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Sample records for improved toxicity prediction

  1. An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

    International Nuclear Information System (INIS)

    Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula; Heinzle, Elmar

    2009-01-01

    Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac, tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC 50 values 100 μM or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.

  2. Rs895819 in MIR27A improves the predictive value of DPYD variants to identify patients at risk of severe fluoropyrimidine-associated toxicity.

    Science.gov (United States)

    Meulendijks, Didier; Henricks, Linda M; Amstutz, Ursula; Froehlich, Tanja K; Largiadèr, Carlo R; Beijnen, Jos H; de Boer, Anthonius; Deenen, Maarten J; Cats, Annemieke; Schellens, Jan H M

    2016-06-01

    The objective of this study was to determine whether genotyping of MIR27A polymorphisms rs895819A>G and rs11671784C>T can be used to improve the predictive value of DPYD variants to identify patients at risk of severe fluoropyrimidine-associated toxicity (FP-toxicity). Patients treated previously in a prospective study with fluoropyrimidine-based chemotherapy were genotyped for rs895819 and rs11671784, and DPYD c.2846A>T, c.1679T>G, c.1129-5923C>G and c.1601G>A. The predictive value of MIR27A variants for early-onset grade ≥3 FP-toxicity, alone or in combination with DPYD variants, was tested in multivariable logistic regression models. Random-effects meta-analysis was performed, including previously published data. A total of 1,592 patients were included. Allele frequencies of rs895819 and rs11671784 were 0.331 and 0.020, respectively. In DPYD wild-type patients, MIR27A variants did not affect risk of FP-toxicity (OR 1.3 for ≥1 variant MIR27A allele vs. none, 95% CI: 0.87-1.82, p = 0.228). In contrast, in patients carrying DPYD variants, the presence of ≥1 rs895819 variant allele was associated with increased risk of FP-toxicity (OR 4.9, 95% CI: 1.24-19.7, p = 0.023). Rs11671784 was not associated with FP-toxicity (OR 2.9, 95% CI: 0.47-18.0, p = 0.253). Patients carrying a DPYD variant and rs895819 were at increased risk of FP-toxicity compared to patients wild type for rs895819 and DPYD (OR 2.4, 95% CI: 1.27-4.37, p = 0.007), while patients with a DPYD variant but without a MIR27A variant were not (OR 0.3 95% CI: 0.06-1.17, p = 0.081). In meta-analysis, rs895819 remained significantly associated with FP-toxicity in DPYD variant allele carriers, OR 5.4 (95% CI: 1.83-15.7, p = 0.002). This study demonstrates the clinical validity of combined MIR27A/DPYD screening to identify patients at risk of severe FP-toxicity. © 2016 UICC.

  3. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  4. Predictive acute toxicity tests with pesticides.

    Science.gov (United States)

    Brown, V K

    1983-01-01

    By definition pesticides are biocidal products and this implies a probability that pesticides may be acutely toxic to species other than the designated target species. The ways in which pesticides are manufactured, formulated, packaged, distributed and used necessitates a potential for the exposure of non-target species although the technology exists to minimize adventitious exposure. The occurrence of deliberate exposure of non-target species due to the misuse of pesticides is known to happen. The array of predictive acute toxicity tests carried out on pesticides and involving the use of laboratory animals can be justified as providing data on which hazard assessment can be based. This paper addresses the justification and rationale of this statement.

  5. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

    Science.gov (United States)

    Nendza, Monika; Müller, Martin; Wenzel, Andrea

    2017-03-22

    Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR

  6. Toxicity challenges in environmental chemicals: Prediction of ...

    Science.gov (United States)

    Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do

  7. Genomics and the prediction of xenobiotic toxicity

    International Nuclear Information System (INIS)

    Meyer, Urs-A.; Gut, Josef

    2002-01-01

    The systematic identification and functional analysis of human genes is revolutionizing the study of disease processes and the development and rational use of drugs. It increasingly enables medicine to make reliable assessments of the individual risk to acquire a particular disease, raises the number and specificity of drug targets and explains interindividual variation of the effectiveness and toxicity of drugs. Mutant alleles at a single gene locus for more than 20 drug metabolizing enzymes are some of the best studied individual risk factors for adverse drug reactions and xenobiotic toxicity. Increasingly, genetic polymorphisms of transporter and receptor systems are also recognized as causing interindividual variation in drug response and drug toxicity. However, pharmacogenetic and toxicogenetic factors rarely act alone; they produce a phenotype in concert with other variant genes and with environmental factors. Environmental factors may affect gene expression in many ways. For instance, numerous drugs induce their own and the metabolism of other xenobiotics by interacting with nuclear receptors such as AhR, PPAR, PXR and CAR. Genomics is providing the information and technology to analyze these complex situations to obtain individual genotypic and gene expression information to assess the risk of toxicity

  8. Novel view on predicting acute toxicity: Decomposing toxicity data in species vulnerability and chemical potency.

    NARCIS (Netherlands)

    Jager, D.T.; Posthuma, L.; Zwart, D.D.; van de Meent, D.

    2007-01-01

    Chemical risk assessment usually applies empirical methods to predict toxicant effects on different species. We propose a more mechanism-oriented approach, and introduce a method to decompose toxicity data in a contribution from the chemical (potency) and from the exposed species (vulnerability). We

  9. Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity.

    Science.gov (United States)

    Roy, Kunal; Das, Rudra Narayan; Popelier, Paul L A

    2015-05-01

    Predictive toxicology using chemometric tools can be very useful in order to fill the data gaps for ionic liquids (ILs) with limited available experimental toxicity information, in view of their growing industrial uses. Though originally promoted as green chemicals, ILs have now been shown to possess considerable toxicity against different ecological endpoints. Against this background, quantitative structure-activity relationship (QSAR) models have been developed here for the toxicity of ILs against the green algae Scenedesmus vacuolatus using computed descriptors with definite physicochemical meaning. The final models emerged from E-state indices, extended topochemical atom (ETA) indices and quantum topological molecular similarity (QTMS) indices. The developed partial least squares models support the established mechanism of toxicity of ionic liquids in terms of a surfactant action of cations and chaotropic action of anions. The models have been developed within the guidelines of the Organization of Economic Co-operation and Development (OECD) for regulatory QSAR models, and they have been validated both internally and externally using multiple strategies and also tested for applicability domain. A preliminary attempt has also been made, for the first time, to develop interspecies quantitative toxicity-toxicity relationship (QTTR) models for the algal toxicity of ILs with Daphnia toxicity, which should be interesting while predicting toxicity of ILs for an endpoint when the data for the other are available.

  10. eMolTox: prediction of molecular toxicity with confidence.

    Science.gov (United States)

    Ji, Changge; Svensson, Fredrik; Zoufir, Azedine; Bender, Andreas

    2018-03-07

    In this work we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. chicago.ji@gmail.com or ab454@cam.ac.uk. Supplementary data are available at Bioinformatics online.

  11. Predicting dietborne metal toxicity from metal influxes

    Science.gov (United States)

    Croteau, M.-N.; Luoma, S.N.

    2009-01-01

    Dietborne metal uptake prevails for many species in nature. However, the links between dietary metal exposure and toxicity are not well understood. Sources of uncertainty include the lack of suitable tracers to quantify exposure for metals such as copper, the difficulty to assess dietary processes such as food ingestion rate, and the complexity to link metal bioaccumulation and effects. We characterized dietborne copper, nickel, and cadmium influxes in a freshwater gastropod exposed to diatoms labeled with enriched stable metal isotopes. Metal influxes in Lymnaea stagnalis correlated linearly with dietborne metal concentrations over a range encompassing most environmental exposures. Dietary Cd and Ni uptake rate constants (kuf) were, respectively, 3.3 and 2.3 times higher than that for Cu. Detoxification rate constants (k detox) were similar among metals and appeared 100 times higher than efflux rate constants (ke). Extremely high Cu concentrations reduced feeding rates, causing the relationship between exposure and influx to deviate from linearity; i.e., Cu uptake rates leveled off between 1500 and 1800 nmol g-1 day-1. L. stagnalis rapidly takes up Cu, Cd, and Ni from food but detoxifies the accumulated metals, instead of reducing uptake or intensifying excretion. Above a threshold uptake rate, however, the detoxification capabilities of L. stagnalis are overwhelmed.

  12. Prediction of toxic metals concentration using artificial intelligence techniques

    Science.gov (United States)

    Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.

    2011-12-01

    Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.

  13. Use of passive samplers for improving oil toxicity and spill effects assessment

    International Nuclear Information System (INIS)

    Letinski, Daniel; Parkerton, Thomas; Redman, Aaron; Manning, Ryan; Bragin, Gail; Febbo, Eric; Palandro, David; Nedwed, Tim

    2014-01-01

    Highlights: • Methods to quantify dissolved hydrocarbons needed to link oil exposures to toxicity. • Solid phase microextraction (SPME) fibers used to measure dissolved hydrocarbons. • SPME results reliably predicted acute toxicity for range of dispersed oils. • Oil droplets and chemical dispersant did not significantly contribute to toxicity. • SPME analysis improves oil exposure assessment in lab and field studies. - Abstract: Methods that quantify dissolved hydrocarbons are needed to link oil exposures to toxicity. Solid phase microextraction (SPME) fibers can serve this purpose. If fibers are equilibrated with oiled water, dissolved hydrocarbons partition to and are concentrated on the fiber. The absorbed concentration (C polymer ) can be quantified by thermal desorption using GC/FID. Further, given that the site of toxic action is hypothesized as biota lipid and partitioning of hydrocarbons to lipid and fibers is well correlated, C polymer is hypothesized to be a surrogate for toxicity prediction. To test this method, toxicity data for physically and chemically dispersed oils were generated for shrimp, Americamysis bahia, and compared to test exposures characterized by C polymer . Results indicated that C polymer reliably predicted toxicity across oils and dispersions. To illustrate field application, SPME results are reported for oil spills at the Ohmsett facility. SPME fibers provide a practical tool to improve characterization of oil exposures and predict effects in future lab and field studies

  14. Extensive review of fish embryo acute toxicities for the prediction of GHS acute systemic toxicity categories.

    Science.gov (United States)

    Scholz, Stefan; Ortmann, Julia; Klüver, Nils; Léonard, Marc

    2014-08-01

    Distribution and marketing of chemicals require appropriate labelling of health, physical and environmental hazards according to the United Nations global harmonisation system (GHS). Labelling for (human) acute toxicity categories is based on experimental findings usually obtained by oral, dermal or inhalative exposure of rodents. There is a strong societal demand for replacing animal experiments conducted for safety assessment of chemicals. Fish embryos are considered as alternative to animal testing and are proposed as predictive model both for environmental and human health effects. Therefore, we tested whether LC50s of the fish embryo acute toxicity test would allow effectively predicting of acute mammalian toxicity categories. A database of published fish embryo LC50 containing 641 compounds was established. For these compounds corresponding rat oral LD50 were identified resulting in 364 compounds for which both fish embryo LC50 and rat LD50 was available. Only a weak correlation of fish embryo LC50 and rat oral LD50 was obtained. Fish embryos were also not able to effectively predict GHS oral acute toxicity categories. We concluded that due to fundamental exposure protocol differences (single oral dose versus water-borne exposure) a reverse dosimetry approach is needed to explore the predictive capacity of fish embryos. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Nomogram to predict rectal toxicity following prostate cancer radiotherapy.

    Directory of Open Access Journals (Sweden)

    Jean-Bernard Delobel

    Full Text Available To identify predictors of acute and late rectal toxicity following prostate cancer radiotherapy (RT, while integrating the potential impact of RT technique, dose escalation, and moderate hypofractionation, thus enabling us to generate a nomogram for individual prediction.In total, 972 patients underwent RT for localized prostate cancer, to a total dose of 70 Gy or 80 Gy, using two different fractionations (2 Gy or 2.5 Gy/day, by means of several RT techniques (3D conformal RT [3DCRT], intensity-modulated RT [IMRT], or image-guided RT [IGRT]. Multivariate analyses were performed to identify predictors of acute and late rectal toxicity. A nomogram was generated based on the logistic regression model used to predict the 3-year rectal toxicity risk, with its accuracy assessed by dividing the cohort into training and validation subgroups.Mean follow-up for the entire cohort was 62 months, ranging from 6 to 235. The rate of acute Grade ≥2 rectal toxicity was 22.2%, decreasing when combining IMRT and IGRT, compared to 3DCRT (RR = 0.4, 95%CI: 0.3-0.6, p<0.01. The 5-year Grade ≥2 risks for rectal bleeding, urgency/tenesmus, diarrhea, and fecal incontinence were 9.9%, 4.5%, 2.8%, and 0.4%, respectively. The 3-year Grade ≥2 risk for overall rectal toxicity increased with total dose (p<0.01, RR = 1.1, 95%CI: 1.0-1.1 and dose per fraction (2Gy vs. 2.5Gy (p = 0.03, RR = 3.3, 95%CI: 1.1-10.0, and decreased when combining IMRT and IGRT (RR = 0.50, 95% CI: 0.3-0.8, p<0.01. Based on these three parameters, a nomogram was generated.Dose escalation and moderate hypofractionation increase late rectal toxicity. IMRT combined with IGRT markedly decreases acute and late rectal toxicity. Performing combined IMRT and IGRT can thus be envisaged for dose escalation and moderate hypofractionation. Our nomogram predicts the 3-year rectal toxicity risk by integrating total dose, fraction dose, and RT technique.

  16. Predictive QSAR Models for the Toxicity of Disinfection Byproducts

    Directory of Open Access Journals (Sweden)

    Litang Qin

    2017-10-01

    Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  17. Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

    Science.gov (United States)

    Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

    2017-10-09

    Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  18. Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates

    Science.gov (United States)

    Patino, Reynaldo; VanLandeghem, Matthew M.; Denny, Shawn

    2016-01-01

    Golden alga (Prymnesium parvum) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from ∼ 80% to ∼ 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.

  19. Toxicity prediction of compounds from turmeric (Curcuma longa L).

    Science.gov (United States)

    Balaji, S; Chempakam, B

    2010-10-01

    Turmeric belongs to the ginger family Zingiberaceae. Currently, cheminformatics approaches are not employed in any of the spices to study the medicinal properties traditionally attributed to them. The aim of this study is to find the most efficacious molecule which does not have any toxic effects. In the present study, toxicity of 200 chemical compounds from turmeric were predicted (includes bacterial mutagenicity, rodent carcinogenicity and human hepatotoxicity). The study shows out of 200 compounds, 184 compounds were predicted as toxigenic, 136 compounds are mutagenic, 153 compounds are carcinogenic and 64 compounds are hepatotoxic. To cross validate our results, we have chosen the popular curcumin and found that curcumin and its derivatives may cause dose dependent hepatotoxicity. The results of these studies indicate that, in contrast to curcumin, few other compounds in turmeric which are non-mutagenic, non-carcinogenic, non-hepatotoxic, and do not have any side-effects. Hence, the cost-effective approach presented in this paper could be used to filter toxic compounds from the drug discovery lifecycle. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  20. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-02-01

    For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  1. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Directory of Open Access Journals (Sweden)

    Hongbin Yang

    2018-02-01

    Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  2. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-01-01

    During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  3. Complex versus simple models: ion-channel cardiac toxicity prediction.

    Science.gov (United States)

    Mistry, Hitesh B

    2018-01-01

    There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  4. Complex versus simple models: ion-channel cardiac toxicity prediction

    Directory of Open Access Journals (Sweden)

    Hitesh B. Mistry

    2018-02-01

    Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  5. The current status of biomarkers for predicting toxicity

    Science.gov (United States)

    Campion, Sarah; Aubrecht, Jiri; Boekelheide, Kim; Brewster, David W; Vaidya, Vishal S; Anderson, Linnea; Burt, Deborah; Dere, Edward; Hwang, Kathleen; Pacheco, Sara; Saikumar, Janani; Schomaker, Shelli; Sigman, Mark; Goodsaid, Federico

    2013-01-01

    Introduction There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity. Areas covered This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled ‘Translational Biomarkers in Toxicology.’ The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process. Expert opinion There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process. PMID:23961847

  6. Predicting Pulmonary O2 Toxicity: A New Look at the Unit Pulmonary Toxicity Dose

    Science.gov (United States)

    1985-05-01

    beatl loeged# aNy ahepes in Vo was considered error. The assumptieo was shoe eapeova to a 10 below the "safe’! I02 should have produced se daeromeat...Naval Medical Research Institute SethesdolMO 20814-505b NMRI 86-52 December 1986 PREDICTING4 PULMONARY 0 2 TOXICITY: j k,, NEW LOOK AT THE UNIT...distribution is unlimited °Q0- C(-" Naval Medical Research LLj and Development Command ,, J Bethesda, Maryland 20814-5044 -4 • Department of the Navy Naval

  7. Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)

    Science.gov (United States)

    A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

  8. Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

    Science.gov (United States)

    Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

  9. In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

    Science.gov (United States)

    Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

    2015-01-01

    The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

  10. GHS additivity formula: can it predict the acute systemic toxicity of agrochemical formulations that contain acutely toxic ingredients?

    Science.gov (United States)

    Van Cott, Andrew; Hastings, Charles E; Landsiedel, Robert; Kolle, Susanne; Stinchcombe, Stefan

    2018-02-01

    In vivo acute systemic testing is a regulatory requirement for agrochemical formulations. GHS specifies an alternative computational approach (GHS additivity formula) for calculating the acute toxicity of mixtures. We collected acute systemic toxicity data from formulations that contained one of several acutely-toxic active ingredients. The resulting acute data set includes 210 formulations tested for oral toxicity, 128 formulations tested for inhalation toxicity and 31 formulations tested for dermal toxicity. The GHS additivity formula was applied to each of these formulations and compared with the experimental in vivo result. In the acute oral assay, the GHS additivity formula misclassified 110 formulations using the GHS classification criteria (48% accuracy) and 119 formulations using the USEPA classification criteria (43% accuracy). With acute inhalation, the GHS additivity formula misclassified 50 formulations using the GHS classification criteria (61% accuracy) and 34 formulations using the USEPA classification criteria (73% accuracy). For acute dermal toxicity, the GHS additivity formula misclassified 16 formulations using the GHS classification criteria (48% accuracy) and 20 formulations using the USEPA classification criteria (36% accuracy). This data indicates the acute systemic toxicity of many formulations is not the sum of the ingredients' toxicity (additivity); but rather, ingredients in a formulation can interact to result in lower or higher toxicity than predicted by the GHS additivity formula. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. Pharmacogenetics predictive of response and toxicity in acute lymphoblastic leukemia therapy.

    Science.gov (United States)

    Mei, Lin; Ontiveros, Evelena P; Griffiths, Elizabeth A; Thompson, James E; Wang, Eunice S; Wetzler, Meir

    2015-07-01

    Acute lymphoblastic leukemia (ALL) is a relatively rare disease in adults accounting for no more than 20% of all cases of acute leukemia. By contrast with the pediatric population, in whom significant improvements in long term survival and even cure have been achieved over the last 30years, adult ALL remains a significant challenge. Overall survival in this group remains a relatively poor 20-40%. Modern research has focused on improved pharmacokinetics, novel pharmacogenetics and personalized principles to optimize the efficacy of the treatment while reducing toxicity. Here we review the pharmacogenetics of medications used in the management of patients with ALL, including l-asparaginase, glucocorticoids, 6-mercaptopurine, methotrexate, vincristine and tyrosine kinase inhibitors. Incorporating recent pharmacogenetic data, mainly from pediatric ALL, will provide novel perspective of predicting response and toxicity in both pediatric and adult ALL therapies. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH).

    Science.gov (United States)

    Bruce, Erica D; Autenrieth, Robin L; Burghardt, Robert C; Donnelly, K C; McDonald, Thomas J

    2008-01-01

    Quantitative structure-activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict toxic endpoints for three bioassays specific to several stages of carcinogenesis. The ethoxyresorufin O-deethylase assay (EROD), the Salmonella/microsome assay, and a gap junction intercellular communication (GJIC) assay were chosen for their ability to measure toxic endpoints specific to activation-, induction-, and promotion-related effects of polycyclic aromatic hydrocarbons (PAH). Shape-electronic, spatial, information content, and topological descriptors proved to be important descriptors in predicting the toxicity of PAH in these bioassays. Bioassay-based toxic equivalency factors (TEF(B)) were developed for several PAH using the quantitative structure-toxicity relationships (QSTR) developed. Predicting toxicity for a specific PAH compound, such as a bioassay-based potential potency (PP(B)) or a TEF(B), is possible by combining the predicted behavior from the QSTR models. These toxicity estimates may then be incorporated into a risk assessment for compounds that lack toxicity data. Accurate toxicity predictions are made by examining each type of endpoint important to the process of carcinogenicity, and a clearer understanding between composition and toxicity can be obtained.

  13. Improvement in Shrimp Hatchery Procedures for Toxicity Tests

    International Nuclear Information System (INIS)

    Nor Azizah Marsiddi; Fazliana Mohd Saaya; Anee Suryani Sued

    2015-01-01

    Toxicity testing of brine shrimp Artemia salina Brine shrimp lethality assay is a screening test to determine half the dose mortality (LC50) for its shrimp given certain herbal extract at a concentration tested. The shrimp child mortality half a dose indicator to determine level of toxicity before further testing done on animal cell culture and animal experiments also on the mouse. The use of new hardware, namely Artemio 1 has increased its shrimp production at a rate that more and faster than the use of the black box hatching previously taken from the method by Solis, 1993. brine shrimp eggs from Artemio mix also easier to use because it contains egg and sea salt have been ready mixed for use in experiments. In conclusion, this method improvements help increase the number of offspring produced shrimp and produce experimental method easier than previous methods. (author)

  14. Potential carcinogenicity predicted by computational toxicity evaluation of thiophosphate pesticides using QSTR/QSCarciAR model.

    Science.gov (United States)

    Petrescu, Alina-Maria; Ilia, Gheorghe

    2017-07-01

    This study presents in silico prediction of toxic activities and carcinogenicity, represented by the potential carcinogenicity DSSTox/DBS, based on vector regression with a new Kernel activity, and correlating the predicted toxicity values through a QSAR model, namely: QSTR/QSCarciAR (quantitative structure toxicity relationship/quantitative structure carcinogenicity-activity relationship) described by 2D, 3D descriptors and biological descriptors. The results showed a connection between carcinogenicity (compared to the structure of a compound) and toxicity, as a basis for future studies on this subject, but each prediction is based on structurally similar compounds and the reactivation of the substructures of these compounds.

  15. In silico toxicology: computational methods for the prediction of chemical toxicity

    KAUST Repository

    Raies, Arwa B.; Bajic, Vladimir B.

    2016-01-01

    Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

  16. In silico toxicology: computational methods for the prediction of chemical toxicity

    KAUST Repository

    Raies, Arwa B.

    2016-01-06

    Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

  17. Predicting toxic heavy metal movements in upper Sanyati catchment ...

    African Journals Online (AJOL)

    Water samples from boreholes located in areas where mining, mineral processing and agricultural activities were dominant, yielded the highest values of toxic heavy metals. Dilution Attenuation Factor (DAF) for each toxic heavy metal was calculated to observe metal behaviour along the contaminant path for each season.

  18. Audiovisual biofeedback improves motion prediction accuracy.

    Science.gov (United States)

    Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

    2013-04-01

    The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

  19. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

    Science.gov (United States)

    Zhen, Xin; Chen, Jiawei; Zhong, Zichun; Hrycushko, Brian; Zhou, Linghong; Jiang, Steve; Albuquerque, Kevin; Gu, Xuejun

    2017-11-01

    Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

  20. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Directory of Open Access Journals (Sweden)

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  1. Toxicity evaluation and prediction of toxic chemicals on activated sludge system.

    Science.gov (United States)

    Cai, Bijing; Xie, Li; Yang, Dianhai; Arcangeli, Jean-Pierre

    2010-05-15

    The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC(50-M) and IC(50-S) were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24h) and short-term (30min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC(50-M) of the tested chemicals were higher than those of IC(50-S). In addition, quantitative structure-activity relationships (QSARs) models developed from IC(50-M) were more stable and accurate than those of IC(50-S). The multiple linear models based on molecular descriptors and K(ow) presented better reliability than single linear models based on K(ow). Among these molecular descriptors, E(lumo) was the most important impact factor for evaluation of mid-term toxicity. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  2. Predicting molybdenum toxicity to higher plants: Estimation of toxicity threshold values

    Energy Technology Data Exchange (ETDEWEB)

    McGrath, S.P., E-mail: steve.mcgrath@bbsrc.ac.u [Soil Science Department, Centre for Soils and Ecosystems Function, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Mico, C.; Zhao, F.J.; Stroud, J.L. [Soil Science Department, Centre for Soils and Ecosystems Function, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Zhang, H.; Fozard, S. [Division of Environmental Science, Lancaster University, Lancaster LA1 4YQ (United Kingdom)

    2010-10-15

    Four plant species (oilseed rape, Brassica napus L.; red clover, Trifolium pratense L.; ryegrass, Lolium perenne L.; and tomato, Lycopersicon esculentum L.) were tested on ten soils varying widely in soil properties to assess molybdenum (Mo) toxicity. A larger range (66-fold-609-fold) of added Mo concentrations resulting in 50% inhibition of yield (ED{sub 50}) was found among soils than among plant species (2-fold-38-fold), which illustrated that the soils differed widely in the expression of Mo toxicity. Toxicity thresholds based on soil solution Mo narrowed the variation among soils compared to thresholds based on added Mo concentrations. We conclude that plant bioavailability of Mo in soil depends on Mo solubility, but this alone did not decrease the variability in observed toxicity enough to be used in risk assessment and that other soil properties influencing Mo toxicity to plants need to be considered. - Mo toxicity thresholds varied widely in different soils and therefore soil properties need to be taken into account in order to assess the risk of Mo exposure.

  3. Non toxic additives for improved fabric filter performance

    Energy Technology Data Exchange (ETDEWEB)

    Bustard, C.J.; Baldrey, K.E.; Ebner, T.G. [ADA Technologies, Inc., Englewood, CO (United States)] [and others

    1995-11-01

    The overall objective of this three-phase Small Business innovative Research (SBIR) program funded by the Department of Energy pittsburgh Energy Technology Center (PETC) is to commercialize a technology based upon the use of non-toxic, novel flue gas conditioning agents to improve particulate air toxic control and overall fabric filter performance. The ultimate objective of the Phase II program currently in progress is to demonstrate that the candidate additives are successful at full-scale on flue gas from a coal-fired utility boiler. This paper covers bench-scale field tests conducted during the period February through May, 1995. The bench-scale additives testing was conducted on a flue gas slipstream taken upstream of the existing particulate control device at a utility power plant firing a Texas lignite coal. These tests were preceded by extensive testing with additives in the laboratory using a simulated flue gas stream and re-dispersed flyash from the same power plant. The bench-scale field testing was undertaken to demonstrate the performance with actual flue gas of the bet candidate additives previously identified in the laboratory. Results from the bench-scale tests will be used to establish operating parameters for a larger-scale demonstration on either a single baghouse compartment or a full baghouse at the same site.

  4. The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

    Science.gov (United States)

    Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

    2016-10-01

    The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of

  5. Predicting molybdenum toxicity to higher plants: Influence of soil properties

    Energy Technology Data Exchange (ETDEWEB)

    McGrath, S.P., E-mail: steve.mcgrath@bbsrc.ac.u [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Mico, C. [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom); Curdy, R. [Laboratory for Environmental Biotechnology (LBE), Swiss Federal Institute of Technology Lausanne (EPFL) Station 6 CH, 1015 Lausanne (Switzerland); Zhao, F.J. [Soil Science Department, Centre for Soils and Ecosystems Functions, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ (United Kingdom)

    2010-10-15

    The effect of soil properties on the toxicity of molybdenum (Mo) to four plant species was investigated. Soil organic carbon or ammonium-oxalate extractable Fe oxides were found to be the best predictors of the 50% effective dose (ED{sub 50}) of Mo in different soils, explaining > 65% of the variance in ED{sub 50} for four species except for ryegrass (26-38%). Molybdenum concentrations in soil solution and consequently plant uptake were increased when soil pH was artificially raised because sorption of Mo to amorphous oxides is greatly reduced at high pH. The addition of sulphate significantly decreased Mo uptake by oilseed rape. For risk assessment, we suggest that Mo toxicity values for plants should be normalised using soil amorphous iron oxide concentrations. - Amorphous iron oxides or organic carbon were found to be the best predictors of the toxicity threshold values of Mo to higher plants on different soils.

  6. Predicting molybdenum toxicity to higher plants: Influence of soil properties

    International Nuclear Information System (INIS)

    McGrath, S.P.; Mico, C.; Curdy, R.; Zhao, F.J.

    2010-01-01

    The effect of soil properties on the toxicity of molybdenum (Mo) to four plant species was investigated. Soil organic carbon or ammonium-oxalate extractable Fe oxides were found to be the best predictors of the 50% effective dose (ED 50 ) of Mo in different soils, explaining > 65% of the variance in ED 50 for four species except for ryegrass (26-38%). Molybdenum concentrations in soil solution and consequently plant uptake were increased when soil pH was artificially raised because sorption of Mo to amorphous oxides is greatly reduced at high pH. The addition of sulphate significantly decreased Mo uptake by oilseed rape. For risk assessment, we suggest that Mo toxicity values for plants should be normalised using soil amorphous iron oxide concentrations. - Amorphous iron oxides or organic carbon were found to be the best predictors of the toxicity threshold values of Mo to higher plants on different soils.

  7. Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

    Science.gov (United States)

    Furuhama, A; Hasunuma, K; Hayashi, T I; Tatarazako, N

    2016-05-01

    We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

  8. Genetic markers for prediction of normal tissue toxicity after radiotherapy

    DEFF Research Database (Denmark)

    Alsner, Jan; Andreassen, Christian Nicolaj; Overgaard, Jens

    2008-01-01

    During the last decade, a number of studies have supported the hypothesis that there is an important genetic component to the observed interpatient variability in normal tissue toxicity after radiotherapy. This review summarizes the candidate gene association studies published so far on the risk...

  9. Predictive value of cell assays for developmental toxicity and embryotoxicity of conazole fungicides

    DEFF Research Database (Denmark)

    Sørensen, Karin Dreisig; Taxvig, Camilla; Kjærstad, Mia Birkhøj

    2013-01-01

    in reasonably good agreement with available in vivo effects. Ketoconazole and epoxiconazole are the most potent embryotoxic compounds, whereas prochloraz belongs to the most potent developmental toxicants. In conclusion, a rough prediction of the ranking of these conazole fungicides for in vivo toxicity data...

  10. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

    International Nuclear Information System (INIS)

    Nigsch, Florian; Mitchell, John B.O.

    2008-01-01

    The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

  11. Predictive factors for gastroduodenal toxicity based on endoscopy following radiotherapy in patients with hepatocellular carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, H. [Sungkyunkwan Univ., Seoul (Korea, Republic of). Dept. of Health Sciences and Technology; Oh, D.; Park, H.C.; Han, Y.; Lim, D.H. [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of). Dept. of Radiation Oncology; Kang, S.W. [Korea Univ., Seoul (Korea, Republic of). Dept. of Radiologic Science; Paik, S.W. [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of). Dept. of Medicine

    2013-07-15

    Purpose: The aim of this work was to determine predictive factors for gastroduodenal (GD) toxicity in hepatocellular carcinoma (HCC) patients who were treated with radiotherapy (RT). Patients and methods: A total of 90 HCC patients who underwent esophagogastroduodenoscopy (EGD) before and after RT were enrolled. RT was delivered as 30-50 Gy (median 37.5 Gy) in 2-5 Gy (median 3.5 Gy) per fraction. All endoscopic findings were reviewed and GD toxicities related to RT were graded by the Common Toxicity Criteria for Adverse Events, version 3.0. The predictive factors for the {>=} grade 2 GD toxicity were investigated. Results: Endoscopic findings showed erosive gastritis in 14 patients (16 %), gastric ulcers in 8 patients (9 %), erosive duodenitis in 15 patients (17 %), and duodenal ulcers in 14 patients (16 %). Grade 2 toxicity developed in 19 patients (21 %) and grade 3 toxicity developed in 8 patients (9 %). V{sub 25} for stomach and V{sub 35} for duodenum (volume receiving a RT dose of more than x Gy) were the most predictive factors for {>=} grade 2 toxicity. The gastric toxicity rate at 6 months was 2.9 % for V{sub 25} {<=} 6.3 % and 57.1 % for V{sub 25} > 6.3 %. The duodenal toxicity rate at 6 months was 9.4 % for V{sub 35} > 5.4 % and 45.9 % for V{sub 35} > 5.4 %. By multivariate analysis including the clinical factors, V{sub 25} for stomach and V{sub 35} for duodenum were the significant factors. Conclusion: EGD revealed that GD toxicity is common following RT for HCC. V{sub 25} for the stomach and V{sub 35} for the duodenum were the significant factors to predict {>=} grade 2 GD toxicity. (orig.)

  12. Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example.

    Science.gov (United States)

    Li, Tong; Liu, Shu-Shen; Qu, Rui; Liu, Hai-Ling

    2017-10-01

    The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC 50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC 30 , and EC 70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs. Copyright © 2017. Published by Elsevier Inc.

  13. Predicting the risk of developmental toxicity from in vitro assays

    International Nuclear Information System (INIS)

    Spielmann, Horst

    2005-01-01

    Reproductive toxicity refers to the adverse effects of a substance on any aspect of the reproductive cycle, including the impairment of reproductive function, the induction of adverse effects in the embryo, such as growth retardation, malformations, and death. Due to the complexity of the mammalian reproductive cycle, it is impossible to model the whole cycle in a single in vitro system in order to detect chemical effects on mammalian reproduction. However, the cycle can be broken down in its biological components which may be studied individually or in combination. This approach has the advantage that the target tissue/organ of a developmental toxicant can be identified. In specific areas of developmental toxicity, a number of useful and promising in vitro models are already available. The individual tests may be used as building blocks of a tiered testing strategy. So far, research has focused on developing and validating tests covering only a few components of the reproductive cycle, in particular organogenesis of the embryo, reflecting important concerns for teratogenic chemicals. During the last three decades, a number of established models and promising new developments have emerged that will be discussed, e.g. culture of mammalian embryos and embryonic cells and tissues and the use of embryonic stem cells

  14. ProTox: a web server for the in silico prediction of rodent oral toxicity.

    Science.gov (United States)

    Drwal, Malgorzata N; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R; Preissner, Robert

    2014-07-01

    Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Inclusion of functional information from perfusion SPECT improves predictive value of dose-volume parameters in lung toxicity outcome after radiotherapy for non-small cell lung cancer: A prospective study

    DEFF Research Database (Denmark)

    Farr, Katherina P; Kallehauge, Jesper F; Møller, Ditte S

    2015-01-01

    for corresponding standard parameters, but they were not significantly different from each other. CONCLUSION: SPECT-based functional parameters were better to predict the risk of RP compared to standard CT-based dose-volume parameters. Functional parameters may be useful to guide radiotherapy planning in order...

  16. Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

    International Nuclear Information System (INIS)

    West, Paul R.; Weir, April M.; Smith, Alan M.; Donley, Elizabeth L.R.; Cezar, Gabriela G.

    2010-01-01

    Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

  17. Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

    International Nuclear Information System (INIS)

    Keshavarz, Mohammad Hossein; Gharagheizi, Farhad; Shokrolahi, Arash; Zakinejad, Sajjad

    2012-01-01

    Highlights: ► A novel method is introduced for desk calculation of toxicity of benzoic acid derivatives. ► There is no need to use QSAR and QSTR methods, which are based on computer codes. ► The predicted results of 58 compounds are more reliable than those predicted by QSTR method. ► The present method gives good predictions for further 324 benzoic acid compounds. - Abstract: Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD 50 with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure–toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model.

  18. Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition

    DEFF Research Database (Denmark)

    Sørli, Jorid Birkelund; Huang, Yishi; Da Silva, Emilie

    2018-01-01

    impregnation products using the constant flow through set-up of the constrained drop surfactometer to determine if they inhibited LS function or not. The same products were tested in a mouse inhalation bioassay to determine their toxicity in vivo. The sensitivity was 100%, i.e. the in vitro method predicted...... the chemical composition of the products and induction of toxicity. The currently accepted method for determination of acute inhalation toxicity is based on experiments on animals; it is time-consuming, expensive and causes stress for the animals. Impregnation products are present on the market in large...... numbers and amounts and exhibit great variety. Therefore, an alternative method to screen for acute inhalation toxicity is needed. The aim of our study was to determine if inhibition of lung surfactant by impregnation products in vitro could accurately predict toxicity in vivo in mice. We tested 21...

  19. Predictive Models of target organ and Systemic toxicities (BOSC)

    Science.gov (United States)

    The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

  20. Predicting_Systemic_Toxicity_Effects_ArchTox_2017_Data

    Data.gov (United States)

    U.S. Environmental Protection Agency — In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was...

  1. Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition.

    Science.gov (United States)

    Sørli, Jorid B; Huang, Yishi; Da Silva, Emilie; Hansen, Jitka S; Zuo, Yi Y; Frederiksen, Marie; Nørgaard, Asger W; Ebbehøj, Niels E; Larsen, Søren T; Hougaard, Karin S

    2018-01-01

    Private consumers and professionals may experience acute inhalation toxicity after inhaling aerosolized impregnation products. The distinction between toxic and non-toxic products is difficult to make for producers and product users alike, as there is no clearly described relationship between the chemical composition of the products and induction of toxicity. The currently accepted method for determination of acute inhalation toxicity is based on experiments on animals; it is time-consuming, expensive and causes stress for the animals. Impregnation products are present on the market in large numbers and amounts and exhibit great variety. Therefore, an alternative method to screen for acute inhalation toxicity is needed. The aim of our study was to determine if inhibition of lung surfactant by impregnation products in vitro could accurately predict toxicity in vivo in mice. We tested 21 impregnation products using the constant flow through set-up of the constrained drop surfactometer to determine if the products inhibited surfactant function or not. The same products were tested in a mouse inhalation bioassay to determine their toxicity in vivo. The sensitivity was 100%, i.e., the in vitro method predicted all the products that were toxic for mice to inhale. The specificity of the in vitro test was 63%, i.e., the in vitro method found three false positives in the 21 tested products. Six of the products had been involved in accidental human inhalation where they caused acute inhalation toxicity. All of these six products inhibited lung surfactant function in vitro and were toxic to mice.

  2. THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY: AN EXPANDED VIEW OF CHEMICAL TOXICITY

    Science.gov (United States)

    A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. T...

  3. Improving Flash Flood Prediction in Multiple Environments

    Science.gov (United States)

    Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

    2009-12-01

    Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

  4. Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method

    International Nuclear Information System (INIS)

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-01-01

    Highlights: • A comprehensive database on toxicity of ionic liquids (ILs) was established. • Relationship between structure and toxicity of IL has been analyzed qualitatively. • Two new QSAR models were developed for predicting toxicity of ILs to IPC-81. • Accuracy of proposed nonlinear SVM model is much higher than the linear MLR model. • The established models can be explored in designing novel green agents. - Abstract: A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure–Activity relationships (QSAR) model is conducted to predict the toxicities (EC 50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R 2 ) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R 2 and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs

  5. Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas

    OpenAIRE

    de Polo, A; Scrimshaw, MD

    2012-01-01

    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 SETAC. An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by ...

  6. Toxicity ratios: Their use and abuse in predicting the risk from induced cancer

    International Nuclear Information System (INIS)

    Mays, C.W.; Taylor, G.N.; Lloyd, R.D.

    1986-01-01

    The toxicity ratio concept assumes the validity of certain relationships. In some examples for bone sarcoma induction, the approximate toxicity of 239 Pu in man can be calculated algebraically from the observed toxicity in the radium-dial painters and the ratio of 239 Pu/ 226 Ra toxicities in suitable laboratory mammals. In a species highly susceptible to bone sarcoma induction, the risk coefficients for both 239 Pu and 226 Ra are elevated, but the toxicity ratio of 239 Pu to 226 Ra tends to be similar to the ratio in resistant species. Among the tested species the toxicity ratio of 239 Pu to 226 Ra ranged from 6 to 22 (a fourfold range), whereas their relative sensitivities to 239 Pu varied by a factor of 150. The toxicity ratio approach can also be used to estimate the actinide risk to man from liver cancer, by comparing to the Thorotrast patients; from lung cancer, by comparing to the uranium miners and the atomic-bomb survivors; and from neutron-induced cancers, by comparing to cancers induced by gamma rays. The toxicity ratio can be used to predict the risk to man from a specific type of cancer that has been reliably induced by a reference radiation in humans and that can be induced by both the reference and the investigated radiation in suitable laboratory animals. 26 refs., 3 figs., 1 tab

  7. Improving Earth/Prediction Models to Improve Network Processing

    Science.gov (United States)

    Wagner, G. S.

    2017-12-01

    The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

  8. Improving contact prediction along three dimensions.

    Directory of Open Access Journals (Sweden)

    Christoph Feinauer

    2014-10-01

    Full Text Available Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i filter and align the raw sequence data representing the evolutionarily related proteins; (ii choose a predictive model to describe a sequence alignment; (iii infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.

