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Sample records for accurately predict metabolic

  1. NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

    PubMed Central

    Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R.; Samatova, Nagiza F.

    2012-01-01

    Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to

  2. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    PubMed

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  3. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    PubMed

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  4. Hounsfield unit density accurately predicts ESWL success.

    PubMed

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p < 0.0001). We determined by receiver operator curve (ROC) that HDV of 93 or less carries a 90% or better chance of stone freedom following ESWL for upper tract calculi between 5-15mm.

  5. FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes.

    PubMed

    Kirchmair, Johannes; Williamson, Mark J; Afzal, Avid M; Tyzack, Jonathan D; Choy, Alison P K; Howlett, Andrew; Rydberg, Patrik; Glen, Robert C

    2013-11-25

    FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted. PMID:24219364

  6. On the Accurate Prediction of CME Arrival At the Earth

    NASA Astrophysics Data System (ADS)

    Zhang, Jie; Hess, Phillip

    2016-07-01

    We will discuss relevant issues regarding the accurate prediction of CME arrival at the Earth, from both observational and theoretical points of view. In particular, we clarify the importance of separating the study of CME ejecta from the ejecta-driven shock in interplanetary CMEs (ICMEs). For a number of CME-ICME events well observed by SOHO/LASCO, STEREO-A and STEREO-B, we carry out the 3-D measurements by superimposing geometries onto both the ejecta and sheath separately. These measurements are then used to constrain a Drag-Based Model, which is improved through a modification of including height dependence of the drag coefficient into the model. Combining all these factors allows us to create predictions for both fronts at 1 AU and compare with actual in-situ observations. We show an ability to predict the sheath arrival with an average error of under 4 hours, with an RMS error of about 1.5 hours. For the CME ejecta, the error is less than two hours with an RMS error within an hour. Through using the best observations of CMEs, we show the power of our method in accurately predicting CME arrival times. The limitation and implications of our accurate prediction method will be discussed.

  7. Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach.

    PubMed

    Saa, Pedro A; Nielsen, Lars K

    2016-01-01

    Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values, and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions. PMID:27417285

  8. Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach

    PubMed Central

    Saa, Pedro A.; Nielsen, Lars K.

    2016-01-01

    Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values, and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions. PMID:27417285

  9. Passive samplers accurately predict PAH levels in resident crayfish.

    PubMed

    Paulik, L Blair; Smith, Brian W; Bergmann, Alan J; Sower, Greg J; Forsberg, Norman D; Teeguarden, Justin G; Anderson, Kim A

    2016-02-15

    Contamination of resident aquatic organisms is a major concern for environmental risk assessors. However, collecting organisms to estimate risk is often prohibitively time and resource-intensive. Passive sampling accurately estimates resident organism contamination, and it saves time and resources. This study used low density polyethylene (LDPE) passive water samplers to predict polycyclic aromatic hydrocarbon (PAH) levels in signal crayfish, Pacifastacus leniusculus. Resident crayfish were collected at 5 sites within and outside of the Portland Harbor Superfund Megasite (PHSM) in the Willamette River in Portland, Oregon. LDPE deployment was spatially and temporally paired with crayfish collection. Crayfish visceral and tail tissue, as well as water-deployed LDPE, were extracted and analyzed for 62 PAHs using GC-MS/MS. Freely-dissolved concentrations (Cfree) of PAHs in water were calculated from concentrations in LDPE. Carcinogenic risks were estimated for all crayfish tissues, using benzo[a]pyrene equivalent concentrations (BaPeq). ∑PAH were 5-20 times higher in viscera than in tails, and ∑BaPeq were 6-70 times higher in viscera than in tails. Eating only tail tissue of crayfish would therefore significantly reduce carcinogenic risk compared to also eating viscera. Additionally, PAH levels in crayfish were compared to levels in crayfish collected 10 years earlier. PAH levels in crayfish were higher upriver of the PHSM and unchanged within the PHSM after the 10-year period. Finally, a linear regression model predicted levels of 34 PAHs in crayfish viscera with an associated R-squared value of 0.52 (and a correlation coefficient of 0.72), using only the Cfree PAHs in water. On average, the model predicted PAH concentrations in crayfish tissue within a factor of 2.4 ± 1.8 of measured concentrations. This affirms that passive water sampling accurately estimates PAH contamination in crayfish. Furthermore, the strong predictive ability of this simple model suggests

  10. Plant diversity accurately predicts insect diversity in two tropical landscapes.

    PubMed

    Zhang, Kai; Lin, Siliang; Ji, Yinqiu; Yang, Chenxue; Wang, Xiaoyang; Yang, Chunyan; Wang, Hesheng; Jiang, Haisheng; Harrison, Rhett D; Yu, Douglas W