  9. Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

    International Nuclear Information System (INIS)

    Niazi, Ali; Jameh-Bozorghi, Saeed; Nori-Shargh, Davood

    2008-01-01

    A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC 50 ) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC 50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC 50 ) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares

  10. Prediction of human population responses to toxic compounds by a collaborative competition.

    Science.gov (United States)

    Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

    2015-09-01

    The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

  11. Predictive factors for acute and late urinary toxicity after permanent interstitial brachytherapy in Japanese patients

    International Nuclear Information System (INIS)

    Tanimoto, Ryuta; Bekku, Kensuke; Katayama, Norihisa

    2013-01-01

    The objectives of this study were to describe the frequency of and to determine predictive factors associated with Radiation Therapy Oncology Group urinary toxicity in prostate brachytherapy patients. From January 2004 to April 2011, 466 consecutive Japanese patients underwent permanent iodine-125-seed brachytherapy (median follow up 48 months). International Prostate Symptom Score and Radiation Therapy Oncology Group toxicity data were prospectively collected. Prostate volume, International Prostate Symptom Score before and after brachytherapy, and postimplant analysis were examined for an association with urinary toxicity, defined as Radiation Therapy Oncology Group urinary toxicity of Grade 1 or higher. Logistic regression analysis was used to examine the factors associated with urinary toxicity. The rate of Radiation Therapy Oncology Group urinary toxicity grade 1 or higher at 1, 6, 12, 24, 36 and 48 months was 67%, 40%, 21%, 31%, 27% and 28%, respectively. Grade 2 or higher urinary toxicity was less than 1% at each time-point. International Prostate Symptom Score was highest at 3 months and returned to normal 12 months after brachytherapy. On multivariate analysis, patients with a larger prostate size, greater baseline International Prostate Symptom Score, higher prostate V100, higher prostate V150, higher prostate D90 and a greater number of seeds had more acute urinary toxicities at 1 month and 12 months after brachytherapy. On multivariate analysis, significant predictors for urinary toxicity at 1 month and 12 months were a greater baseline International Prostate Symptom Score and prostate V100. Most urinary symptoms are tolerated and resolved within 12 months after prostate brachytherapy. Acute and late urinary toxicity after brachytherapy is strongly related to the baseline International Prostate Symptom Score and prostate V100. (author)

  12. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

    Science.gov (United States)

    Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

    2013-09-01

    The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Challenges for the development of a biotic ligand model predicting copper toxicity in estuaries and seas.

    Science.gov (United States)

    de Polo, Anna; Scrimshaw, Mark D

    2012-02-01

    An effort is ongoing to develop a biotic ligand model (BLM) that predicts copper (Cu) toxicity in estuarine and marine environments. At present, the BLM accounts for the effects of water chemistry on Cu speciation, but it does not consider the influence of water chemistry on the physiology of the organisms. We discuss how chemistry affects Cu toxicity not only by controlling its speciation, but also by affecting the osmoregulatory physiology of the organism, which varies according to salinity. In an attempt to understand the mechanisms of Cu toxicity and predict its impacts, we explore the hypothesis that the common factor linking the main toxic effects of Cu is the enzyme carbonic anhydrase (CA), because it is a Cu target with multiple functions and salinity-dependent expression and activity. According to this hypothesis, the site of action of Cu in marine fish may be not only the gill, but also the intestine, because in this tissue CA plays an important role in ion transport and water adsorption. Therefore, the BLM of Cu toxicity to marine fish should also consider the intestine as a biotic ligand. Finally, we underline the need to incorporate the osmotic gradient into the BLM calculations to account for the influence of physiology on Cu toxicity. Copyright © 2011 SETAC.

  14. Improving reptile ecological risk assessment: oral and dermal toxicity of pesticides to a common lizard species (Sceloporus occidentalis).

    Science.gov (United States)

    Weir, Scott M; Yu, Shuangying; Talent, Larry G; Maul, Jonathan D; Anderson, Todd A; Salice, Christopher J

    2015-08-01

    Reptiles have been understudied in ecotoxicology, which limits consideration in ecological risk assessments. The goals of the present study were 3-fold: to improve oral and dermal dosing methodologies for reptiles, to generate reptile toxicity data for pesticides, and to correlate reptile and avian toxicity. The authors first assessed the toxicity of different dosing vehicles: 100 μL of water, propylene glycol, and acetone were not toxic. The authors then assessed the oral and dermal toxicity of 4 pesticides following the up-and-down procedure. Neither brodifacoum nor chlorothalonil caused mortality at doses ≤ 1750 μg/g. Under the "neat pesticide" oral exposure, endosulfan (median lethal dose [LD50] = 9.8 μg/g) was more toxic than λ-cyhalothrin (LD50 = 916.5 μg/g). Neither chemical was toxic via dermal exposure. An acetone dosing vehicle increased λ-cyhalothrin toxicity (oral LD50 = 9.8 μg/g; dermal LD50 = 17.5 μg/g), but not endosulfan. Finally, changes in dosing method and husbandry significantly increased dermal λ-cyhalothrin LD50s, which highlights the importance of standardized methods. The authors combined data from the present study with other reptile LD50s to correlate with available avian data. When only definitive LD50s were used in the analysis, a strong correlation was found between avian and reptile toxicity. The results suggest it is possible to build predictive relationships between avian and reptile LD50s. More research is needed, however, to understand trends associated with chemical classes and modes of action. © 2015 SETAC.

  15. Prediction of toxicity and comparison of alternatives using WebTEST (Web-services Toxicity Estimation Software Tool)(Bled Slovenia)

    Science.gov (United States)

    A Java-based web service is being developed within the US EPA’s Chemistry Dashboard to provide real time estimates of toxicity values and physical properties. WebTEST can generate toxicity predictions directly from a simple URL which includes the endpoint, QSAR method, and ...

  16. Random Forests to Predict Rectal Toxicity Following Prostate Cancer Radiation Therapy

    International Nuclear Information System (INIS)

    Ospina, Juan D.; Zhu, Jian; Chira, Ciprian; Bossi, Alberto; Delobel, Jean B.; Beckendorf, Véronique; Dubray, Bernard; Lagrange, Jean-Léon; Correa, Juan C.

    2014-01-01

    Purpose: To propose a random forest normal tissue complication probability (RF-NTCP) model to predict late rectal toxicity following prostate cancer radiation therapy, and to compare its performance to that of classic NTCP models. Methods and Materials: Clinical data and dose-volume histograms (DVH) were collected from 261 patients who received 3-dimensional conformal radiation therapy for prostate cancer with at least 5 years of follow-up. The series was split 1000 times into training and validation cohorts. A RF was trained to predict the risk of 5-year overall rectal toxicity and bleeding. Parameters of the Lyman-Kutcher-Burman (LKB) model were identified and a logistic regression model was fit. The performance of all the models was assessed by computing the area under the receiving operating characteristic curve (AUC). Results: The 5-year grade ≥2 overall rectal toxicity and grade ≥1 and grade ≥2 rectal bleeding rates were 16%, 25%, and 10%, respectively. Predictive capabilities were obtained using the RF-NTCP model for all 3 toxicity endpoints, including both the training and validation cohorts. The age and use of anticoagulants were found to be predictors of rectal bleeding. The AUC for RF-NTCP ranged from 0.66 to 0.76, depending on the toxicity endpoint. The AUC values for the LKB-NTCP were statistically significantly inferior, ranging from 0.62 to 0.69. Conclusions: The RF-NTCP model may be a useful new tool in predicting late rectal toxicity, including variables other than DVH, and thus appears as a strong competitor to classic NTCP models

  17. Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

    Science.gov (United States)

    Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

    2016-09-01

    The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

  18. Sirc-cvs cytotoxicity test: an alternative for predicting rodent acute systemic toxicity.

    Science.gov (United States)

    Kitagaki, Masato; Wakuri, Shinobu; Hirota, Morihiko; Tanaka, Noriho; Itagaki, Hiroshi

    2006-10-01

    An in vitro crystal violet staining method using the rabbit cornea-derived cell line (SIRC-CVS) has been developed as an alternative to predict acute systemic toxicity in rodents. Seventy-nine chemicals, the in vitro cytotoxicity of which was already reported by the Multicenter Evaluation of In vitro Toxicity (MEIC) and ICCVAM/ECVAM, were selected as test compounds. The cells were incubated with the chemicals for 72 hrs and the IC(50) and IC(35) values (microg/mL) were obtained. The results were compared to the in vivo (rat or mouse) "most toxic" oral, intraperitoneal, subcutaneous and intravenous LD(50) values (mg/kg) taken from the RTECS database for each of the chemicals by using Pearson's correlation statistics. The following parameters were calculated: accuracy, sensitivity, specificity, prevalence, positive predictability, and negative predictability. Good linear correlations (Pearson's coefficient; r>0.6) were observed between either the IC(50) or the IC(35) values and all the LD(50) values. Among them, a statistically significant high correlation (r=0.8102, p50) values and the oral LD(50) values. By using the cut-off concentrations of 2,000 mg/kg (LD(50)) and 4,225 microg/mL (IC(50)), no false negatives were observed, and the accuracy was 84.8%. From this, it is concluded that this method could be used to predict the acute systemic toxicity potential of chemicals in rodents.

  19. Use of computer-assisted prediction of toxic effects of chemical substances

    International Nuclear Information System (INIS)

    Simon-Hettich, Brigitte; Rothfuss, Andreas; Steger-Hartmann, Thomas

    2006-01-01

    The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of 'alternative methods'. The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the

  20. A novel approach for predicting the uptake and toxicity of metallic and metalloid ions

    Science.gov (United States)

    Wang, Peng

    2011-01-01

    Electrostatic nature of plant plasma membrane (PM) plays significant roles in the ion uptake and toxicity. Electrical potential at the PM exterior surface (ψ0o) influences ion distribution at the PM exterior surface, and the depolarization of ψ0o negativity increases the electrical driving force for cation transport, but decreases the driving force for anion transport across the PMs. Assessing environmental risks of toxic ions has been a difficult task because the ion concentration (activity) in medium is not directly corrected to its potential effects. Medium characteristics like the content of major cations have important influences on the bioavailability and toxicity of ions in natural waters and soils. Models such as the Free Ion Activity Model (FIAM) and the Biotic Ligand Model (BLM), as usually employed, neglect the ψ0o and hence often lead to false conclusions about interaction mechanisms between toxic ions and major cations for biology. The neglect of ψ0o is not inconsistent with its importance, and possibly reflects the difficulty in the measurement of ψ0o. Based on the dual effects of the ψ0o, electrostatic models were developed to better predict the uptake and toxicity of metallic and metalloid ions. These results suggest that the electrostatic models provides a more robust mechanistic framework to assess metal(loid) ecotoxicity and predict critical metal(loid) concentrations linked to a biological effect, indicating its potential utility in risk assessment of metal(loid)s in water and terrestrial ecosystems. PMID:21386661

  1. NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis.

    Science.gov (United States)

    Lienemann, Kai; Plötz, Thomas; Pestel, Sabine

    2008-01-01

    The aim of safety pharmacology is early detection of compound-induced side-effects. NMR-based urine analysis followed by multivariate data analysis (metabonomics) identifies efficiently differences between toxic and non-toxic compounds; but in most cases multiple administrations of the test compound are necessary. We tested the feasibility of detecting proximal tubule kidney toxicity and phospholipidosis with metabonomics techniques after single compound administration as an early safety pharmacology approach. Rats were treated orally, intravenously, inhalatively or intraperitoneally with different test compounds. Urine was collected at 0-8 h and 8-24 h after compound administration, and (1)H NMR-patterns were recorded from the samples. Variation of post-processing and feature extraction methods led to different views on the data. Support Vector Machines were trained on these different data sets and then aggregated as experts in an Ensemble. Finally, validity was monitored with a cross-validation study using a training, validation, and test data set. Proximal tubule kidney toxicity could be predicted with reasonable total classification accuracy (85%), specificity (88%) and sensitivity (78%). In comparison to alternative histological studies, results were obtained quicker, compound need was reduced, and very importantly fewer animals were needed. In contrast, the induction of phospholipidosis by the test compounds could not be predicted using NMR-based urine analysis or the previously published biomarker PAG. NMR-based urine analysis was shown to effectively predict proximal tubule kidney toxicity after single compound administration in rats. Thus, this experimental design allows early detection of toxicity risks with relatively low amounts of compound in a reasonably short period of time.

  2. Plasma citrulline levels predict intestinal toxicity in patients treated with pelvic radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Onal, Cem; Kotek, Ayse; Arslan, Gungor; Topkan, Erkan (Dept. of Radiation Oncology, Baskent Univ. Faculty of Medicine, Adana (Turkey)), E-mail: hcemonal@hotmail.com; Unal, Birsel (Dept. of Biochemistry, Baskent Univ. Faculty of Medicine, Ankara (Turkey)); Yavuz, Aydin; Yavuz, Melek (Dept. of Radiation Oncology, Akdeniz Univ. Faculty of Medicine, Antalya (Turkey))

    2011-11-15

    Background. Radiotherapy (RT) for abdominal and pelvic malignancies often causes severe small bowel toxicity. Citrulline concentrations are known to decrease with intestinal failure. We thus evaluated the feasibility of plasma citrulline levels in predicting radiation-induced intestinal toxicity. Material and methods. Fifty-three patients (36 prostate cancer, 17 endometrial cancer) who received 45 Gy pelvic RT using conventional fractionation were prospectively evaluated. Patients with prostate cancer received an additional 25-30.6 Gy conformal boost. Plasma citrulline levels were assessed on day 0, mid- (week 3) and post-RT (week 8), and four months post-RT. Dose-volume histogram, citrulline concentration changes, and weekly intestinal toxicity scores were analyzed. Results. Mean age was 63 years (range: 43-81 years) and mean baseline citrulline concentration was 38.0 +- 10.1 mumol/l. Citrulline concentrations were significantly reduced at week 3 (27.4 +- 5.9 mumol/l; p < 0.0001), treatment end (29.9 +- 8.8 mumol/l; p < 0.0001), and four months post-treatment (34.3 +- 12.1; p 0.01). The following factor pairs were significantly positively correlated: Citrulline concentration/mean bowel dose during, end of treatment, and four months post-RT; dose-volume parameters/citrulline change groups; cumulative mean radiation dose/intestinal toxicity at end and four months post-RT; citrulline changes/intestinal toxicity during and end of RT. Citrulline concentration changes significantly differed during treatment according to RTOG intestinal toxicity grades (p < 0.0001). Although the citrulline changes differed significantly within RTOG intestinal toxicity grades (p = 0.003), the difference between Grade 0 and Grade 1 did not differ significantly at the end of the treatment. At four months after RT, no significant differences were apparent. Conclusion. Citrulline-based assessment scores are objective and should be considered in measuring radiation-induced intestinal toxicity

  3. A re-evaluation of PETROTOX for predicting acute and chronic toxicity of petroleum substances.

    Science.gov (United States)

    Redman, Aaron D; Parkerton, Thomas F; Leon Paumen, Miriam; Butler, Josh D; Letinski, Daniel J; den Haan, Klass

    2017-08-01

    The PETROTOX model was developed to perform aquatic hazard assessment of petroleum substances based on substance composition. The model relies on the hydrocarbon block method, which is widely used for conducting petroleum substance risk assessments providing further justification for evaluating model performance. Previous work described this model and provided a preliminary calibration and validation using acute toxicity data for limited petroleum substance. The objective of the present study was to re-evaluate PETROTOX using expanded data covering both acute and chronic toxicity endpoints on invertebrates, algae, and fish for a wider range of petroleum substances. The results indicated that recalibration of 2 model parameters was required, namely, the algal critical target lipid body burden and the log octanol-water partition coefficient (K OW ) limit, used to account for reduced bioavailability of hydrophobic constituents. Acute predictions from the updated model were compared with observed toxicity data and found to generally be within a factor of 3 for algae and invertebrates but overestimated fish toxicity. Chronic predictions were generally within a factor of 5 of empirical data. Furthermore, PETROTOX predicted acute and chronic hazard classifications that were consistent or conservative in 93 and 84% of comparisons, respectively. The PETROTOX model is considered suitable for the purpose of characterizing petroleum substance hazard in substance classification and risk assessments. Environ Toxicol Chem 2017;36:2245-2252. © 2017 SETAC. © 2017 SETAC.

  4. Predicted risks of radiogenic cardiac toxicity in two pediatric patients undergoing photon or proton radiotherapy

    International Nuclear Information System (INIS)

    Zhang, Rui; Howell, Rebecca M; Homann, Kenneth; Giebeler, Annelise; Taddei, Phillip J; Mahajan, Anita; Newhauser, Wayne D

    2013-01-01

    Hodgkin disease (HD) and medulloblastoma (MB) are common malignancies found in children and young adults, and radiotherapy is part of the standard treatment. It was reported that these patients who received radiation therapy have an increased risk of cardiovascular late effects. We compared the predicted risk of developing radiogenic cardiac toxicity after photon versus proton radiotherapies for a pediatric patient with HD and a pediatric patient with MB. In the treatment plans, each patient’s heart was contoured in fine detail, including substructures of the pericardium and myocardium. Risk calculations took into account both therapeutic and stray radiation doses. We calculated the relative risk (RR) of cardiac toxicity using a linear risk model and the normal tissue complication probability (NTCP) values using relative seriality and Lyman models. Uncertainty analyses were also performed. The RR values of cardiac toxicity for the HD patient were 7.27 (proton) and 8.37 (photon), respectively; the RR values for the MB patient were 1.28 (proton) and 8.39 (photon), respectively. The predicted NTCP values for the HD patient were 2.17% (proton) and 2.67% (photon) for the myocardium, and were 2.11% (proton) and 1.92% (photon) for the whole heart. The predicted ratios of NTCP values (proton/photon) for the MB patient were much less than unity. Uncertainty analyses revealed that the predicted ratio of risk between proton and photon therapies was sensitive to uncertainties in the NTCP model parameters and the mean radiation weighting factor for neutrons, but was not sensitive to heart structure contours. The qualitative findings of the study were not sensitive to uncertainties in these factors. We conclude that proton and photon radiotherapies confer similar predicted risks of cardiac toxicity for the HD patient in this study, and that proton therapy reduced the predicted risk for the MB patient in this study

  5. A hypothetical model for predicting the toxicity of high aspect ratio nanoparticles (HARN)

    Science.gov (United States)

    Tran, C. L.; Tantra, R.; Donaldson, K.; Stone, V.; Hankin, S. M.; Ross, B.; Aitken, R. J.; Jones, A. D.

    2011-12-01

    The ability to predict nanoparticle (dimensional structures which are less than 100 nm in size) toxicity through the use of a suitable model is an important goal if nanoparticles are to be regulated in terms of exposures and toxicological effects. Recently, a model to predict toxicity of nanoparticles with high aspect ratio has been put forward by a consortium of scientists. The High aspect ratio nanoparticles (HARN) model is a platform that relates the physical dimensions of HARN (specifically length and diameter ratio) and biopersistence to their toxicity in biological environments. Potentially, this model is of great public health and economic importance, as it can be used as a tool to not only predict toxicological activity but can be used to classify the toxicity of various fibrous nanoparticles, without the need to carry out time-consuming and expensive toxicology studies. However, this model of toxicity is currently hypothetical in nature and is based solely on drawing similarities in its dimensional geometry with that of asbestos and synthetic vitreous fibres. The aim of this review is two-fold: (a) to present findings from past literature, on the physicochemical property and pathogenicity bioassay testing of HARN (b) to identify some of the challenges and future research steps crucial before the HARN model can be accepted as a predictive model. By presenting what has been done, we are able to identify scientific challenges and research directions that are needed for the HARN model to gain public acceptance. Our recommendations for future research includes the need to: (a) accurately link physicochemical data with corresponding pathogenicity assay data, through the use of suitable reference standards and standardised protocols, (b) develop better tools/techniques for physicochemical characterisation, (c) to develop better ways of monitoring HARN in the workplace, (d) to reliably measure dose exposure levels, in order to support future epidemiological

  6. A hypothetical model for predicting the toxicity of high aspect ratio nanoparticles (HARN)

    International Nuclear Information System (INIS)

    Tran, C. L.; Tantra, R.; Donaldson, K.; Stone, V.; Hankin, S. M.; Ross, B.; Aitken, R. J.; Jones, A. D.

    2011-01-01

    The ability to predict nanoparticle (dimensional structures which are less than 100 nm in size) toxicity through the use of a suitable model is an important goal if nanoparticles are to be regulated in terms of exposures and toxicological effects. Recently, a model to predict toxicity of nanoparticles with high aspect ratio has been put forward by a consortium of scientists. The High aspect ratio nanoparticles (HARN) model is a platform that relates the physical dimensions of HARN (specifically length and diameter ratio) and biopersistence to their toxicity in biological environments. Potentially, this model is of great public health and economic importance, as it can be used as a tool to not only predict toxicological activity but can be used to classify the toxicity of various fibrous nanoparticles, without the need to carry out time-consuming and expensive toxicology studies. However, this model of toxicity is currently hypothetical in nature and is based solely on drawing similarities in its dimensional geometry with that of asbestos and synthetic vitreous fibres. The aim of this review is two-fold: (a) to present findings from past literature, on the physicochemical property and pathogenicity bioassay testing of HARN (b) to identify some of the challenges and future research steps crucial before the HARN model can be accepted as a predictive model. By presenting what has been done, we are able to identify scientific challenges and research directions that are needed for the HARN model to gain public acceptance. Our recommendations for future research includes the need to: (a) accurately link physicochemical data with corresponding pathogenicity assay data, through the use of suitable reference standards and standardised protocols, (b) develop better tools/techniques for physicochemical characterisation, (c) to develop better ways of monitoring HARN in the workplace, (d) to reliably measure dose exposure levels, in order to support future epidemiological

  7. A Novel Two-Step Hierarchial Quantitative Structure-Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents

    Science.gov (United States)

    Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...

  8. Predicting refinery effluent toxicity on the basis of hydrocarbon composition determined by GCxGC analysis

    Energy Technology Data Exchange (ETDEWEB)

    Whale, G. [and others

    2013-04-15

    A high resolution analytical method for determining hydrocarbon blocks in petroleum products by comprehensive two-dimensional gas chromatography (GCxGC) was used for the analysis of petroleum hydrocarbons extracted from refinery effluents. From 105 CONCAWE refineries in Europe 111 refinery effluents were collected in the period June 2008 to March 2009 (CONCAWE, 2010). The effluents were analysed for metals, standard effluent parameters (including Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), oil in water (OiW), GCxGC speciated hydrocarbons, BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) and volatile organic compounds. This report describes the subsequent analysis of the GCxGC data, as described in hydrocarbon blocks, and uses the PETROTOX model, to predict the environmental toxicity (i.e. ecotoxicity) of the discharged effluents. A further analysis was undertaken to address the potential environmental impact of these predicted effects initially using default dilution factors and then,when necessary site specific factors. The report describes all the methods used to arrive at the predictions, and shows that for the majority of refinery effluents direct toxicity effects in the effluents are not anticipated. Furthermore, when applying either the EU Risk Assessment Technical Guidance Document (TGD) default dilution factors or site specific dilution factors, none of the refineries are predicted to exerting either acute or chronic toxicity to organisms in the receiving aquatic environment, based on their hydrocarbon composition present in the effluent samples.

  9. Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

    Science.gov (United States)

    Nu’aim, M. N.; Bustam, M. A.

    2018-04-01

    By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

  10. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

    2014-03-15

    Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

  11. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    International Nuclear Information System (INIS)

    Singh, Kunwar P.; Gupta, Shikha

    2014-01-01

    Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R 2 ) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R 2 and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

  12. Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

    Science.gov (United States)

    Lee, Jia-Ying Joey; Miller, James Alastair; Basu, Sreetama; Kee, Ting-Zhen Vanessa; Loo, Lit-Hsin

    2018-06-01

    Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

  13. An Improved Algorithm for Predicting Free Recalls

    Science.gov (United States)

    Laming, Donald

    2008-01-01

    Laming [Laming, D. (2006). "Predicting free recalls." "Journal of Experimental Psychology: Learning, Memory, and Cognition," 32, 1146-1163] has shown that, in a free-recall experiment in which the participants rehearsed out loud, entire sequences of recalls could be predicted, to a useful degree of precision, from the prior sequences of stimuli…

  14. Chemical predictive modelling to improve compound quality.

    Science.gov (United States)

    Cumming, John G; Davis, Andrew M; Muresan, Sorel; Haeberlein, Markus; Chen, Hongming

    2013-12-01

    The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.

  15. Prediction of paraquat exposure and toxicity in clinically ill poisoned patients: a model based approach.

    Science.gov (United States)

    Wunnapuk, Klintean; Mohammed, Fahim; Gawarammana, Indika; Liu, Xin; Verbeeck, Roger K; Buckley, Nicholas A; Roberts, Michael S; Musuamba, Flora T

    2014-10-01

    Paraquat poisoning is a medical problem in many parts of Asia and the Pacific. The mortality rate is extremely high as there is no effective treatment. We analyzed data collected during an ongoing cohort study on self-poisoning and from a randomized controlled trial assessing the efficacy of immunosuppressive therapy in hospitalized paraquat-intoxicated patients. The aim of this analysis was to characterize the toxicokinetics and toxicodynamics of paraquat in this population. A non-linear mixed effects approach was used to perform a toxicokinetic/toxicodynamic population analysis in a cohort of 78 patients. The paraquat plasma concentrations were best fitted by a two compartment toxicokinetic structural model with first order absorption and first order elimination. Changes in renal function were used for the assessment of paraquat toxicodynamics. The estimates of toxicokinetic parameters for the apparent clearance, the apparent volume of distribution and elimination half-life were 1.17 l h(-1) , 2.4 l kg(-1) and 87 h, respectively. Renal function, namely creatinine clearance, was the most significant covariate to explain between patient variability in paraquat clearance.This model suggested that a reduction in paraquat clearance occurred within 24 to 48 h after poison ingestion, and afterwards the clearance was constant over time. The model estimated that a paraquat concentration of 429 μg l(-1) caused 50% of maximum renal toxicity. The immunosuppressive therapy tested during this study was associated with only 8% improvement of renal function. The developed models may be useful as prognostic tools to predict patient outcome based on patient characteristics on admission and to assess drug effectiveness during antidote drug development. © 2014 The British Pharmacological Society.

  16. Plasma citrulline levels predict intestinal toxicity in patients treated with pelvic radiotherapy

    International Nuclear Information System (INIS)

    Onal, Cem; Kotek, Ayse; Arslan, Gungor; Topkan, Erkan; Unal, Birsel; Yavuz, Aydin; Yavuz, Melek

    2011-01-01

    Background. Radiotherapy (RT) for abdominal and pelvic malignancies often causes severe small bowel toxicity. Citrulline concentrations are known to decrease with intestinal failure. We thus evaluated the feasibility of plasma citrulline levels in predicting radiation-induced intestinal toxicity. Material and methods. Fifty-three patients (36 prostate cancer, 17 endometrial cancer) who received 45 Gy pelvic RT using conventional fractionation were prospectively evaluated. Patients with prostate cancer received an additional 25-30.6 Gy conformal boost. Plasma citrulline levels were assessed on day 0, mid- (week 3) and post-RT (week 8), and four months post-RT. Dose-volume histogram, citrulline concentration changes, and weekly intestinal toxicity scores were analyzed. Results. Mean age was 63 years (range: 43-81 years) and mean baseline citrulline concentration was 38.0 ± 10.1 μmol/l. Citrulline concentrations were significantly reduced at week 3 (27.4 ± 5.9 μmol/l; p < 0.0001), treatment end (29.9 ± 8.8 μmol/l; p < 0.0001), and four months post-treatment (34.3 ± 12.1; p 0.01). The following factor pairs were significantly positively correlated: Citrulline concentration/mean bowel dose during, end of treatment, and four months post-RT; dose-volume parameters/citrulline change groups; cumulative mean radiation dose/intestinal toxicity at end and four months post-RT; citrulline changes/intestinal toxicity during and end of RT. Citrulline concentration changes significantly differed during treatment according to RTOG intestinal toxicity grades (p < 0.0001). Although the citrulline changes differed significantly within RTOG intestinal toxicity grades (p = 0.003), the difference between Grade 0 and Grade 1 did not differ significantly at the end of the treatment. At four months after RT, no significant differences were apparent. Conclusion. Citrulline-based assessment scores are objective and should be considered in measuring radiation-induced intestinal toxicity

  17. Serum Creatinine Versus Plasma Methotrexate Levels to Predict Toxicities in Children Receiving High-dose Methotrexate.

    Science.gov (United States)

    Tiwari, Priya; Thomas, M K; Pathania, Subha; Dhawan, Deepa; Gupta, Y K; Vishnubhatla, Sreenivas; Bakhshi, Sameer

    2015-01-01

    Facilities for measuring methotrexate (MTX) levels are not available everywhere, potentially limiting administration of high-dose methotrexate (HDMTX). We hypothesized that serum creatinine alteration after HDMTX administration predicts MTX clearance. Overall, 122 cycles in 50 patients of non-Hodgkin lymphoma or acute lymphoblastic leukemia aged ≤18 years receiving HDMTX were enrolled prospectively. Plasma MTX levels were measured at 12, 24, 36, 48, 60, and 72 hours; serum creatinine was measured at baseline, 24, 48, and 72 hours. Correlation of plasma MTX levels with creatinine levels and changes in creatinine from baseline (Δ creatinine) were evaluated. Plasma MTX levels at 72 hours showed positive correlation with serum creatinine at 48 hours (P = .011) and 72 hours (P = .013) as also Δ creatinine at 48 hours (P = .042) and 72 hours (P = .045). However, cut-off value of either creatinine or Δ creatinine could not be established to reliably predict delayed MTX clearance. Greater than 50% Δ creatinine at 48 and 72 hours significantly predicted grade 3/4 leucopenia (P = .036 and P = .001, respectively) and thrombocytopenia (P = .012 and P = .009, respectively) but not mucositis (P = .827 and P = .910, respectively). Delayed MTX elimination did not predict any grade 3/4 toxicity. In spite of demonstration of significant correlation between serum creatinine and Δ creatinine with plasma MTX levels at 72 hours, cut-off value of either variable to predict MTX delay could not be established. Thus, either of these cannot be used as a surrogate for plasma MTX estimation. Interestingly, Δ creatinine effectively predicted hematological toxicities, which were not predicted by delayed MTX clearance.

  18. Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

    Science.gov (United States)

    Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

    2018-06-15

    Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Early hematologic changes during prostate cancer radiotherapy predictive for late urinary and bowel toxicity

    Energy Technology Data Exchange (ETDEWEB)

    Pinkawa, Michael; Djukic, Victoria; Klotz, Jens; Holy, Richard; Eble, Michael J. [RWTH Aachen University, Department of Radiation Oncology, Aachen (Germany); Ribbing, Carolina [RWTH Aachen University, Department of Diagnostic and Interventional Radiology, Aachen (Germany)

    2015-10-15

    The primary objective of the study was to identify early hematologic changes predictive for radiotherapy (RT)-associated genitourinary and gastrointestinal toxicity. In a group of 91 prostate cancer patients presenting for primary (n = 51) or postoperative (n = 40) curative RT, blood samples (blood count, acute phase proteins, and cytokines) were analyzed before (T1), three times during (T2-T4), and 6-8 weeks after (T5) radiotherapy. Before RT (baseline), on the last day (acute toxicity), a median of 2 months and 16 months (late toxicity) after RT, patients responded to a validated questionnaire (Expanded Prostate Cancer Index Composite). Acute score changes > 20 points and late changes > 10 points were considered clinically relevant. Radiotherapy resulted in significant changes of hematologic parameters, with the largest effect on lymphocytes (mean decrease of 31-45 %) and significant dependence on target volume. C-reactive protein (CRP) elevation > 5 mg/l and hemoglobin level decrease ≥ 5 G/1 at T2 were found to be independently predictive for acute urinary toxicity (p < 0.01, respectively). CRP elevation was predominantly detected in primary prostate RT (p = 0.02). Early lymphocyte level elevation ≥ 0.3G/l at T2 was protective against late urinary and bowel toxicity (p = 0.02, respectively). Other significant predictive factors for late bowel toxicity were decreasing hemoglobin levels (cut-off ≥ 5 G/l) at T2 (p = 0.04); changes of TNF-α (tumor necrosis factor; p = 0.03) and ferritin levels (p = 0.02) at T5. All patients with late bowel toxicity had interleukin (IL)-6 levels < 1.5 ng/l at T2 (63 % without; p = 0.01). Early hematologic changes during prostate cancer radiotherapy are predictive for late urinary and bowel toxicity. (orig.) [German] Das primaere Ziel der Studie war die Identifikation von fruehen haematologischen Veraenderungen mit praediktiver Bedeutung fuer radiotherapieassoziierte genitourinale und gastrointestinale Toxizitaet. In einer

  20. Large-scale assessment of the zebrafish embryo as a possible predictive model in toxicity testing.

    Directory of Open Access Journals (Sweden)

    Shaukat Ali

    Full Text Available BACKGROUND: In the drug discovery pipeline, safety pharmacology is a major issue. The zebrafish has been proposed as a model that can bridge the gap in this field between cell assays (which are cost-effective, but low in data content and rodent assays (which are high in data content, but less cost-efficient. However, zebrafish assays are only likely to be useful if they can be shown to have high predictive power. We examined this issue by assaying 60 water-soluble compounds representing a range of chemical classes and toxicological mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: Over 20,000 wild-type zebrafish embryos (including controls were cultured individually in defined buffer in 96-well plates. Embryos were exposed for a 96 hour period starting at 24 hours post fertilization. A logarithmic concentration series was used for range-finding, followed by a narrower geometric series for LC(50 determination. Zebrafish embryo LC(50 (log mmol/L, and published data on rodent LD(50 (log mmol/kg, were found to be strongly correlated (using Kendall's rank correlation tau and Pearson's product-moment correlation. The slope of the regression line for the full set of compounds was 0.73403. However, we found that the slope was strongly influenced by compound class. Thus, while most compounds had a similar toxicity level in both species, some compounds were markedly more toxic in zebrafish than in rodents, or vice versa. CONCLUSIONS: For the substances examined here, in aggregate, the zebrafish embryo model has good predictivity for toxicity in rodents. However, the correlation between zebrafish and rodent toxicity varies considerably between individual compounds and compound class. We discuss the strengths and limitations of the zebrafish model in light of these findings.

  1. Prediction of Chest Wall Toxicity From Lung Stereotactic Body Radiotherapy (SBRT)

    Energy Technology Data Exchange (ETDEWEB)

    Stephans, Kevin L., E-mail: stephak@ccf.org [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States); Djemil, Toufik; Tendulkar, Rahul D. [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States); Robinson, Cliff G. [Department of Radiation Oncology, Siteman Cancer Center, Washington University, St Louis, MO (United States); Reddy, Chandana A.; Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH (United States)

    2012-02-01

    Purpose: To determine patient, tumor, and treatment factors related to the development of late chest wall toxicity after lung stereotactic body radiotherapy (SBRT). Methods and Materials: We reviewed a registry of 134 patients treated with lung SBRT to 60 Gy in 3 fractions who had greater than 1 year of clinical follow-up and no history of multiple treatments to the same lobe (n = 48). Patients were treated as per Radiation Therapy Oncology Group Protocol 0236 without specific chest wall avoidance criteria. The chest wall was retrospectively contoured. Thirty-two lesions measured less than 3 cm, and sixteen measured 3 to 5 cm. The median planning target volume was 29 cm{sup 3}. Results: With a median follow-up of 18.8 months, 10 patients had late symptomatic chest wall toxicity (4 Grade 1 and 6 Grade 2) at a median of 8.8 months after SBRT. No patient characteristics (age, diabetes, hypertension, peripheral vascular disease, or body mass index) were predictive for toxicity, whereas there was a trend for continued smoking (p = 0.066; odds ratio [OR], 4.4). Greatest single tumor dimension (p = 0.047; OR, 2.63) and planning target volume (p = 0.040; OR, 1.04) were correlated with toxicity, whereas distance from tumor edge to chest wall and gross tumor volume did not reach statistical significance. Volumes of chest wall receiving 30 Gy (V30) through 70 Gy (V70) were all highly significant, although this correlation weakened for V65 and V70 and maximum chest wall point dose only trended to significance (p = 0.06). On multivariate analysis, tumor volume was no longer correlated with toxicity and only V30 through V60 remained statistically significant. Conclusions: Tumor size and chest wall dosimetry are correlated to late chest wall toxicity. Only chest wall V30 through V60 remained significant on multivariate analysis. Restricting V30 to 30 cm{sup 3} or less and V60 to 3 cm{sup 3} or less should result in a 10% to 15% risk of late chest wall toxicity or lower.

  2. Clinical use of the hyperthermia treatment planning system HyperPlan to predict effectiveness and toxicity

    International Nuclear Information System (INIS)

    Sreenivasa, Geetha; Gellermann, Johanna; Rau, Beate; Nadobny, Jacek; Schlag, Peter; Deuflhard, Peter; Felix, Roland; Wust, Peter

    2003-01-01

    Purpose: The main aim is to prove the clinical practicability of the hyperthermia treatment planning system HyperPlan on a β-test level. Data and observations obtained from clinical hyperthermia are compared with the numeric methods FE (finite element) and FDTD (finite difference time domain), respectively. Methods and Materials: The planning system HyperPlan is built on top of the modular, object-oriented platform for visualization and model generation AMIRA. This system already contains powerful algorithms for image processing, geometric modeling, and three-dimensional graphics display. A number of hyperthermia-specific modules are provided, enabling the creation of three-dimensional tetrahedral patient models suitable for treatment planning. Two numeric methods, FE and FDTD, are implemented in HyperPlan for solving Maxwell's equations. Both methods base their calculations on segmented (contour based) CT or MR image data. A tetrahedral grid is generated from the segmented tissue boundaries, consisting of approximately 80,000 tetrahedrons per patient. The FE method necessitates, primarily, this tetrahedral grid for the calculation of the E-field. The FDTD method, on the other hand, calculates the E-field on a cubical grid, but also requires a tetrahedral grid for correction at electrical interfaces. In both methods, temperature distributions are calculated on the tetrahedral grid by solving the bioheat transfer equation with the FE method. Segmentation, grid generation, E-field, and temperature calculation can be carried out in clinical practice at an acceptable time expenditure of about 1-2 days. Results: All 30 patients we analyzed with cervical, rectal, and prostate carcinoma exhibit a good correlation between the model calculations and the attained clinical data regarding acute toxicity (hot spots), prediction of easy-to-heat or difficult-to-heat patients, and the dependency on various other individual parameters. We could show sufficient agreement between

  3. Improvements in disruption prediction at ASDEX Upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Aledda, R., E-mail: raffaele.aledda@diee.unica.it; Cannas, B., E-mail: cannas@diee.unica.it; Fanni, A., E-mail: fanni@diee.unica.it; Pau, A., E-mail: alessandro.pau@diee.unica.it; Sias, G., E-mail: giuliana.sias@diee.unica.it

    2015-10-15

    Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

  4. Improvements in disruption prediction at ASDEX Upgrade

    International Nuclear Information System (INIS)

    Aledda, R.; Cannas, B.; Fanni, A.; Pau, A.; Sias, G.

    2015-01-01

    Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

  5. Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines.

    Science.gov (United States)

    DeForest, David K; Brix, Kevin V; Tear, Lucinda M; Adams, William J

    2018-01-01

    The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC. © 2017 SETAC.

  6. Recent Improvements in IERS Rapid Service/Prediction Center Products

    National Research Council Canada - National Science Library

    Stamatakos, N; Luzum, B; Wooden, W

    2007-01-01

    ...) at USNO has made several improvements to its combination and pre- diction products. These improvements are due to the inclusion of new input data sources as well as modifications to the combination and prediction algorithms...

  7. Improved techniques for predicting spacecraft power

    International Nuclear Information System (INIS)

    Chmielewski, A.B.

    1987-01-01

    Radioisotope Thermoelectric Generators (RTGs) are going to supply power for the NASA Galileo and Ulysses spacecraft now scheduled to be launched in 1989 and 1990. The duration of the Galileo mission is expected to be over 8 years. This brings the total RTG lifetime to 13 years. In 13 years, the RTG power drops more than 20 percent leaving a very small power margin over what is consumed by the spacecraft. Thus it is very important to accurately predict the RTG performance and be able to assess the magnitude of errors involved. The paper lists all the error sources involved in the RTG power predictions and describes a statistical method for calculating the tolerance

  8. Improving LMA predictions with non standard interactions

    CERN Document Server

    Das, C R

    2010-01-01

    It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

  9. Work-principle model for predicting toxic fumes of nonideal explosives

    Energy Technology Data Exchange (ETDEWEB)

    Wieland, Michael S. [National Institute of Occupational Safety and Health, Pittsburgh Research Center, P.O. Box 18070, Pittsburgh, PA 15236-0070 (United States)

    2004-08-01

    The work-principle from thermodynamics was used to formulate a model for predicting toxic fumes from mining explosives in underground chamber tests, where rapid turbulent combustion within the surrounding air noticeably changes the resulting concentrations. Two model constants were required to help characterize the reaction zone undergoing rapid chemical transformations in conjunction with heat transfer and work output: a stoichiometry mixing fraction and a reaction-quenching temperature. Rudimentary theory with an unsteady uniform concentration gradient was taken to characterize the combustion zone, yielding 75% for the mixing fraction. Four quenching temperature trends were resolved and compared to test results of ammonium nitrate compositions with different fuel-oil percentages (ANFO). The quenching temperature 2345 K was the optimum choice for fitting the two major components of fume toxicity: carbon monoxide (CO) and total nitrogen oxides (NO{sub X}). The resulting two-constant model was used to generate comparisons for test results of ANFO compositions with additives. Though respectable fits were usually found, charge formulations which reacted weakly could not be resolved numerically. The work-principle model yields toxic concentrations for a range of charge formulations, making it a useful tool for investigating the potential hazard of released fumes and reducing the risk of unwanted incidents. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  10. Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products

    Directory of Open Access Journals (Sweden)

    John Wheeler

    2012-07-01

    Full Text Available Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR, has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50 for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

  11. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. Predicting the formation and the dispersion of toxic combustion products from the fires of dangerous substances

    Science.gov (United States)

    Nevrlý, V.; Bitala, P.; Danihelka, P.; Dobeš, P.; Dlabka, J.; Hejzlar, T.; Baudišová, B.; Míček, D.; Zelinger, Z.

    2012-04-01

    Natural events, such as wildfires, lightning or earthquakes represent a frequent trigger of industrial fires involving dangerous substances. Dispersion of smoke plume from such fires and the effects of toxic combustion products are one of the reference scenarios expected in the framework of major accident prevention. Nowadays, tools for impact assessment of these events are rather missing. Detailed knowledge of burning material composition, atmospheric conditions, and other factors are required in order to describe quantitatively the source term of toxic fire products and to evaluate the parameters of smoke plume. Nevertheless, an assessment of toxic emissions from large scale fires involves a high degree of uncertainty, because of the complex character of physical and chemical processes in the harsh environment of uncontrolled flame. Among the others, soot particle formation can be mentioned as still being one of the unresolved problems in combustion chemistry, as well as decomposition pathways of chemical substances. Therefore, simplified approach for estimating the emission factors from outdoor fires of dangerous chemicals, utilizable for major accident prevention and preparedness, was developed and the case study illustrating the application of the proposed method was performed. ALOFT-FT software tool based on large eddy simulation of buoyant fire plumes was employed for predicting the local toxic contamination in the down-wind vicinity of the fire. The database of model input parameters can be effectively modified enabling the simulation of the smoke plume from pool fires or jet fires of arbitrary flammable (or combustible) gas, liquid or solid. This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic via the project LD11012 (in the frame of the COST CM0901 Action) and the Ministry of Environment of the Czech Republic (project no. SPII 1a10 45/70).

  13. Improved distribution and reduced toxicity of adriamycin bound to albumin-heparin microspheres

    NARCIS (Netherlands)

    Cremers, Harry; Cremers, H.F.M.; Bayon, L.G.; Verrijk, R.; Wesseling, M.M.; Wondergem, J.; Heuff, G.; Kwon, G.S.; Bae, Y.H.; Feijen, Jan; Kim, S.W.

    1995-01-01

    Adriamycin (ADR) was formulated in albumin-heparin conjugate microspheres (AHCMS) to improve site-specific delivery and to reduce the toxicity of the drug. The effect of formulating ADR in AHCMS was investigated upon intrahepatic administration to male Wag/Rij rats. After intraveno-portal (i.v.p.)

  14. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  15. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    International Nuclear Information System (INIS)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa

    2014-01-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation

  16. Prediction of Chemical Carcinogenicity in Rodents from in vitro Genetic Toxicity Assays

    Science.gov (United States)

    Tennant, Raymond W.; Margolin, Barry H.; Shelby, Michael D.; Zeiger, Errol; Haseman, Joseph K.; Spalding, Judson; Caspary, William; Resnick, Michael; Stasiewicz, Stanley; Anderson, Beth; Minor, Robert

    1987-05-01

    Four widely used in vitro assays for genetic toxicity were evaluated for their ability to predict the carcinogenicity of selected chemicals in rodents. These assays were mutagenesis in Salmonella and mouse lymphoma cells and chromosome aberrations and sister chromatid exchanges in Chinese hamster ovary cells. Seventy-three chemicals recently tested in 2-year carcinogenicity studies conducted by the National Cancer Institute and the National Toxicology Program were used in this evaluation. Test results from the four in vitro assays did not show significant differences in individual concordance with the rodent carcinogenicity results; the concordance of each assay was approximately 60 percent. Within the limits of this study there was no evidence of complementarity among the four assays, and no battery of tests constructed from these assays improved substantially on the overall performance of the Salmonella assay. The in vitro assays which represented a range of three cell types and four end points did show substantial agreement among themselves, indicating that chemicals positive in one in vitro assay tended to be positive in the other in vitro assays. To help put this project into its proper context, we emphasize certain features of the study: 1) Standard protocols were used to mimic the major use of STTs worldwide--screening for mutagens and carcinogens; no attempt was made to optimize protocols for specific chemicals. 2) The 73 NTP chemicals and their 60% incidence of carcinogenicity are probably not representative of the universe of chemicals but rather reflect the recent chemical selection process for the NTP carcinogenicity assay. 3) The small, diverse group of chemicals precludes a meaningful evaluation of the predictive utility of chemical structure information. 4) The NTP is currently testing these same 73 chemicals in two in vivo STTs for chromosomal effects. 5) Complete data for an additional group of 30 to 40 NTP chemicals will be gathered on

  17. Improving plant availability by predicting reactor trips

    International Nuclear Information System (INIS)

    Frank, M.V.; Epstein, S.A.

    1986-01-01

    Management Ahnalysis Company (MAC) has developed and applied two complementary software packages called RiTSE and RAMSES. Together they provide an mini-computer workstation for maintenance and operations personnel to dramatically reduce inadvertent reactor trips. They are intended to be used by those responsible at the plant for authorizing work during operation (such as a clearance coordinator or shift foreman in U.S. plants). They discover and represent all components, processes, and their interactions that could case a trip. They predict if future activities at the plant would cause a reactor trip, provide a reactor trip warning system and aid in post-trip cause analysis. RAMSES is a general reliability engineering software package that uses concepts of artificial intelligence to provide unique capabilities on personal and mini-computers

  18. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

  19. From basic physics to mechanisms of toxicity: the ``liquid drop'' approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles

    Science.gov (United States)

    Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Kuz'min, Victor; Puzyn, Tomasz; Leszczynski, Jerzy

    2014-10-01

    Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the ``liquid drop'' model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were

  20. Verification and improvement of a predictive model for radionuclide migration

    International Nuclear Information System (INIS)

    Miller, C.W.; Benson, L.V.; Carnahan, C.L.

    1982-01-01

    Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

  1. A novel two-dimensional liquid chromatographic system for the online toxicity prediction of pharmaceuticals and related substances

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jian; Xu, Li [Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030 (China); Shi, Zhi-guo, E-mail: shizg@whu.edu.cn [Department of Chemistry, Wuhan University, Wuhan 430072 (China); Hu, Min [Hubei Instrument for Food and Drug Control, Wuhan (China)

    2015-08-15

    Highlights: • A novel two-dimensional liquid chromatographic system was developed. • The 1st dimension was ODS to separate components in the sample. • The 2nd dimension was biopartitioning micellar chromatography to predict toxicity. • The system was used to screen toxicity of pharmaceuticals and related substances. • It was promising for fast online toxicity screening of complex sample in one step. - Abstract: In this study, a novel two-dimensional liquid chromatographic (2D-LC) system was developed for simultaneous separation and toxicity prediction of pharmaceutical and its related substances. A conventional ODS column was used on the 1st-D to separate the sample; while, bio-partitioning micellar chromatography served as the 2nd-D to predict toxicity of the components. The established system was tested for the toxicity of ibuprofen and its impurities with known toxicity. With only one injection, ibuprofen and its impurities were separated on the 1st-D; and LC50 values of individual impurity were obtained based on the quantitative retention–activity relationships, which agreed well with the reported data. Furthermore, LC50 values of photolysis transformation products (TPs) of carprofen, ketoprofen and diclofenac acid (as unknown compounds) were screened in this 2D-LC system, which could be an indicator of the toxicity of these TPs and was meaningful for the environmental monitoring and drinking water treatment. The established 2D-LC system was cost-effective, time-saving and reliable, and was promising for fast online screening of toxicity of known and unknown analytes in the complex sample in a single step. It may find applications in environment, pharmaceutical and food, etc.

  2. A novel two-dimensional liquid chromatographic system for the online toxicity prediction of pharmaceuticals and related substances

    International Nuclear Information System (INIS)

    Li, Jian; Xu, Li; Shi, Zhi-guo; Hu, Min

    2015-01-01

    Highlights: • A novel two-dimensional liquid chromatographic system was developed. • The 1st dimension was ODS to separate components in the sample. • The 2nd dimension was biopartitioning micellar chromatography to predict toxicity. • The system was used to screen toxicity of pharmaceuticals and related substances. • It was promising for fast online toxicity screening of complex sample in one step. - Abstract: In this study, a novel two-dimensional liquid chromatographic (2D-LC) system was developed for simultaneous separation and toxicity prediction of pharmaceutical and its related substances. A conventional ODS column was used on the 1st-D to separate the sample; while, bio-partitioning micellar chromatography served as the 2nd-D to predict toxicity of the components. The established system was tested for the toxicity of ibuprofen and its impurities with known toxicity. With only one injection, ibuprofen and its impurities were separated on the 1st-D; and LC50 values of individual impurity were obtained based on the quantitative retention–activity relationships, which agreed well with the reported data. Furthermore, LC50 values of photolysis transformation products (TPs) of carprofen, ketoprofen and diclofenac acid (as unknown compounds) were screened in this 2D-LC system, which could be an indicator of the toxicity of these TPs and was meaningful for the environmental monitoring and drinking water treatment. The established 2D-LC system was cost-effective, time-saving and reliable, and was promising for fast online screening of toxicity of known and unknown analytes in the complex sample in a single step. It may find applications in environment, pharmaceutical and food, etc

  3. Photodegradation kinetics, transformation, and toxicity prediction of ketoprofen, carprofen, and diclofenac acid in aqueous solutions.

    Science.gov (United States)

    Li, Jian; Ma, Li-Yun; Li, Lu-Shuang; Xu, Li

    2017-12-01

    Photodegradation of 3 commonly used nonsteroidal anti-inflammatory drugs, ketoprofen, carprofen, and diclofenac acid, was conducted under ultraviolet (UV) irradiation. The kinetic results showed that the 3 pharmaceuticals obeyed the first-order reaction with decreasing rate constants of 1.54 × 10 -4 , 5.91 × 10 -5 , and 7.78 × 10 -6  s -1 for carprofen, ketoprofen, and diclofenac acid, respectively. Moreover, the main transformation products were identified by ion-pair liquid-liquid extraction combined with injection port derivatization-gas chromatography-mass spectrometry and high-performance liquid chromatography-quadrupole-time of flight mass spectrometric analysis. There were 8, 3, and 6 transformation products identified for ketoprofen, carprofen, and diclofenac acid, respectively. Decarboxylation, dechlorination, oxidation, demethylation, esterification, and cyclization were proposed to be associated with the transformation of the 3 pharmaceuticals. Toxicity prediction of the transformation products was conducted on the EPI Suite software based on ECOSAR model, and the results indicate that some of the transformation products were more toxic than the parent compounds. The present study provides the foundation to understand the transformation behavior of the studied pharmaceuticals under UV irradiation. Environ Toxicol Chem 2017;36:3232-3239. © 2017 SETAC. © 2017 SETAC.

  4. In silico prediction of microRNAs on fluoride induced sperm toxicity in mice.

    Science.gov (United States)

    Raghunath, Azhwar; Jeyabaskar, Dhivyalakshmi; Sundarraj, Kiruthika; Panneerselvam, Lakshmikanthan; Perumal, Ekambaram

    2016-12-01

    Fluorosis is an endemic global problem causing male reproductive impairment. F mediates male reproductive toxicity in mice down-regulating 63 genes involved in diverse biological processes - apoptosis, cell cycle, cell signaling, chemotaxis, electron transport, glycolysis, oxidative stress, sperm capacitation and spermatogenesis. We predicted the miRNAs down-regulating these 63 genes using TargetScan, DIANA and MicroCosm. The prediction tools identified 3059 miRNAs targeting 63 genes. Of the predicted interactions, 11 miRNAs (mmu-miR-103, -107, -122, -188a, -199a-5p, -205, -340-5p, -345-3p, -452-5p, -499, -878-3p) were commonly found in the three tools utilized and seven miRNAs (miR-9-5p, miR-511-3p, miR-7b-5p, miR-30e-5p, miR-17-5p, miR-122-5p and miR-541-5p) targeting six genes (Traf3, Rock2, Rgs8, Atp1b2, Cacna2d1 and Aldoa) were already validated experimentally in mice. The miRNA-mRNA network of the predicted miRNAs with its respective targets revealed the complex interaction within a biological process leading to sperm dysfunction on exposure to F. Our findings not only suggest that the predicted miRs furnish evidence, but also have the potential to serve as non-invasive biomarkers on F-induced sperm dysfunction. Our data contribute towards elucidating the function of miRNAs in the fluoride induced infertility. miRNA molecular pathways in F intoxication will open new avenues on the use of antagomirs in recovering fertility. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

    Science.gov (United States)

    Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discri...

  6. Combined Adjuvant Radiochemotherapy With IMRT/XELOX Improves Outcome With Low Renal Toxicity in Gastric Cancer

    International Nuclear Information System (INIS)

    Boda-Heggemann, Judit; Hofheinz, Ralf-Dieter; Weiss, Christel; Mennemeyer, Philipp; Mai, Sabine K.; Hermes, Petra; Wertz, Hansjoerg; Post, Stefan; Massner, Bernd; Hieber, Udo; Hochhaus, Andreas; Wenz, Frederik; Lohr, Frank

    2009-01-01

    Objectives: Adjuvant radiochemotherapy improves survival of patients with advanced gastric cancer. We assessed in two sequential cohorts whether improved radiotherapy technique (IMRT) together with intensified chemotherapy improves outcome vs. conventional three-dimensional conformal radiotherapy (3D-CRT) and standard chemotherapy in these patients while maintaining or reducing renal toxicity. Materials and Methods: Sixty consecutive patients treated for gastric cancer either with 3D-CRT (n = 27) and IMRT (n = 33) were evaluated. More than 70% had undergone D2 resection. Although there was a slight imbalance in R0 status between cohorts, N+ status was balanced. Chemotherapy consisted predominantly of 5-fluorouracil/folinic acid (n = 36) in the earlier cohort and mostly of oxaliplatin/capecitabine (XELOX, n = 24) in the later cohort. Primary end points were overall survival (OS), disease-free survival (DFS), and renal toxicity based on creatinine levels. Results: Median follow-up (FU) of all patients in the 3D-CRT group was 18 months and in the IMRT group 22 months (median FU of surviving patients 67 months in the 3D-CRT group and 25 months in the IMRT group). Overall median survival (and DFS) were 18 (13) months in the 3D-CRT group and both not reached in the IMRT group (p = 0.0492 and 0.0216). Actuarial 2-year survival was 37% and 67% in the 3D-CRT and IMRT groups, respectively. No late renal toxicity >Grade 2 (LENT-SOMA scale) was observed in either cohort. Conclusion: When comparing sequentially treated patient cohorts with similar characteristics, OS and DFS improved with the use of IMRT and intensified chemotherapy without signs of increased renal toxicity.

  7. Text mining improves prediction of protein functional sites.

    Directory of Open Access Journals (Sweden)

    Karin M Verspoor

    Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  8. Text Mining Improves Prediction of Protein Functional Sites

    Science.gov (United States)

    Cohn, Judith D.; Ravikumar, Komandur E.

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

  9. Toxicity assessment of silica coated iron oxide nanoparticles and biocompatibility improvement by surface engineering.

    Directory of Open Access Journals (Sweden)

    Maria Ada Malvindi

    Full Text Available We have studied in vitro toxicity of iron oxide nanoparticles (NPs coated with a thin silica shell (Fe3O4/SiO2 NPs on A549 and HeLa cells. We compared bare and surface passivated Fe3O4/SiO2 NPs to evaluate the effects of the coating on the particle stability and toxicity. NPs cytotoxicity was investigated by cell viability, membrane integrity, mitochondrial membrane potential (MMP, reactive oxygen species (ROS assays, and their genotoxicity by comet assay. Our results show that NPs surface passivation reduces the oxidative stress and alteration of iron homeostasis and, consequently, the overall toxicity, despite bare and passivated NPs show similar cell internalization efficiency. We found that the higher toxicity of bare NPs is due to their stronger in-situ degradation, with larger intracellular release of iron ions, as compared to surface passivated NPs. Our results indicate that surface engineering of Fe3O4/SiO2 NPs plays a key role in improving particles stability in biological environments reducing both cytotoxic and genotoxic effects.

  10. Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae.

    Science.gov (United States)

    Gao, Yongfei; Feng, Jianfeng; Kang, Lili; Xu, Xin; Zhu, Lin

    2018-01-01

    The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

    Science.gov (United States)

    Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

    2011-03-01

    There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

  12. Nitrite addition to acidified sludge significantly improves digestibility, toxic metal removal, dewaterability and pathogen reduction

    Science.gov (United States)

    Du, Fangzhou; Keller, Jürg; Yuan, Zhiguo; Batstone, Damien J.; Freguia, Stefano; Pikaar, Ilje

    2016-12-01

    Sludge management is a major issue for water utilities globally. Poor digestibility and dewaterability are the main factors determining the cost for sludge management, whereas pathogen and toxic metal concentrations limit beneficial reuse. In this study, the effects of low level nitrite addition to acidified sludge to simultaneously enhance digestibility, toxic metal removal, dewaterability and pathogen reduction were investigated. Waste activated sludge (WAS) from a full-scale waste water treatment plant was treated at pH 2 with 10 mg NO2--N/L for 5 h. Biochemical methane potential tests showed an increase in the methane production of 28%, corresponding to an improvement from 247 ± 8 L CH4/kg VS to 317 ± 1 L CH4/kg VS. The enhanced removal of toxic metals further increased the methane production by another 18% to 360 ± 6 L CH4/kg VS (a total increase of 46%). The solids content of dewatered sludge increased from 14.6 ± 1.4% in the control to 18.2 ± 0.8%. A 4-log reduction for both total coliforms and E. coli was achieved. Overall, this study highlights the potential of acidification with low level nitrite addition as an effective and simple method achieving multiple improvements in terms of sludge management.

  13. Improving toxicity assessment of pesticide mixtures: the use of polar passive sampling devices extracts in microalgae toxicity tests

    Directory of Open Access Journals (Sweden)

    Sandra KIM TIAM

    2016-09-01

    Full Text Available Complexity of contaminants exposure needs to be taking in account for an appropriate evaluation of risks related to mixtures of pesticides released in the ecosystems. Toxicity assessment of such mixtures can be made through a variety of toxicity tests reflecting different level of biological complexity. This paper reviews the recent developments of passive sampling techniques for polar compounds, especially Polar Organic Chemical Integrative Samplers (POCIS and Chemcatcher® and the principal assessment techniques using microalgae in laboratory experiments. The progresses permitted by the coupled use of such passive samplers and ecotoxicology testing as well as their limitations are presented. Case studies combining passive sampling devices (PSD extracts and toxicity assessment toward microorganisms at different biological scales from single organisms to communities level are presented. These case studies, respectively aimed i at characterizing the toxic potential of waters using dose-response curves, and ii at performing microcosm experiments with increased environmental realism in the toxicant exposure in term of cocktail composition and concentration. Finally perspectives and limitations of such approaches for future applications in the area of environmental risk assessment are discussed.

  14. Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?

    Science.gov (United States)

    Meyer, Joseph S; Traudt, Elizabeth M; Ranville, James F

    2018-01-01

    In aquatic toxicology, a toxicity-prediction model is generally deemed acceptable if its predicted median lethal concentrations (LC50 values) or median effect concentrations (EC50 values) are within a factor of 2 of their paired, observed LC50 or EC50 values. However, that rule of thumb is based on results from only two studies: multiple LC50 values for the fathead minnow (Pimephales promelas) exposed to Cu in one type of exposure water, and multiple EC50 values for Daphnia magna exposed to Zn in another type of exposure water. We tested whether the factor-of-2 rule of thumb also is supported in a different dataset in which D. magna were exposed separately to Cd, Cu, Ni, or Zn. Overall, the factor-of-2 rule of thumb appeared to be a good guide to evaluating the acceptability of a toxicity model's underprediction or overprediction of observed LC50 or EC50 values in these acute toxicity tests.

  15. COMPUTER-BASED PREDICTION OF TOXICITY USING THE ELECTRON-CONFORMATIONAL METHOD. APPLICATION TO FRAGRANCE ALLERGENS AND OTHER ENVIRONMENTAL POLLUTANTS

    Directory of Open Access Journals (Sweden)

    Natalia N. Gorinchoy

    2012-06-01

    Full Text Available The electron-conformational (EC method is employed for the toxicophore (Tph identification and quantitative prediction of toxicity using the training set of 24 compounds that are considered as fragrance allergens. The values of a=LD50 in oral exposure of rats were chosen as a measure of toxicity. EC parameters are evaluated on the base of conformational analysis and ab initio electronic structure calculations (including solvent influence. The Tph consists of four sites which in this series of compounds are represented by three carbon and one oxygen atoms, but may be any other atoms that have the same electronic and geometric features within the tolerance limits. The regression model taking into consideration the Tph flexibility, anti-Tph shielding, and influence of out-of-Tph functional groups predicts well the experimental values of toxicity (R2 = 0.93 with a reasonable leaveone- out cross-validation.

  16. Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells.

    Science.gov (United States)

    Developing predictions of in vivo developmental toxicity of ToxCast chemicals using mouse embryonic stem cells S. Hunter, M. Rosen, M. Hoopes, H. Nichols, S. Jeffay, K. Chandler1, Integrated Systems Toxicology Division, National Health and Environmental Effects Research Labor...

  17. Human Pluripotent Stem Cell-Based Assay Predicts Developmental Toxicity Potential of ToxCast Chemicals (ACT meeting)

    Science.gov (United States)

    Worldwide initiatives to screen for toxicity potential among the thousands of chemicals currently in use require inexpensive and high-throughput in vitro models to meet their goals. The devTOX quickPredict platform is an in vitro human pluripotent stem cell-based assay used to as...

  18. Current and future perspectives on the development, evaluation and application of in silico approaches for predicting toxicity

    Science.gov (United States)

    Safety-related problems continue to be one of the major reasons of attrition in drug development. Non-testing approaches to predict toxicity could form part of the solution. This review provides a perspective of current status of non-testing approaches available for the predictio...

  19. Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships (MCBIOS)

    Science.gov (United States)

    Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships Jie Liu1,2, Richard Judson1, Matthew T. Martin1, Huixiao Hong3, Imran Shah1 1National Center for Computational Toxicology (NCCT), US EPA, RTP, NC...

  20. Improving anticancer activity and reducing systemic toxicity of doxorubicin by self-assembled polymeric micelles

    International Nuclear Information System (INIS)

    Gou Maling; Shi Huashan; Guo Gang; Men Ke; Zhang Juan; Li Zhiyong; Luo Feng; Qian Zhiyong; Wei Yuquan; Zheng Lan; Zhao Xia

    2011-01-01

    In an attempt to improve anticancer activity and reduce systemic toxicity of doxorubicin (Dox), we encapsulated Dox in monomethoxy poly(ethylene glycol)-poly(ε-caprolactone) (MPEG-PCL) micelles by a novel self-assembly procedure without using surfactants, organic solvents or vigorous stirring. These Dox encapsulated MPEG-PCL (Dox/MPEG-PCL) micelles with drug loading of 4.2% were monodisperse and ∼ 20 nm in diameter. The Dox can be released from the Dox/MPEG-PCL micelles; the Dox-release at pH 5.5 was faster than that at pH 7.0. Encapsulation of Dox in MPEG-PCL micelles enhanced the cellular uptake and cytotoxicity of Dox on the C-26 colon carcinoma cell in vitro, and slowed the extravasation of Dox in the transgenic zebrafish model. Compared to free Dox, Dox/MPEG-PCL micelles were more effective in inhibiting tumor growth in the subcutaneous C-26 colon carcinoma and Lewis lung carcinoma models, and prolonging survival of mice bearing these tumors. Dox/MPEG-PCL micelles also induced lower systemic toxicity than free Dox. In conclusion, incorporation of Dox in MPEG-PCL micelles enhanced the anticancer activity and decreased the systemic toxicity of Dox; these Dox/MPEG-PCL micelles are an interesting formulation of Dox and may have potential clinical applications in cancer therapy.

  1. Improving anticancer activity and reducing systemic toxicity of doxorubicin by self-assembled polymeric micelles

    Energy Technology Data Exchange (ETDEWEB)

    Gou Maling; Shi Huashan; Guo Gang; Men Ke; Zhang Juan; Li Zhiyong; Luo Feng; Qian Zhiyong; Wei Yuquan [State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041 (China); Zheng Lan; Zhao Xia, E-mail: anderson-qian@163.com [West China Second University Hospital, West China Women' s and Children' s Hospital, Sichuan University, Chengdu 610041 (China)

    2011-03-04

    In an attempt to improve anticancer activity and reduce systemic toxicity of doxorubicin (Dox), we encapsulated Dox in monomethoxy poly(ethylene glycol)-poly({epsilon}-caprolactone) (MPEG-PCL) micelles by a novel self-assembly procedure without using surfactants, organic solvents or vigorous stirring. These Dox encapsulated MPEG-PCL (Dox/MPEG-PCL) micelles with drug loading of 4.2% were monodisperse and {approx} 20 nm in diameter. The Dox can be released from the Dox/MPEG-PCL micelles; the Dox-release at pH 5.5 was faster than that at pH 7.0. Encapsulation of Dox in MPEG-PCL micelles enhanced the cellular uptake and cytotoxicity of Dox on the C-26 colon carcinoma cell in vitro, and slowed the extravasation of Dox in the transgenic zebrafish model. Compared to free Dox, Dox/MPEG-PCL micelles were more effective in inhibiting tumor growth in the subcutaneous C-26 colon carcinoma and Lewis lung carcinoma models, and prolonging survival of mice bearing these tumors. Dox/MPEG-PCL micelles also induced lower systemic toxicity than free Dox. In conclusion, incorporation of Dox in MPEG-PCL micelles enhanced the anticancer activity and decreased the systemic toxicity of Dox; these Dox/MPEG-PCL micelles are an interesting formulation of Dox and may have potential clinical applications in cancer therapy.

  2. Improved synthesis of N-benzylaminoferrocene-based prodrugs and evaluation of their toxicity and antileukemic activity.

    Science.gov (United States)

    Daum, Steffen; Chekhun, Vasiliy F; Todor, Igor N; Lukianova, Natalia Yu; Shvets, Yulia V; Sellner, Leopold; Putzker, Kerstin; Lewis, Joe; Zenz, Thorsten; de Graaf, Inge A M; Groothuis, Geny M M; Casini, Angela; Zozulia, Oleksii; Hampel, Frank; Mokhir, Andriy

    2015-02-26

    We report on an improved method of synthesis of N-benzylaminoferrocene-based prodrugs and demonstrate its applicability by preparing nine new aminoferrocenes. Their effect on the viability of selected cancer cells having different p53 status was studied. The obtained data are in agreement with the hypothesis that the toxicity of aminoferrocenes is not dependent upon p53 status. Subsequently the toxicity of a selected prodrug (4) was investigated ex vivo using rat precision cut liver slices and in vivo on hybrid male mice BDF1. In both experiments no toxicity was observed: ex vivo, up to 10 μM; in vivo, up to 6 mg/kg. Finally, prodrug 4 was shown to extend the survival of BDF1 mice carrying L1210 leukemia from 13.7 ± 0.6 days to 17.5 ± 0.7 days when injected daily 6 times at a dose of 26 μg/kg starting from the second day after injection of L1210 cells.

  3. Dosimetric factors predictive of late toxicity in prostate cancer radiotherapy; Radiotherapie prostatique: prediction de la toxicite tardive a partir des donnees dosimetriques

    Energy Technology Data Exchange (ETDEWEB)

    Crevoisier, R. de [Departement de radiotherapie, centre Eugene-Marquis, 35 - Rennes (France); Inserm, U 642, 35 - Rennes (France); Fiorino, C. [Medical Physics Department, San Raffaele Scientific Institute, Melghera, Milan (Italy); Dubray, B. [Departement de radiotherapie et de physique medicale, centre Henri-Becquerel, 76 - Rouen (France); EA 4108, UFR de medecine-pharmacie, QuantIF-LITIS, 76 - Rouen (France)

    2010-10-15

    Dose escalation in prostate cancer is made possible due to technological advances and to precise dose-volume constraints to limit normal tissue damage. This article is a literature review focusing on the correlations between exposure (doses and volumes) of organs at risk (OAR) and rectal, urinary, sexual and bone toxicity, as well as on mathematical models aiming at toxicity prediction. Dose-volume constraint recommendations are presented that have been shown to be associated with reduced rectal damage. Indeed, the clinical data is relatively strong for late rectal toxicity (bleeding), with constraints put on both the volume of the rectum receiving high doses ({>=}70 Gy) and the volume receiving intermediate doses (40 to 60 Gy). Predictive models of rectal toxicity (Normal Tissue Complication Probability) appear to accurately estimate toxicity risks. The correlations are much weaker for the bulb and the femoral heads, and nearly do not exist for the bladder. Further prospective studies are required, ideally taking into account patient-related risk factors (co-morbidities and their specific treatments), assays of normal tissue hypersensitivity to ionizing radiation and mathematical models applied on 3D images acquired under the treatment machine (e.g. Cone Beam CT). (authors)

  4. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  5. Improved Modeling and Prediction of Surface Wave Amplitudes

    Science.gov (United States)

    2017-05-31

    AFRL-RV-PS- AFRL-RV-PS- TR-2017-0162 TR-2017-0162 IMPROVED MODELING AND PREDICTION OF SURFACE WAVE AMPLITUDES Jeffry L. Stevens, et al. Leidos...data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented...SUBTITLE Improved Modeling and Prediction of Surface Wave Amplitudes 5a. CONTRACT NUMBER FA9453-14-C-0225 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER

  6. Lead toxicity to Lemna minor predicted using a metal speciation chemistry approach.

    Science.gov (United States)

    Antunes, Paula M C; Kreager, Nancy J

    2014-10-01

    In the present study, predictive measures for Pb toxicity and Lemna minor were developed from bioassays with 7 surface waters having varied chemistries (0.5-12.5 mg/L dissolved organic carbon, pH of 5.4-8.3, and water hardness of 8-266 mg/L CaCO3 ). As expected based on water quality, 10%, 20%, and 50% inhibitory concentration (IC10, IC20, and IC50, respectively) values expressed as percent net root elongation (%NRE) varied widely (e.g., IC20s ranging from 306 nM to >6920 nM total dissolved Pb), with unbounded values limited by Pb solubility. In considering chemical speciation, %NRE variability was better explained when both Pb hydroxides and the free lead ion were defined as bioavailable (i.e., f{OH} ) and colloidal Fe(III)(OH)3 precipitates were permitted to form and sorb metals (using FeOx as the binding phase). Although cause and effect could not be established because of covariance with alkalinity (p = 0.08), water hardness correlated strongly (r(2)  = 0.998, p minor and highlight the importance of chemical speciation in Pb-based risk assessments for aquatic macrophytes. © 2014 SETAC.

  7. An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

    Science.gov (United States)

    Liu, Tao; Chen, Lei; Pan, Xiaoyong

    2018-05-31

    Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  8. Combining gene prediction methods to improve metagenomic gene annotation

    Directory of Open Access Journals (Sweden)

    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  9. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  10. ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

    Directory of Open Access Journals (Sweden)

    Ashok K. Sharma

    2017-11-01

    Full Text Available The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93% and Matthews's correlation coefficient (0.84. The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87 on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84 than the multi-linear regression (MLR and partial least square regression (PLSR models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2 performed better (R2 = 0.68 in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity

  11. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  12. Improving orbit prediction accuracy through supervised machine learning

    Science.gov (United States)

    Peng, Hao; Bai, Xiaoli

    2018-05-01

    Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

  13. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

    Science.gov (United States)

    Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

    2017-04-01

    Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R training 2 and R test 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R training 2 and R test 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R training 2 and R test 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Comparison of the capacity of two biotic ligand models to predict chronic copper toxicity to two Daphnia magna clones and formulation of a generalized bioavailability model.

    Science.gov (United States)

    Van Regenmortel, Tina; Janssen, Colin R; De Schamphelaere, Karel A C

    2015-07-01

    Although it is increasingly recognized that biotic ligand models (BLMs) are valuable in the risk assessment of metals in aquatic systems, the use of 2 differently structured and parameterized BLMs (1 in the United States and another in the European Union) to obtain bioavailability-based chronic water quality criteria for copper is worthy of further investigation. In the present study, the authors evaluated the predictive capacity of these 2 BLMs for a large dataset of chronic copper toxicity data with 2 Daphnia magna clones, termed K6 and ARO. One BLM performed best with clone K6 data, whereas the other performed best with clone ARO data. In addition, there was an important difference between the 2 BLMs in how they predicted the bioavailability of copper as a function of pH. These modeling results suggested that the effect of pH on chronic copper toxicity is different between the 2 clones considered, which was confirmed with additional chronic toxicity experiments. Finally, because fundamental differences in model structure between the 2 BLMs made it impossible to create an average BLM, a generalized bioavailability model (gBAM) was developed. Of the 3 gBAMs developed, the authors recommend the use of model gBAM-C(uni), which combines a log-linear relation between the 21-d median effective concentration (expressed as free Cu(2+) ion activity) and pH, with more conventional BLM-type competition constants for sodium, calcium, and magnesium. This model can be considered a first step in further improving the accuracy of chronic toxicity predictions of copper as a function of water chemistry (for a variety of Daphnia magna clones), even beyond the robustness of the current BLMs used in regulatory applications. © 2015 SETAC.

  15. Improved survival for elderly married glioblastoma patients. Better treatment delivery, less toxicity, and fewer disease complications

    International Nuclear Information System (INIS)

    Putz, Florian; Goerig, Nicole; Knippen, Stefan; Gryc, Thomas; Semrau, Sabine; Lettmaier, Sebastian; Fietkau, Rainer; Putz, Tobias; Eyuepoglu, Ilker; Roessler, Karl

    2016-01-01

    Marital status is a well-described prognostic factor in patients with gliomas but the observed survival difference is unexplained in the available population-based studies. A series of 57 elderly glioblastoma patients (≥70 years) were analyzed retrospectively. Patients received radiotherapy or chemoradiation with temozolomide. The prognostic significance of marital status was assessed. Disease complications, toxicity, and treatment delivery were evaluated in detail. Overall survival was significantly higher in married than in unmarried patients (median, 7.9 vs. 4.0 months; p = 0.006). The prognostic significance of marital status was preserved in the multivariate analysis (HR, 0.41; p = 0.011). Married patients could receive significantly higher daily temozolomide doses (mean, 53.7 mg/m"2 vs. 33.1 mg/m"2; p = 0.020), were more likely to receive maintenance temozolomide (45.7 % vs. 11.8 %; p = 0.016), and had to be hospitalized less frequently during radiotherapy (55.0 % vs. 88.2 %; p = 0.016). Of the patients receiving temozolomide, married patients showed significantly lower rates of hematologic and liver toxicity. Most complications were infectious or neurologic in nature. Complications of any grade were more frequent in unmarried patients (58.8 % vs. 30.0 %; p = 0.041) with the incidence of grade 3-5 complications being particularly elevated (47.1 % vs. 15.0 %; p = 0.004). We found poorer treatment delivery as well as an unexpected severe increase in toxicity and disease complications in elderly unmarried glioblastoma patients. Marital status may be an important predictive factor for clinical decision-making and should be addressed in further studies. (orig.) [de

  16. Improved survival for elderly married glioblastoma patients : Better treatment delivery, less toxicity, and fewer disease complications.

    Science.gov (United States)

    Putz, Florian; Putz, Tobias; Goerig, Nicole; Knippen, Stefan; Gryc, Thomas; Eyüpoglu, Ilker; Rössler, Karl; Semrau, Sabine; Lettmaier, Sebastian; Fietkau, Rainer

    2016-11-01

    Marital status is a well-described prognostic factor in patients with gliomas but the observed survival difference is unexplained in the available population-based studies. A series of 57 elderly glioblastoma patients (≥70 years) were analyzed retrospectively. Patients received radiotherapy or chemoradiation with temozolomide. The prognostic significance of marital status was assessed. Disease complications, toxicity, and treatment delivery were evaluated in detail. Overall survival was significantly higher in married than in unmarried patients (median, 7.9 vs. 4.0 months; p = 0.006). The prognostic significance of marital status was preserved in the multivariate analysis (HR, 0.41; p = 0.011). Married patients could receive significantly higher daily temozolomide doses (mean, 53.7 mg/m² vs. 33.1 mg/m²; p = 0.020), were more likely to receive maintenance temozolomide (45.7 % vs. 11.8 %; p = 0.016), and had to be hospitalized less frequently during radiotherapy (55.0 % vs. 88.2 %; p = 0.016). Of the patients receiving temozolomide, married patients showed significantly lower rates of hematologic and liver toxicity. Most complications were infectious or neurologic in nature. Complications of any grade were more frequent in unmarried patients (58.8 % vs. 30.0 %; p = 0.041) with the incidence of grade 3-5 complications being particularly elevated (47.1 % vs. 15.0 %; p = 0.004). We found poorer treatment delivery as well as an unexpected severe increase in toxicity and disease complications in elderly unmarried glioblastoma patients. Marital status may be an important predictive factor for clinical decision-making and should be addressed in further studies.

  17. Innovative predictive maintenance concepts to improve life cycle management

    NARCIS (Netherlands)

    Tinga, Tiedo

    2014-01-01

    For naval systems with typically long service lives, high sustainment costs and strict availability requirements, an effective and efficient life cycle management process is very important. In this paper four approaches are discussed to improve that process: physics of failure based predictive

  18. Precision-cut intestinal slices as an in vitro model to predict NSAID induced intestinal toxicity

    NARCIS (Netherlands)

    Niu, Xiaoyu; van der Bijl, Henk; Groothuis, Geny; de Graaf, Inge

    2013-01-01

    Non-steroidal anti-inflammatory drugs (NSAIDs) are associated with high prevalence of gastro-intestinal side-effects. In vivo studies suggest that uncoupling of oxidative phosphorylation is an important cause of the toxicity and that the toxicity is aggravated by enterohepatic circulation.

  19. A Conceptual Framework for Predicting the Toxicity of Reactive Chemicals: Modeling Soft Electrophilicity

    Science.gov (United States)

    Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point...

  20. Enhancing the applicability and predictability of the embryonic stem cell test for developmental toxicity

    NARCIS (Netherlands)

    de Jong, E.

    2012-01-01

    Within the full risk assessment of a chemical, developmental toxicity testing is one of the endpoints that require the highest percentage of experimental animals. With the high number of experimental animals, cost and time involved in in vivo developmental toxicity testing there is an urgent need

  1. Improving urban wind flow predictions through data assimilation

    Science.gov (United States)

    Sousa, Jorge; Gorle, Catherine

    2017-11-01

    Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

  2. Improved Wind Speed Prediction Using Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2018-05-01

    Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

  3. Toxicity and cosmetic outcome of hypofractionated whole-breast radiotherapy: predictive clinical and dosimetric factors

    International Nuclear Information System (INIS)

    Ciammella, Patrizia; Podgornii, Ala; Galeandro, Maria; Micera, Renato; Ramundo, Dafne; Palmieri, Tamara; Cagni, Elisabetta; Iotti, Cinzia

    2014-01-01

    The objective of this study is to evaluate toxicity and cosmetic outcome in breast cancer patients treated with adjuvant hypo fractionated radiotherapy to the whole breast, and to identify the risk factors for toxicity. Two hundred twelve women with early breast cancer underwent conserving surgery were enrolled in the study. The patients received 40.05 Gy in 15 daily fractions, 2.67 Gy per fraction. The boost to the tumor bed was administered with a total dose of 9 Gy in 3 consecutive fractions in 55 women. Physician-rated acute and late toxicity and cosmetic outcome (both subjective and objective) were prospectively assessed during and after radiotherapy. In our population study the mean age was 63 with the 17% (36 pts) of the women younger than 50 years. The median follow-up was 34 months. By the end of RT, 35 patients out of 212 (16%) no acute toxicity, according to the RTOG criteria, while 145 (68%) and 31 patients (15%) developed grade 1 and grade 2 acute skin toxicity, respectively. Late skin toxicity evaluation was available for all 212 patients with a minimum follow up of 8 months. The distribution of toxicity was: 39 pts (18%) with grade 1 and 2 pts (1%) with grade 2. No worse late skin toxicity was observed. Late subcutaneous grade 0-1 toxicity was recorded in 208 patients (98%) and grade 2 toxicity in 3 patients (2%), while grade 3 was observed in 1 patient only. At last follow up, a subjective and objective good or excellent cosmetic outcome was reported in 93% and 92% of the women, respectively. At univariate and multivariate analysis, the late skin toxicity was correlated with the additional boost delivery (p=0.007 and p=0.023). Regarding the late subcutaneous tissue, a correlation with diabetes was found (p=0.0283). These results confirm the feasibility and safety of the hypofractionated radiotherapy in patients with early breast cancer. In our population the boost administration was resulted to be a significant adverse prognostic factor for acute

  4. Solar radio proxies for improved satellite orbit prediction

    Science.gov (United States)

    Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

    2017-12-01

    Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

  5. Improved hybrid optimization algorithm for 3D protein structure prediction.

    Science.gov (United States)

    Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

    2014-07-01

    A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

  6. ABILITY OF ECOSAR, TOPKAT, NEURAL NETWORKS, AND ASTER TO PREDICT TOXICITY OF CHEMICALS TO AQUATIC BIOTA

    Science.gov (United States)

    The Canadian Environmental Protection Act (CEPA) which provides the basis for assessing and managing toxic substances in Canada, is being revised. Several new mandates have been introduced in the Act...

  7. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit

    2015-04-16

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  8. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

    2015-01-01

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  9. Nanoparticle delivery of chemosensitizers improve chemotherapy efficacy without incurring additional toxicity

    Science.gov (United States)

    Caster, Joseph M.; Sethi, Manish; Kowalczyk, Sonya; Wang, Edina; Tian, Xi; Nabeel Hyder, Sayed; Wagner, Kyle T.; Zhang, Ying-Ao; Kapadia, Chintan; Man Au, Kin; Wang, Andrew Z.