    2016-09-01

    Plant diversity surely determines arthropod diversity, but only moderate correlations between arthropod and plant species richness had been observed until Basset et al. (Science, 338, 2012 and 1481) finally undertook an unprecedentedly comprehensive sampling of a tropical forest and demonstrated that plant species richness could indeed accurately predict arthropod species richness. We now require a high-throughput pipeline to operationalize this result so that we can (i) test competing explanations for tropical arthropod megadiversity, (ii) improve estimates of global eukaryotic species diversity, and (iii) use plant and arthropod communities as efficient proxies for each other, thus improving the efficiency of conservation planning and of detecting forest degradation and recovery. We therefore applied metabarcoding to Malaise-trap samples across two tropical landscapes in China. We demonstrate that plant species richness can accurately predict arthropod (mostly insect) species richness and that plant and insect community compositions are highly correlated, even in landscapes that are large, heterogeneous and anthropogenically modified. Finally, we review how metabarcoding makes feasible highly replicated tests of the major competing explanations for tropical megadiversity. PMID:27474399

  11. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; et al

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  12. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

  13. Passive samplers accurately predict PAH levels in resident crayfish.

    PubMed

    Paulik, L Blair; Smith, Brian W; Bergmann, Alan J; Sower, Greg J; Forsberg, Norman D; Teeguarden, Justin G; Anderson, Kim A

    2016-02-15

    Contamination of resident aquatic organisms is a major concern for environmental risk assessors. However, collecting organisms to estimate risk is often prohibitively time and resource-intensive. Passive sampling accurately estimates resident organism contamination, and it saves time and resources. This study used low density polyethylene (LDPE) passive water samplers to predict polycyclic aromatic hydrocarbon (PAH) levels in signal crayfish, Pacifastacus leniusculus. Resident crayfish were collected at 5 sites within and outside of the Portland Harbor Superfund Megasite (PHSM) in the Willamette River in Portland, Oregon. LDPE deployment was spatially and temporally paired with crayfish collection. Crayfish visceral and tail tissue, as well as water-deployed LDPE, were extracted and analyzed for 62 PAHs using GC-MS/MS. Freely-dissolved concentrations (Cfree) of PAHs in water were calculated from concentrations in LDPE. Carcinogenic risks were estimated for all crayfish tissues, using benzo[a]pyrene equivalent concentrations (BaPeq). ∑PAH were 5-20 times higher in viscera than in tails, and ∑BaPeq were 6-70 times higher in viscera than in tails. Eating only tail tissue of crayfish would therefore significantly reduce carcinogenic risk compared to also eating viscera. Additionally, PAH levels in crayfish were compared to levels in crayfish collected 10 years earlier. PAH levels in crayfish were higher upriver of the PHSM and unchanged within the PHSM after the 10-year period. Finally, a linear regression model predicted levels of 34 PAHs in crayfish viscera with an associated R-squared value of 0.52 (and a correlation coefficient of 0.72), using only the Cfree PAHs in water. On average, the model predicted PAH concentrations in crayfish tissue within a factor of 2.4 ± 1.8 of measured concentrations. This affirms that passive water sampling accurately estimates PAH contamination in crayfish. Furthermore, the strong predictive ability of this simple model suggests

  14. Mouse models of human AML accurately predict chemotherapy response

    PubMed Central

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S.; Zhao, Zhen; Rappaport, Amy R.; Luo, Weijun; McCurrach, Mila E.; Yang, Miao-Miao; Dolan, M. Eileen; Kogan, Scott C.; Downing, James R.; Lowe, Scott W.

    2009-01-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  15. Lipoprotein Metabolism Indicators Improve Cardiovascular Risk Prediction

    PubMed Central

    van Schalkwijk, Daniël B.; de Graaf, Albert A.; Tsivtsivadze, Evgeni; Parnell, Laurence D.; van der Werff-van der Vat, Bianca J. C.; van Ommen, Ben; van der Greef, Jan; Ordovás, José M.

    2014-01-01

    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. PMID:24667559

  16. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  17. Fast and accurate predictions of covalent bonds in chemical space

    NASA Astrophysics Data System (ADS)

    Chang, K. Y. Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (˜1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H 2+ . Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  18. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  19. Efficient Reconstruction of Predictive Consensus Metabolic Network Models.