    2015-01-01

    Chemosensitizers can improve the therapeutic index of chemotherapy and overcome treatment resistance. Successful translation of chemosensitizers depends on the development of strategies that can preferentially deliver chemosensitizers to tumors while avoiding normal tissue. We hypothesized that nanoparticle (NP) formulation of chemosensitizers can improve their delivery to tumors which can in turn improve their therapeutic index. To demonstrate the proof of principle of this approach, we engineered NP formulations of two chemosensitizers, the PI3-kindase inhibitor wortmanin (Wtmn) and the PARP inhibitor olaparib. NP Wtmn and NP olaparib were evaluated as chemosensitizers using lung cancer cells and breast cancer cells respectively. We found Wtmn to be an efficient chemosensitizer in all tested lung-cancer cell lines reducing tumor cell growth between 20 and 60% compared to drug alone. NP formulation did not decrease its efficacy in vitro. Olaparib showed less consistent chemosensitization as a free drug or in NP formulation. NP Wtmn was further evaluated as a chemosensitizer using mouse models of lung cancer. We found that NP Wtmn is an effective chemosensitizer and more effective than free Wtmn showing a 32% reduction in tumor growth compared to free Wtmn when given with etoposide. Importantly, NP Wtmn was able to sensitize the multi-drug resistant H69AR cells to etoposide. Additionally, the combination of NP Wtmn and etoposide chemotherapy did not significantly increase toxicity. The present study demonstrates the proof of principle of using NP formulation of chemosensitizing drugs to improve the therapeutic index of chemotherapy.

  10. Machine Learning Principles Can Improve Hip Fracture Prediction

    DEFF Research Database (Denmark)

    Kruse, Christian; Eiken, Pia; Vestergaard, Peter

    2017-01-01

    Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

  11. Predictive Maintenance: One key to improved power plant availability

    International Nuclear Information System (INIS)

    Mobley; Allen, J.W.

    1986-01-01

    Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

  12. Prediction of acute toxicity of cadmium and lead to zebrafish larvae by using a refined toxicokinetic-toxicodynamic model

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Yongfei; Feng, Jianfeng, E-mail: fengjf@nankai.edu.cn; Zhu, Lin, E-mail: zhulin@nankai.edu.cn

    2015-12-15

    Highlights: • We developed a BLM-aided TK-TD model that considers the effects of H{sup +}. • The time-course metal concentration in larvae was well described by the TK model. • The time-course survival of zebrafish larvae was well simulated by the TD model. - Abstract: The biotic ligand model (BLM) and the toxicokinetic-toxicodynamic (TK-TD) model are essential in predicting the acute toxicity of metals in various species and exposure conditions; however, these models are usually separately utilized. In this study, a mechanistic TK-TD model was developed to predict the acute toxicity of 10{sup −6} M Cd and 10{sup −6} M Pb to zebrafish (Danio rerio) larvae. The novel approach links the BLM with relevant TK processes to simulate the bioaccumulation processes of Cd or Pb as a function of the maximum uptake rate of each metal, the affinity constants, and the concentrations of free metal ions and H{sup +} in test solutions. Results showed that the refined TK-TD model can accurately predict the accumulation and acute toxicity of Cd and Pb to zebrafish larvae at pH 5.5, 6.5, and 7.0.

  13. Prediction of acute toxicity of cadmium and lead to zebrafish larvae by using a refined toxicokinetic-toxicodynamic model

    International Nuclear Information System (INIS)

    Gao, Yongfei; Feng, Jianfeng; Zhu, Lin

    2015-01-01

    Highlights: • We developed a BLM-aided TK-TD model that considers the effects of H"+. • The time-course metal concentration in larvae was well described by the TK model. • The time-course survival of zebrafish larvae was well simulated by the TD model. - Abstract: The biotic ligand model (BLM) and the toxicokinetic-toxicodynamic (TK-TD) model are essential in predicting the acute toxicity of metals in various species and exposure conditions; however, these models are usually separately utilized. In this study, a mechanistic TK-TD model was developed to predict the acute toxicity of 10"−"6 M Cd and 10"−"6 M Pb to zebrafish (Danio rerio) larvae. The novel approach links the BLM with relevant TK processes to simulate the bioaccumulation processes of Cd or Pb as a function of the maximum uptake rate of each metal, the affinity constants, and the concentrations of free metal ions and H"+ in test solutions. Results showed that the refined TK-TD model can accurately predict the accumulation and acute toxicity of Cd and Pb to zebrafish larvae at pH 5.5, 6.5, and 7.0.

  14. Tumor histology and location predict deep nuclei toxicity: Implications for late effects from focal brain irradiation.

    Science.gov (United States)

    Plaga, Alexis; Shields, Lisa B E; Sun, David A; Vitaz, Todd W; Spalding, Aaron C

    2012-01-01

    Normal tissue toxicity resulting from both disease and treatment is an adverse side effect in the management of patients with central nervous system malignancies. We tested the hypothesis that despite these improvements, certain tumors place patients at risk for neurocognitive, neuroendocrine, and neurosensory late effects. Defining patient groups at risk for these effects could allow for development of preventive strategies. Fifty patients with primary brain tumors underwent radiation planning with magnetic resonance imaging scan and computed tomography datasets. Organs at risk (OAR) responsible for neurocognitive, neuroendocrine, and neurosensory function were defined. Inverse-planned intensity-modulated radiation therapy was optimized with priority given to target coverage while penalties were assigned to exceeding normal tissue tolerances. Tumor laterality, location, and histology were compared with OAR doses, and analysis of variance was performed to determine the significance of any observed correlation. The ipsilateral hippocampus exceeded dose limits in frontal (74%), temporal (94%), and parietal (100%) lobe tumor locations. The contralateral hippocampus was at risk in the following tumor locations: frontal (53%), temporal (83%), or parietal (50%) lobe. Patients with high-grade glioma were at risk for ipsilateral (88%) and contralateral (73%) hippocampal damage (P <0.05 compared with other histologies). The pituitary gland and hypothalamus exceeded dose tolerances in patients with pituitary tumors (both 100%) and high-grade gliomas (50% and 75%, P <0.05 compared with other histologies), respectively. Despite application of modern radiation therapy, certain tumor locations and histologies continue to place patients at risk for morbidity. Patients with high-grade gliomas or tumors located in the frontal, temporal, or parietal lobes are at risk for neurocognitive decline, likely because of larger target volumes and higher radiation doses. Data from this study

  15. Tumor histology and location predict deep nuclei toxicity: Implications for late effects from focal brain irradiation

    International Nuclear Information System (INIS)

    Plaga, Alexis; Shields, Lisa B.E.; Sun, David A.; Vitaz, Todd W.; Spalding, Aaron C.

    2012-01-01

    Normal tissue toxicity resulting from both disease and treatment is an adverse side effect in the management of patients with central nervous system malignancies. We tested the hypothesis that despite these improvements, certain tumors place patients at risk for neurocognitive, neuroendocrine, and neurosensory late effects. Defining patient groups at risk for these effects could allow for development of preventive strategies. Fifty patients with primary brain tumors underwent radiation planning with magnetic resonance imaging scan and computed tomography datasets. Organs at risk (OAR) responsible for neurocognitive, neuroendocrine, and neurosensory function were defined. Inverse-planned intensity-modulated radiation therapy was optimized with priority given to target coverage while penalties were assigned to exceeding normal tissue tolerances. Tumor laterality, location, and histology were compared with OAR doses, and analysis of variance was performed to determine the significance of any observed correlation. The ipsilateral hippocampus exceeded dose limits in frontal (74%), temporal (94%), and parietal (100%) lobe tumor locations. The contralateral hippocampus was at risk in the following tumor locations: frontal (53%), temporal (83%), or parietal (50%) lobe. Patients with high-grade glioma were at risk for ipsilateral (88%) and contralateral (73%) hippocampal damage (P <0.05 compared with other histologies). The pituitary gland and hypothalamus exceeded dose tolerances in patients with pituitary tumors (both 100%) and high-grade gliomas (50% and 75%, P <0.05 compared with other histologies), respectively. Despite application of modern radiation therapy, certain tumor locations and histologies continue to place patients at risk for morbidity. Patients with high-grade gliomas or tumors located in the frontal, temporal, or parietal lobes are at risk for neurocognitive decline, likely because of larger target volumes and higher radiation doses. Data from this study

  16. Tumor histology and location predict deep nuclei toxicity: Implications for late effects from focal brain irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Plaga, Alexis; Shields, Lisa B.E. [Norton Neuroscience Institute, Louisville, KY (United States); Sun, David A.; Vitaz, Todd W. [Norton Neuroscience Institute, Louisville, KY (United States); Brain Tumor Center, Norton Healthcare, Louisville, KY (United States); Spalding, Aaron C., E-mail: acspalding1@gmail.com [Brain Tumor Center, Norton Healthcare, Louisville, KY (United States); Norton Cancer Institute, Radiation Center, Kosair Children' s Hospital, Louisville, KY (United States)

    2012-10-01

    Normal tissue toxicity resulting from both disease and treatment is an adverse side effect in the management of patients with central nervous system malignancies. We tested the hypothesis that despite these improvements, certain tumors place patients at risk for neurocognitive, neuroendocrine, and neurosensory late effects. Defining patient groups at risk for these effects could allow for development of preventive strategies. Fifty patients with primary brain tumors underwent radiation planning with magnetic resonance imaging scan and computed tomography datasets. Organs at risk (OAR) responsible for neurocognitive, neuroendocrine, and neurosensory function were defined. Inverse-planned intensity-modulated radiation therapy was optimized with priority given to target coverage while penalties were assigned to exceeding normal tissue tolerances. Tumor laterality, location, and histology were compared with OAR doses, and analysis of variance was performed to determine the significance of any observed correlation. The ipsilateral hippocampus exceeded dose limits in frontal (74%), temporal (94%), and parietal (100%) lobe tumor locations. The contralateral hippocampus was at risk in the following tumor locations: frontal (53%), temporal (83%), or parietal (50%) lobe. Patients with high-grade glioma were at risk for ipsilateral (88%) and contralateral (73%) hippocampal damage (P <0.05 compared with other histologies). The pituitary gland and hypothalamus exceeded dose tolerances in patients with pituitary tumors (both 100%) and high-grade gliomas (50% and 75%, P <0.05 compared with other histologies), respectively. Despite application of modern radiation therapy, certain tumor locations and histologies continue to place patients at risk for morbidity. Patients with high-grade gliomas or tumors located in the frontal, temporal, or parietal lobes are at risk for neurocognitive decline, likely because of larger target volumes and higher radiation doses. Data from this study

  17. Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

    Science.gov (United States)

    Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

    2017-11-15

    Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  18. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  19. Ringer's lactate improves liver recovery in a murine model of acetaminophen toxicity

    Directory of Open Access Journals (Sweden)

    Yang Runkuan

    2011-11-01

    Full Text Available Abstract Background Acetaminophen (APAP overdose induces massive hepatocyte necrosis. Liver regeneration is a vital process for survival after a toxic insult. Since hepatocytes are mostly in a quiescent state (G0, the regeneration process requires the priming of hepatocytes by cytokines such as TNF-α and IL-6. Ringer's lactate solution (RLS has been shown to increase serum TNF-α and IL-6 in patients and experimental animals; in addition, RLS also provides lactate, which can be used as an alternative metabolic fuel to meet the higher energy demand by liver regeneration. Therefore, we tested whether RLS therapy improves liver recovery after APAP overdose. Methods C57BL/6 male mice were intraperitoneally injected with a single dose of APAP (300 mg/kg dissolved in 1 mL sterile saline. Following 2 hrs of APAP challenge, the mice were given 1 mL RLS or Saline treatment every 12 hours for a total of 72 hours. Results 72 hrs after APAP challenge, compared to saline-treated group, RLS treatment significantly lowered serum transaminases (ALT/AST and improved liver recovery seen in histopathology. This beneficial effect was associated with increased hepatic tissue TNF-α concentration, enhanced hepatic NF-κB DNA binding and increased expression of cell cycle protein cyclin D1, three important factors in liver regeneration. Conclusion RLS improves liver recovery from APAP hepatotoxicity.

  20. Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

    International Nuclear Information System (INIS)

    Wurzbach, Richard N.

    2002-01-01

    On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

  1. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

    Energy Technology Data Exchange (ETDEWEB)

    Carriger, John F. [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States); Martin, Todd M. [U.S. Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, OH, 45220 (United States); Barron, Mace G., E-mail: barron.mace@epa.gov [U.S. Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, FL, 32561 (United States)

    2016-11-15

    Highlights: • A Bayesian network was developed to classify chemical mode of action (MoA). • The network was based on the aquatic toxicity MoA for over 1000 chemicals. • A Markov blanket algorithm selected a subset of theoretical molecular descriptors. • Sensitivity analyses found influential descriptors for classifying the MoAs. • Overall precision of the Bayesian MoA classification model was 80%. - Abstract: The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by

  2. Genetic tests for predicting the toxicity and efficacy of anticancer chemotherapy.

    Science.gov (United States)

    Mladosievicova, B; Carter, A; Kristova, V

    2007-01-01

    The standard anticancer therapy based "on one size fits all" modality has been determined to be ineffective or to be the cause of adverse drug reactions in many oncologic patients. Most pharmacogenetic and pharmacogenomic studies so far have been focused on toxicity of anticancer drugs such as 6-mercaptopurine, thioguanine, irinotecan, methotrexate, 5-fluorouracil (5-FU). Variation in genes are known to influence not only toxicity, but also efficacy of chemotherapeutics such as platinum analogues, 5-FU and irinotecan. The majority of current pharmacogenetic studies focus on single enzyme deficiencies as predictors of drug effects; however effects of most anticancer drugs are determined by the interplay of several gene products. These effects are polygenic in nature. This review briefly describes genetic variations that may impact efficacy and toxicity of drugs used in cancer chemotherapy.

  3. Solar radio proxies for improved satellite orbit prediction

    Directory of Open Access Journals (Sweden)

    Yaya Philippe

    2017-01-01

    Full Text Available Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model performs better with (past and predicted values of the 30 cm radio flux than with the 10.7 flux.

  4. Benthic Light Availability Improves Predictions of Riverine Primary Production

    Science.gov (United States)

    Kirk, L.; Cohen, M. J.

    2017-12-01

    Light is a fundamental control on photosynthesis, and often the only control strongly correlated with gross primary production (GPP) in streams and rivers; yet it has received far less attention than nutrients. Because benthic light is difficult to measure in situ, surrogates such as open sky irradiance are often used. Several studies have now refined methods to quantify canopy and water column attenuation of open sky light in order to estimate the amount of light that actually reaches the benthos. Given the additional effort that measuring benthic light requires, we should ask if benthic light always improves our predictions of GPP compared to just open sky irradiance. We use long-term, high-resolution dissolved oxygen, turbidity, dissolved organic matter (fDOM), and irradiance data from streams and rivers in north-central Florida, US across gradients of size and color to build statistical models of benthic light that predict GPP. Preliminary results on a large, clear river show only modest model improvements over open sky irradiance, even in heavily canopied reaches with pulses of tannic water. However, in another spring-fed river with greater connectivity to adjacent wetlands - and hence larger, more frequent pulses of tannic water - the model improved dramatically with the inclusion of fDOM (model R2 improved from 0.28 to 0.68). River shade modeling efforts also suggest that knowing benthic light will greatly enhance our ability to predict GPP in narrower, forested streams flowing in particular directions. Our objective is to outline conditions where an assessment of benthic light conditions would be necessary for riverine metabolism studies or management strategies.

  5. Assessment of Pharmacogenomic Panel Assay for Prediction of Taxane Toxicities: Preliminary Results

    Directory of Open Access Journals (Sweden)

    Raffaele Di Francia

    2017-11-01

    Full Text Available Backbone: Paclitaxel and docetaxel are the primary taxane anticancer drugs regularly used to treat, breast, gastric, ovarian, head/neck, lung, and genitourinary neoplasm. Suspension of taxane treatments compromising patient benefits is more frequently caused by peripheral neuropathy and allergy, than to tumor progression. Several strategies for preventing toxicity have been investigated so far. Recently, findings on the genetic variants associated with toxicity and resistance to taxane-based chemotherapy have been reported.Methods: An extensive panel of five polymorphisms on four candidate genes (ABCB1, CYP2C8*3, CYP3A4*1B, XRCC3, previously validated as significant markers related to paclitaxel and Docetaxel toxicity, are analyzed and discussed. We genotyped 76 cancer patients, and 35 of them received paclitaxel or docetaxel-based therapy. What is more, an early outline evaluation of the genotyping costs and benefit was assessed.Results: Out of 35 patients treated with a taxane, six (17.1% had adverse neuropathy events. Pharmacogenomics analysis showed no correlation between candidate gene polymorphisms and toxicity, except for the XRCC3 AG+GG allele [OR 2.61 (95% CI: 0.91–7.61] which showed a weak significant trend of risk of neurotoxicities vs. the AG allele [OR 1.52 (95% CI: 0.51–4.91] P = 0.03.Summary: Based on our experimental results and data from the literature, we propose a useful and low-cost genotyping panel assay for the prevention of toxicity in patients undergoing taxane-based therapy. With the individual pharmacogenomics profile, clinicians will have additional information to plan the better treatment for their patients to minimize toxicity and maximize benefits, including determining cost-effectiveness for national healthcare sustainability.

  6. Semi-quantitative assessments of dextran toxicity on corneal endothelium: conceptual design of a predictive algorithm.

    Science.gov (United States)

    Filev, Filip; Oezcan, Ceprail; Feuerstacke, Jana; Linke, Stephan J; Wulff, Birgit; Hellwinkel, Olaf J C

    2017-03-01

    Dextran is added to corneal culture medium for at least 8 h prior to transplantation to ensure that the cornea is osmotically dehydrated. It is presumed that dextran has a certain toxic effect on corneal endothelium but the degree and the kinetics of this effect have not been quantified so far. We consider that such data regarding the toxicity of dextran on the corneal endothelium could have an impact on scheduling and logistics of corneal preparation in eye banking. In retrospective statistic analyses, we compared the progress of corneal endothelium (endothelium cell loss per day) of 1334 organ-cultured corneal explants in media with and without dextran. Also, the influence of donor-age, sex and cause of death on the observed dextran-mediated effect on endothelial cell counts was studied. Corneas cultured in dextran-free medium showed a mean endothelium cell count decrease of 0.7% per day. Dextran supplementation led to a mean endothelium cell loss of 2.01% per day; this reflects an increase by the factor of 2.9. The toxic impact of dextran was found to be time dependent; while the prevailing part of the effect was observed within the first 24 h after dextran-addition. Donor age, sex and cause of death did not seem to have an influence on the dextran-mediated toxicity. Based on these findings, we could design an algorithm which approximately describes the kinetics of dextran-toxicity. We reproduced the previously reported toxic effect of dextran on the corneal endothelium in vitro. Additionally, this is the first work that provides an algorithmic instrument for the semi-quantitative calculation of the putative endothelium cell count decrease in dextran containing medium for a given incubation time and could thus influence the time management and planning of corneal transplantations.

  7. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    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

  8. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available 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

  9. Improvement of energy expenditure prediction from heart rate during running

    International Nuclear Information System (INIS)

    Charlot, Keyne; Borne, Rachel; Richalet, Jean-Paul; Chapelot, Didier; Pichon, Aurélien; Cornolo, Jérémy; Brugniaux, Julien Vincent

    2014-01-01

    We aimed to develop new equations that predict exercise-induced energy expenditure (EE) more accurately than previous ones during running by including new parameters as fitness level, body composition and/or running intensity in addition to heart rate (HR). Original equations predicting EE were created from data obtained during three running intensities (25%, 50% and 70% of HR reserve) performed by 50 subjects. Five equations were conserved according to their accuracy assessed from error rates, interchangeability and correlations analyses: one containing only basic parameters, two containing VO 2max  or speed at VO 2max  and two including running speed with or without HR. Equations accuracy was further tested in an independent sample during a 40 min validation test at 50% of HR reserve. It appeared that: (1) the new basic equation was more accurate than pre-existing equations (R 2  0.809 versus. 0,737 respectively); (2) the prediction of EE was more accurate with the addition of VO 2max  (R 2  = 0.879); and (3) the equations containing running speed were the most accurate and were considered to have good agreement with indirect calorimetry. In conclusion, EE estimation during running might be significantly improved by including running speed in the predictive models, a parameter readily available with treadmill or GPS. (paper)

  10. Healthy, wealthy, and wise: retirement planning predicts employee health improvements.

    Science.gov (United States)

    Gubler, Timothy; Pierce, Lamar

    2014-09-01

    Are poor physical and financial health driven by the same underlying psychological factors? We found that the decision to contribute to a 401(k) retirement plan predicted whether an individual acted to correct poor physical-health indicators revealed during an employer-sponsored health examination. Using this examination as a quasi-exogenous shock to employees' personal-health knowledge, we examined which employees were more likely to improve their health, controlling for differences in initial health, demographics, job type, and income. We found that existing retirement-contribution patterns and future health improvements were highly correlated. Employees who saved for the future by contributing to a 401(k) showed improvements in their abnormal blood-test results and health behaviors approximately 27% more often than noncontributors did. These findings are consistent with an underlying individual time-discounting trait that is both difficult to change and domain interdependent, and that predicts long-term individual behaviors in multiple dimensions. © The Author(s) 2014.

  11. Improving mortality outcomes of Stevens Johnson syndrome/toxic epidermal necrolysis: A regional burns centre experience.

    Science.gov (United States)

    Nizamoglu, M; Ward, J A; Frew, Q; Gerrish, H; Martin, N; Shaw, A; Barnes, D; Shelly, O; Philp, B; El-Muttardi, N; Dziewulski, P

    2018-05-01

    Stevens Johnson Syndrome/toxic epidermal necrolysis (SJS/TEN) are rare, potentially fatal desquamative disorders characterised by large areas of partial thickness skin and mucosal loss. The degree of epidermal detachment that occurs has led to SJS/TEN being described as a burn-like condition. These patients benefit from judicious critical care, early debridement and meticulous wound care. This is best undertaken within a multidisciplinary setting led by clinicians experienced in the management of massive skin loss and its sequelae. In this study, we examined the clinical outcomes of SJS/TEN overlap & TEN patients managed by our regional burns service over a 12-year period. We present our treatment model for other burn centres treating SJS/TEN patients. A retrospective case review was performed for all patients with a clinical diagnosis of TEN or SJS/TEN overlap admitted to our paediatric and adult burns centre between June 2004 and December 2016. Patient demographics, percentage total body surface area (%TBSA), mucosal involvement, causation, severity of illness score (SCORTEN), length of stay and survival were appraised with appropriate statistical analysis performed using Graph Pad Prism 7.02 Software. During the study period, 42 patients (M26; F: 16) with TEN (n=32) and SJS/TEN overlap (n=10) were managed within our burns service. Mean %TBSA of cutaneous involvement was 57% (range 10-100%) and mean length of stay (LOS) was 27 days (range 1-144 days). We observed 4 deaths in our series compared to 16 predicted by SCORTEN giving a standardised mortality ratio (SMR) of 24%. Management in our burns service with an aggressive wound care protocol involving debridement of blistered epidermis and wound closure with synthetic and biological dressings seems to have produced benefits in mortality when compared to predicted outcomes. Copyright © 2017 Elsevier Ltd and ISBI. All rights reserved.

  12. Acute toxicity prediction to threatened and endangered species using Interspecies Correlation Estimation (ICE) models

    Science.gov (United States)

    Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA’s Internet application Web-ICE is a suite of Interspecies Correlati...

  13. QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish

    NARCIS (Netherlands)

    Zvinavashe, E.; Berg, H. van den; Soffers, A.E.M.F.; Vervoort, J.; Freidig, A.; Murk, A.J.; Rietjens, I.M.C.M.

    2008-01-01

    Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of

  14. Quantitative Prediction of Systemic Toxicity Points of Departure (OpenTox USA 2017)

    Science.gov (United States)

    Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative models based on chemical structure information, are c...

  15. An overview of data mining algorithms in drug induced toxicity prediction.

    Science.gov (United States)

    Omer, Ankur; Singh, Poonam; Yadav, N K; Singh, R K

    2014-04-01

    The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

  16. Use of pharmacogenomics in predicting bleomycin-induced pulmonary toxicity in testicular cancer patients.

    NARCIS (Netherlands)

    Nuver, J; Van Zweeden, M; Holzik, ML; Meijer, C; Hoekstra, H; Suurmeijer, A; Hofstra, R; Groen, H; Sleijfer, D; Gietema, J

    2004-01-01

    4531 Background:Use of bleomycin, important for treatment efficacy in testicular cancer, is limited by its pulmonary toxicity. Bleomycin is mainly excreted by the kidneys, but can also be inactivated by bleomycin hydrolase (BLH). An A1450G polymorphic site in the BLH gene results in an amino acid

  17. Improving Permafrost Hydrology Prediction Through Data-Model Integration

    Science.gov (United States)

    Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

    2017-12-01

    The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

  18. Improving consensus contact prediction via server correlation reduction.

    Science.gov (United States)

    Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

    2009-05-06

    Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  19. Improving consensus contact prediction via server correlation reduction

    Directory of Open Access Journals (Sweden)

    Xu Jinbo

    2009-05-01

    Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  20. Combining specificity determining and conserved residues improves functional site prediction

    Directory of Open Access Journals (Sweden)

    Gelfand Mikhail S

    2009-06-01

    Full Text Available Abstract Background Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core. Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. Results Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs, as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. Conclusion The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.

  1. LIFETIME PREDICTIONS OF TOXIC AND RADIOACTIVE WASTE DISPOSAL AND REMEDIATION SCHEMES

    International Nuclear Information System (INIS)

    Wesolowski, D.J.; Ewing, R.C.; Bruno, J.

    2005-01-01

    Nuclear power production epitomizes the need for predictive geoscience (Ewing, 2004). Current global carbon emissions of ∼7 Gt/y, largely from fossil fuel consumption, are expected to grow and result in a variety of adverse global effects, including acid rain, toxic smog, and hypothetically, sea level rise and increased frequency and severity of adverse weather conditions. One of the most reliable and sufficiently large alternative sources of energy is nuclear power, which currently provides about 17% of the world's electricity, equivalent to a reduction in carbon emissions of ∼0.5 Gt/y. The U.S. currently consumes ∼40% of the world's fossil fuel production, but generates only about 20% of it's electricity from nuclear plants. One major factor inhibiting increased power production form this source in the US. is the lack of a licensed repository for spent nuclear fuel, and Yucca Mountain is the only site being considered at this time. The licensing issue hinges on DOE's ability to present a credible case before the Nuclear Regulatory Commission that releases of radionuclides from the repository will not pose a threat to the accessible environment. This case is being built using a performance assessment model that incorporates a thermochemical database (EQ3/6) fed by experiments and theoretical developments in aqueous geochemistry and fluid rock interactions, coupled reaction/transport models which combine both the chemical and physical aspects of fluid and heat transport through porous and fractured media, geohazard and climate change models, and information gleaned from natural analogs. The assessment period is currently 10,000 years, but this has recently been challenged in a court of law, and may be extended to 300,000 years or more. Yucca Mountain has a design capacity that only marginally exceeds the current U.S. inventory of commercial spent fuel, currently stored on site at power plants through the country. Some analysts suggest that in order to have a

  2. High-Density Real-Time PCR-Based in Vivo Toxicogenomic Screen to Predict Organ-Specific Toxicity

    Directory of Open Access Journals (Sweden)

    Laszlo G. Puskas

    2011-09-01

    Full Text Available Toxicogenomics, based on the temporal effects of drugs on gene expression, is able to predict toxic effects earlier than traditional technologies by analyzing changes in genomic biomarkers that could precede subsequent protein translation and initiation of histological organ damage. In the present study our objective was to extend in vivo toxicogenomic screening from analyzing one or a few tissues to multiple organs, including heart, kidney, brain, liver and spleen. Nanocapillary quantitative real-time PCR (QRT-PCR was used in the study, due to its higher throughput, sensitivity and reproducibility, and larger dynamic range compared to DNA microarray technologies. Based on previous data, 56 gene markers were selected coding for proteins with different functions, such as proteins for acute phase response, inflammation, oxidative stress, metabolic processes, heat-shock response, cell cycle/apoptosis regulation and enzymes which are involved in detoxification. Some of the marker genes are specific to certain organs, and some of them are general indicators of toxicity in multiple organs. Utility of the nanocapillary QRT-PCR platform was demonstrated by screening different references, as well as discovery of drug-like compounds for their gene expression profiles in different organs of treated mice in an acute experiment. For each compound, 896 QRT-PCR were done: four organs were used from each of the treated four animals to monitor the relative expression of 56 genes. Based on expression data of the discovery gene set of toxicology biomarkers the cardio- and nephrotoxicity of doxorubicin and sulfasalazin, the hepato- and nephrotoxicity of rotenone, dihydrocoumarin and aniline, and the liver toxicity of 2,4-diaminotoluene could be confirmed. The acute heart and kidney toxicity of the active metabolite SN-38 from its less toxic prodrug, irinotecan could be differentiated, and two novel gene markers for hormone replacement therapy were identified

  3. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

  4. Postimplantation Analysis Enables Improvement of Dose-Volume Histograms and Reduction of Toxicity for Permanent Seed Implantation

    International Nuclear Information System (INIS)

    Wust, Peter; Postrach, Johanna; Kahmann, Frank; Henkel, Thomas; Graf, Reinhold; Cho, Chie Hee; Budach, Volker; Boehmer, Dirk

    2008-01-01

    Purpose: To demonstrate how postimplantation analysis is useful for improving permanent seed implantation and reducing toxicity. Patients and Methods: We evaluated 197 questionnaires completed by patients after permanent seed implantation (monotherapy between 1999 and 2003). For 70% of these patients, a computed tomography was available to perform postimplantation analysis. The index doses and volumes of the dose-volume histograms (DVHs) were determined and categorized with respect to the date of implantation. Differences in symptom scores relative to pretherapeutic status were analyzed with regard to follow-up times and DVH descriptors. Acute and subacute toxicities in a control group of 117 patients from an earlier study (June 1999 to September 2001) by Wust et al. (2004) were compared with a matched subgroup from this study equaling 110 patients treated between October 2001 and August 2003. Results: Improved performance, identifying a characteristic time dependency of DVH parameters (after implantation) and toxicity scores, was demonstrated. Although coverage (volume covered by 100% of the prescription dose of the prostate) increased slightly, high-dose regions decreased with the growing experience of the users. Improvement in the DVH and a reduction of toxicities were found in the patient group implanted in the later period. A decline in symptoms with follow-up time counteracts this gain of experience and must be considered. Urinary and sexual discomfort was enhanced by dose heterogeneities (e.g., dose covering 10% of the prostate volume, volume covered by 200% of prescription dose). In contrast, rectal toxicities correlated with exposed rectal volumes, especially the rectal volume covered by 100% of the prescription dose. Conclusion: The typical side effects occurring after permanent seed implantation can be reduced by improving the dose distributions. An improvement in dose distributions and a reduction of toxicities were identified with elapsed time between

  5. A New Model for Predicting Acute Mucosal Toxicity in Head-and-Neck Cancer Patients Undergoing Radiotherapy With Altered Schedules

    International Nuclear Information System (INIS)

    Strigari, Lidia; Pedicini, Piernicola; D’Andrea, Marco; Pinnarò, Paola; Marucci, Laura; Giordano, Carolina; Benassi, Marcello

    2012-01-01

    Purpose: One of the worst radiation-induced acute effects in treating head-and-neck (HN) cancer is grade 3 or higher acute (oral and pharyngeal) mucosal toxicity (AMT), caused by the killing/depletion of mucosa cells. Here we aim to testing a predictive model of the AMT in HN cancer patients receiving different radiotherapy schedules. Methods and Materials: Various radiotherapeutic schedules have been reviewed and classified as tolerable or intolerable based on AMT severity. A modified normal tissue complication probability (NTCP) model has been investigated to describe AMT data in radiotherapy regimens, both conventional and altered in dose and overall treatment time (OTT). We tested the hypothesis that such a model could also be applied to identify intolerable treatment and to predict AMT. This AMT NTCP model has been compared with other published predictive models to identify schedules that are either tolerable or intolerable. The area under the curve (AUC) was calculated for all models, assuming treatment tolerance as the gold standard. The correlation between AMT and the predicted toxicity rate was assessed by a Pearson correlation test. Results: The AMT NTCP model was able to distinguish between acceptable and intolerable schedules among the data available for the study (AUC = 0.84, 95% confidence interval = 0.75-0.92). In the equivalent dose at 2 Gy/fraction (EQD2) vs OTT space, the proposed model shows a trend similar to that of models proposed by other authors, but was superior in detecting some intolerable schedules. Moreover, it was able to predict the incidence of ≥G3 AMT. Conclusion: The proposed model is able to predict ≥G3 AMT after HN cancer radiotherapy, and could be useful for designing altered/hypofractionated schedules to reduce the incidence of AMT.

  6. Development of a Set of Nomograms to Predict Acute Lower Gastrointestinal Toxicity for Prostate Cancer 3D-CRT

    International Nuclear Information System (INIS)

    Valdagni, Riccardo; Rancati, Tiziana; Fiorino, Claudio; Fellin, Gianni; Magli, Alessandro; Baccolini, Michela; Bianchi, Carla; Cagna, Emanuela; Greco, Carlo; Mauro, Flora A.; Monti, Angelo F.; Munoz, Fernando; Stasi, Michele; Franzone, Paola; Vavassori, Vittorio

    2008-01-01

    Purpose: To predict acute Radiation Therapy Oncology Group (RTOG)/European Organization for Research and Treatment of Cancer (EORTC) and Subjective Objective Signs Management and Analysis/Late Effect of Normal Tissue (SOMA/LENT) toxicities of the lower gastrointestinal (LGI) syndrome in patients with prostate cancer undergoing three-dimensional conformal radiotherapy using a tool (nomogram) that takes into account clinical and dosimetric variables that proved to be significant in the Italian Association for Radiation Oncology (AIRO) Group on Prostate Cancer (AIROPROS) 0102 trial. Methods and Materials: Acute rectal toxicity was scored in 1,132 patients by using both the RTOG/EORTC scoring system and a 10-item self-assessed questionnaire. Correlation between clinical variables/dose-volume histogram constraints and rectal toxicity was investigated by means of multivariate logistic analyses. Multivariate logistic analyses results were used to create nomograms predicting the symptoms of acute LGI syndrome. Results: Mean rectal dose was a strong predictor of Grade 2-3 RTOG/EORTC acute LGI toxicity (p 0.0004; odds ratio (OR) = 1.035), together with hemorrhoids (p = 0.02; OR 1.51), use of anticoagulants/antiaggregants (p = 0.02; OR = 0.63), and androgen deprivation (AD) (p = 0.04; OR = 0.65). Diabetes (p = 0.34; OR 1.28) and pelvic node irradiation (p = 0.11; OR = 1.56) were significant variables to adjust toxicity prediction. Bleeding was related to hemorrhoids (p = 0.02; OR = 173), AD (p = 0.17; OR = 0.67), and mean rectal dose (p 0.009; OR = 1.024). Stool frequency was related to seminal vesicle irradiation (p = 0.07; OR = 6.46), AD administered for more than 3 months (p = 0.002; OR = 0.32), and the percent volume of rectum receiving more than 60 Gy (V60Gy) V60 (p = 0.02; OR = 1.02). Severe fecal incontinence depended on seminal vesicle irradiation (p = 0.14; OR = 4.5) and V70 (p = 0.033; OR 1.029). Conclusions: To the best of our knowledge, this work presents the

  7. Improved prediction of aerodynamic noise from wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Guidati, G.; Bareiss, R.; Wagner, S. [Univ. of Stuttgart, Inst. of Aerodynamics and Gasdynamics, Stuttgart (Germany)

    1997-12-31

    This paper focuses on an improved prediction model for inflow-turbulence noise which takes the true airfoil shape into account. Predictions are compared to the results of acoustic measurements on three 2D-models of 0.25 m chord. Two of the models have NACA-636xx airfoils of 12% and 18% relative thickness. The third airfoil was acoustically optimized by using the new prediction model. In the experiments the turbulence intensity of the flow was strongly increased by mounting a grid with 60 mm wide meshes and 12 mm thick rods onto the tunnel exhaust nozzle. The sound radiated from the airfoil was distinguished by the tunnel background noise by using an acoustic antenna consisting of a cross array of 36 microphones in total. An application of a standard beam-forming algorithm allows to determine how much noise is radiated from different parts of the models. This procedure normally results in a peak at the leading and trailing edge of the airfoil. The strength of the leading-edge peak is taken as the source strength for inflow-turbulence noise. (LN) 14 refs.

  8. Predictive factors of gastroduodenal toxicity in cirrhotic patients after three-dimensional conformal radiotherapy for hepatocellular carcinoma

    International Nuclear Information System (INIS)

    Kim, Haeyoung; Lim, Do Hoon; Paik, Seung Woon; Yoo, Byung Chul; Koh, Kwang Gheol; Lee, Joon Hyoek; Choi, Moon Seok; Park, Won; Park, Hee Chul; Huh, Seung Jae; Choi, Doo Ho; Ahn, Yong Chan

    2009-01-01

    Background and purpose: To identify predictive factors for the development of gastroduodenal toxicity (GDT) in cirrhotic patients treated with three-dimensional conformal radiotherapy (3D-CRT) for hepatocellular carcinoma (HCC). Materials and methods: We retrospectively analyzed dose-volume histograms (DVHs) and clinical records of 73 cirrhotic patients treated with 3D-CRT for HCC. The median radiation dose was 36 Gy (range, 30-54 Gy) with a daily dose of 3 Gy. The grade of GDT was defined by the Common Toxicity Criteria Version 2. The predictive factors of grade 3 GDT were identified. Results: Grade 3 GDT was found in 9 patients. Patient's age and the percentage of gastroduodenal volume receiving more than 35 Gy (V 35 ) significantly affected the development of grade 3 GDT. Patients over 50 years of age developed grade 3 GDT more frequently than patients under 50 years of age. The risk of grade 3 GDT grew exponentially as V 35 increased. The 1-year actuarial rate of grade 3 GDT in patients with V 35 35 ≥5% (4% vs. 48%, p 35 were the most predictive factors for the development of grade 3 GDT in patients treated with RT.

  9. Can biomechanical variables predict improvement in crouch gait?

    Science.gov (United States)

    Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

    2011-01-01

    Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

  10. Improved nucleic acid descriptors for siRNA efficacy prediction.

    Science.gov (United States)

    Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

    2013-02-01

    Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

  11. Improved survival for elderly married glioblastoma patients. Better treatment delivery, less toxicity, and fewer disease complications

    Energy Technology Data Exchange (ETDEWEB)

    Putz, Florian; Goerig, Nicole; Knippen, Stefan; Gryc, Thomas; Semrau, Sabine; Lettmaier, Sebastian; Fietkau, Rainer [Friedrich-Alexander-University Erlangen-Nuremberg, Department of Radiation Oncology, Erlangen (Germany); Putz, Tobias [University of Bamberg, Professorship of Demography, Bamberg (Germany); Eyuepoglu, Ilker; Roessler, Karl [Friedrich-Alexander-University Erlangen-Nuremberg, Department of Neurosurgery, Erlangen (Germany)

    2016-11-15

    Marital status is a well-described prognostic factor in patients with gliomas but the observed survival difference is unexplained in the available population-based studies. A series of 57 elderly glioblastoma patients (≥70 years) were analyzed retrospectively. Patients received radiotherapy or chemoradiation with temozolomide. The prognostic significance of marital status was assessed. Disease complications, toxicity, and treatment delivery were evaluated in detail. Overall survival was significantly higher in married than in unmarried patients (median, 7.9 vs. 4.0 months; p = 0.006). The prognostic significance of marital status was preserved in the multivariate analysis (HR, 0.41; p = 0.011). Married patients could receive significantly higher daily temozolomide doses (mean, 53.7 mg/m{sup 2} vs. 33.1 mg/m{sup 2}; p = 0.020), were more likely to receive maintenance temozolomide (45.7 % vs. 11.8 %; p = 0.016), and had to be hospitalized less frequently during radiotherapy (55.0 % vs. 88.2 %; p = 0.016). Of the patients receiving temozolomide, married patients showed significantly lower rates of hematologic and liver toxicity. Most complications were infectious or neurologic in nature. Complications of any grade were more frequent in unmarried patients (58.8 % vs. 30.0 %; p = 0.041) with the incidence of grade 3-5 complications being particularly elevated (47.1 % vs. 15.0 %; p = 0.004). We found poorer treatment delivery as well as an unexpected severe increase in toxicity and disease complications in elderly unmarried glioblastoma patients. Marital status may be an important predictive factor for clinical decision-making and should be addressed in further studies. (orig.) [German] Fuer verheiratete Patienten mit malignen Gliomen ist ein verbessertes Gesamtueberleben gut beschrieben. Die zugrunde liegenden Mechanismen konnten bislang jedoch in den verfuegbaren bevoelkerungsbezogenen Arbeiten nicht erklaert werden. Eine Serie von 57 aelteren Patienten mit

  12. Modeling Chronic Toxicity: A Comparison of Experimental Variability With (QSAR/Read-Across Predictions

    Directory of Open Access Journals (Sweden)

    Christoph Helma

    2018-04-01

    Full Text Available This study compares the accuracy of (QSAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

  13. Ginseng Purified Dry Extract, BST204, Improved Cancer Chemotherapy-Related Fatigue and Toxicity in Mice

    Directory of Open Access Journals (Sweden)

    Hyun-Jung Park

    2015-01-01

    Full Text Available Cancer related fatigue (CRF is one of the most common side effects of cancer and its treatments. A large proportion of cancer patients experience cancer-related physical and central fatigue so new strategies are needed for treatment and improved survival of these patients. BST204 was prepared by incubating crude ginseng extract with ginsenoside-β-glucosidase. The purpose of the present study was to examine the effects of BST204, mixture of ginsenosides on 5-fluorouracil (5-FU-induced CRF, the glycogen synthesis, and biochemical parameters in mice. The mice were randomly divided into the following groups: the naïve normal (normal, the HT-29 cell inoculated (xenograft, xenograft and 5-FU treated (control, xenograft + 5-FU + BST204-treated (100 and 200 mg/kg (BST204, and xenograft + 5-FU + modafinil (13 mg/kg treated group (modafinil. Running wheel activity and forced swimming test were used for evaluation of CRF. Muscle glycogen, serum inflammatory cytokines, aspartic aminotransferase (AST, alanine aminotransferase (ALT, creatinine (CRE, white blood cell (WBC, neutrophil (NEUT, red blood cell (RBC, and hemoglobin (HGB were measured. Treatment with BST204 significantly increased the running wheel activity and forced swimming time compared to the control group. Consistent with the behavioral data, BST204 markedly increased muscle glycogen activity and concentrations of WBC, NEUT, RBC, and HGB. Also, tumor necrosis factor-α (TNF-α and interleukin-6 (IL-6, AST, ALT, and CRE levels in the serum were significantly reduced in the BST204-treated group compared to the control group. This result suggests that BST204 may improve chemotherapy-related fatigue and adverse toxic side effects.

  14. Prediction of clinical toxicity in localized cervical carcinoma by radio-induced apoptosis study in peripheral blood lymphocytes (PBLs)

    International Nuclear Information System (INIS)

    Bordón, Elisa; Henríquez Hernández, Luis Alberto; Lara, Pedro C; Pinar, Beatriz; Fontes, Fausto; Rodríguez Gallego, Carlos; Lloret, Marta

    2009-01-01

    Cervical cancer is treated mainly by surgery and radiotherapy. Toxicity due to radiation is a limiting factor for treatment success. Determination of lymphocyte radiosensitivity by radio-induced apoptosis arises as a possible method for predictive test development. The aim of this study was to analyze radio-induced apoptosis of peripheral blood lymphocytes. Ninety four consecutive patients suffering from cervical carcinoma, diagnosed and treated in our institution, and four healthy controls were included in the study. Toxicity was evaluated using the Lent-Soma scale. Peripheral blood lymphocytes were isolated and irradiated at 0, 1, 2 and 8 Gy during 24, 48 and 72 hours. Apoptosis was measured by flow cytometry using annexin V/propidium iodide to determine early and late apoptosis. Lymphocytes were marked with CD45 APC-conjugated monoclonal antibody. Radiation-induced apoptosis (RIA) increased with radiation dose and time of incubation. Data strongly fitted to a semi logarithmic model as follows: RIA = βln(Gy) + α. This mathematical model was defined by two constants: α, is the origin of the curve in the Y axis and determines the percentage of spontaneous cell death and β, is the slope of the curve and determines the percentage of cell death induced at a determined radiation dose (β = ΔRIA/Δln(Gy)). Higher β values (increased rate of RIA at given radiation doses) were observed in patients with low sexual toxicity (Exp(B) = 0.83, C.I. 95% (0.73-0.95), p = 0.007; Exp(B) = 0.88, C.I. 95% (0.82-0.94), p = 0.001; Exp(B) = 0.93, C.I. 95% (0.88-0.99), p = 0.026 for 24, 48 and 72 hours respectively). This relation was also found with rectal (Exp(B) = 0.89, C.I. 95% (0.81-0.98), p = 0.026; Exp(B) = 0.95, C.I. 95% (0.91-0.98), p = 0.013 for 48 and 72 hours respectively) and urinary (Exp(B) = 0.83, C.I. 95% (0.71-0.97), p = 0.021 for 24 hours) toxicity. Radiation induced apoptosis at different time points and radiation doses fitted to a semi logarithmic model defined

  15. Improving student success using predictive models and data visualisations

    Directory of Open Access Journals (Sweden)

    Hanan Ayad

    2012-08-01

    Full Text Available The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50–60%. At the college level in the US only 30% of students graduate from 2-year colleges in 3 years or less and approximately 50% graduate from 4-year colleges in 5 years or less. A basic challenge in delivering global education, therefore, is improving student success. By student success we mean improving retention, completion and graduation rates. In this paper we describe a Student Success System (S3 that provides a holistic, analytical view of student academic progress.1 The core of S3 is a flexible predictive modelling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualisations for reaching diagnostic insights and a case management tool for managing interventions. S3's open modular architecture will also allow integration and plug-ins with both open and proprietary software. Powered by learning analytics, S3 is intended as an end-to-end solution for identifying at-risk students, understanding why they are at risk, designing interventions to mitigate that risk and finally closing the feedback look by tracking the efficacy of the applied intervention.

  16. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  17. Suppression of a NAC-like transcription factor gene improves boron-toxicity tolerance in rice.

    Science.gov (United States)

    Ochiai, Kumiko; Shimizu, Akifumi; Okumoto, Yutaka; Fujiwara, Toru; Matoh, Toru

    2011-07-01

    We identified a gene responsible for tolerance to boron (B) toxicity in rice (Oryza sativa), named BORON EXCESS TOLERANT1. Using recombinant inbred lines derived from the B-toxicity-sensitive indica-ecotype cultivar IR36 and the tolerant japonica-ecotype cultivar Nekken 1, the region responsible for tolerance to B toxicity was narrowed to 49 kb on chromosome 4. Eight genes are annotated in this region. The DNA sequence in this region was compared between the B-toxicity-sensitive japonica cultivar Wataribune and the B-toxicity-tolerant japonica cultivar Nipponbare by eco-TILLING analysis and revealed a one-base insertion mutation in the open reading frame sequence of the gene Os04g0477300. The gene encodes a NAC (NAM, ATAF, and CUC)-like transcription factor and the function of the transcript is abolished in B-toxicity-tolerant cultivars. Transgenic plants in which the expression of Os04g0477300 is abolished by RNA interference gain tolerance to B toxicity.

  18. Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach

    International Nuclear Information System (INIS)

    Su Tingzhi; Guan Xiaohong; Tang Yulin; Gu Guowei; Wang Jianmin

    2010-01-01

    Toxic anionic elements such as arsenic, selenium, and vanadium often co-exist in groundwater. These elements may impact each other when adsorption methods are used to remove them. In this study, we investigated the competitive adsorption behavior of As(V), Se(IV), and V(V) onto activated alumina under different pH and surface loading conditions. Results indicated that these anionic elements interfered with each other during adsorption. A speciation-based model was developed to quantify the competitive adsorption behavior of these elements. This model could predict the adsorption data well over the pH range of 1.5-12 for various surface loading conditions, using the same set of adsorption constants obtained from single-sorbate systems. This model has great implications in accurately predicting the field capacity of activated alumina under various local water quality conditions when multiple competitive anionic elements are present.

  19. A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

    Science.gov (United States)

    Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

    2017-08-01

    The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

  20. Improved prediction and tracking of volcanic ash clouds

    Science.gov (United States)

    Mastin, Larry G.; Webley, Peter

    2009-01-01

    During the past 30??years, more than 100 airplanes have inadvertently flown through clouds of volcanic ash from erupting volcanoes. Such encounters have caused millions of dollars in damage to the aircraft and have endangered the lives of tens of thousands of passengers. In a few severe cases, total engine failure resulted when ash was ingested into turbines and coating turbine blades. These incidents have prompted the establishment of cooperative efforts by the International Civil Aviation Organization and the volcanological community to provide rapid notification of eruptive activity, and to monitor and forecast the trajectories of ash clouds so that they can be avoided by air traffic. Ash-cloud properties such as plume height, ash concentration, and three-dimensional ash distribution have been monitored through non-conventional remote sensing techniques that are under active development. Forecasting the trajectories of ash clouds has required the development of volcanic ash transport and dispersion models that can calculate the path of an ash cloud over the scale of a continent or a hemisphere. Volcanological inputs to these models, such as plume height, mass eruption rate, eruption duration, ash distribution with altitude, and grain-size distribution, must be assigned in real time during an event, often with limited observations. Databases and protocols are currently being developed that allow for rapid assignment of such source parameters. In this paper, we summarize how an interdisciplinary working group on eruption source parameters has been instigating research to improve upon the current understanding of volcanic ash cloud characterization and predictions. Improved predictions of ash cloud movement and air fall will aid in making better hazard assessments for aviation and for public health and air quality. ?? 2008 Elsevier B.V.

  1. Chemical agnostic hazard prediction: Statistical inference of toxicity pathways - data for Figure 2

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO...

  2. Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)

    Science.gov (United States)

    EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.

  3. Climatic extremes improve predictions of spatial patterns of tree species

    Science.gov (United States)

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

    2009-01-01

    Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

  4. Improved apparatus for predictive diagnosis of rotator cuff disease

    Science.gov (United States)

    Pillai, Anup; Hall, Brittany N.; Thigpen, Charles A.; Kwartowitz, David M.

    2014-03-01

    Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.

  5. Improving predictive capabilities of environmental change with GLOBE data

    Science.gov (United States)

    Robin, Jessica Hill

    This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (>35°) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (>60°) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March--mid May) when vegetation is greening up (NDVI 0.60). Results from the second study showed that a climate threshold of 153 +/- 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the interannual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.

  6. The Urgent Need for Improved Climate Models and Predictions

    Science.gov (United States)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  7. Improving Flood Predictions in Data-Scarce Basins

    Science.gov (United States)

    Vimal, Solomon; Zanardo, Stefano; Rafique, Farhat; Hilberts, Arno

    2017-04-01

    Flood modeling methodology at Risk Management Solutions Ltd. has evolved over several years with the development of continental scale flood risk models spanning most of Europe, the United States and Japan. Pluvial (rain fed) and fluvial (river fed) flood maps represent the basis for the assessment of regional flood risk. These maps are derived by solving the 1D energy balance equation for river routing and 2D shallow water equation (SWE) for overland flow. The models are run with high performance computing and GPU based solvers as the time taken for simulation is large in such continental scale modeling. These results are validated with data from authorities and business partners, and have been used in the insurance industry for many years. While this methodology has been proven extremely effective in regions where the quality and availability of data are high, its application is very challenging in other regions where data are scarce. This is generally the case for low and middle income countries, where simpler approaches are needed for flood risk modeling and assessment. In this study we explore new methods to make use of modeling results obtained in data-rich contexts to improve predictive ability in data-scarce contexts. As an example, based on our modeled flood maps in data-rich countries, we identify statistical relationships between flood characteristics and topographic and climatic indicators, and test their generalization across physical domains. Moreover, we apply the Height Above Nearest Drainage (HAND)approach to estimate "probable" saturated areas for different return period flood events as functions of basin characteristics. This work falls into the well-established research field of Predictions in Ungauged Basins.

  8. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

    Directory of Open Access Journals (Sweden)

    Thomas W. Frazier

    2014-03-01

    Full Text Available This report evaluates whether classification tree algorithms (CTA may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD. Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS cohort (629 youth, 148 with BPSD and 481 without BPSD. Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4% relative to logistic regression (77.6%. However, CTA showed increased sensitivity (0.28 vs. 0.18 at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%. High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%. Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data; these may increase the clinical utility of CTA models further.

  9. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  10. Designing quantitative structure activity relationships to predict specific toxic endpoints for polybrominated diphenyl ethers in mammalian cells.

    Science.gov (United States)

    Rawat, S; Bruce, E D

    2014-01-01

    Polybrominated diphenyl ethers (PBDEs) are known as effective flame retardants and have vast industrial application in products like plastics, building materials and textiles. They are found to be structurally similar to thyroid hormones that are responsible for regulating metabolism in the body. Structural similarity with the hormones poses a threat to human health because, once in the system, PBDEs have the potential to affect thyroid hormone transport and metabolism. This study was aimed at designing quantitative structure-activity relationship (QSAR) models for predicting toxic endpoints, namely cell viability and apoptosis, elicited by PBDEs in mammalian cells. Cell viability was evaluated quantitatively using a general cytotoxicity bioassay using Janus Green dye and apoptosis was evaluated using a caspase assay. This study has thus modelled the overall cytotoxic influence of PBDEs at an early and a late endpoint by the Genetic Function Approximation method. This research was a twofold process including running in vitro bioassays to collect data on the toxic endpoints and modeling the evaluated endpoints using QSARs. Cell viability and apoptosis responses for Hep G2 cells exposed to PBDEs were successfully modelled with an r(2) of 0.97 and 0.94, respectively.

  11. Prediction of the relative toxicity of environmental toxins as a function of behavioral and non-behavioral endpoints

    International Nuclear Information System (INIS)

    Young, R.W.

    1979-01-01

    This study was conducted in order to examine the differential effects of behavioral and non-behavioral endpoints on the prediction of the relative toxicity of an environmental toxin. The effects of ionizing radiation were taken as the model for this evaluation. Forty rhesus monkeys were irradiated in groups of four at five different dose levels of high energy neuton and Bremsstrahlung radiations. Measures of behavioral performance, emesis and mortality were taken for each subject in order to test the hypotheses that behavioral indices would be more sensitive to gamma radiation than would physiological indices and that the physiological indices would be more sensitive to neutron radiations than would behavioral indices. The results supported these hypotheses

  12. IMPROVING STRUCTURE-LINKED ACCESS TO PUBLICLY AVAILABLE CHEMICAL TOXICITY INFORMATION

    Science.gov (United States)

    Hepatotoxicity of the Herbicide Alachlor Associated with Glutathione Depletion, Oxidative Damage and Protein S-Cysteinyl Adduction.Toxicity of the herbicide alachlor (2-chloro-2',6'-diethtl-N-[methoxtmethtl]-acetanilide) has been attributed to cytochrome P450-dependent me...

  13. Synthesis of β-cyclodextrin hydrogel nanoparticles for improving the solubility of dexibuprofen: characterization and toxicity evaluation.

    Science.gov (United States)

    Khalid, Qandeel; Ahmad, Mahmood; Minhas, Muhammad Usman

    2017-11-01

    This study was aimed to enhance aqueous solubility of dexibuprofen through designing β-cyclodextrin (βCD) hydrogel nanoparticles and to evaluate toxicological potential through acute toxicity studies in rats. Dexibuprofen is a non-steroidal analgesic and anti-inflammatory drug that is one of safest over the counter medications. However, its clinical effectiveness is hampered due to poor aqueous solubility. βCD hydrogel nanoparticles were prepared and characterized by percent yield, drug loading, solubilization efficiency, FTIR, XRD, DSC, FESEM and in-vitro dissolution studies. Acute oral toxicity study was conducted to assess safety of oral administration of prepared βCD hydrogel nanoparticles. βCD hydrogel nanoparticles dramatically enhanced the drug loading and solubilization efficiency of dexibuprofen in aqueous media. FTIR, TGA and DSC studies confirmed the formation of new and a stable nano-polymeric network and interactions of dexibuprofen with these nanoparticles. Resulting nanoparticles were highly porous with 287 nm in size. XRD analysis revealed pronounced reduction in crystalline nature of dexibuprofen within nanoparticles. Release of dexibuprofen in βCD hydrogel nanoparticles was significantly higher compared with dexibuprofen tablet at pH 1.2 and 6.8. In acute toxicity studies, no significant changes in behavioral, physiological, biochemical or histopathologic parameters of animals were observed. The efficient preparation, high solubility, excellent physicochemical characteristics, improved dissolution and non-toxic βCD hydrogel nanoparticles may be a promising approach for oral delivery of lipophilic drugs.

  14. Improved insecticidal toxicity by fusing Cry1Ac of Bacillus thuringiensis with Av3 of Anemonia viridis.

    Science.gov (United States)

    Yan, Fu; Cheng, Xing; Ding, Xuezhi; Yao, Ting; Chen, Hanna; Li, Wenping; Hu, Shengbiao; Yu, Ziquan; Sun, Yunjun; Zhang, Youming; Xia, Liqiu

    2014-05-01

    Av3, a neurotoxin of Anemonia viridis, is toxic to crustaceans and cockroaches but inactive in mammals. In the present study, Av3 was expressed in Escherichia coli Origami B (DE3) and purified by reversed-phase liquid chromatography. The purified Av3 was injected into the hemocoel of Helicoverpa armigera, rendering the worm paralyzed. Then, Av3 was expressed alone or fusion expressed with the Cry1Ac in acrystalliferous strain Cry(-)B of Bacillus thuringiensis. The shape of Cry1Ac was changed by fusion with Av3. The expressed fusion protein, Cry1AcAv3, formed irregular rhombus- or crescent-shaped crystalline inclusions, which is quite different from the shape of original Cry1Ac crystals. The toxicity of Cry1Ac was improved by fused expression. Compared with original Cry1Ac expressed in Cry(-)B, the oral toxicity of Cry1AcAv3 to H. armigera was elevated about 2.6-fold. No toxicity was detected when Av3 was expressed in Cry(-)B alone. The present study confirmed that marine toxins could be used in bio-control and implied that fused expression with other insecticidal proteins could be an efficient way for their application.

  15. The integral biologically effective dose to predict brain stem toxicity of hypofractionated stereotactic radiotherapy

    International Nuclear Information System (INIS)

    Clark, Brenda G.; Souhami, Luis; Pla, Conrado; Al-Amro, Abdullah S.; Bahary, Jean-Paul; Villemure, Jean-Guy; Caron, Jean-Louis; Olivier, Andre; Podgorsak, Ervin B.

    1998-01-01

    Purpose: The aim of this work was to develop a parameter for use during fractionated stereotactic radiotherapy treatment planning to aid in the determination of the appropriate treatment volume and fractionation regimen that will minimize risk of late damage to normal tissue. Materials and Methods: We have used the linear quadratic model to assess the biologically effective dose at the periphery of stereotactic radiotherapy treatment volumes that impinge on the brain stem. This paper reports a retrospective study of 77 patients with malignant and benign intracranial lesions, treated between 1987 and 1995, with the dynamic rotation technique in 6 fractions over a period of 2 weeks, to a total dose of 42 Gy prescribed at the 90% isodose surface. From differential dose-volume histograms, we evaluated biologically effective dose-volume histograms and obtained an integral biologically-effective dose (IBED) in each case. Results: Of the 77 patients in the study, 36 had target volumes positioned so that the brain stem received more than 1% of the prescribed dose, and 4 of these, all treated for meningioma, developed serious late damage involving the brain stem. Other than type of lesion, the only significant variable was the volume of brain stem exposed. An analysis of the IBEDs received by these 36 patients shows evidence of a threshold value for late damage to the brain stem consistent with similar thresholds that have been determined for external beam radiotherapy. Conclusions: We have introduced a new parameter, the IBED, that may be used to represent the fractional effective dose to structures such as the brain stem that are partially irradiated with stereotactic dose distributions. The IBED is easily calculated prior to treatment and may be used to determine appropriate treatment volumes and fractionation regimens minimizing possible toxicity to normal tissue

  16. Use of watershed factors to predict consumer surfactant toxic units in the upper Trinity river, Texas

    DEFF Research Database (Denmark)

    Johnson, David; Sanderson, Hans; Atkinson, Sam

    2009-01-01

    Surfactants are high production volume chemicals that are used in a wide assortment of "down-the-drain" consumer products. Wastewater treatment plants (WWTPs) generally remove 85 to more than 99% of all surfactants from influents, but residual concentrations are discharged into receiving waters v...... the potential to be a reliable and inexpensive method of predicting water and habitat quality in the upper Trinity River watershed and perhaps other highly urbanized watersheds in semi-arid regions. © 2009 Elsevier B.V. All rights reserved. Udgivelsesdato: June 15...

  17. A predictive maintenance approach for improved nuclear plant availability

    International Nuclear Information System (INIS)

    Verma, R.M.P.; Pandya, M.B.; Kini, M.P.

    1979-01-01

    Predictive maintenance programme as against preventive maintenance programme aims at diagnosing, inspecting, monitoring, and objective condition-checking of equipment. It helps in forecasting failures, and scheduling the optimal frequencies for overhauls, replacements, lubrication etc. It also helps in establishing work load, manpower, resource planning and inventory control. Various stages of predictive maintenance programme for a nuclear power plant are outlined. A partial list of instruments for predictive maintenance is given. (M.G.B.)

  18. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

    Directory of Open Access Journals (Sweden)

    Sun Zhangzhen

    2012-08-01

    Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

  19. Combining Physical and Biologic Parameters to Predict Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Definitive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Stenmark, Matthew H. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Cai Xuwei [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shanghai Cancer Hospital, Fudan University, Shanghai (China); Shedden, Kerby [Department of Biostatistics, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Hayman, James A. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Yuan Shuanghu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shangdong Cancer Hospital, Jinan (China); Ritter, Timothy [Veterans Affairs Medical Center, Ann Arbor, Michigan (United States); Ten Haken, Randall K.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Kong Fengming, E-mail: fengkong@med.umich.edu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Veterans Affairs Medical Center, Ann Arbor, Michigan (United States)

    2012-10-01

    Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (P<.001) compared to either variable alone. Conclusions: Combining inflammatory cytokines with physical dosimetric factors may provide a more accurate model for RILT prediction. Future study with a larger number of cases and events is needed to validate such findings.

  20. Using road topology to improve cyclist path prediction

    NARCIS (Netherlands)

    Pool, E.A.I.; Kooij, J.F.P.; Gavrila, D.; Ioannou, Petros; Zhang, Wei-Bin; Lu, Meng

    2017-01-01

    We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected

  1. Genomic selection: genome-wide prediction in plant improvement.

    Science.gov (United States)

    Desta, Zeratsion Abera; Ortiz, Rodomiro

    2014-09-01

    Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Human contamination by persistent toxic substances: the rationale to improve exposure assessment.

    Science.gov (United States)

    Porta, Miquel

    2015-10-01

    We know quite a lot about the generalized human contamination by environmental chemical agents; this statement is fully compatible with the view that most countries lack the necessary monitoring systems. We also know quite a lot about the toxic effects of environmental pollutants; this statement is fully compatible with the proposal that we need both more research and more energetic policies to decrease human contamination by such pollutants. Unsurprisingly, we know too little about the (environmental and social) causes and the etiopathogenesis (mechanisms) of the most prevalent diseases, and we will continue to miss relevant causes and mechanisms if we neglect the toxic chemicals that commonly contaminate humans, worldwide. Basic, clinical end environmental-epidemiological research on human health should more often consider integrating biomarkers of internal dose of environmental chemical pollutants. When we act in more responsible, rational, and scientific ways; when we become less dismissive towards environmental hazards; and when we thus neglect less the generalized human contamination by environmental chemical agents and their toxic effects, we will expand mechanistic biologic knowledge, and we shall as well increase the effectiveness of interventions and policies that enable the primary prevention of human diseases which cause huge amounts of economic burden and human suffering.

  3. A Novel Method for Predicting Late Genitourinary Toxicity After Prostate Radiation Therapy and the Need for Age-Based Risk-Adapted Dose Constraints

    International Nuclear Information System (INIS)

    Ahmed, Awad A.; Egleston, Brian; Alcantara, Pino; Li, Linna; Pollack, Alan; Horwitz, Eric M.; Buyyounouski, Mark K.

    2013-01-01

    Background: There are no well-established normal tissue sparing dose–volume histogram (DVH) criteria that limit the risk of urinary toxicity from prostate radiation therapy (RT). The aim of this study was to determine which criteria predict late toxicity among various DVH parameters when contouring the entire solid bladder and its contents versus the bladder wall. The area under the histogram curve (AUHC) was also analyzed. Methods and Materials: From 1993 to 2000, 503 men with prostate cancer received 3-dimensional conformal RT (median follow-up time, 71 months). The whole bladder and the bladder wall were contoured in all patients. The primary endpoint was grade ≥2 genitourinary (GU) toxicity occurring ≥3 months after completion of RT. Cox regressions of time to grade ≥2 toxicity were estimated separately for the entire bladder and bladder wall. Concordance probability estimates (CPE) assessed model discriminative ability. Before training the models, an external random test group of 100 men was set aside for testing. Separate analyses were performed based on the mean age (≤ 68 vs >68 years). Results: Age, pretreatment urinary symptoms, mean dose (entire bladder and bladder wall), and AUHC (entire bladder and bladder wall) were significant (P 68 years. Conclusion: The AUHC method based on bladder wall volumes was superior for predicting late GU toxicity. Age >68 years was associated with late grade ≥2 GU toxicity, which suggests that risk-adapted dose constraints based on age should be explored

  4. Dynamic Filtering Improves Attentional State Prediction with fNIRS

    Science.gov (United States)

    Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

    2016-01-01

    Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

  5. Plasma exchange combining with plasma bilirubin adsorption effectively removes toxic substances and improves liver functions of hepatic failure patients.

    Science.gov (United States)

    Che, X-Q; Li, Z-Q; Chen, Z; Guo, D; Jia, Q-Y; Jiang, S-C; Cai, J

    2018-02-01

    Hepatic failure (HF) is a kind of complex disease characterizing with liver dysfunction and a few clinical complications. Artificial liver support system (ALSS) has been applied to HF patients to improve dysfunctional liver in recent years. This study aims to evaluate therapeutic effects of ALSS approaches, including plasma exchange (PE), plasma diafiltration (PDF) and plasma bilirubin adsorption (PBA), on liver function of HF patients. This study is a retrospective analysis involving 516 patients diagnosed as HF between February 2014 and February 2015. Patients were randomly divided into PE, PDF, PE plus PBA, and PDF plus PBA group. Meanwhile, single-drug group and combined-drug group were also divided. The liver functions, capability of removing toxic substances and coagulation functions were evaluated both pre-treatment and post-treatment. The side effects and hospital improvement rate were also observed post-treatment. Hospital improvement rate achieves to 69.6%. TBIL levels and MELD scores were significantly decreased post-treatment compared to pre-treatment (phigher compared to PE and PDF group (p=0.002, 0.002, respectively). MELD scores were significantly decreased post-treatment compared to pre-treatment in each group (pbetter role in removing toxic substances, improving liver functions of HF patients.

  6. The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

    Directory of Open Access Journals (Sweden)

    Montserrat Cases

    2014-11-01

    Full Text Available The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP protection and set up of adequate controlled vocabularies and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds. In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys are presented as decision support knowledge-based tools for drug development process at an early stage.

  7. Dose-volume effects for pelvic bone marrow in predicting hematological toxicity in prostate cancer radiotherapy with pelvic node irradiation.

    Science.gov (United States)

    Sini, Carla; Fiorino, Claudio; Perna, Lucia; Noris Chiorda, Barbara; Deantoni, Chiara Lucrezia; Bianchi, Marco; Sacco, Vincenzo; Briganti, Alberto; Montorsi, Francesco; Calandrino, Riccardo; Di Muzio, Nadia; Cozzarini, Cesare

    2016-01-01

    To prospectively identify clinical/dosimetric predictors of acute/late hematologic toxicity (HT) in chemo-naÏve patients treated with whole-pelvis radiotherapy (WPRT) for prostate cancer. Data of 121 patients treated with adjuvant/salvage WPRT were analyzed (static-field IMRT n=19; VMAT/Rapidarc n=57; Tomotherapy n=45). Pelvic bone marrow (BM) was delineated as ilium (IL), lumbosacral, lower and whole pelvis (WP), and the relative DVHs were calculated. HT was graded both according to CTCAE v4.03 and as variation in percentage relative to baseline. Logistic regression was used to analyze association between HT and clinical/DVHs factors. Significant differences (p<0.005) in the DVH of BM volumes between different techniques were found: Tomotherapy was associated with larger volumes receiving low doses (3-20 Gy) and smaller receiving 40-50 Gy. Lower baseline absolute values of WBC, neutrophils and lymphocytes (ALC) predicted acute/late HT (p ⩽ 0.001). Higher BM V40 was associated with higher risk of acute Grade3 (OR=1.018) or late Grade2 lymphopenia (OR=1.005). Two models predicting lymphopenia were developed, both including baseline ALC, and BM WP-V40 (AUC=0.73) and IL-V40+smoking (AUC=0.904) for acute/late respectively. Specific regions of pelvic BM predicting acute/late lymphopenia, a risk factor for viral infections, were identified. The 2-variable models including specific constraints to BM may help reduce HT. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  8. Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action

    DEFF Research Database (Denmark)

    Sanderson, Hans; Thomsen, Marianne

    2009-01-01

    data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68...

  9. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

    Science.gov (United States)

    Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

    2018-02-05

    Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  10. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

    Directory of Open Access Journals (Sweden)

    Li-Yue Bai

    2018-02-01

    Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  11. Characterisation of a radiation-resistant strain of bacillus thuringiensis subsp. Aizawai with improved toxicity to larval plutella xylostella

    International Nuclear Information System (INIS)

    Mahadi, N.M.; Boo, J.M.L.; Jangi, M.S.

    1989-01-01

    A radiation-resistant strain of Bacillus thuringiensis subsp. Aizawai which was previously shown to be more toxic against larval Plutell xylostella was further characterized. Some of the growth characteristics of the mutant strain were quite different from those of the parent strain. In shake flask culture, its lag period was shorter and its cell yield was lower. The growth rate, however, was the same as that of the parent. Electron microscope studies show that the insecticidal parasporal crystals from the mutant strain are significantly bigger than those produced by the parent strain. The average length and width of the crystals were 1.25 and 0.53 um respectively whereas those of the parent were 0.87 and 0.35 um, respectively. The crystals from the mutant strain were also more toxic. The LC 50 was 0.30 ug crystal protein per ml as against 0.66 ug crystal protein per ml for those from the parent strain. Protein profile of the crystals obtained with SDS-PA gel electrophoresis showed that the mutant strain produced an additional polypeptide of 143 KDa polypeptide. The mutant strain also has an additional high molecular weight plasmid. The improved toxicity may have been brought about by a number of factors including an alteration in the regulatory mechanism that control the synthesis of the polypeptides that make up the crystals. (Auth.). 5 figs.; 21 refs.; 2 tabs

  12. Interpreting Disruption Prediction Models to Improve Plasma Control

    Science.gov (United States)

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

  13. Improving acute kidney injury diagnostics using predictive analytics.

    Science.gov (United States)

    Basu, Rajit K; Gist, Katja; Wheeler, Derek S

    2015-12-01

    Acute kidney injury (AKI) is a multifactorial syndrome affecting an alarming proportion of hospitalized patients. Although early recognition may expedite management, the ability to identify patients at-risk and those suffering real-time injury is inconsistent. The review will summarize the recent reports describing advancements in the area of AKI epidemiology, specifically focusing on risk scoring and predictive analytics. In the critical care population, the primary underlying factors limiting prediction models include an inability to properly account for patient heterogeneity and underperforming metrics used to assess kidney function. Severity of illness scores demonstrate limited AKI predictive performance. Recent evidence suggests traditional methods for detecting AKI may be leveraged and ultimately replaced by newer, more sophisticated analytical tools capable of prediction and identification: risk stratification, novel AKI biomarkers, and clinical information systems. Additionally, the utility of novel biomarkers may be optimized through targeting using patient context, and may provide more granular information about the injury phenotype. Finally, manipulation of the electronic health record allows for real-time recognition of injury. Integrating a high-functioning clinical information system with risk stratification methodology and novel biomarker yields a predictive analytic model for AKI diagnostics.

  14. The use of adverse outcome pathway-based toxicity predictions: A case study evaluating the effects of imazalil on fathead minnow reproduction

    Science.gov (United States)

    Product Description: As a means to increase the efficiency of chemical safety assessment, there is an interest in using data from molecular and cellular bioassays, conducted in a highly automated fashion using modern robotics, to predict toxicity in humans and wildlife. The prese...

  15. Extending the Derek-Meteor Workflow to Predict Chemical-Toxicity Space Impacted by Metabolism: Application to ToxCast and Tox21 Chemical Inventories

    Science.gov (United States)

    A central aim of EPA’s ToxCast project is to use in vitro high-throughput screening (HTS) profiles to build predictive models of in vivo toxicity. Where assays lack metabolic capability, such efforts may need to anticipate the role of metabolic activation (or deactivation). A wo...

  16. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Improving Marital Prediction: A Model and a Pilot Study.

    Science.gov (United States)

    Dean, Dwight G.; Lucas, Wayne L.

    A model for the prediction of marital adjustment is proposed which presents selected social background factors (e.g., education) and interactive factors (e.g., Bienvenu's Communication scale, Hurvitz' Role Inventory, Dean's Emotional Maturity and Commitment scales, Rosenberg's Self-Esteem scale) in order to account for as much of the variance in…

  18. How Predictive Analytics and Choice Architecture Can Improve Student Success

    Science.gov (United States)

    Denley, Tristan

    2014-01-01

    This article explores the challenges that students face in navigating the curricular structure of post-secondary degree programs, and how predictive analytics and choice architecture can play a role. It examines Degree Compass, a course recommendation system that successfully pairs current students with the courses that best fit their talents and…

  19. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    user

    2010-10-04

    Oct 4, 2010 ... Available online at http://www.academicjournals.org/AJEST ... robust technique for predicting optimal image resolution for the mapping of savannah ecosystems was developed. .... whether to purchase multi-spectral imagery acquired by GeoEye-2 ..... Analysis of the spectral behaviour of the pasture class in.

  20. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    Past studies to predict optimal image resolution required for generating spatial information for savannah ecosystems have yielded different outcomes, hence providing a knowledge gap that was investigated in the present study. The postulation, for the present study, was that by graphically solving two simultaneous ...

  1. Combining disparate data sources for improved poverty prediction and mapping.

    Science.gov (United States)

    Pokhriyal, Neeti; Jacques, Damien Christophe

    2017-11-14

    More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of "Big Data" to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84-0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. Copyright © 2017 the Author(s). Published by PNAS.

  2. Trajectory Analysis and Prediction for Improved Pedestrian Safety

    DEFF Research Database (Denmark)

    Møgelmose, Andreas; Trivedi, Mohan M.; Moeslund, Thomas B.

    2015-01-01

    This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pede...

  3. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...

  4. Selection procedures in sports: Improving predictions of athletes’ future performance

    NARCIS (Netherlands)

    den Hartigh, Jan Rudolf; Niessen, Anna; Frencken, Wouter; Meijer, Rob R.

    The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the

  5. Antimony Toxicity

    OpenAIRE

    Sundar, Shyam; Chakravarty, Jaya

    2010-01-01

    Antimony toxicity occurs either due to occupational exposure or during therapy. Occupational exposure may cause respiratory irritation, pneumoconiosis, antimony spots on the skin and gastrointestinal symptoms. In addition antimony trioxide is possibly carcinogenic to humans. Improvements in working conditions have remarkably decreased the incidence of antimony toxicity in the workplace. As a therapeutic, antimony has been mostly used for the treatment of leishmaniasis and schistosomiasis. The...

  6. Improved part-of-speech prediction in suffix analysis.

    Directory of Open Access Journals (Sweden)

    Mario Fruzangohar

    Full Text Available MOTIVATION: Predicting the part of speech (POS tag of an unknown word in a sentence is a significant challenge. This is particularly difficult in biomedicine, where POS tags serve as an input to training sophisticated literature summarization techniques, such as those based on Hidden Markov Models (HMM. Different approaches have been taken to deal with the POS tagger challenge, but with one exception--the TnT POS tagger--previous publications on POS tagging have omitted details of the suffix analysis used for handling unknown words. The suffix of an English word is a strong predictor of a POS tag for that word. As a pre-requisite for an accurate HMM POS tagger for biomedical publications, we present an efficient suffix prediction method for integration into a POS tagger. RESULTS: We have implemented a fully functional HMM POS tagger using experimentally optimised suffix based prediction. Our simple suffix analysis method, significantly outperformed the probability interpolation based TnT method. We have also shown how important suffix analysis can be for probability estimation of a known word (in the training corpus with an unseen POS tag; a common scenario with a small training corpus. We then integrated this simple method in our POS tagger and determined an optimised parameter set for both methods, which can help developers to optimise their current algorithm, based on our results. We also introduce the concept of counting methods in maximum likelihood estimation for the first time and show how counting methods can affect the prediction result. Finally, we describe how machine-learning techniques were applied to identify words, for which prediction of POS tags were always incorrect and propose a method to handle words of this type. AVAILABILITY AND IMPLEMENTATION: Java source code, binaries and setup instructions are freely available at http://genomes.sapac.edu.au/text_mining/pos_tagger.zip.

  7. Comment on 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study'.

    Science.gov (United States)

    Valdes, Gilmer; Interian, Yannet

    2018-03-15

    The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study' with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.

  8. Phenotyping of UGT1A1 Activity Using Raltegravir Predicts Pharmacokinetics and Toxicity of Irinotecan in FOLFIRI.

    Directory of Open Access Journals (Sweden)

    Lawrence Soon-U Lee

    Full Text Available Irinotecan toxicity correlates with UGT1A1 activity. We explored whether phenotyping UGT1A1 using a probe approach works better than current genotyping methods.Twenty-four Asian cancer patients received irinotecan as part of the FOLFIRI regimen. Subjects took raltegravir 400 mg orally and intravenous midazolam 1 mg. Pharmacokinetic analyses were performed using WinNonLin and NONMEM. Genomic DNA was isolated and screened for the known genetic variants in UGT1A1 and CYP3A4/5.SN-38G/SN-38 AUC ratio correlated well with Raltegravir glucuronide/ Raltegravir AUC ratio (r = 0.784 p<0.01. Midazolam clearance correlated well with irinotecan clearance (r = 0.563 p<0.01. SN-38 AUC correlated well with Log10Nadir Absolute Neutrophil Count (ANC (r = -0.397 p<0.05. Significant correlation was found between nadir ANC and formation rate constant of raltegravir glucuronide (r = 0.598, P<0.005, but not UGT1A1 genotype.Raltegravir glucuronide formation is a good predictor of nadir ANC, and can predict neutropenia in East Asian patients. Prospective studies with dose adjustments should be done to develop raltegravir as a probe to optimize irinotecan therapy.Clinicaltrials.gov NCT00808184.

  9. Prediction of the bioavailability of potentially toxic elements in freshwaters. Comparison between speciation models and passive samplers.

    Science.gov (United States)

    Sierra, Jordi; Roig, Neus; Giménez Papiol, Gemma; Pérez-Gallego, Elena; Schuhmacher, Marta

    2017-12-15

    The aim of this work is to predict the bioavailability of the Potentially Toxic Elements (PTEs) Cd, Pb, Hg, Ni, Cu, Zn, As, Cr and Se in 6 sites within the Ebro River basin. In situ Diffusive gradient in thin-films (DGTs) and classical sampling have been used and compared. The potentially bioavailable fractions of each PTE was estimated by modelling their chemical speciation using three programs (WHAM 7.0, Visual MINTEQ 3.1 and Bio-met), following the suggestions published in recent European regulations. Results of the equilibrium-based models WHAM 7.0 and Visual MINTEQ 3.1 indicate that As, Cd, Ni, Se and Zn, predominate as free metals ions or forming inorganic soluble complexes. Copper, Pb and Hg bioavailability is conditioned by their affinity to dissolved humic substances. According to Visual MINTEQ 3.1, Cr is subjected to redox reactions, being Cr (VI) present (at low concentrations) in the studied rivers. According to Bio-met model, the bioavailability of Cu and Zn is highly influenced by soluble organic matter and water hardness, respectively. For most PTEs, the bioavailability estimated by deploying DGTs in river waters tends to be slightly lower than the estimation obtained with speciation models, since in real conditions more environmental factors take place comparing to the finite number of parameters considered in models. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Improving the cyanide toxicity tolerance of anaerobic reactor: Microbial interactions and toxin reduction

    International Nuclear Information System (INIS)

    Gupta, Pragya; Ahammad, S.Z.; Sreekrishnan, T.R.

    2016-01-01

    Highlights: • Anaerobic batch study of 110 days. • Acclimatization for cyanide biodegradation. • Understanding inhibitory effects of cyanide on methane generation and VFA production. • Identification of microorganisms tolerant to cyanide. • Community analysis using DGGE and qPCR analyses. - Abstract: Anaerobic biological treatment of high organics containing wastewater is amongst the preferred treatment options but poor tolerance to toxins makes its use prohibitive. In this study, efforts have been made to understand the key parameters for developing anaerobic reactor, resilient to cyanide toxicity. A laboratory scale anaerobic batch reactor was set up to treat cyanide containing wastewater. The reactor was inoculated with anaerobic sludge obtained from a wastewater treatment plant and fresh cow dung in the ratio of 3:1. The focus was on acclimatization and development of cyanide-degrading biomass and to understand the toxic effects of cyanide on the dynamic equilibrium between various microbial groups. The sludge exposed to cyanide was found to have higher bacterial diversity than the control. It was observed that certain hydrogenotrophic methanogens and bacterial groups were able to grow and produce methane in the presence of cyanide. Also, it was found that hydrogen utilizing methanogens were more cyanide tolerant than acetate utilizing methanogens. So, effluents from various industries like electroplating, coke oven plant, petroleum refining, explosive manufacturing, and pesticides industries which are having high concentrations of cyanide can be treated by favoring the growth of the tolerant microbes in the reactors. It will provide much better treatment efficiency by overcoming the inhibitory effects of cyanide to certain extent.

  11. Improving the cyanide toxicity tolerance of anaerobic reactor: Microbial interactions and toxin reduction

    Energy Technology Data Exchange (ETDEWEB)

    Gupta, Pragya; Ahammad, S.Z.; Sreekrishnan, T.R., E-mail: sree@iitd.ac.in

    2016-09-05

    Highlights: • Anaerobic batch study of 110 days. • Acclimatization for cyanide biodegradation. • Understanding inhibitory effects of cyanide on methane generation and VFA production. • Identification of microorganisms tolerant to cyanide. • Community analysis using DGGE and qPCR analyses. - Abstract: Anaerobic biological treatment of high organics containing wastewater is amongst the preferred treatment options but poor tolerance to toxins makes its use prohibitive. In this study, efforts have been made to understand the key parameters for developing anaerobic reactor, resilient to cyanide toxicity. A laboratory scale anaerobic batch reactor was set up to treat cyanide containing wastewater. The reactor was inoculated with anaerobic sludge obtained from a wastewater treatment plant and fresh cow dung in the ratio of 3:1. The focus was on acclimatization and development of cyanide-degrading biomass and to understand the toxic effects of cyanide on the dynamic equilibrium between various microbial groups. The sludge exposed to cyanide was found to have higher bacterial diversity than the control. It was observed that certain hydrogenotrophic methanogens and bacterial groups were able to grow and produce methane in the presence of cyanide. Also, it was found that hydrogen utilizing methanogens were more cyanide tolerant than acetate utilizing methanogens. So, effluents from various industries like electroplating, coke oven plant, petroleum refining, explosive manufacturing, and pesticides industries which are having high concentrations of cyanide can be treated by favoring the growth of the tolerant microbes in the reactors. It will provide much better treatment efficiency by overcoming the inhibitory effects of cyanide to certain extent.

  12. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

  13. Improved predictions of nuclear data: A continued challenge in astrophysics

    International Nuclear Information System (INIS)

    Goriely, S.

    2001-01-01

    Although important effort has been devoted in the last decades to measure reaction cross sections and decay half-lives of interest in astrophysics, most of the nuclear astrophysics applications still require the use of theoretical predictions to estimate experimentally unknown rates. The nuclear ingredients to the reaction or weak interaction models should preferentially be estimated from microscopic or semi-microscopic global predictions based on sound and reliable nuclear models which, in turn, can compete with more phenomenological highly-parametrized models in the reproduction of experimental data. The latest developments made in deriving the nuclear inputs of relevance in astrophysics applications are reviewed. It mainly concerns nuclear structure properties (atomic masses, deformations, radii, etc...), nuclear level densities, nucleon and α-optical potentials, γ-ray and Gamow-Teller strength functions

  14. Toxicity Estimation Software Tool (TEST)

    Science.gov (United States)

    The Toxicity Estimation Software Tool (TEST) was developed to allow users to easily estimate the toxicity of chemicals using Quantitative Structure Activity Relationships (QSARs) methodologies. QSARs are mathematical models used to predict measures of toxicity from the physical c...

  15. Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech

    OpenAIRE

    劉, 麗清

    2015-01-01

    Linear prediction (LP) analysis has been applied to speech system over the last few decades. LP technique is well-suited for speech analysis due to its ability to model speech production process approximately. Hence LP analysis has been widely used for speech enhancement, low-bit-rate speech coding in cellular telephony, speech recognition, characteristic parameter extraction (vocal tract resonances frequencies, fundamental frequency called pitch) and so on. However, the performance of the co...

  16. Improving Transit Predictions of Known Exoplanets with TERMS

    Directory of Open Access Journals (Sweden)

    Mahadevan S.

    2011-02-01

    Full Text Available Transiting planet discoveries have largely been restricted to the short-period or low-periastron distance regimes due to the bias inherent in the geometric transit probability. Through the refinement of planetary orbital parameters, and hence reducing the size of transit windows, long-period planets become feasible targets for photometric follow-up. Here we describe the TERMS project that is monitoring these host stars at predicted transit times.

  17. Multilevel Empirical Bayes Modeling for Improved Estimation of Toxicant Formulations to Suppress Parasitic Sea Lamprey in the Upper Great Lakes

    Science.gov (United States)

    Hatfield, L.A.; Gutreuter, S.; Boogaard, M.A.; Carlin, B.P.

    2011-01-01

    Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data. ?? 2011, The International Biometric Society.

  18. Clinical Factors Predicting Late Severe Urinary Toxicity After Postoperative Radiotherapy for Prostate Carcinoma: A Single-Institute Analysis of 742 Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cozzarini, Cesare, E-mail: cozzarini.cesare@hsr.it [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Fiorino, Claudio [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Da Pozzo, Luigi Filippo [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Alongi, Filippo; Berardi, Genoveffa; Bolognesi, Angelo [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Briganti, Alberto [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Broggi, Sara [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Deli, Aniko [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Guazzoni, Giorgio [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Perna, Lucia [Department of Medical Physics, San Raffaele Scientific Institute, Milan (Italy); Pasetti, Marcella; Salvadori, Giovannella [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy); Montorsi, Francesco; Rigatti, Patrizio [Department of Urology, San Raffaele Scientific Institute, Milan (Italy); Di Muzio, Nadia [Department of Radiotherapy, San Raffaele Scientific Institute, Milan (Italy)

    2012-01-01

    Purpose: To investigate the clinical factors independently predictive of long-term severe urinary sequelae after postprostatectomy radiotherapy. Patients and Methods: Between 1993 and 2005, 742 consecutive patients underwent postoperative radiotherapy with either adjuvant (n = 556; median radiation dose, 70.2 Gy) or salvage (n = 186; median radiation dose, 72 Gy) intent. Results: After a median follow-up of 99 months, the 8-year risk of Grade 2 or greater and Grade 3 late urinary toxicity was almost identical (23.9% vs. 23.7% and 12% vs. 10%) in the adjuvant and salvage cohorts, respectively. On univariate analysis, acute toxicity was significantly predictive of late Grade 2 or greater sequelae in both subgroups (p <.0001 in both cases), and hypertension (p = .02) and whole-pelvis radiotherapy (p = .02) correlated significantly in the adjuvant cohort only. The variables predictive of late Grade 3 sequelae were acute Grade 2 or greater toxicity in both groups and whole-pelvis radiotherapy (8-year risk of Grade 3 events, 21% vs. 11%, p = .007), hypertension (8-year risk, 18% vs. 10%, p = .005), age {<=} 62 years at RT (8-year risk, 16% vs. 11%, p = .04) in the adjuvant subset, and radiation dose >72 Gy (8-year risk, 19% vs. 6%, p = .007) and age >71 years (8-year risk, 16% vs. 6%, p = .006) in the salvage subgroup. Multivariate analysis confirmed the independent predictive role of all the covariates indicated as statistically significant on univariate analysis. Conclusions: The risk of late Grade 2 or greater and Grade 3 urinary toxicity was almost identical, regardless of the RT intent. In the salvage cohort, older age and greater radiation doses resulted in a worse toxicity profile, and younger, hypertensive patients experienced a greater rate of severe late sequelae in the adjuvant setting. The causes of this latter correlation and apparently different etiopathogenesis of chronic damage in the two subgroups were unclear and deserve additional investigation.

  19. Can dosimetric parameters predict acute hematologic toxicity in rectal cancer patients treated with intensity-modulated pelvic radiotherapy?

    International Nuclear Information System (INIS)

    Wan, Juefeng; Liu, Kaitai; Li, Kaixuan; Li, Guichao; Zhang, Zhen

    2015-01-01

    To identify dosimetric parameters associated with acute hematologic toxicity (HT) in rectal cancer patients undergoing concurrent chemotherapy and intensity-modulated pelvic radiotherapy. Ninety-three rectal cancer patients receiving concurrent capecitabine and pelvic intensity-modulated radiation therapy (IMRT) were analyzed. Pelvic bone marrow (PBM) was contoured for each patient and divided into three subsites: lumbosacral spine (LSS), ilium, and lower pelvis (LP). The volume of each site receiving 5–40 Gy (V 5, V10, V15, V20, V30, and V40, respectively) as well as patient baseline clinical characteristics was calculated. The endpoint for hematologic toxicity was grade ≥ 2 (HT2+) leukopenia, neutropenia, anemia or thrombocytopenia. Logistic regression was used to analyze correlation between dosimetric parameters and grade ≥ 2 hematologic toxicity. Twenty-four in ninety-three patients experienced grade ≥ 2 hematologic toxicity. Only the dosimetric parameter V40 of lumbosacral spine was correlated with grade ≥ 2 hematologic toxicity. Increased pelvic lumbosacral spine V40 (LSS-V40) was associated with an increased grade ≥ 2 hematologic toxicity (p = 0.041). Patients with LSS-V40 ≥ 60 % had higher rates of grade ≥ 2 hematologic toxicity than did patients with lumbosacral spine V40 < 60 % (38.3 %, 18/47 vs.13 %, 6/46, p =0.005). On univariate and multivariate logistic regression analysis, lumbosacral spine V40 and gender was also the variable associated with grade ≥ 2 hematologic toxicity. Female patients were observed more likely to have grade ≥ 2 hematologic toxicity than male ones (46.9 %, 15/32 vs 14.8 %, 9/61, p =0.001). Lumbosacral spine -V40 was associated with clinically significant grade ≥ 2 hematologic toxicity. Keeping the lumbosacral spine -V40 < 60 % was associated with a 13 % risk of grade ≥ 2 hematologic toxicity in rectal cancer patients undergoing concurrent chemoradiotherapy

  20. Improving 3D structure prediction from chemical shift data

    Energy Technology Data Exchange (ETDEWEB)

    Schot, Gijs van der [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Zhang, Zaiyong [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany); Vernon, Robert [University of Washington, Department of Biochemistry (United States); Shen, Yang [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States); Vranken, Wim F. [VIB, Department of Structural Biology (Belgium); Baker, David [University of Washington, Department of Biochemistry (United States); Bonvin, Alexandre M. J. J., E-mail: a.m.j.j.bonvin@uu.nl [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Lange, Oliver F., E-mail: oliver.lange@tum.de [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany)

    2013-09-15

    We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50-100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 A RMSD from the reference)

  1. Improving Prediction of Large-scale Regime Transitions

    Science.gov (United States)

    Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.

    2017-12-01

    Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these

  2. Improving Student Success Using Predictive Models and Data Visualisations

    Science.gov (United States)

    Essa, Alfred; Ayad, Hanan

    2012-01-01

    The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from…

  3. Improving the TRIGA facility maintenance by predictive maintenance techniques

    International Nuclear Information System (INIS)

    Preda, M.; Sabau, C.; Barbalata, E.

    1997-01-01

    This work deals with the specific operation of equipment in radioactive environment or in conditions allowing radioactive contamination. The requirements of remote operation ensuring the operators' protection are presented. Also, the requirements of international standards issued by IAEA-Vienna are reviewed. The organizational withdraws of the maintenance activities, based on the standards and maintenance and repair directives still in force, are shown. It is emphasized the fact that this type of maintenance was adequate to a given level of technical development, characteristic for pre-computerized industry, but, at present, it is obsolete and uneconomic both in utilization and maintenance. Such a system constitutes already a burden hindering the efforts of maximizing the availability, maintenance, prolongation the service life of equipment and utilities, finally, of increasing the efficiency of complex installations. Moreover, the predictive maintenance techniques are strongly requested by the character of radioactive installations precluding the direct access in given zones (a potential risk of irradiation or radioactive contamination) of installations during operation. The results obtained by applying the predictive maintenance techniques in the operation of the double circuit irradiation loop, used in the TRIGA reactors, are presented

  4. Antimony Toxicity

    Directory of Open Access Journals (Sweden)

    Shyam Sundar

    2010-12-01

    Full Text Available Antimony toxicity occurs either due to occupational exposure or during therapy. Occupational exposure may cause respiratory irritation, pneumoconiosis, antimony spots on the skin and gastrointestinal symptoms. In addition antimony trioxide is possibly carcinogenic to humans. Improvements in working conditions have remarkably decreased the incidence of antimony toxicity in the workplace. As a therapeutic, antimony has been mostly used for the treatment of leishmaniasis and schistosomiasis. The major toxic side-effects of antimonials as a result of therapy are cardiotoxicity (~9% of patients and pancreatitis, which is seen commonly in HIV and visceral leishmaniasis co-infections. Quality control of each batch of drugs produced and regular monitoring for toxicity is required when antimonials are used therapeutically.

  5. Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

    Science.gov (United States)

    Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

    2018-02-01

    In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and

  6. Ionizing radiation-induced DNA damage and repair as a potential biomarker in biodosimetry, cancer risk analysis and for prediction of radiotherapy induced toxicity

    International Nuclear Information System (INIS)

    Satish Rao, B.S.

    2017-01-01

    Lymphocytes isolated from peripheral blood from 100 healthy individuals, 232 cancer patients (cervical, breast cancer and head and neck cancer) irradiated in vitro or in vivo were used for measuring DNA damage and repair. The microscopic method of the γ-H2AX assay was adopted to elucidate the significance of DSB in biodosimetry, cancer risk susceptibility, and normal tissue toxicity prediction. We validated the use of H2AX assay in early triage biodosimetry by using lymphocytes from cervical cancer patients exposed to radiotherapy. Further, the basal and residual damage was significantly higher in cancer individuals compared to the healthy individuals. In cancer patients undergoing radiotherapy, we could able to show the increase in normal tissue toxicity with decreased DSB repair capacity. In conclusion this study indicates the DSB estimation by γ-H2AX foci analysis can serve as a tool to understand the triage of radiation exposed individuals, identifying individuals at cancer risk and normal tissue toxicity

  7. Improving, characterizing and predicting the lifetime of organic photovoltaics

    DEFF Research Database (Denmark)

    Gevorgyan, Suren A.; Heckler, Ilona Maria; Bundgaard, Eva

    2017-01-01

    This review summarizes the recent progress in the stability and lifetime of organic photovoltaics (OPVs). In particular, recently proposed solutions to failure mechanisms in different layers of the device stack are discussed comprising both structural and chemical modifications. Upscaling...... characterization reported recently. Lifetime testing and determination is another challenge in the field of organic solar cells and the final sections of this review discuss the testing protocols as well as the generic marker for device lifetime and the methodology for comparing all the lifetime landmarks in one...... common diagram. These tools were used to determine the baselines for OPV lifetime tested under different ageing conditions. Finally, the current status of lifetime for organic solar cells is presented and predictions are made for progress in the near future....

  8. Predicting occurrence of juvenile shark habitat to improve conservation planning.

    Science.gov (United States)

    Oh, Beverly Z L; Sequeira, Ana M M; Meekan, Mark G; Ruppert, Jonathan L W; Meeuwig, Jessica J

    2017-06-01

    Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark-monitoring data on large scales (100s-1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of habitat suitability for juvenile sharks. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490,515 km 2 of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species-level models had higher accuracy (ĸ ≥ 0.69) and deviance explained (≥48%) than our order-level model (ĸ = 0.36 and deviance explained of 10%). Maps of predicted occurrence revealed different species-specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species-focused approach. Highly suitable habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non-extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across

  9. Prediction of clinical toxicity in locally advanced head and neck cancer patients by radio-induced apoptosis in peripheral blood lymphocytes (PBLs)

    International Nuclear Information System (INIS)

    Bordón, Elisa; Henríquez-Hernández, Luis Alberto; Lara, Pedro C; Ruíz, Ana; Pinar, Beatriz; Rodríguez-Gallego, Carlos; Lloret, Marta

    2010-01-01

    Head and neck cancer is treated mainly by surgery and radiotherapy. Normal tissue toxicity due to x-ray exposure is a limiting factor for treatment success. Many efforts have been employed to develop predictive tests applied to clinical practice. Determination of lymphocyte radio-sensitivity by radio-induced apoptosis arises as a possible method to predict tissue toxicity due to radiotherapy. The aim of the present study was to analyze radio-induced apoptosis of peripheral blood lymphocytes in head and neck cancer patients and to explore their role in predicting radiation induced toxicity. Seventy nine consecutive patients suffering from head and neck cancer, diagnosed and treated in our institution, were included in the study. Toxicity was evaluated using the Radiation Therapy Oncology Group scale. Peripheral blood lymphocytes were isolated and irradiated at 0, 1, 2 and 8 Gy during 24 hours. Apoptosis was measured by flow cytometry using annexin V/propidium iodide. Lymphocytes were marked with CD45 APC-conjugated monoclonal antibody. Radiation-induced apoptosis increased in order to radiation dose and fitted to a semi logarithmic model defined by two constants: α and β. α, as the origin of the curve in the Y axis determining the percentage of spontaneous cell death, and β, as the slope of the curve determining the percentage of cell death induced at a determined radiation dose, were obtained. β value was statistically associated to normal tissue toxicity in terms of severe xerostomia, as higher levels of apoptosis were observed in patients with low toxicity (p = 0.035; Exp(B) 0.224, I.C.95% (0.060-0.904)). These data agree with our previous results and suggest that it is possible to estimate the radiosensitivity of peripheral blood lymphocytes from patients determining the radiation induced apoptosis with annexin V/propidium iodide staining. β values observed define an individual radiosensitivity profile that could predict late toxicity due to radiotherapy

  10. Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans.

    Science.gov (United States)

    Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H

    2018-02-01

    More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  11. Improved Synthesis of N-Benzylaminoferrocene-Based Prodrugs and Evaluation of Their Toxicity and Antileukemic Activity

    NARCIS (Netherlands)

    Daum, Steffen; Chekhun, Vasiliy F; Todor, Igor N; Lukianova, Natalia Yu; Shvets, Yulia V; Sellner, Leopold; Putzker, Kerstin; Lewis, Joe; Zenz, Thorsten; de Graaf, Inge A M; Groothuis, Geny M M; Casini, Angela; Zozulia, Oleksii; Hampel, Frank; Mokhir, Andriy

    2015-01-01

    We report on an improved method of synthesis of N-benzylaminoferrocene-based prodrugs and demonstrate its applicability by preparing nine new aminoferrocenes. Their effect on the viability of selected cancer cells having different p53 status was studied. The obtained data are in agreement with the

  12. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  13. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  14. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  15. Improving the reliability of fishery predictions under climate change

    DEFF Research Database (Denmark)

    Brander, Keith

    2015-01-01

    The increasing number of publications assessing impacts of climate change on marine ecosystems and fisheries attests to rising scientific and public interest. A selection of recent papers, dealing more with biological than social and economic aspects, is reviewed here, with particular attention...... to the reliability of projections of climate impacts on future fishery yields. The 2014 Intergovernmental Panel on Climate Change (IPCC) report expresses high confidence in projections that mid- and high-latitude fish catch potential will increase by 2050 and medium confidence that low-latitude catch potential...... understanding of climate impacts, such as how to improve coupled models from physics to fish and how to strengthen confidence in analysis of time series...

  16. Verification and improvement of predictive algorithms for radionuclide migration

    International Nuclear Information System (INIS)

    Carnahan, C.L.; Miller, C.W.; Remer, J.S.

    1984-01-01

    This research addresses issues relevant to numerical simulation and prediction of migration of radionuclides in the environment of nuclear waste repositories. Specific issues investigated are the adequacy of current numerical codes in simulating geochemical interactions affecting radionuclide migration, the level of complexity required in chemical algorithms of transport models, and the validity of the constant-k/sub D/ concept in chemical transport modeling. An initial survey of the literature led to the conclusion that existing numerical codes did not encompass the full range of chemical and physical phenomena influential in radionuclide migration. Studies of chemical algorithms have been conducted within the framework of a one-dimensional numerical code that simulates the transport of chemically reacting solutes in a saturated porous medium. The code treats transport by dispersion/diffusion and advection, and equilibrium-controlled proceses of interphase mass transfer, complexation in the aqueous phase, pH variation, and precipitation/dissolution of secondary solids. Irreversible, time-dependent dissolution of solid phases during transport can be treated. Mass action, transport, and sorptive site constraint equations are expressed in differential/algebraic form and are solved simultaneously. Simulations using the code show that use of the constant-k/sub D/ concept can produce unreliable results in geochemical transport modeling. Applications to a field test and laboratory analogs of a nuclear waste repository indicate that a thermodynamically based simulator of chemical transport can successfully mimic real processes provided that operative chemical mechanisms and associated data have been correctly identified and measured, and have been incorporated in the simulator. 17 references, 10 figures

  17. Improving Radar QPE's in Complex Terrain for Improved Flash Flood Monitoring and Prediction

    Science.gov (United States)

    Cifelli, R.; Streubel, D. P.; Reynolds, D.

    2010-12-01

    Quantitative Precipitation Estimation (QPE) is extremely challenging in regions of complex terrain due to a combination of issues related to sampling. In particular, radar beams are often blocked or scan above the liquid precipitation zone while rain gauge density is often too low to properly characterize the spatial distribution of precipitation. Due to poor radar coverage, rain gauge networks are used by the National Weather Service (NWS) River Forecast Centers as the principal source for QPE across the western U.S. The California Nevada River Forecast Center (CNRFC) uses point rainfall measurements and historical rainfall runoff relationships to derive river stage forecasts. The point measurements are interpolated to a 4 km grid using Parameter-elevation Regressions on Independent Slopes Model (PRISM) data to develop a gridded 6-hour QPE product (hereafter referred to as RFC QPE). Local forecast offices can utilize the Multi-sensor Precipitation Estimator (MPE) software to improve local QPE’s and thus local flash flood monitoring and prediction. MPE uses radar and rain gauge data to develop a combined QPE product at 1-hour intervals. The rain gauge information is used to bias correct the radar precipitation estimates so that, in situations where the rain gauge density and radar coverage are adequate, MPE can take advantage of the spatial coverage of the radar and the “ground truth” of the rain gauges to provide an accurate QPE. The MPE 1-hour QPE analysis should provide better spatial and temporal resolution for short duration hydrologic events as compared to 6-hour analyses. These hourly QPEs are then used to correct radar derived rain rates used by the Flash Flood Monitoring and Prediction (FFMP) software in forecast offices for issuance of flash flood warnings. Although widely used by forecasters across the eastern U.S., MPE is not used extensively by the NWS in the west. Part of the reason for the lack of use of MPE across the west is that there has

  18. Is It Time to Tailor the Prediction of Radio-Induced Toxicity in Prostate Cancer Patients? Building the First Set of Nomograms for Late Rectal Syndrome

    International Nuclear Information System (INIS)

    Valdagni, Riccardo; Kattan, Michael W.; Rancati, Tiziana; Yu Changhong; Vavassori, Vittorio; Fellin, Giovanni; Cagna, Elena; Gabriele, Pietro; Mauro, Flora Anna; Baccolini, Micaela; Bianchi, Carla; Menegotti, Loris; Monti, Angelo F.; Stasi, Michele; Giganti, Maria Olga

    2012-01-01

    Purpose: Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer. Methods and Materials: This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718 adult men in the AIROPROS 0102 trial. Doses were between 70 and 80 Gy. Nomograms were created based on multivariable logistic regression analysis. Three endpoints were considered: G2 to G3 late rectal bleeding (52/718 events), G3 late rectal bleeding (24/718 events), and G2 to G3 late fecal incontinence (LINC, 19/718 events). Results: Inputs for the nomogram for G2 to G3 late rectal bleeding estimation were as follows: presence of abdominal surgery before RT, percentage volume of rectum receiving >75 Gy (V75Gy), and nomogram-based estimation of the probability of G2 to G3 acute gastrointestinal toxicity (continuous variable, which was estimated using a previously published nomogram). G3 late rectal bleeding estimation was based on abdominal surgery before RT, V75Gy, and NOMACU. Prediction of G2 to G3 late fecal incontinence was based on abdominal surgery before RT, presence of hemorrhoids, use of antihypertensive medications (protective factor), and percentage volume of rectum receiving >40 Gy. Conclusions: We developed and internally validated the first set of nomograms available in the literature for the prediction of radio-induced toxicity in prostate cancer patients. Calculations included dosimetric as well as clinical variables to help radiation oncologists predict late rectal morbidity, thus introducing the possibility of RT plan corrections to better tailor treatment to the patient’s characteristics, to avoid unnecessary worsening of quality of life, and to provide support to the patient in selecting the best therapeutic approach.

  19. Is It Time to Tailor the Prediction of Radio-Induced Toxicity in Prostate Cancer Patients? Building the First Set of Nomograms for Late Rectal Syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Valdagni, Riccardo [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Radiotherapy, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan (Italy); Kattan, Michael W. [Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH (United States); Rancati, Tiziana, E-mail: tiziana.rancati@istitutotumori.mi.it [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Yu Changhong [Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH (United States); Vavassori, Vittorio [Radiotherapy and Medical Physics, Ospedale di Circolo, Varese (Italy); Department of Radiotherapy, Humanitas - Gavazzeni, Bergamo (Italy); Fellin, Giovanni [Radiotherapy and Medical Physics, Ospedale Santa Chiara, Trento (Italy); Cagna, Elena [Department of Radiotherapy and Medical Physics, Ospedale Sant' Anna, Como (Italy); Gabriele, Pietro [Department of Radiotherapy and Medical Physics, Institute for Cancer Research and Treatment, Candiolo (Italy); Mauro, Flora Anna; Baccolini, Micaela [Department of Radiotherapy and Medical Physics, Ospedale Villa Maria Cecilia, Lugo (Italy); Bianchi, Carla [Radiotherapy and Medical Physics, Ospedale di Circolo, Varese (Italy); Menegotti, Loris [Radiotherapy and Medical Physics, Ospedale Santa Chiara, Trento (Italy); Monti, Angelo F. [Department of Radiotherapy and Medical Physics, Ospedale Sant' Anna, Como (Italy); Stasi, Michele [Department of Radiotherapy and Medical Physics, Institute for Cancer Research and Treatment, Candiolo (Italy); Giganti, Maria Olga [Prostate Program, Scientific Directorate, Fondazione IRCCS-Istituto Nazionale Tumori, Milan (Italy); Dept. of Oncology, Ospedale Niguarda, Milan (Italy); and others

    2012-04-01

    Purpose: Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer. Methods and Materials: This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718 adult men in the AIROPROS 0102 trial. Doses were between 70 and 80 Gy. Nomograms were created based on multivariable logistic regression analysis. Three endpoints were considered: G2 to G3 late rectal bleeding (52/718 events), G3 late rectal bleeding (24/718 events), and G2 to G3 late fecal incontinence (LINC, 19/718 events). Results: Inputs for the nomogram for G2 to G3 late rectal bleeding estimation were as follows: presence of abdominal surgery before RT, percentage volume of rectum receiving >75 Gy (V75Gy), and nomogram-based estimation of the probability of G2 to G3 acute gastrointestinal toxicity (continuous variable, which was estimated using a previously published nomogram). G3 late rectal bleeding estimation was based on abdominal surgery before RT, V75Gy, and NOMACU. Prediction of G2 to G3 late fecal incontinence was based on abdominal surgery before RT, presence of hemorrhoids, use of antihypertensive medications (protective factor), and percentage volume of rectum receiving >40 Gy. Conclusions: We developed and internally validated the first set of nomograms available in the literature for the prediction of radio-induced toxicity in prostate cancer patients. Calculations included dosimetric as well as clinical variables to help radiation oncologists predict late rectal morbidity, thus introducing the possibility of RT plan corrections to better tailor treatment to the patient's characteristics, to avoid unnecessary worsening of quality of life, and to provide support to the patient in selecting the best therapeutic approach.

  20. Using synchronization in multi-model ensembles to improve prediction

    Science.gov (United States)

    Hiemstra, P.; Selten, F.

    2012-04-01

    In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of

  1. Improving models to predict phenological responses to global change

    Energy Technology Data Exchange (ETDEWEB)

    Richardson, Andrew D. [Harvard College, Cambridge, MA (United States)

    2015-11-25

    The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms. Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall). We conducted a study to investigate the phenological response of northern peatland communities to global change. Field work was conducted at the SPRUCE experiment in northern Minnesota, where we installed 10 digital cameras. Imagery from the cameras is being used to track shifts in plant phenology driven by elevated carbon dioxide and elevated temperature in the different SPRUCE experimental treatments. Camera imagery and derived products (“greenness”) is being posted in near-real time on a publicly available web page (http://phenocam.sr.unh.edu/webcam/gallery/). The images will provide a permanent visual record of the progression of the experiment over the next 10 years. Integrated with other measurements collected as part of the SPRUCE program, this study is providing insight into the degree to which phenology may mediate future shifts in carbon uptake and storage by peatland ecosystems. In the future, these data will be used to develop improved models of vegetation phenology, which will be tested against ground observations collected by a local collaborator.

  2. Dipeptide Piracetam Analogue Noopept Improves Viability of Hippocampal HT-22 Neurons in the Glutamate Toxicity Model.

    Science.gov (United States)

    Antipova, T A; Nikolaev, S V; Ostrovskaya, P U; Gudasheva, T A; Seredenin, S B

    2016-05-01

    Effect of noopept (N-phenylacetyl-prolylglycine ethyl ester) on viability of neurons exposed to neurotoxic action of glutamic acid (5 mM) was studied in vitro in immortalized mouse hippocampal HT-22 neurons. Noopept added to the medium before or after glutamic acid improved neuronal survival in a concentration range of 10-11-10-5 M. Comparison of the effective noopept concentrations determined in previous studies on cultured cortical and cerebellar neurons showed that hippocampal neurons are more sensitive to the protective effect of noopept.

  3. Data Prediction for Public Events in Professional Domains Based on Improved RNN- LSTM

    Science.gov (United States)

    Song, Bonan; Fan, Chunxiao; Wu, Yuexin; Sun, Juanjuan

    2018-02-01

    The traditional data services of prediction for emergency or non-periodic events usually cannot generate satisfying result or fulfill the correct prediction purpose. However, these events are influenced by external causes, which mean certain a priori information of these events generally can be collected through the Internet. This paper studied the above problems and proposed an improved model—LSTM (Long Short-term Memory) dynamic prediction and a priori information sequence generation model by combining RNN-LSTM and public events a priori information. In prediction tasks, the model is qualified for determining trends, and its accuracy also is validated. This model generates a better performance and prediction results than the previous one. Using a priori information can increase the accuracy of prediction; LSTM can better adapt to the changes of time sequence; LSTM can be widely applied to the same type of prediction tasks, and other prediction tasks related to time sequence.

  4. Development of a Combined In Vitro Physiologically Based Kinetic (PBK) and Monte Carlo Modelling Approach to Predict Interindividual Human Variation in Phenol-Induced Developmental Toxicity.

    Science.gov (United States)

    Strikwold, Marije; Spenkelink, Bert; Woutersen, Ruud A; Rietjens, Ivonne M C M; Punt, Ans

    2017-06-01

    With our recently developed in vitro physiologically based kinetic (PBK) modelling approach, we could extrapolate in vitro toxicity data to human toxicity values applying PBK-based reverse dosimetry. Ideally information on kinetic differences among human individuals within a population should be considered. In the present study, we demonstrated a modelling approach that integrated in vitro toxicity data, PBK modelling and Monte Carlo simulations to obtain insight in interindividual human kinetic variation and derive chemical specific adjustment factors (CSAFs) for phenol-induced developmental toxicity. The present study revealed that UGT1A6 is the primary enzyme responsible for the glucuronidation of phenol in humans followed by UGT1A9. Monte Carlo simulations were performed taking into account interindividual variation in glucuronidation by these specific UGTs and in the oral absorption coefficient. Linking Monte Carlo simulations with PBK modelling, population variability in the maximum plasma concentration of phenol for the human population could be predicted. This approach provided a CSAF for interindividual variation of 2.0 which covers the 99th percentile of the population, which is lower than the default safety factor of 3.16 for interindividual human kinetic differences. Dividing the dose-response curve data obtained with in vitro PBK-based reverse dosimetry, with the CSAF provided a dose-response curve that reflects the consequences of the interindividual variability in phenol kinetics for the developmental toxicity of phenol. The strength of the presented approach is that it provides insight in the effect of interindividual variation in kinetics for phenol-induced developmental toxicity, based on only in vitro and in silico testing. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  5. Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction.

    Science.gov (United States)

    Chen, Chia-Yen; Han, Jiali; Hunter, David J; Kraft, Peter; Price, Alkes L

    2015-09-01

    Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction. © 2015 WILEY PERIODICALS, INC.

  6. Fluorine-18-fluorocholine PET/CT parameters predictive for hematological toxicity to radium-223 therapy in castrate-resistant prostate cancer patients with bone metastases: a pilot study.

    Science.gov (United States)

    Vija Racaru, Lavinia; Sinigaglia, Mathieu; Kanoun, Salim; Ben Bouallègue, Fayçal; Tal, Ilan; Brillouet, Sévérine; Bauriaud-Mallet, Mathilde; Zerdoud, Slimane; Dierickx, Lawrence; Vallot, Delphine; Caselles, Olivier; Gabiache, Erwan; Pascal, Pierre; Courbon, Frederic

    2018-05-21

    This study aims to predict hematological toxicity induced by Ra therapy. We investigated the value of metabolically active bone tumor volume (MBTV) and total bone lesion activity (TLA) calculated on pretreatment fluorine-18-fluorocholine (F-FCH) PET/CT in castrate-resistant prostate cancer (CRPC) patients with bone metastases treated with Ra radionuclide therapy. F-FCH PET/CT imaging was performed in 15 patients with CRPC before treatment with Ra. Bone metastatic disease was quantified on the basis of the maximum standardized uptake value (SUV), total lesion activity (TLA=MBTV×SUVmean), or MBTV/height (MBTV/H) and TLA/H. F-FCH PET/CT bone tumor burden and activity were analyzed to identify which parameters could predict hematological toxicity [on hemoglobin (Hb), platelets (PLTs), and lymphocytes] while on Ra therapy. Pearson's correlation was used to identify the correlations between age, prostate-specific antigen, and F-FCH PET parameters. MBTV ranged from 75 to 1259 cm (median: 392 cm). TLA ranged from 342 to 7198 cm (median: 1853 cm). Patients benefited from two to six cycles of Ra (n=56 cycles in total). At the end of Ra therapy, five of the 15 (33%) patients presented grade 2/3 toxicity on Hb and lymphocytes, whereas three of the 15 (20%) patients presented grade 2/3 PLT toxicity.Age was correlated negatively with both MBTV (r=-0.612, P=0.015) and TLA (r=-0.596, P=0.018). TLA, TLA/H, and MBTV/H predicted hematological toxicity on Hb, whereas TLA/H and MBTV/H predicted toxicity on PLTs at the end of Ra cycles. Receiver operating characteristic curve analysis allowed to define the cutoffs for MBTV (915 cm) and TLA (4198 cm) predictive for PLT toxicity, with an accuracy of 0.92 and 0.99. Tumor bone burden calculation is feasible with F-FCH PET/CT with freely available open-source software. In this pilot study, baseline F-FCH PET/CT markers (TLA, MBTV) have shown abilities to predict Hb and PLT toxicity after Ra therapy and could be explored for

  7. Addition of contaminant bioavailability and species susceptibility to a sediment toxicity assessment: Application in an urban stream in China

    International Nuclear Information System (INIS)

    Li, Huizhen; Sun, Baoquan; Chen, Xin; Lydy, Michael J.; You, Jing

    2013-01-01

    Sediments collected from an urban creek in China exhibited high acute toxicity to Hyalella azteca with 81.3% of sediments being toxic. A toxic unit (TU) estimation demonstrated that the pyrethroid, cypermethrin, was the major contributor to toxicity. The traditional TU approach, however, overestimated the toxicity. Reduced bioavailability of sediment-associated cypermethrin due to sequestration explained the overestimation. Additionally, antagonism among multiple contaminants and species susceptibility to various contaminants also contributed to the unexpectedly low toxicity to H. azteca. Bioavailable TUs derived from the bioavailability-based approaches, Tenax extraction and matrix-solid phase microextraction (matrix-SPME), showed better correlations with the noted toxicity compared to traditional TUs. As the first successful attempt to use matrix-SPME for estimating toxicity caused by emerging insecticides in field sediment, the present study found freely dissolved cypermethrin concentrations significantly improved the prediction of sediment toxicity to H. azteca compared to organic carbon normalized and Tenax extractable concentrations. Highlights: •Over 80% sediments from an urban stream in China were acutely toxic to H. azteca. •Toxic unit analysis showed cypermethrin was the major contributor to toxicity. •The traditional toxic unit approach overestimated sediment toxicity. •Reduced bioavailability was the reason for overestimating sediment toxicity. •Freely dissolved cypermethrin concentrations greatly improved toxicity prediction. -- Field sediment toxicity caused by current-use pesticides could be more accurately evaluated by incorporating bioavailability measurements into the toxic unit analysis

  8. Advanced Materials Test Methods for Improved Life Prediction of Turbine Engine Components

    National Research Council Canada - National Science Library

    Stubbs, Jack

    2000-01-01

    Phase I final report developed under SBIR contract for Topic # AF00-149, "Durability of Turbine Engine Materials/Advanced Material Test Methods for Improved Use Prediction of Turbine Engine Components...

  9. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

    Science.gov (United States)

    Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

    2010-07-01

    PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

  10. Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

    Science.gov (United States)

    2017-12-01

    19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

  11. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  12. Improved understanding of physics processes in pedestal structure, leading to improved predictive capability for ITER

    International Nuclear Information System (INIS)

    Groebner, R.J.; Snyder, P.B.; Leonard, A.W.; Chang, C.S.; Maingi, R.; Boyle, D.P.; Diallo, A.; Hughes, J.W.; Davis, E.M.; Ernst, D.R.; Landreman, M.; Xu, X.Q.; Boedo, J.A.; Cziegler, I.; Diamond, P.H.; Eldon, D.P.; Callen, J.D.; Canik, J.M.; Elder, J.D.; Fulton, D.P.

    2013-01-01

    Joint experiment/theory/modelling research has led to increased confidence in predictions of the pedestal height in ITER. This work was performed as part of a US Department of Energy Joint Research Target in FY11 to identify physics processes that control the H-mode pedestal structure. The study included experiments on C-Mod, DIII-D and NSTX as well as interpretation of experimental data with theory-based modelling codes. This work provides increased confidence in the ability of models for peeling–ballooning stability, bootstrap current, pedestal width and pedestal height scaling to make correct predictions, with some areas needing further work also being identified. A model for pedestal pressure height has made good predictions in existing machines for a range in pressure of a factor of 20. This provides a solid basis for predicting the maximum pedestal pressure height in ITER, which is found to be an extrapolation of a factor of 3 beyond the existing data set. Models were studied for a number of processes that are proposed to play a role in the pedestal n e and T e profiles. These processes include neoclassical transport, paleoclassical transport, electron temperature gradient turbulence and neutral fuelling. All of these processes may be important, with the importance being dependent on the plasma regime. Studies with several electromagnetic gyrokinetic codes show that the gradients in and on top of the pedestal can drive a number of instabilities. (paper)

  13. Use of a (Quantitative) Structure-Activity Relationship [(Q)SAR] model to predict the toxicity of naphthenic acids

    DEFF Research Database (Denmark)

    Frank, Richard; Sanderson, Hans; Kavanagh, Richard

    2010-01-01

    Naphthenic acids (NAs) are a complex mixture of carboxylic acids that are natural constituents of oil sand found in north-eastern Alberta, Canada.  NAs are released and concentrated in the alkaline water used in the extraction of bitumen from oil sand sediment.  NAs have been identified...... as the principal toxic components of oil sands process-affected water (OSPW), and microbial degradation of lower molecular weight (MW) NAs decreases the toxicity of NA mixtures in OSPW.  Analysis by proton nuclear magnetic resonance spectroscopy indicated that larger, more cyclic NAs contain greater carboxylic...

  14. QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna

    International Nuclear Information System (INIS)

    Roy, Kunal; Das, Rudra Narayan

    2013-01-01

    Highlights: • Ionic liquids are not intrinsically ‘green chemicals’ and require toxicological assessment. • Predictive QSTR models have been developed for toxicity of ILs to Daphnia magna. • Simple two dimensional descriptors were used to reduce the computational burden. • Discriminant and regression based models showed appreciable predictivity and reproducibility. • The extracted features can be explored in designing novel environmentally-friendly agents. -- Abstract: Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical

  15. Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models

    DEFF Research Database (Denmark)

    Rohde, Palle Duun; Demontis, Ditte; Børglum, Anders

    is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature...... contains the causal variants. Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia.......6% for the null model). Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data....

  16. Improvement of gas entrainment prediction method. Introduction of surface tension effect

    International Nuclear Information System (INIS)

    Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

    2010-01-01

    A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

  17. Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school.

    Science.gov (United States)

    Maurer, Urs; Bucher, Kerstin; Brem, Silvia; Benz, Rosmarie; Kranz, Felicitas; Schulz, Enrico; van der Mark, Sanne; Steinhausen, Hans-Christoph; Brandeis, Daniel

    2009-08-15

    More struggling readers could profit from additional help at the beginning of reading acquisition if dyslexia prediction were more successful. Currently, prediction is based only on behavioral assessment of early phonological processing deficits associated with dyslexia, but it might be improved by adding brain-based measures. In a 5-year longitudinal study of children with (n = 21) and without (n = 23) familial risk for dyslexia, we tested whether neurophysiological measures of automatic phoneme and tone deviance processing obtained in kindergarten would improve prediction of reading over behavioral measures alone. Together, neurophysiological and behavioral measures obtained in kindergarten significantly predicted reading in school. Particularly the late mismatch negativity measure that indicated hemispheric lateralization of automatic phoneme processing improved prediction of reading ability over behavioral measures. It was also the only significant predictor for long-term reading success in fifth grade. Importantly, this result also held for the subgroup of children at familial risk. The results demonstrate that brain-based measures of processing deficits associated with dyslexia improve prediction of reading and thus may be further evaluated to complement clinical practice of dyslexia prediction, especially in targeted populations, such as children with a familial risk.

  18. Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity - Poster at Teratology Society Annual Meeting

    Science.gov (United States)

    EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

  19. Evaluation of an adherent mouse embryonic stem cell in vitro assay to predict developmental toxicity of ToxCast chemicals.

    Science.gov (United States)

    The potential for most environmental chemicals to produce developmental toxicity is unknown. Mouse embryonic stem cell (mESC) assays are an alternative in vitro model to assess chemicals. The chemical space evaluated using mESC and compared to in vivo is limited. We used an adher...

  20. Predicting Developmental Toxicity of ToxCast Phase I Chemicals Using Human Embryonic Stem Cells and Metabolomics

    Science.gov (United States)

    EPA’s ToxRefDB contains prenatal guideline study data from rats and rabbits for over 240 chemicals that overlap with the ToxCast in vitro high throughput screening project. A subset of these compounds were tested in Stemina Biomarker Discovery's developmental toxicity platform, a...

  1. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  2. TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

    Science.gov (United States)

    Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

    2017-09-01

    α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD PHP, MySQL and Apache, with all major browsers supported.

  3. Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

    Science.gov (United States)

    Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

    2013-05-01

    Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

  4. Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

    Science.gov (United States)

    Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

    2015-12-01

    Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

  5. Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-12-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

  6. Silicon enhanced salt tolerance by improving the root water uptake and decreasing the ion toxicity in cucumber

    Directory of Open Access Journals (Sweden)

    Shiwen eWang

    2015-09-01

    Full Text Available Although the effects of silicon application on enhancing plant salt tolerance have been widely investigated, the underlying mechanism has remained unclear. In this study, seedlings of cucumber, a medium silicon accumulator plant, grown in 0.83 mM silicon solution for two weeks were exposed to 65 mM NaCl solution for another one week. The dry weight and shoot/root ratio were reduced by salt stress, but silicon application significantly alleviated these decreases. The chlorophyll concentration, net photosynthetic rate, transpiration rate and leaf water content were higher in plants treated with silicon than in untreated plants under salt stress conditions. Further investigation showed that salt stress decreased root hydraulic conductance (Lp, but that silicon application moderated this salt-induced decrease in Lp. The higher Lp in silicon-treated plants may account for the superior plant water balance. Moreover, silicon application significantly decreased Na+ concentration in the leaves while increasing K+ concentration. Simultaneously, both free and conjugated types of polyamines were maintained at high levels in silicon-treated plants, suggesting that polyamines may be involved in the ion toxicity. Our results indicate that silicon enhances the salt tolerance of cucumber through improving plant water balance by increasing the Lp and reducing Na+ content by increasing polyamine accumulation.

  7. Identification and comparison of predictive models of rectal and bladder toxicity in the case of prostatic irradiation; Identification et comparaison de modeles predictifs de toxicite rectale et vesicale en cas d'irradiation prostatique

    Energy Technology Data Exchange (ETDEWEB)

    Gnep, K.; Chira, C.; Le Prise, E.; Crevoisier, R. de [Centre Eugene-Marquis, Rennes (France); Zhu, J.; Simon, A.; Ospina Arango, J.D. [Inserm U642, Rennes (France); Messai, T.; Bossi, A. [Institut Gustave-Roussy, Villejuif (France); Beckendorf, V. [Centre Alexis-Vautrin, Nancy (France)

    2011-10-15

    More than 400 patients have been treated by conformational radiation therapy for a localized prostate adenocarcinoma and some have been selected according to the availability of dose-volume histograms. Predictive models of rectal and bladder toxicity have been compared: LKB, Logit EUD and Poisson EUD for rectal toxicity, LKB, Logit EUD, Poisson EUD and Schultheiss for bladder toxicity. Results suggest that these models could be used during the inverse planning of intensity-modulated radiation therapy in order to decrease toxicity. Short communication

  8. Comparative evaluation of genetic toxicity patterns of carcinogens and noncarcinogens: strategies for predictive use of short-term assays

    International Nuclear Information System (INIS)

    Tennant, R.W.; Spalding, J.W.; Stasiewicz, S.; Caspary, W.D.; Mason, J.M.; Resnick, M.A.

    1987-01-01

    The results of a recent comprehensive evaluation of the relationship between four measures of in vitro genetic toxicity and the capacity of the chemicals to induce neoplasia in rodents carry some important implications. The results showed that while the Salmonella mutagenesis assay detected only about half of the carcinogenes as mutagens, the other three in vitro assays (mutagenesis in MOLY cells or induction of aberrations or SCEs in CHO cells) did not complement Salmonella since they failed to effectively discriminate between the carcinogens and noncarcinogens found negative in the Salmonella assay. The specificity of the Salmonella assay for this group of 73 chemicals was relatively high (only 4 of 29 noncarcinogens were positive). Therefore, the authors have begun to evaluate in vivo genetic toxicity assays for their ability to complement Salmonella in the identification of carcinogens

  9. Analyzing the capacity of the Daphnia magna and Pseudokirchneriella subcapitata bioavailability models to predict chronic zinc toxicity at high pH and low calcium concentrations and formulation of a generalized bioavailability model for D. magna.

    Science.gov (United States)

    Van Regenmortel, Tina; Berteloot, Olivier; Janssen, Colin R; De Schamphelaere, Karel A C

    2017-10-01

    Risk assessment in the European Union implements Zn bioavailability models to derive predicted-no-effect concentrations for Zn. These models are validated within certain boundaries (i.e., pH ≤ 8 and Ca concentrations ≥ 5mg/L), but a substantial fraction of the European surface waters falls outside these boundaries. Therefore, we evaluated whether the chronic Zn biotic ligand model (BLM) for Daphnia magna and the chronic bioavailability model for Pseudokirchneriella subcapitata could be extrapolated to pH > 8 and Ca concentrations model can accurately predict Zn toxicity for Ca concentrations down to 0.8 mg/L and pH values up to 8.5. Because the chronic Zn BLM for D. magna could not be extrapolated beyond its validity boundaries for pH, a generalized bioavailability model (gBAM) was developed. Of 4 gBAMs developed, we recommend the use of gBAM-D, which combines a log-linear relation between the 21-d median effective concentrations (expressed as free Zn 2+ ion activity) and pH, with more conventional BLM-type competition constants for Na, Ca, and Mg. This model is a first step in further improving the accuracy of chronic toxicity predictions of Zn as a function of water chemistry, which can decrease the uncertainty in implementing the bioavailability-based predicted-no-effect concentration in the risk assessment of high-pH and low-Ca concentration regions in Europe. Environ Toxicol Chem 2017;36:2781-2798. © 2017 SETAC. © 2017 SETAC.

  10. Novel high dose rate lip brachytherapy technique to improve dose homogeneity and reduce toxicity by customized mold

    International Nuclear Information System (INIS)

    Feldman, Jon; Appelbaum, Limor; Sela, Mordechay; Voskoboinik, Ninel; Kadouri, Sarit; Weinberger, Jeffrey; Orion, Itzhak; Meirovitz, Amichay

    2014-01-01

    The purpose of this study is to describe a novel brachytherapy technique for lip Squamous Cell Carcinoma, utilizing a customized mold with embedded brachytherapy sleeves, which separates the lip from the mandible, and improves dose homogeneity. Seven patients with T2 lip cancer treated with a “sandwich” technique of High Dose Rate (HDR) brachytherapy to the lip, consisting of interstitial catheters and a customized mold with embedded catheters, were reviewed for dosimetry and outcome using 3D planning. Dosimetric comparison was made between the “sandwich” technique to “classic” – interstitial catheters only plan. We compared dose volume histograms for Clinical Tumor Volume (CTV), normal tissue “hot spots” and mandible dose. We are reporting according to the ICRU 58 and calculated the Conformal Index (COIN) to show the advantage of our technique. The seven patients (ages 36–81 years, male) had median follow-up of 47 months. Four patients received Brachytherapy and External Beam Radiation Therapy, 3 patients received brachytherapy alone. All achieved local control, with excellent esthetic and functional results. All patients are disease free. The Customized Mold Sandwich technique (CMS) reduced the high dose region receiving 150% (V150) by an average of 20% (range 1–47%), The low dose region (les then 90% of the prescribed dose) improved by 73% in average by using the CMS technique. The COIN value for the CMS was in average 0.92 as opposed to 0.88 for the interstitial catheter only. All differences (excluding the low dose region) were statistically significant. The CMS technique significantly reduces the high dose volume and increases treatment homogeneity. This may reduce the potential toxicity to the lip and adjacent mandible, and results in excellent tumor control, cosmetic and functionality

  11. Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.

    Directory of Open Access Journals (Sweden)

    Kais Gadhoumi

    Full Text Available Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5% and specificity (25.1% of total time under warning for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.

  12. Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

    Science.gov (United States)

    Ober, Ulrike; Huang, Wen; Magwire, Michael; Schlather, Martin; Simianer, Henner; Mackay, Trudy F C

    2015-01-01

    The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

  13. Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

    Directory of Open Access Journals (Sweden)

    Ulrike Ober

    Full Text Available The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17% of the genetic variance among lines in females (males, the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

  14. Pitfalls in Prediction Modeling for Normal Tissue Toxicity in Radiation Therapy: An Illustration With the Individual Radiation Sensitivity and Mammary Carcinoma Risk Factor Investigation Cohorts

    Energy Technology Data Exchange (ETDEWEB)

    Mbah, Chamberlain, E-mail: chamberlain.mbah@ugent.be [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium); Department of Mathematical Modeling, Statistics, and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent (Belgium); Thierens, Hubert [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium); Thas, Olivier [Department of Mathematical Modeling, Statistics, and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent (Belgium); National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, New South Wales (Australia); De Neve, Jan [Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent (Belgium); Chang-Claude, Jenny; Seibold, Petra; Botma, Akke [Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg (Germany); West, Catharine [Translational Radiobiology Group, Institute of Cancer Sciences, Radiotherapy Related Research, Christie Hospital NHS Trust, University of Manchester, Manchester (United Kingdom); De Ruyck, Kim [Department of Basic Medical Sciences, Faculty of Health Sciences, Ghent University, Ghent (Belgium)

    2016-08-01

    Purpose: To identify the main causes underlying the failure of prediction models for radiation therapy toxicity to replicate. Methods and Materials: Data were used from two German cohorts, Individual Radiation Sensitivity (ISE) (n=418) and Mammary Carcinoma Risk Factor Investigation (MARIE) (n=409), of breast cancer patients with similar characteristics and radiation therapy treatments. The toxicity endpoint chosen was telangiectasia. The LASSO (least absolute shrinkage and selection operator) logistic regression method was used to build a predictive model for a dichotomized endpoint (Radiation Therapy Oncology Group/European Organization for the Research and Treatment of Cancer score 0, 1, or ≥2). Internal areas under the receiver operating characteristic curve (inAUCs) were calculated by a naïve approach whereby the training data (ISE) were also used for calculating the AUC. Cross-validation was also applied to calculate the AUC within the same cohort, a second type of inAUC. Internal AUCs from cross-validation were calculated within ISE and MARIE separately. Models trained on one dataset (ISE) were applied to a test dataset (MARIE) and AUCs calculated (exAUCs). Results: Internal AUCs from the naïve approach were generally larger than inAUCs from cross-validation owing to overfitting the training data. Internal AUCs from cross-validation were also generally larger than the exAUCs, reflecting heterogeneity in the predictors between cohorts. The best models with largest inAUCs from cross-validation within both cohorts had a number of common predictors: hypertension, normalized total boost, and presence of estrogen receptors. Surprisingly, the effect (coefficient in the prediction model) of hypertension on telangiectasia incidence was positive in ISE and negative in MARIE. Other predictors were also not common between the 2 cohorts, illustrating that overcoming overfitting does not solve the problem of replication failure of prediction models completely

  15. Suppression of a NAC-Like Transcription Factor Gene Improves Boron-Toxicity Tolerance in Rice1

    Science.gov (United States)

    Ochiai, Kumiko; Shimizu, Akifumi; Okumoto, Yutaka; Fujiwara, Toru; Matoh, Toru

    2011-01-01

    We identified a gene responsible for tolerance to boron (B) toxicity in rice (Oryza sativa), named BORON EXCESS TOLERANT1. Using recombinant inbred lines derived from the B-toxicity-sensitive indica-ecotype cultivar IR36 and the tolerant japonica-ecotype cultivar Nekken 1, the region responsible for tolerance to B toxicity was narrowed to 49 kb on chromosome 4. Eight genes are annotated in this region. The DNA sequence in this region was compared between the B-toxicity-sensitive japonica cultivar Wataribune and the B-toxicity-tolerant japonica cultivar Nipponbare by eco-TILLING analysis and revealed a one-base insertion mutation in the open reading frame sequence of the gene Os04g0477300. The gene encodes a NAC (NAM, ATAF, and CUC)-like transcription factor and the function of the transcript is abolished in B-toxicity-tolerant cultivars. Transgenic plants in which the expression of Os04g0477300 is abolished by RNA interference gain tolerance to B toxicity. PMID:21543724

  16. Measured Copper Toxicity to Cnesterodon decemmaculatus (Pisces: Poeciliidae and Predicted by Biotic Ligand Model in Pilcomayo River Water: A Step for a Cross-Fish-Species Extrapolation

    Directory of Open Access Journals (Sweden)

    María Victoria Casares

    2012-01-01

    Full Text Available In order to determine copper toxicity (LC50 to a local species (Cnesterodon decemmaculatus in the South American Pilcomayo River water and evaluate a cross-fish-species extrapolation of Biotic Ligand Model, a 96 h acute copper toxicity test was performed. The dissolved copper concentrations tested were 0.05, 0.19, 0.39, 0.61, 0.73, 1.01, and 1.42 mg Cu L-1. The 96 h Cu LC50 calculated was 0.655 mg L-1 (0.823-0.488. 96-h Cu LC50 predicted by BLM for Pimephales promelas was 0.722 mg L-1. Analysis of the inter-seasonal variation of the main water quality parameters indicates that a higher protective effect of calcium, magnesium, sodium, sulphate, and chloride is expected during the dry season. The very high load of total suspended solids in this river might be a key factor in determining copper distribution between solid and solution phases. A cross-fish-species extrapolation of copper BLM is valid within the water quality parameters and experimental conditions of this toxicity test.

  17. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    Science.gov (United States)

    Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

    2017-01-01

    Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

  18. An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

    Directory of Open Access Journals (Sweden)

    Guoxing Li

    2015-01-01

    Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

  19. Irinotecan-loaded double-reversible thermogel with improved antitumor efficacy without initial burst effect and toxicity for intramuscular administration.

    Science.gov (United States)

    Din, Fakhar Ud; Kim, Dong Wuk; Choi, Ju Yeon; Thapa, Raj Kumar; Mustapha, Omer; Kim, Dong Shik; Oh, Yu-Kyoung; Ku, Sae Kwang; Youn, Yu Seok; Oh, Kyung Taek; Yong, Chul Soon; Kim, Jong Oh; Choi, Han-Gon

    2017-05-01

    Intramuscularly administered, anti-tumour drugs induce severe side effects due to their direct contact with body tissues and initial burst effect. In this study, to solve this problem, a novel double-reversible thermogel system (DRTG) for the intramuscular administration of irinotecan was developed. This irinotecan-loaded DRTG was prepared by dispersing the irinotecan-loaded thermoreversible solid lipid nanoparticles (SLNs) in the thermoreversible hydrogel. In DRTG, the former was solid at 25°C but converted to liquid at 36.5°C; in contrast, the latter existed in a liquid form but transformed to gel state in the body. The DRTG was easily administered intramuscularly. Its particle size and drug content were not noticeably changeable, resulting that it was stable at 40°C for at least 6months. Compared to the irinotecan-loaded solution and conventional hydrogel, the DRTG significantly delayed drug release, leading to a reduced burst effect. Moreover, it showed decreased C max and maintained the sustained plasma concentrations at a relatively low level for the long period of 60h in rats, resulting in ameliorated side effects of the anti-tumour drug. Furthermore, it gave significantly improved anti-tumour efficacy in tumour-bearing mice compared to the hydrogel but, unlike the conventional hydrogel, induced no body weight loss and local damage to the muscle. Thus, this DRTG with improved antitumor efficacy without initial burst effect and toxicity could provide a potential pharmaceutical system for the intramuscular administration of irinotecan. Intramuscularly administered, anti-tumour drugs induce severe side effects due to their direct contact with body tissues and initial burst effect. To solve this problem, we developed a novel double-reversible thermogel system (DRTG) for the intramuscular administration of irinotecan. Unlike the conventional hydrogel, the DRTG is a dispersion of the irinotecan-loaded thermoreversible solid lipid nanoparticles in the

  20. Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

    Science.gov (United States)

    Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.

    2017-11-01

    The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

  1. The ChemScreen project to design a pragmatic alternative approach to predict reproductive toxicity of chemicals

    DEFF Research Database (Denmark)

    van der Burg, Bart; Wedebye, Eva Bay; Dietrich, Daniel R.

    2015-01-01

    to validate the test panel using mechanistic approaches. We are actively engaged in promoting regulatory acceptance of the tools developed as an essential step towards practical application, including case studies for read-across purposes. With this approach, a significant saving in animal use and associated......There is a great need for rapid testing strategies for reproductive toxicity testing, avoiding animal use. The EU Framework program 7 project ChemScreen aimed to fill this gap in a pragmatic manner preferably using validated existing tools and place them in an innovative alternative testing...

  2. Exogenous nitric oxide improves salt tolerance during establishment of Jatropha curcas seedlings by ameliorating oxidative damage and toxic ion accumulation.

    Science.gov (United States)

    Gadelha, Cibelle Gomes; Miranda, Rafael de Souza; Alencar, Nara Lídia M; Costa, José Hélio; Prisco, José Tarquinio; Gomes-Filho, Enéas

    2017-05-01

    Jatropha curcas is an oilseed species that is considered an excellent alternative energy source for fossil-based fuels for growing in arid and semiarid regions, where salinity is becoming a stringent problem to crop production. Our working hypothesis was that nitric oxide (NO) priming enhances salt tolerance of J. curcas during early seedling development. Under NaCl stress, seedlings arising from NO-treated seeds showed lower accumulation of Na + and Cl - than those salinized seedlings only, which was consistent with a better growth for all analyzed time points. Also, although salinity promoted a significant increase in hydrogen peroxide (H 2 O 2 ) content and membrane damage, the harmful effects were less aggressive in NO-primed seedlings. The lower oxidative damage in NO-primed stressed seedlings was attributed to operation of a powerful antioxidant system, including greater glutathione (GSH) and ascorbate (AsA) contents as well as catalase (CAT) and glutathione reductase (GR) enzyme activities in both endosperm and embryo axis. Priming with NO also was found to rapidly up-regulate the JcCAT1, JcCAT2, JcGR1 and JcGR2 gene expression in embryo axis, suggesting that NO-induced salt responses include functional and transcriptional regulations. Thus, NO almost completely abolished the deleterious salinity effects on reserve mobilization and seedling growth. In conclusion, NO priming improves salt tolerance of J. curcas during seedling establishment by inducing an effective antioxidant system and limiting toxic ion and reactive oxygen species (ROS) accumulation. Copyright © 2017 Elsevier GmbH. All rights reserved.

  3. Reduction in relapse rate of radioiodine therapy in patients of toxic multinodular goiter: a quality improvement project

    International Nuclear Information System (INIS)

    Mitra, Sujata; Muthu, Sonai G.

    2012-01-01

    Radioiodine ( 131 I) therapy is the definitive treatment of toxic multinodular goiter (TMNG). Treatment failure may result in relapse after 131 I therapy. The present study was undertaken to reduce treatment failure rate of 131 I therapy in TMNG patients. Multiple causes may have lead to treatment failure of 131 I in TMNG patients making it difficult to establish a direct cause-effect relationship and take corrective action. Therefore, the JURAN methodology of quality improvement was applied. The treatment failure rate in 80 TMNG patients treated with 131 I in the period 2003-06 was 29%. The root cause analysis identified delay in decision to radioablate and concomitant antithyroid drugs (ATD) with 131 I therapy as factors leading to relapse. In 2007, a change in management was introduced with decision to radioablate all TMNG patients not remitting at 1 year of ATD and to withdraw ATD for 2 weeks prior to 131 I therapy. A total of 63 patients of TMNG followed the changed protocol between 2007 and 2009. Further analysis showed that one of the factors identified in the initial brainstorming (high iodide pool in the patient) had not been addressed in the protocol currently followed. The protocol was modified to include patient preparation and implemented after standardization. The post- 131 I relapse rate in patients treated after implementation of the new protocol from 2007 to 2009 was 18% which further reduced to 16% in 2011 after modification of the protocol. The failure rate of 131 I therapy in TMNG reduced from 29% to 16% through standardization of the treatment procedure achieved by the use of Juran Methodology that helped to identify process-related defects. (author)

  4. Reduction in relapse rate of radioiodine therapy in patients of toxic multinodular goiter: a quality improvement project

    Energy Technology Data Exchange (ETDEWEB)

    Mitra, Sujata; Muthu, Sonai G., E-mail: sujatamitra@tatasteel.com [Department of Nuclear Medicine, Tata Main Hospital, Jamshedpur (India)

    2012-01-15

    Radioiodine ({sup 131}I) therapy is the definitive treatment of toxic multinodular goiter (TMNG). Treatment failure may result in relapse after {sup 131}I therapy. The present study was undertaken to reduce treatment failure rate of {sup 131}I therapy in TMNG patients. Multiple causes may have lead to treatment failure of {sup 131}I in TMNG patients making it difficult to establish a direct cause-effect relationship and take corrective action. Therefore, the JURAN methodology of quality improvement was applied. The treatment failure rate in 80 TMNG patients treated with {sup 131}I in the period 2003-06 was 29%. The root cause analysis identified delay in decision to radioablate and concomitant antithyroid drugs (ATD) with {sup 131}I therapy as factors leading to relapse. In 2007, a change in management was introduced with decision to radioablate all TMNG patients not remitting at 1 year of ATD and to withdraw ATD for 2 weeks prior to {sup 131}I therapy. A total of 63 patients of TMNG followed the changed protocol between 2007 and 2009. Further analysis showed that one of the factors identified in the initial brainstorming (high iodide pool in the patient) had not been addressed in the protocol currently followed. The protocol was modified to include patient preparation and implemented after standardization. The post-{sup 131}I relapse rate in patients treated after implementation of the new protocol from 2007 to 2009 was 18% which further reduced to 16% in 2011 after modification of the protocol. The failure rate of {sup 131}I therapy in TMNG reduced from 29% to 16% through standardization of the treatment procedure achieved by the use of Juran Methodology that helped to identify process-related defects. (author)

  5. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

    Science.gov (United States)

    Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

    2016-11-01

    Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

  6. Improving the ecological relevance of toxicity tests on scleractinian corals: Influence of season, life stage, and seawater temperature

    Science.gov (United States)

    Hedouin, Laetitia; Wolf, Ruth E.; Phillips, Jeff; Gates, Ruth D.

    2016-01-01

    Metal pollutants in marine systems are broadly acknowledged as deleterious: however, very little data exist for tropical scleractinian corals. We address this gap by investigating how life-history stage, season and thermal stress influence the toxicity of copper (Cu) and lead (Pb) in the coral Pocillopora damicornis. Our results show that under ambient temperature, adults and larvae appear to tolerate exposure to unusually high levels of copper (96 h-LC50 ranging from 167 to 251 μg Cu L−1) and lead (from 477 to 742 μg Pb L−1). Our work also highlights that warmer conditions (seasonal and experimentally manipulated) reduce the tolerance of adults and larvae to Cu toxicity. Despite a similar trend observed for the response of larvae to Pb toxicity to experimentally induced increase in temperature, surprisingly adults were more resistant in warmer condition to Pb toxicity. In the summer adults were less resistant to Cu toxicity (96 h-LC50 = 175 μg L−1) than in the winter (251 μg L−1). An opposite trend was observed for the Pb toxicity on adults between summer and winter (96 h-LC50 of 742 vs 471 μg L−1, respectively). Larvae displayed a slightly higher sensitivity to Cu and Pb than adults. An experimentally induced 3 °C increase in temperature above ambient decreased larval resistance to Cu and Pb toxicity by 23–30% (96 h-LC50 of 167 vs 129 μg Cu L−1 and 681 vs 462 μg Pb L−1).

  7. [Source identification of toxic wastewaters in a petrochemical industrial park].

    Science.gov (United States)

    Yang, Qian; Yu, Yin; Zhou, Yue-Xi; Chen, Xue-Min; Fu, Xiao-Yong; Wang, Miao

    2014-12-01

    Petrochemical wastewaters have toxic impacts on the microorganisms in biotreatment processes, which are prone to cause deterioration of effluent quality of the wastewater treatment plants. In this study, the inhibition effects of activated sludge's oxygen consumption were tested to evaluate the toxicity of production wastewaters in a petrochemical industrial park. The evaluation covered the wastewaters from not only different production units in the park, but also different production nodes in each unit. No direct correlation was observed between the toxicity effects and the organic contents, suggesting that the toxic properties of the effluents could not be predicted by the organic contents. In view of the variation of activated sludge sensitivity among different tests, the toxicity data were standardized according to the concentration-effect relationships of the standard toxic substance 3, 5-dichlorophenol on each day, in order to improve the comparability among the toxicity data. Furthermore, the Quality Emission Load (QEL) of corresponding standard toxic substance was calculated by multiplying the corresponding 3, 5-dichlorophenol concentration and the wastewater flow quantity, to indicate the toxicity emission contribution of each wastewater to the wastewater treatment plant. According to the rank list of the toxicity contribution of wastewater from different units and nodes, the sources of toxic wastewater in the petrochemical industrial park were clearly identified. This study provides effective guidance for source control of wastewater toxicity in the large industrial park.

  8. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  9. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

    2018-01-01

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  10. Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems

    Directory of Open Access Journals (Sweden)

    Ali Azadeh

    2015-06-01

    Full Text Available Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2. Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS. Mean absolute percentage errors (MAPEs resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

  11. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.

    2018-03-20

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  12. Improving local clustering based top-L link prediction methods via asymmetric link clustering information

    Science.gov (United States)

    Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

    2018-02-01

    Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

  13. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

    Science.gov (United States)

    Adhikari, Badri; Hou, Jie; Cheng, Jianlin

    2018-05-01

    Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. chengji@missouri.edu. Supplementary data are available at Bioinformatics online.

  14. Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory.

    Science.gov (United States)

    Panaite, Vanessa; Hindash, Alexandra Cowden; Bylsma, Lauren M; Small, Brent J; Salomon, Kristen; Rottenberg, Jonathan

    2016-01-01

    Respiratory sinus arrhythmia (RSA) reactivity, an index of cardiac vagal tone, has been linked to self-regulation and the severity and course of depression (Rottenberg, 2007). Although initial data supports the proposition that RSA withdrawal during a sad film is a specific predictor of depression course (Fraguas, 2007; Rottenberg, 2005), the robustness and specificity of this finding are unclear. To provide a stronger test, RSA reactivity to three emotion films (happy, sad, fear) and to a more robust stressor, a speech task, were examined in currently depressed individuals (n=37), who were assessed for their degree of symptomatic improvement over 30weeks. Robust RSA reactivity to the sad film uniquely predicted overall symptom improvement over 30weeks. RSA reactivity to both sad and stressful stimuli predicted the speed and maintenance of symptomatic improvement. The current analyses provide the most robust support to date that RSA withdrawal to sad stimuli (but not stressful) has specificity in predicting the overall symptomatic improvement. In contrast, RSA reactivity to negative stimuli (both sad and stressful) predicted the trajectory of depression course. Patients' engagement with sad stimuli may be an important sign to attend to in therapeutic settings. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

    Science.gov (United States)

    W. Brad Smith

    1983-01-01

    A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

  16. Improved model predictive control for high voltage quality in microgrid applications

    DEFF Research Database (Denmark)

    Dragicevic, T.; Al hasheem, Mohamed; Lu, M.

    2017-01-01

    This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based...

  17. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  18. NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

    Science.gov (United States)

    Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

    2017-12-01

    NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

  19. Improved prediction of signal peptides: SignalP 3.0

    DEFF Research Database (Denmark)

    Bendtsen, Jannick Dyrløv; Nielsen, Henrik; von Heijne, G.

    2004-01-01

    We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the ...

  20. A two-stage approach for improved prediction of residue contact maps

    Directory of Open Access Journals (Sweden)

    Pollastri Gianluca

    2006-03-01

    Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

  1. Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

    Directory of Open Access Journals (Sweden)

    Otto Savolainen

    Full Text Available The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D risk that would improve prediction of T2D over current risk markers.Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629. Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA, smoking, serum adiponectin alone, and in combination with metabolomics had the largest areas under the curve (AUC (0.794 (95% confidence interval [0.738-0.850] and 0.808 [0.749-0.867] respectively, with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]. Prediction based on non-blood based measures was 0.638 [0.565-0.711].Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

  2. LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

    DEFF Research Database (Denmark)

    Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

    2012-01-01

    Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

  3. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  4. Facile Synthesis of Novel Coumarin Derivatives, Antimicrobial Analysis, Enzyme Assay, Docking Study, ADMET Prediction and Toxicity Study

    Directory of Open Access Journals (Sweden)

    Shailee V. Tiwari

    2017-07-01

    Full Text Available The work reports the synthesis under solvent-free condition using the ionic liquid [Et3NH][HSO4] as a catalyst of fifteen novel 3-((dicyclohexylamino(substituted phenyl/heteryl-methyl-4-hydroxy-2H-chromen-2-onederivatives 4a–o as potential antimicrobial agents. The structures of the synthesized compounds were confirmed by IR, 1H-NMR, 13C-NMR, mass spectral studies and elemental analyses. All the synthesized compounds were evaluated for their in vitro antifungal and antibacterial activity. The compound 4k bearing 4-hydroxy-3-ethoxy group on the phenyl ring was found to be the most active antifungal agent. The compound 4e bearing a 2,4-difluoro group on the phenyl ring was found to be the most active antibacterial agent. The mode of action of the most promising antifungal compound 4k was established by an ergosterol extraction and quantitation assay. From the assay it was found that 4k acts by inhibition of ergosterol biosynthesis in C. albicans. Molecular docking studies revealed a highly spontaneous binding ability of the tested compounds to the active site of lanosterol 14α-demethylase, which suggests that the tested compounds inhibit the synthesis of this enzyme. The synthesized compounds were analyzed for in silico ADMET properties to establish oral drug like behavior and showed satisfactory results. To establish the antimicrobial selectivity and safety, the most active compounds 4e and 4k were further tested for cytotoxicity against human cancer cell line HeLa and were found to be non-cytotoxic in nature. An in vivo acute oral toxicity study was also performed for the most active compounds 4e and 4k and results indicated that the compounds are non-toxic.

  5. Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L. Improved by Accounting for Linkage Disequilibrium

    Directory of Open Access Journals (Sweden)

    Guillaume P. Ramstein

    2016-04-01

    Full Text Available Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

  6. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Directory of Open Access Journals (Sweden)

    Michael F Sloma

    2017-11-01

    Full Text Available Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

  7. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Science.gov (United States)

    Sloma, Michael F; Mathews, David H

    2017-11-01

    Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

  8. Improving the accuracy of protein secondary structure prediction using structural alignment

    Directory of Open Access Journals (Sweden)

    Gallin Warren J

    2006-06-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

  9. Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

    Science.gov (United States)

    Aghakouchak, Amir; Tourian, Mohammad J.

    2015-04-01

    Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

  10. An improved method for predicting brittleness of rocks via well logs in tight oil reservoirs

    Science.gov (United States)

    Wang, Zhenlin; Sun, Ting; Feng, Cheng; Wang, Wei; Han, Chuang

    2018-06-01

    There can be no industrial oil production in tight oil reservoirs until fracturing is undertaken. Under such conditions, the brittleness of the rocks is a very important factor. However, it has so far been difficult to predict. In this paper, the selected study area is the tight oil reservoirs in Lucaogou formation, Permian, Jimusaer sag, Junggar basin. According to the transformation of dynamic and static rock mechanics parameters and the correction of confining pressure, an improved method is proposed for quantitatively predicting the brittleness of rocks via well logs in tight oil reservoirs. First, 19 typical tight oil core samples are selected in the study area. Their static Young’s modulus, static Poisson’s ratio and petrophysical parameters are measured. In addition, the static brittleness indices of four other tight oil cores are measured under different confining pressure conditions. Second, the dynamic Young’s modulus, Poisson’s ratio and brittleness index are calculated using the compressional and shear wave velocity. With combination of the measured and calculated results, the transformation model of dynamic and static brittleness index is built based on the influence of porosity and clay content. The comparison of the predicted brittleness indices and measured results shows that the model has high accuracy. Third, on the basis of the experimental data under different confining pressure conditions, the amplifying factor of brittleness index is proposed to correct for the influence of confining pressure on the brittleness index. Finally, the above improved models are applied to formation evaluation via well logs. Compared with the results before correction, the results of the improved models agree better with the experimental data, which indicates that the improved models have better application effects. The brittleness index prediction method of tight oil reservoirs is improved in this research. It is of great importance in the optimization of

  11. Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates

    Science.gov (United States)

    In an effort to ensure chemical safety while reducing reliance on animal testing, USEPA and L’Oréal have collaborated to address a major challenge in chemical safety assessment using alternative approaches: the prediction of points-of-departure (POD) of systemic effects. Systemic...

  12. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  13. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  14. Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

    Science.gov (United States)

    Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

    2016-08-07

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Science.gov (United States)

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  16. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Directory of Open Access Journals (Sweden)

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  17. Utilizing multiple scale models to improve predictions of extra-axial hemorrhage in the immature piglet.

    Science.gov (United States)

    Scott, Gregory G; Margulies, Susan S; Coats, Brittany

    2016-10-01

    Traumatic brain injury (TBI) is a leading cause of death and disability in the USA. To help understand and better predict TBI, researchers have developed complex finite element (FE) models of the head which incorporate many biological structures such as scalp, skull, meninges, brain (with gray/white matter differentiation), and vasculature. However, most models drastically simplify the membranes and substructures between the pia and arachnoid membranes. We hypothesize that substructures in the pia-arachnoid complex (PAC) contribute substantially to brain deformation following head rotation, and that when included in FE models accuracy of extra-axial hemorrhage prediction improves. To test these hypotheses, microscale FE models of the PAC were developed to span the variability of PAC substructure anatomy and regional density. The constitutive response of these models were then integrated into an existing macroscale FE model of the immature piglet brain to identify changes in cortical stress distribution and predictions of extra-axial hemorrhage (EAH). Incorporating regional variability of PAC substructures substantially altered the distribution of principal stress on the cortical surface of the brain compared to a uniform representation of the PAC. Simulations of 24 non-impact rapid head rotations in an immature piglet animal model resulted in improved accuracy of EAH prediction (to 94 % sensitivity, 100 % specificity), as well as a high accuracy in regional hemorrhage prediction (to 82-100 % sensitivity, 100 % specificity). We conclude that including a biofidelic PAC substructure variability in FE models of the head is essential for improved predictions of hemorrhage at the brain/skull interface.

  18. Hematological toxicity in radioimmunotherapy is predicted both by the computed absorbed whole body dose (cGy) and by the administered dose (mCi)

    International Nuclear Information System (INIS)

    Marquez, Sheri D.; Knox, Susan J.; Trisler, Kirk D.; Goris, Michael L.

    1997-01-01

    Purpose/Objective: Radioimmunotherapy (RIT) has yielded encouraging response rates in patients with recurrent non-Hodgkin's lymphoma, but myelotoxicity remains the dose limiting factor. Dose optimization is theoretically possible, since a pretreatment biodistribution study with tracer doses allows for a fairly accurate estimate of the whole body (and by implication the bone marrow) dose in patients. It has been shown that the radiation dose as a function of the administered dose varies widely from patient to patient. The pretreatment study could therefore be used to determine the maximum tolerable dose for each individual patient. The purpose of this study was to examine whether the administered dose or the estimated whole body absorbed radiation dose were indeed predictors of bone marrow toxicity. Materials and Methods: We studied two cohorts of patients to determine if the computed integral whole body or marrow dose is predictive of myelotoxicity. The first cohort consisted of 13 patients treated with Yttrium-90 labeled anti-CD20 (2B8) monoclonal antibody. Those patients were treated in a dose escalation protocol, based on the administered dose, without correction for weight or body surface. The computed whole body dose varied from 41 to 129 cGy. The second cohort (6 patients) were treated with Iodine-131 labeled anti-CD20 (B1) antibody. In this group the administered dose was tailored to deliver an estimated 75 cGy whole body dose. The administered dose varied from 54 to 84 mCi of Iodine-131. For each patient, white blood cell count with differential, hemoglobin, hematocrit, and platelet levels were measured before and at regular intervals after RIT was administered. Using linear regression analysis, a relationship between administered dose, absorbed dose and myelotoxicity was determined for each patient cohort. Results: Marrow toxicity was measured by the absolute decrease in white blood cell (DWBC), platelet (DPLAT), and neutrophil (DN) values. In the Yttrium

  19. Improving the Accuracy of a Heliocentric Potential (HCP Prediction Model for the Aviation Radiation Dose

    Directory of Open Access Journals (Sweden)

    Junga Hwang

    2016-12-01

    Full Text Available The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs, flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA. However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015. In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1 real-time daily sunspot assessments, (2 predictions of the daily HCP by our prediction algorithm, and (3 calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

  20. The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

    Science.gov (United States)

    Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

    2017-01-01

    Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2  = 0.18). The use of routinely available pre-operative patient factors improves the prediction of operative

  1. Reliable B cell epitope predictions: impacts of method development and improved benchmarking

    DEFF Research Database (Denmark)

    Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole

    2012-01-01

    biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping...... evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version...

  2. Methods to improve genomic prediction and GWAS using combined Holstein populations

    DEFF Research Database (Denmark)

    Li, Xiujin

    The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

  3. Improved prediction of reservoir behavior through integration of quantitative geological and petrophysical data

    Energy Technology Data Exchange (ETDEWEB)

    Auman, J. B.; Davies, D. K.; Vessell, R. K.

    1997-08-01

    Methodology that promises improved reservoir characterization and prediction of permeability, production and injection behavior during primary and enhanced recovery operations was demonstrated. The method is based on identifying intervals of unique pore geometry by a combination of image analysis techniques and traditional petrophysical measurements to calculate rock type and estimate permeability and saturation. Results from a complex carbonate and sandstone reservoir were presented as illustrative examples of the versatility and high level of accuracy of this method in predicting reservoir quality. 16 refs., 5 tabs., 14 figs.

  4. Evaluation and improvements of a mayfly, Neocloeon (Centroptilum) triangulifer (Ephemeroptera: Baetidae) toxicity test method - SETAC Europe 2016

    Science.gov (United States)

    A recently published test method for Neocloeon triangulifer assessed the survival and growth of larval mayflies exposed to several reference toxicants (NaCl, KCl, and CuSO4). Results were not able to be replicated in subsequent experiments. To identify potential sources of variab...

  5. Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

    Science.gov (United States)

    Ickes, Jacob C.

    Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

  6. Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

    Science.gov (United States)

    Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

    2015-09-01

    Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

  7. Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

    Science.gov (United States)

    Daigle, Matthew John; Goebel, Kai Frank

    2010-01-01

    Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

  8. A study on improvement of analytical prediction model for spacer grid pressure loss coefficients

    International Nuclear Information System (INIS)

    Lim, Jonh Seon

    2002-02-01

    Nuclear fuel assemblies used in the nuclear power plants consist of the nuclear fuel rods, the control rod guide tubes, an instrument guide tube, spacer grids,a bottom nozzle, a top nozzle. The spacer grid is the most important component of the fuel assembly components for thermal hydraulic and mechanical design and analyses. The spacer grids fixed with the guide tubes support the fuel rods and have the very important role to activate thermal energy transfer by the coolant mixing caused to the turbulent flow and crossflow in the subchannels. In this paper, the analytical spacer grid pressure loss prediction model has been studied and improved by considering the test section wall to spacer grid gap pressure loss independently and applying the appropriate friction drag coefficient to predict pressure loss more accurately at the low Reynolds number region. The improved analytical model has been verified based on the hydraulic pressure drop test results for the spacer grids of three types with 5x5, 16x16, 17x17 arrays, respectively. The pressure loss coefficients predicted by the improved analytical model are coincident with those test results within ±12%. This result shows that the improved analytical model can be used for research and design change of the nuclear fuel assembly

  9. Poster - 47: A parametrized prediction model of rectal toxicity in focal SBRT of low risk prostate cancer

    Energy Technology Data Exchange (ETDEWEB)

    Stevens, Todd; Bauman, Glenn [Saint John Regional Hospital, London Regional Cancer Program (Canada)

    2016-08-15

    There has been a recent trend towards watchful waiting in place of intervention for early stage prostate cancer (CaP). However, this approach can allow for disease progression, and subsequent whole-gland therapies such as prostatectomy and whole gland irradiation can result in functional deficits or rectal toxicities or both. A controversial alternative approach for this patient cohort is the use of focal therapy, where the treatment is focussed on an identified dominant index lesion (DIL). This work aims to investigate the treatment parameters for focal SBRT of the prostate under which clinically acceptable rectal NTCP levels can be achieved. For each of 25 low risk CaP patients, a hypothetical 2 cc DIL was modeled in the right-posterior quadrant of the prostate, and was used to build a PTV as the target for SBRT simulation. An SBRT prescriptions of 41 Gy and 37 Gy in 5 fractions were chosen, corresponding to the boost levels used in previous CaP dose escalation studies. DVH data were exported and used to calculate rectal NTCP values based on the Lyman-Kutcher-Burman (LKB) model using the QUANTEC reccommended model parameters. Rectal NTCP dependence on DIL-to-rectum separation, dose level, and DIL volume were investigated. The final goal of this ongoing work is to create a map of the maximum allowable prescription dose for a given patient geometry that achieves a clinically acceptable rectal NTCP level.

  10. Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database.

    Science.gov (United States)

    Dieguez-Santana, Karel; Pham-The, Hai; Villegas-Aguilar, Pedro J; Le-Thi-Thu, Huong; Castillo-Garit, Juan A; Casañola-Martin, Gerardo M

    2016-12-01

    In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative contributions. In a next step, a median-size database of nearly 8000 phenolic compounds extracted from ChEMBL was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report is very useful to screen chemical databases for finding the compounds responsible of aquatic contamination in the biomarker used in the current work. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

    Science.gov (United States)

    Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

    2016-09-01

    Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

  12. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

    Science.gov (United States)

    Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

    2017-01-01

    For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

  13. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Eric R. Edelman

    2017-06-01

    Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

  14. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  15. Nanosecond pulsed electric field incorporation technique to predict molecular mechanisms of teratogenicity and developmental toxicity of estradiol-17β on medaka embryos.

    Science.gov (United States)

    Yamaguchi, Akemi; Ishibashi, Hiroshi; Kono, Susumu; Iida, Midori; Uchida, Masaya; Arizono, Koji; Tominaga, Nobuaki

    2018-05-01

    Herein, we propose using a nanosecond pulsed electric field (nsPEF) technique to assess teratogenicity and embryonic developmental toxicity of estradiol-17β (E 2 ) and predict the molecular mechanisms of teratogenicity and embryonic developmental defects caused by E 2 on medaka (Oryzias latipes). The 5 hour post-fertilization embryos were exposed to co-treatment with 10 μm E 2 and nsPEF for 2 hours and then continuously cultured under non-E 2 and nsPEF conditions until hatching. Results documented that the time to hatching of embryos was significantly delayed in comparison to the control group and that typical abnormal embryo development, such as the delay of blood vessel formation, was observed. For DNA microarray analysis, 6 day post-fertilization embryos that had been continuously cultured under the non-E 2 and nsPEF condition after 2 hour co-treatments were used. DNA microarray analysis identified 542 upregulated genes and one downregulated gene in the 6 day post-fertilization embryos. Furthermore, bioinformatic analyses using differentially expressed genes revealed that E 2 exposure affected various gene ontology terms, such as response to hormone stimulus. The network analysis also documented that the estrogen receptor α in the mitogen-activated protein kinase signaling pathway may be involved in regulating several transcription factors, such as FOX, AKT1 and epidermal growth factor receptor. These results suggest that our nsPEF technique is a powerful tool for assessing teratogenicity and embryonic developmental toxicity of E 2 and predict their molecular mechanisms in medaka embryos. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Activity Prediction of Schiff Base Compounds using Improved QSAR Models of Cinnamaldehyde Analogues and Derivatives

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2015-10-01

    Full Text Available In past work, QSAR (quantitative structure-activity relationship models of cinnamaldehyde analogues and derivatives (CADs have been used to predict the activities of new chemicals based on their mass concentrations, but these approaches are not without shortcomings. Therefore, molar concentrations were used instead of mass concentrations to determine antifungal activity. New QSAR models of CADs against Aspergillus niger and Penicillium citrinum were established, and the molecular design of new CADs was performed. The antifungal properties of the designed CADs were tested, and the experimental Log AR values were in agreement with the predicted Log AR values. The results indicate that the improved QSAR models are more reliable and can be effectively used for CADs molecular design and prediction of the activity of CADs. These findings provide new insight into the development and utilization of cinnamaldehyde compounds.

  17. A New Approach to Improve Accuracy of Grey Model GMC(1,n in Time Series Prediction

    Directory of Open Access Journals (Sweden)

    Sompop Moonchai

    2015-01-01

    Full Text Available This paper presents a modified grey model GMC(1,n for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n model was verified by two cases: the study of forecasting CO2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n and D-GMC(1,n models.

  18. Improving the Accuracy of Predicting Maximal Oxygen Consumption (VO2pk)

    Science.gov (United States)

    Downs, Meghan E.; Lee, Stuart M. C.; Ploutz-Snyder, Lori; Feiveson, Alan

    2016-01-01

    Maximal oxygen (VO2pk) is the maximum amount of oxygen that the body can use during intense exercise and is used for benchmarking endurance exercise capacity. The most accurate method to determineVO2pk requires continuous measurements of ventilation and gas exchange during an exercise test to maximal effort, which necessitates expensive equipment, a trained staff, and time to set-up the equipment. For astronauts, accurate VO2pk measures are important to assess mission critical task performance capabilities and to prescribe exercise intensities to optimize performance. Currently, astronauts perform submaximal exercise tests during flight to predict VO2pk; however, while submaximal VO2pk prediction equations provide reliable estimates of mean VO2pk for populations, they can be unacceptably inaccurate for a given individual. The error in current predictions and logistical limitations of measuring VO2pk, particularly during spaceflight, highlights the need for improved estimation methods.

  19. Improved time series prediction with a new method for selection of model parameters

    International Nuclear Information System (INIS)

    Jade, A M; Jayaraman, V K; Kulkarni, B D

    2006-01-01

    A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

  20. At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration.

    Science.gov (United States)

    Stegen, James C

    2018-01-01

    To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modeling frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. We can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.

  1. Factoring-in agglomeration of carbon nanotubes and nanofibers for better prediction of their toxicity versus asbestos

    Directory of Open Access Journals (Sweden)

    Murray Ashley R

    2012-04-01

    Full Text Available Abstract Background Carbon nanotubes (CNT and carbon nanofibers (CNF are allotropes of carbon featuring fibrous morphology. The dimensions and high aspect ratio of CNT and CNF have prompted the comparison with naturally occurring asbestos fibers which are known to be extremely pathogenic. While the toxicity and hazardous outcomes elicited by airborne exposure to single-walled CNT or asbestos have been widely reported, very limited data are currently available describing adverse effects of respirable CNF. Results Here, we assessed pulmonary inflammation, fibrosis, oxidative stress markers and systemic immune responses to respirable CNF in comparison to single-walled CNT (SWCNT and asbestos. Pulmonary inflammatory and fibrogenic responses to CNF, SWCNT and asbestos varied depending upon the agglomeration state of the particles/fibers. Foci of granulomatous lesions and collagen deposition were associated with dense particle-like SWCNT agglomerates, while no granuloma formation was found following exposure to fiber-like CNF or asbestos. The average thickness of the alveolar connective tissue - a marker of interstitial fibrosis - was increased 28 days post SWCNT, CNF or asbestos exposure. Exposure to SWCNT, CNF or asbestos resulted in oxidative stress evidenced by accumulations of 4-HNE and carbonylated proteins in the lung tissues. Additionally, local inflammatory and fibrogenic responses were accompanied by modified systemic immunity, as documented by decreased proliferation of splenic T cells ex vivo on day 28 post exposure. The accuracies of assessments of effective surface area for asbestos, SWCNT and CNF (based on geometrical analysis of their agglomeration versus estimates of mass dose and number of particles were compared as predictors of toxicological outcomes. Conclusions We provide evidence that effective surface area along with mass dose rather than specific surface area or particle number are significantly correlated with toxicological

  2. Is serum level of CC chemokine ligand 18 a biomarker for the prediction of radiation induced lung toxicity (RILT)?

    Science.gov (United States)

    Gkika, Eleni; Vach, Werner; Adebahr, Sonja; Schimek-Jasch, Tanja; Brenner, Anton; Brunner, Thomas Baptist; Kaier, Klaus; Prasse, Antje; Müller-Quernheim, Joachim; Grosu, Anca-Ligia; Zissel, Gernot; Nestle, Ursula

    2017-01-01

    The CC chemokine ligand 18 (CCL18) is produced by alveolar macrophages in patients with fibrosing lung disease and its concentration is increased in various fibrotic lung diseases. Furthermore CCL18 is elevated in several malignancies as it is produced by tumor associated macrophages. In this study we aimed to analyze the role of CCL18 as a prognostic biomarker for the development of early radiation induced lung toxicity (RILT), i.e. radiation pneumonitis after thoracic irradiation and its significance in the course of the disease. Sixty seven patients were enrolled prospectively in the study. Patients were treated with irradiation for several thoracic malignancies (lung cancer, esophageal cancer, thymoma), either with conventionally fractionated or hypo-fractionated radiotherapy. The CCL18 serum levels were quantified with ELISA (enzyme-linked immunosorbent assay) at predefined time points: before, during and at the end of treatment as well as in the first and second follow-up. Treatment parameters and functional tests were also correlated with the development of RILT.Fifty three patients were evaluable for this study. Twenty one patients (39%) developed radiologic signs of RILT Grade >1 but only three of them (5.6%) developed clinical symptoms (Grade 2). We could not find any association between the different CCL18 concentrations and a higher incidence of RILT. Statistical significant factors were the planning target volume (odds ratio OR: 1.003, p = 0.010), the volume of the lung receiving > 20 Gy (OR: 1.132 p = 0.004) and age (OR: 0.917, p = 0.008). There was no association between serial CCL18 concentrations with tumor response and overall survival.In our study the dosimetric parameters remained the most potent predictors of RILT. Further studies are needed in order to estimate the role of CCL18 in the development of early RILT.

  3. Is serum level of CC chemokine ligand 18 a biomarker for the prediction of radiation induced lung toxicity (RILT?

    Directory of Open Access Journals (Sweden)

    Eleni Gkika

    Full Text Available The CC chemokine ligand 18 (CCL18 is produced by alveolar macrophages in patients with fibrosing lung disease and its concentration is increased in various fibrotic lung diseases. Furthermore CCL18 is elevated in several malignancies as it is produced by tumor associated macrophages. In this study we aimed to analyze the role of CCL18 as a prognostic biomarker for the development of early radiation induced lung toxicity (RILT, i.e. radiation pneumonitis after thoracic irradiation and its significance in the course of the disease. Sixty seven patients were enrolled prospectively in the study. Patients were treated with irradiation for several thoracic malignancies (lung cancer, esophageal cancer, thymoma, either with conventionally fractionated or hypo-fractionated radiotherapy. The CCL18 serum levels were quantified with ELISA (enzyme-linked immunosorbent assay at predefined time points: before, during and at the end of treatment as well as in the first and second follow-up. Treatment parameters and functional tests were also correlated with the development of RILT.Fifty three patients were evaluable for this study. Twenty one patients (39% developed radiologic signs of RILT Grade >1 but only three of them (5.6% developed clinical symptoms (Grade 2. We could not find any association between the different CCL18 concentrations and a higher incidence of RILT. Statistical significant factors were the planning target volume (odds ratio OR: 1.003, p = 0.010, the volume of the lung receiving > 20 Gy (OR: 1.132 p = 0.004 and age (OR: 0.917, p = 0.008. There was no association between serial CCL18 concentrations with tumor response and overall survival.In our study the dosimetric parameters remained the most potent predictors of RILT. Further studies are needed in order to estimate the role of CCL18 in the development of early RILT.

  4. Factoring-in agglomeration of carbon nanotubes and nanofibers for better prediction of their toxicity versus asbestos.

    Science.gov (United States)

    Murray, Ashley R; Kisin, Elena R; Tkach, Alexey V; Yanamala, Naveena; Mercer, Robert; Young, Shih-Houng; Fadeel, Bengt; Kagan, Valerian E; Shvedova, Anna A

    2012-04-10

    Carbon nanotubes (CNT) and carbon nanofibers (CNF) are allotropes of carbon featuring fibrous morphology. The dimensions and high aspect ratio of CNT and CNF have prompted the comparison with naturally occurring asbestos fibers which are known to be extremely pathogenic. While the toxicity and hazardous outcomes elicited by airborne exposure to single-walled CNT or asbestos have been widely reported, very limited data are currently available describing adverse effects of respirable CNF. Here, we assessed pulmonary inflammation, fibrosis, oxidative stress markers and systemic immune responses to respirable CNF in comparison to single-walled CNT (SWCNT) and asbestos. Pulmonary inflammatory and fibrogenic responses to CNF, SWCNT and asbestos varied depending upon the agglomeration state of the particles/fibers. Foci of granulomatous lesions and collagen deposition were associated with dense particle-like SWCNT agglomerates, while no granuloma formation was found following exposure to fiber-like CNF or asbestos. The average thickness of the alveolar connective tissue--a marker of interstitial fibrosis--was increased 28 days post SWCNT, CNF or asbestos exposure. Exposure to SWCNT, CNF or asbestos resulted in oxidative stress evidenced by accumulations of 4-HNE and carbonylated proteins in the lung tissues. Additionally, local inflammatory and fibrogenic responses were accompanied by modified systemic immunity, as documented by decreased proliferation of splenic T cells ex vivo on day 28 post exposure. The accuracies of assessments of effective surface area for asbestos, SWCNT and CNF (based on geometrical analysis of their agglomeration) versus estimates of mass dose and number of particles were compared as predictors of toxicological outcomes. We provide evidence that effective surface area along with mass dose rather than specific surface area or particle number are significantly correlated with toxicological responses to carbonaceous fibrous nanoparticles. Therefore

  5. Interest of blood markers in predicting radiation-induced toxicity; Interet des marqueurs sanguins dans la prediction de la toxicite radio-induite

    Energy Technology Data Exchange (ETDEWEB)

    Lacombe, J. [Departement de cancerologie radiotherapie, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France); Universite de Montpellier I, 5, boulevard Henri-IV, CS 19044, 34967 Montpellier cedex 2 (France); Laboratoire d' oncoproteomique clinique, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France); Solassol, J. [Laboratoire de biologie cellulaire et hormonale, hopital Arnaud-de-Villeneuve, CHU de Montpellier, 371, avenue du Doyen-Gaston-Giraud, 34295 Montpellier cedex 5 (France); Laboratoire d' oncoproteomique clinique, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France); Coelho, M. [Inserm U896, institut de recherche en cancerologie de Montpellier, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France); Ozsahin, M. [Service de radio-oncologie, centre hospitalier universitaire Vaudois, 1011 Lausanne (Switzerland); Azria, D. [Departement de cancerologie radiotherapie, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France); Universite de Montpellier I, 5, boulevard Henri-IV, CS 19044, 34967 Montpellier cedex 2 (France); Inserm U896, institut de recherche en cancerologie de Montpellier, CRLC Val-d' Aurelle-Paul-Lamarque, rue Croix-Verte, 34298 Montpellier cedex 5 (France)

    2011-08-15

    The oncologic outcome and the total dose are highly correlated with the treatment by ionizing radiation. The dose increase (total or per fraction) may provoke late-side effects that are potentially irreversible. The radiation-induced CD8 lymphocyte apoptotic value and the molecular modifications within the lymphocyte are capable of predicting the level of risk of developing late-side effects after curative intent radiotherapy. In this review, we present the different blood assays in this setting and discuss the current possibilities of researches, namely those involving the proteomic process. (authors)

  6. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Science.gov (United States)

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  7. Improved Seasonal Prediction of European Summer Temperatures With New Five-Layer Soil-Hydrology Scheme

    Science.gov (United States)

    Bunzel, Felix; Müller, Wolfgang A.; Dobrynin, Mikhail; Fröhlich, Kristina; Hagemann, Stefan; Pohlmann, Holger; Stacke, Tobias; Baehr, Johanna

    2018-01-01

    We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.

  8. Breast calcifications. A standardized mammographic reporting and data system to improve positive predictive value

    International Nuclear Information System (INIS)

    Perugini, G.; Bonzanini, B.; Valentino, C.

    1999-01-01

    The purpose of this work is to investigate the usefulness of a standardized reporting and data system in improving the positive predictive value of mammography in breast calcifications. Using the Breast Imaging Reporting and Data System lexicon developed by the American College of Radiology, it is defined 5 descriptive categories of breast calcifications and classified diagnostic suspicion of malignancy on a 3-grade scale (low, intermediate and high). Two radiologists reviewed 117 mammographic studies selected from those of the patients submitted to surgical biopsy for mammographically detected calcifications from January 1993 to December 1997, and classified them according to the above criteria. The positive predictive value was calculated for all examinations and for the stratified groups. Defining a standardized system for assessing and describing breast calcifications helps improve the diagnostic accuracy of mammography in clinical practice [it

  9. An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

    Directory of Open Access Journals (Sweden)

    Jin Xin

    2015-01-01

    Full Text Available To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.

  10. A Model Predictive Control Approach for Fuel Economy Improvement of a Series Hydraulic Hybrid Vehicle

    Directory of Open Access Journals (Sweden)

    Tri-Vien Vu

    2014-10-01

    Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.

  11. Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia.

    Science.gov (United States)

    Menéndez, R; Martínez, R; Reyes, S; Mensa, J; Filella, X; Marcos, M A; Martínez, A; Esquinas, C; Ramirez, P; Torres, A

    2009-07-01

    Prognostic scales provide a useful tool to predict mortality in community-acquired pneumonia (CAP). However, the inflammatory response of the host, crucial in resolution and outcome, is not included in the prognostic scales. The aim of this study was to investigate whether information about the initial inflammatory cytokine profile and markers increases the accuracy of prognostic scales to predict 30-day mortality. To this aim, a prospective cohort study in two tertiary care hospitals was designed. Procalcitonin (PCT), C-reactive protein (CRP) and the systemic cytokines tumour necrosis factor alpha (TNFalpha) and interleukins IL6, IL8 and IL10 were measured at admission. Initial severity was assessed by PSI (Pneumonia Severity Index), CURB65 (Confusion, Urea nitrogen, Respiratory rate, Blood pressure, > or = 65 years of age) and CRB65 (Confusion, Respiratory rate, Blood pressure, > or = 65 years of age) scales. A total of 453 hospitalised CAP patients were included. The 36 patients who died (7.8%) had significantly increased levels of IL6, IL8, PCT and CRP. In regression logistic analyses, high levels of CRP and IL6 showed an independent predictive value for predicting 30-day mortality, after adjustment for prognostic scales. Adding CRP to PSI significantly increased the area under the receiver operating characteristic curve (AUC) from 0.80 to 0.85, that of CURB65 from 0.82 to 0.85 and that of CRB65 from 0.79 to 0.85. Adding IL6 or PCT values to CRP did not significantly increase the AUC of any scale. When using two scales (PSI and CURB65/CRB65) and CRP simultaneously the AUC was 0.88. Adding CRP levels to PSI, CURB65 and CRB65 scales improves the 30-day mortality prediction. The highest predictive value is reached with a combination of two scales and CRP. Further validation of that improvement is needed.

  12. Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

    Science.gov (United States)

    Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

    2010-11-01

    Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

  13. Towards improved hydrologic predictions using data assimilation techniques for water resource management at the continental scale

    Science.gov (United States)

    Naz, Bibi; Kurtz, Wolfgang; Kollet, Stefan; Hendricks Franssen, Harrie-Jan; Sharples, Wendy; Görgen, Klaus; Keune, Jessica; Kulkarni, Ketan

    2017-04-01

    More accurate and reliable hydrologic simulations are important for many applications such as water resource management, future water availability projections and predictions of extreme events. However, simulation of spatial and temporal variations in the critical water budget components such as precipitation, snow, evaporation and runoff is highly uncertain, due to errors in e.g. model structure and inputs (hydrologic parameters and forcings). In this study, we use data assimilation techniques to improve the predictability of continental-scale water fluxes using in-situ measurements along with remotely sensed information to improve hydrologic predications for water resource systems. The Community Land Model, version 3.5 (CLM) integrated with the Parallel Data Assimilation Framework (PDAF) was implemented at spatial resolution of 1/36 degree (3 km) over the European CORDEX domain. The modeling system was forced with a high-resolution reanalysis system COSMO-REA6 from Hans-Ertel Centre for Weather Research (HErZ) and ERA-Interim datasets for time period of 1994-2014. A series of data assimilation experiments were conducted to assess the efficiency of assimilation of various observations, such as river discharge data, remotely sensed soil moisture, terrestrial water storage and snow measurements into the CLM-PDAF at regional to continental scales. This setup not only allows to quantify uncertainties, but also improves streamflow predictions by updating simultaneously model states and parameters utilizing observational information. The results from different regions, watershed sizes, spatial resolutions and timescales are compared and discussed in this study.

  14. Improving behavioral performance under full attention by adjusting response criteria to changes in stimulus predictability.

    Science.gov (United States)

    Katzner, Steffen; Treue, Stefan; Busse, Laura

    2012-09-04

    One of the key features of active perception is the ability to predict critical sensory events. Humans and animals can implicitly learn statistical regularities in the timing of events and use them to improve behavioral performance. Here, we used a signal detection approach to investigate whether such improvements in performance result from changes of perceptual sensitivity or rather from adjustments of a response criterion. In a regular sequence of briefly presented stimuli, human observers performed a noise-limited motion detection task by monitoring the stimulus stream for the appearance of a designated target direction. We manipulated target predictability through the hazard rate, which specifies the likelihood that a target is about to occur, given it has not occurred so far. Analyses of response accuracy revealed that improvements in performance could be accounted for by adjustments of the response criterion; a growing hazard rate was paralleled by an increasing tendency to report the presence of a target. In contrast, the hazard rate did not affect perceptual sensitivity. Consistent with previous research, we also found that reaction time decreases as the hazard rate grows. A simple rise-to-threshold model could well describe this decrease and attribute predictability effects to threshold adjustments rather than changes in information supply. We conclude that, even under conditions of full attention and constant perceptual sensitivity, behavioral performance can be optimized by dynamically adjusting the response criterion to meet ongoing changes in the likelihood of a target.

  15. Reduction in relapse rate of radioiodine therapy in patients of toxic multinodular goiter: A quality improvement project

    OpenAIRE

    Mitra, Sujata; Muthu, Sonai G

    2012-01-01

    Introduction: Radioiodine (I-131) therapy is the definitive treatment of toxic multinodular goiter (TMNG). Treatment failure may result in relapse after I-131 therapy. The present study was undertaken to reduce treatment failure rate of I-131 therapy in TMNG patients. Materials and Methods: Multiple causes may have lead to treatment failure of I-131 in TMNG patients making it difficult to establish a direct cause?effect relationship and take corrective action. Therefore, the JURAN methodology...

  16. An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

    Science.gov (United States)

    Dash, Rajashree

    2017-11-01

    Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

  17. RAPID COMMUNICATION: Improving prediction accuracy of GPS satellite clocks with periodic variation behaviour

    Science.gov (United States)

    Heo, Youn Jeong; Cho, Jeongho; Heo, Moon Beom

    2010-07-01

    The broadcast ephemeris and IGS ultra-rapid predicted (IGU-P) products are primarily available for use in real-time GPS applications. The IGU orbit precision has been remarkably improved since late 2007, but its clock products have not shown acceptably high-quality prediction performance. One reason for this fact is that satellite atomic clocks in space can be easily influenced by various factors such as temperature and environment and this leads to complicated aspects like periodic variations, which are not sufficiently described by conventional models. A more reliable prediction model is thus proposed in this paper in order to be utilized particularly in describing the periodic variation behaviour satisfactorily. The proposed prediction model for satellite clocks adds cyclic terms to overcome the periodic effects and adopts delay coordinate embedding, which offers the possibility of accessing linear or nonlinear coupling characteristics like satellite behaviour. The simulation results have shown that the proposed prediction model outperforms the IGU-P solutions at least on a daily basis.

  18. Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

    Directory of Open Access Journals (Sweden)

    Peng Ren

    Full Text Available Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD to obtain their Intrinsic Mode Functions (IMF. Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.

  19. An efficient and reproducible method for improving growth of a soil alga (Chlorococcum infusionum) for toxicity assays.

    Science.gov (United States)

    Nam, Sun-Hwa; An, Youn-Joo

    2015-12-01

    This study evaluated five methods of soil inoculation using the soil alga Chlorococcum infusionum to determine the most efficient and reproducible method for promoting the growth of soil algae for toxicity testing. The five techniques included application of C. infusionum in a circle on top of the soil, to a central spot on top of the soil, to a central spot in the subsoil, to one side on top of the soil, and application divided between a circle and a central spot on top of the soil. Of these, the first method generated the greatest amount of chlorophyll fluorescence and was the method with the best reproducibility. We evaluated the applicability of this method in an assessment of the toxicity of copper and nickel to C. infusionum in two representative standard soils. Copper (20-75 mg/kg for OECD soil and 20-60 mg/kg Lufa 2.2 soil) and nickel (400-500 mg/kg for OECD soil and 60-100 mg/kg Lufa 2.2 soil) reduced the chlorophyll fluorescence of C. infusionum when the inoculation was delivered in a circle on top of both soil types. To our knowledge, this is the first study to assess the suitability of different soil algal inoculation methods for terrestrial toxicity testing. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  1. Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action

    DEFF Research Database (Denmark)

    Sanderson, Hans; Thomsen, Marianne

    2009-01-01

    data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68...... a comprehensive database based on OECD's standardized measured ecotoxicological data and to evaluate if there is generally cause of greater concern with regards to pharmaceutical aquatic toxicological profiles relative to industrial chemicals. Comparisons were based upon aquatic ecotoxicity classification under...... the United Nations Global Harmonized System for classification and labeling of chemicals (GHS). Moreover, we statistically explored whether the predominant mode-of-action (MOA) for pharmaceuticals is narcosis. We found 275 pharmaceuticals with 569 acute aquatic effect data; 23 pharmaceuticals had chronic...

  2. Factors predicting visual improvement post pars plana vitrectomy for proliferative diabetic retinopathy

    Directory of Open Access Journals (Sweden)

    Evelyn Tai Li Min

    2017-08-01

    Full Text Available AIM: To identify factors predicting visual improvement post vitrectomy for sequelae of proliferative diabetic retinopathy(PDR.METHODS: This was a retrospective analysis of pars plana vitrectomy indicated for sequelae of PDR from Jan. to Dec. 2014 in Hospital Sultanah Bahiyah, Alor Star, Kedah, Malaysia. Data collected included patient demographics, baseline visual acuity(VAand post-operative logMAR best corrected VA at 1y. Data analysis was performed with IBM SPSS Statistics Version 22.0. RESULTS: A total of 103 patients were included. The mean age was 51.2y. On multivariable analysis, each pre-operative positive deviation of 1 logMAR from a baseline VA of 0 logMAR was associated with a post-operative improvement of 0.859 logMAR(P0.001. Likewise, an attached macula pre-operatively was associated with a 0.374(P=0.003logMAR improvement post vitrectomy. Absence of iris neovascularisation and absence of post-operative complications were associated with a post vitrectomy improvement in logMAR by 1.126(P=0.001and 0.377(P=0.005respectively. Absence of long-acting intraocular tamponade was associated with a 0.302(P=0.010improvement of logMAR post vitrectomy.CONCLUSION: Factors associated with visual improvement after vitrectomy are poor pre-operative VA, an attached macula, absence of iris neovascularisation, absence of post-operative complications and abstaining from use of long-acting intraocular tamponade. A thorough understanding of the factors predicting visual improvement will facilitate decision-making in vitreoretinal surgery.

  3. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    International Nuclear Information System (INIS)

    Kostadinova, Radina; Boess, Franziska; Applegate, Dawn; Suter, Laura; Weiser, Thomas; Singer, Thomas; Naughton, Brian; Roth, Adrian

    2013-01-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  4. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    Energy Technology Data Exchange (ETDEWEB)

    Kostadinova, Radina; Boess, Franziska [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Applegate, Dawn [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Suter, Laura; Weiser, Thomas; Singer, Thomas [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Naughton, Brian [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Roth, Adrian, E-mail: adrian_b.roth@roche.com [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland)

    2013-04-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  5. Catchment coevolution: A useful framework for improving predictions of hydrological change?

    Science.gov (United States)

    Troch, Peter A.

    2017-04-01

    The notion that landscape features have co-evolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this presentation we present a general framework of catchment coevolution that could improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building

  6. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    International Nuclear Information System (INIS)

    Rattá, G.A.; Vega, J.; Murari, A.

    2012-01-01

    Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  7. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    Energy Technology Data Exchange (ETDEWEB)

    Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)

    2012-09-15

    Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  8. Prediction and moderation of improvement in cognitive-behavioral and psychodynamic psychotherapy for panic disorder.

    Science.gov (United States)

    Chambless, Dianne L; Milrod, Barbara; Porter, Eliora; Gallop, Robert; McCarthy, Kevin S; Graf, Elizabeth; Rudden, Marie; Sharpless, Brian A; Barber, Jacques P

    2017-08-01

    To identify variables predicting psychotherapy outcome for panic disorder or indicating which of 2 very different forms of psychotherapy-panic-focused psychodynamic psychotherapy (PFPP) or cognitive-behavioral therapy (CBT)-would be more effective for particular patients. Data were from 161 adults participating in a randomized controlled trial (RCT) including these psychotherapies. Patients included 104 women; 118 patients were White, 33 were Black, and 10 were of other races; 24 were Latino(a). Predictors/moderators measured at baseline or by Session 2 of treatment were used to predict change on the Panic Disorder Severity Scale (PDSS). Higher expectancy for treatment gains (Credibility/Expectancy Questionnaire d = -1.05, CI 95% [-1.50, -0.60]), and later age of onset (d = -0.65, CI 95% [-0.98, -0.32]) were predictive of greater change. Both variables were also significant moderators: patients with low expectancy of improvement improved significantly less in PFPP than their counterparts in CBT, whereas this was not the case for patients with average or high levels of expectancy. When patients had an onset of panic disorder later in life (≥27.5 years old), they fared as well in PFPP as CBT. In contrast, at low and mean levels of onset age, CBT was the more effective treatment. Predictive variables suggest possibly fruitful foci for improvement of treatment outcome. In terms of moderation, CBT was the more consistently effective treatment, but moderators identified some patients who would do as well in PFPP as in CBT, thereby widening empirically supported options for treatment of this disorder. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  9. Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

    Directory of Open Access Journals (Sweden)

    Jaehee V. Shim

    2017-09-01

    Full Text Available Tyrosine kinase inhibitors (TKIs are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation–contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

  10. Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction

    Directory of Open Access Journals (Sweden)

    Takemasa Miyoshi

    2012-09-01

    Full Text Available In addition to conventional observations, atmospheric temperature and humidity profile data from the Atmospheric Infrared Sounder (AIRS Version 5 retrieval products are assimilated into the Weather Research and Forecasting (WRF model, using the local ensemble transform Kalman filter (LETKF. Although a naive assimilation of all available quality-controlled AIRS retrieval data yields an inferior analysis, the additional enhancements of adaptive inflation and horizontal data thinning result in a general improvement of numerical weather prediction skill due to AIRS data. In particular, the adaptive inflation method is enhanced so that it no longer assumes temporal homogeneity of the observing network and allows for a better treatment of the temporally inhomogeneous AIRS data. Results indicate that the improvements due to AIRS data are more significant in longer-lead forecasts. Forecasts of Typhoons Sinlaku and Jangmi in September 2008 show improvements due to AIRS data.

  11. Improvement of PM10 prediction in East Asia using inverse modeling

    Science.gov (United States)

    Koo, Youn-Seo; Choi, Dae-Ryun; Kwon, Hi-Yong; Jang, Young-Kee; Han, Jin-Seok

    2015-04-01

    Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10 ㎛ in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia.

  12. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

    Science.gov (United States)

    Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

    2018-02-19

    Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

  13. A national-scale model of linear features improves predictions of farmland biodiversity.

    Science.gov (United States)

    Sullivan, Martin J P; Pearce-Higgins, James W; Newson, Stuart E; Scholefield, Paul; Brereton, Tom; Oliver, Tom H

    2017-12-01

    Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications . This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

  14. Improving MJO Prediction and Simulation Using AGCM Coupled Ocean Model with Refined Vertical Resolution

    Science.gov (United States)

    Tu, Chia-Ying; Tseng, Wan-Ling; Kuo, Pei-Hsuan; Lan, Yung-Yao; Tsuang, Ben-Jei; Hsu, Huang-Hsiung

    2017-04-01

    Precipitation in Taiwan area is significantly influenced by MJO (Madden-Julian Oscillation) in the boreal winter. This study is therefore conducted by toggling the MJO prediction and simulation with a unique model structure. The one-dimensional TKE (Turbulence Kinetic Energy) type ocean model SIT (Snow, Ice, Thermocline) with refined vertical resolution near surface is able to resolve cool skin, as well as diurnal warm layer. SIT can simulate accurate SST and hence give precise air-sea interaction. By coupling SIT with ECHAM5 (MPI-Meteorology), CAM5 (NCAR) and HiRAM (GFDL), the MJO simulations in 20-yrs climate integrations conducted by three SIT-coupled AGCMs are significant improved comparing to those driven by prescribed SST. The horizontal resolutions in ECHAM5, CAM5 and HiRAM are 2-deg., 1-deg and 0.5-deg., respectively. This suggests that the improvement of MJO simulation by coupling SIT is AGCM-resolution independent. This study further utilizes HiRAM coupled SIT to evaluate its MJO forecast skill. HiRAM has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013), but was not as successful in MJO forecast. The preliminary result of the HiRAM-SIT experiment during DYNAMO period shows improved success in MJO forecast. These improvements of MJO prediction and simulation in both hindcast experiments and climate integrations are mainly from better-simulated SST diurnal cycle and diurnal amplitude, which is contributed by the refined vertical resolution near ocean surface in SIT. Keywords: MJO Predictability, DYNAMO

  15. TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

    International Nuclear Information System (INIS)

    Krafft, S; Briere, T; Court, L; Martel, M

    2015-01-01

    Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. A total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP

  16. DSSTOX STRUCTURE-SEARCHABLE PUBLIC TOXICITY DATABASE NETWORK: CURRENT PROGRESS AND NEW INITIATIVES TO IMPROVE CHEMO-BIOINFORMATICS CAPABILITIES

    Science.gov (United States)

    The EPA DSSTox website (http://www/epa.gov/nheerl/dsstox) publishes standardized, structure-annotated toxicity databases, covering a broad range of toxicity disciplines. Each DSSTox database features documentation written in collaboration with the source authors and toxicity expe...

  17. Current status of prediction of drug disposition and toxicity in humans using chimeric mice with humanized liver.

    Science.gov (United States)

    Kitamura, Shigeyuki; Sugihara, Kazumi

    2014-01-01

    1. Human-chimeric mice with humanized liver have been constructed by transplantation of human hepatocytes into several types of mice having genetic modifications that injure endogenous liver cells. Here, we focus on liver urokinase-type plasminogen activator-transgenic severe combined immunodeficiency (uPA/SCID) mice, which are the most widely used human-chimeric mice. Studies so far indicate that drug metabolism, drug transport, pharmacological effects and toxicological action in these mice are broadly similar to those in humans. 2. Expression of various drug-metabolizing enzymes is known to be different between humans and rodents. However, the expression pattern of cytochrome P450, aldehyde oxidase and phase II enzymes in the liver of human-chimeric mice resembles that in humans, not that in the host mice. 3. Metabolism of various drugs, including S-warfarin, zaleplon, ibuprofen, naproxen, coumarin, troglitazone and midazolam, in human-chimeric mice is mediated by human drug-metabolizing enzymes, not by host mouse enzymes, and thus resembles that in humans. 4. Pharmacological and toxicological effects of various drugs in human-chimeric mice are also similar to those in humans. 5. The current consensus is that chimeric mice with humanized liver are useful to predict drug metabolism catalyzed by cytochrome P450, aldehyde oxidase and phase II enzymes in humans in vivo and in vitro. Some remaining issues are discussed in this review.

  18. High-efficiency liposomal encapsulation of a tyrosine kinase inhibitor leads to improved in vivo toxicity and tumor response profile

    Directory of Open Access Journals (Sweden)

    Mukthavaram R

    2013-10-01

    Full Text Available Rajesh Mukthavaram,1 Pengfei Jiang,1 Rohit Saklecha,1 Dmitri Simberg,3,4 Ila Sri Bharati,1 Natsuko Nomura,1 Ying Chao,1 Sandra Pastorino,1 Sandeep C Pingle,1 Valentina Fogal,1 Wolf Wrasidlo,1,2 Milan Makale,1,2 Santosh Kesari1,21Translational Neuro-Oncology Laboratories, 2Department of Neurosciences, 3Solid Tumor Therapeutics Program, Moores Cancer Center, UC San Diego, La Jolla, CA, 4Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Denver, CO, USAAbstract: Staurosporine (STS is a potent pan-kinase inhibitor with marked activity against several chemotherapy-resistant tumor types in vitro. The translational progress of this compound has been hindered by poor pharmacokinetics and toxicity. We sought to determine whether liposomal encapsulation of STS would enhance antitumor efficacy and reduce toxicity, thereby supporting the feasibility of further preclinical development. We developed a novel reverse pH gradient liposomal loading method for STS, with an optimal buffer type and drug-to-lipid ratio. Our approach produced 70% loading efficiency with good retention, and we provide, for the first time, an assessment of the in vivo antitumor activity of STS. A low intravenous dose (0.8 mg/kg inhibited U87 tumors in a murine flank model. Biodistribution showed preferential tumor accumulation, and body weight data, a sensitive index of STS toxicity, was unaffected by liposomal STS, but did decline with the free compound. In vitro experiments revealed that liposomal STS blocked Akt phosphorylation, induced poly(ADP-ribose polymerase cleavage, and produced cell death via apoptosis. This study provides a basis to explore further the feasibility of liposomally encapsulated STS, and potentially related compounds for the management of resistant solid tumors.Keywords: liposomes, staurosporine, glioblastoma, biodistribution, efficacy

  19. Applying a health action model to predict and improve healthy behaviors in coal miners.

    Science.gov (United States)

    Vahedian-Shahroodi, Mohammad; Tehrani, Hadi; Mohammadi, Faeze; Gholian-Aval, Mahdi; Peyman, Nooshin

    2018-05-01

    One of the most important ways to prevent work-related diseases in occupations such as mining is to promote healthy behaviors among miners. This study aimed to predict and promote healthy behaviors among coal miners by using a health action model (HAM). The study was conducted on 200 coal miners in Iran in two steps. In the first step, a descriptive study was implemented to determine predictive constructs and effectiveness of HAM on behavioral intention. The second step involved a quasi-experimental study to determine the effect of an HAM-based education intervention. This intervention was implemented by the researcher and the head of the safety unit based on the predictive construct specified in the first step over 12 sessions of 60 min. The data was collected using an HAM questionnaire and a checklist of healthy behavior. The results of the first step of the study showed that attitude, belief, and normative constructs were meaningful predictors of behavioral intention. Also, the results of the second step revealed that the mean score of attitude and behavioral intention increased significantly after conducting the intervention in the experimental group, while the mean score of these constructs decreased significantly in the control group. The findings of this study showed that HAM-based educational intervention could improve the healthy behaviors of mine workers. Therefore, it is recommended to extend the application of this model to other working groups to improve healthy behaviors.

  20. Partition dataset according to amino acid type improves the prediction of deleterious non-synonymous SNPs

    International Nuclear Information System (INIS)

    Yang, Jing; Li, Yuan-Yuan; Li, Yi-Xue; Ye, Zhi-Qiang

    2012-01-01

    Highlights: ► Proper dataset partition can improve the prediction of deleterious nsSNPs. ► Partition according to original residue type at nsSNP is a good criterion. ► Similar strategy is supposed promising in other machine learning problems. -- Abstract: Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning methods have been applied to train classifiers for sorting disease-associated nsSNPs from neutral ones. The continuously accumulated nsSNP data allows us to further explore better prediction approaches. In this work, we partitioned the training data into 20 subsets according to either original or substituted amino acid type at the nsSNP site. Using support vector machine (SVM), training classification models on each subset resulted in an overall accuracy of 76.3% or 74.9% depending on the two different partition criteria, while training on the whole dataset obtained an accuracy of only 72.6%. Moreover, the dataset was also randomly divided into 20 subsets, but the corresponding accuracy was only 73.2%. Our results demonstrated that partitioning the whole training dataset into subsets properly, i.e., according to the residue type at the nsSNP site, will improve the performance of the trained classifiers significantly, which should be valuable in developing better tools for predicting the disease-association of nsSNPs.

  1. Incorporating Scale-Dependent Fracture Stiffness for Improved Reservoir Performance Prediction

    Science.gov (United States)

    Crawford, B. R.; Tsenn, M. C.; Homburg, J. M.; Stehle, R. C.; Freysteinson, J. A.; Reese, W. C.

    2017-12-01

    We present a novel technique for predicting dynamic fracture network response to production-driven changes in effective stress, with the potential for optimizing depletion planning and improving recovery prediction in stress-sensitive naturally fractured reservoirs. A key component of the method involves laboratory geomechanics testing of single fractures in order to develop a unique scaling relationship between fracture normal stiffness and initial mechanical aperture. Details of the workflow are as follows: tensile, opening mode fractures are created in a variety of low matrix permeability rocks with initial, unstressed apertures in the micrometer to millimeter range, as determined from image analyses of X-ray CT scans; subsequent hydrostatic compression of these fractured samples with synchronous radial strain and flow measurement indicates that both mechanical and hydraulic aperture reduction varies linearly with the natural logarithm of effective normal stress; these stress-sensitive single-fracture laboratory observations are then upscaled to networks with fracture populations displaying frequency-length and length-aperture scaling laws commonly exhibited by natural fracture arrays; functional relationships between reservoir pressure reduction and fracture network porosity, compressibility and directional permeabilities as generated by such discrete fracture network modeling are then exported to the reservoir simulator for improved naturally fractured reservoir performance prediction.

  2. Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.

    Science.gov (United States)

    Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris

    2017-01-01

    This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables "metabolic rate," and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data ( n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated.

  3. Improved prediction for the mass of the W boson in the NMSSM

    International Nuclear Information System (INIS)

    Staal, O.; Zeune, L.

    2015-10-01

    Electroweak precision observables, being highly sensitive to loop contributions of new physics, provide a powerful tool to test the theory and to discriminate between different models of the underlying physics. In that context, the W boson mass, M W , plays a crucial role. The accuracy of the M W measurement has been significantly improved over the last years, and further improvement of the experimental accuracy is expected from future LHC measurements. In order to fully exploit the precise experimental determination, an accurate theoretical prediction for M W in the Standard Model (SM) and extensions of it is of central importance. We present the currently most accurate prediction for the W boson mass in the Next-to-Minimal Supersymmetric extension of the Standard Model (NMSSM), including the full one-loop result and all available higher-order corrections of SM and SUSY type. The evaluation of M W is performed in a flexible framework, which facilitates the extension to other models beyond the SM. We show numerical results for the W boson mass in the NMSSM, focussing on phenomenologically interesting scenarios, in which the Higgs signal can be interpreted as the lightest or second lightest CP-even Higgs boson of the NMSSM. We find that, for both Higgs signal interpretations, the NMSSM M W prediction is well compatible with the measurement. We study the SUSY contributions to M W in detail and investigate in particular the genuine NMSSM effects from the Higgs and neutralino sectors.

  4. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

    Science.gov (United States)

    Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian; Deane, Charlotte M

    2017-05-01

    Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

  5. Improved Performance and Safety for High Energy Batteries Through Use of Hazard Anticipation and Capacity Prediction

    Science.gov (United States)

    Atwater, Terrill

    1993-01-01

    Prediction of the capacity remaining in used high rate, high energy batteries is important information to the user. Knowledge of the capacity remaining in used batteries results in better utilization. This translates into improved readiness and cost savings due to complete, efficient use. High rate batteries, due to their chemical nature, are highly sensitive to misuse (i.e., over discharge or very high rate discharge). Battery failure due to misuse or manufacturing defects could be disastrous. Since high rate, high energy batteries are expensive and energetic, a reliable method of predicting both failures and remaining energy has been actively sought. Due to concerns over safety, the behavior of lithium/sulphur dioxide cells at different temperatures and current drains was examined. The main thrust of this effort was to determine failure conditions for incorporation in hazard anticipation circuitry. In addition, capacity prediction formulas have been developed from test data. A process that performs continuous, real-time hazard anticipation and capacity prediction was developed. The introduction of this process into microchip technology will enable the production of reliable, safe, and efficient high energy batteries.

  6. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.