    PubMed

    van Heck, Ruben G A; Ganter, Mathias; Martins Dos Santos, Vitor A P; Stelling, Joerg

    2016-08-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

  20. Efficient Reconstruction of Predictive Consensus Metabolic Network Models

    PubMed Central

    Martins dos Santos, Vitor A. P.; Stelling, Joerg

    2016-01-01

    Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. PMID:27563720

  1. Metabolic Network Prediction of Drug Side Effects.

    PubMed

    Shaked, Itay; Oberhardt, Matthew A; Atias, Nir; Sharan, Roded; Ruppin, Eytan

    2016-03-23

    Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.7) for >70 drug side effects, including very serious ones such as interstitial nephritis and extrapyramidal disorders. We evaluate AMPP's predictive signal through cross-validation, comparison across multiple versions of a side effects database, and co-occurrence analysis of drug side effect associations in scientific abstracts (hypergeometric p value = 2.2e-40). AMPP outperforms a previous biochemical structure-based method in predicting metabolically based side effects (aggregate AUC = 0.65 versus 0.59). Importantly, AMPP enables the identification of key metabolic reactions and biomarkers that are predictive of specific side effects. Taken together, this work lays a foundation for future detection of metabolically grounded side effects during early stages of drug development. PMID:27135366

  2. Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints.

    PubMed

    Zaretzki, Jed; Boehm, Kevin M; Swamidass, S Joshua

    2015-05-26

    Molecule and atom fingerprints, similar to path-based Daylight fingerprints, can substantially improve the accuracy of P450 site-of-metabolism prediction models. Only two chemical fingerprints have been used in metabolism prediction, so little is known about the importance of fingerprint parameters on site of metabolism predictions. It is possible that different fingerprints might yield more accurate models. Here, we study if tuning fingerprints to specific site of metabolism data sets can lead to improved models. We measure the impact of 484 specific chemical fingerprints on the accuracy of P450 site-of-metabolism prediction models on nine P450 isoform site of metabolism data sets. Using a range of search depths, we study path, circular, and subgraph fingerprints. Two different labelings, also, are considered, both standard SMILES labels and also a labeling that marks ring bonds differently than nonring bonds, enabling ortho, para, and meta positioning of substituents to be more clearly encoded. Optimal fingerprint models chosen by cross-validation performance on the full training data are, on average, 3.8% (Top-2; percent of molecules with a site of metabolism in the top two predictions) and 1.4% (AUC; area under the ROC curve) more accurate than base fingerprint models. These gains represent, respectively, a 25.6% and 16.7% reduction in error. A more rigorous assessment selects fingerprints within each cross-validation fold, sometimes selecting different fingerprints for different folds, but yielding a more reliable estimate of generalization error. In this assessment, averaging the scores from the top few fingerprints yields performances improvements of, on average, 3.0% (Top-2) and 0.7% (AUC). These gains are statistically significant and represent, respectively, a 20.1% and 8.8% reduction in error. Between different isoforms, not many consistencies were observed among the top performing fingerprints, with different fingerprints working best for different

  3. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    PubMed Central

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

  4. Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

    PubMed Central

    Tsianos, George A.; MacFadden, Lisa N.

    2016-01-01

    Physical performance emerges from complex interactions among many physiological systems that are largely driven by the metabolic energy demanded. Quantifying metabolic demand is an essential step for revealing the many mechanisms of physical performance decrement, but accurate predictive models do not exist. The goal of this study was to investigate if a recently developed model of muscle energetics and force could be extended to reproduce the kinematics, kinetics, and metabolic demand of submaximal effort movement. Upright dynamic knee extension against various levels of ergometer load was simulated. Task energetics were estimated by combining the model of muscle contraction with validated models of lower limb musculotendon paths and segment dynamics. A genetic algorithm was used to compute the muscle excitations that reproduced the movement with the lowest energetic cost, which was determined to be an appropriate criterion for this task. Model predictions of oxygen uptake rate (VO2) were well within experimental variability for the range over which the model parameters were confidently known. The model's accurate estimates of metabolic demand make it useful for assessing the likelihood and severity of physical performance decrement for a given task as well as investigating underlying physiologic mechanisms. PMID:27248429

  5. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

    PubMed

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

  6. Accurately Predicting Complex Reaction Kinetics from First Principles

    NASA Astrophysics Data System (ADS)

    Green, William

    Many important systems contain a multitude of reactive chemical species, some of which react on a timescale faster than collisional thermalization, i.e. they never achieve a Boltzmann energy distribution. Usually it is impossible to fully elucidate the processes by experiments alone. Here we report recent progress toward predicting the time-evolving composition of these systems a priori: how unexpected reactions can be discovered on the computer, how reaction rates are computed from first principles, and how the many individual reactions are efficiently combined into a predictive simulation for the whole system. Some experimental tests of the a priori predictions are also presented.

  7. Does more accurate exposure prediction necessarily improve health effect estimates?

    PubMed

    Szpiro, Adam A; Paciorek, Christopher J; Sheppard, Lianne

    2011-09-01

    A unique challenge in air pollution cohort studies and similar applications in environmental epidemiology is that exposure is not measured directly at subjects' locations. Instead, pollution data from monitoring stations at some distance from the study subjects are used to predict exposures, and these predicted exposures are used to estimate the health effect parameter of interest. It is usually assumed that minimizing the error in predicting the true exposure will improve health effect estimation. We show in a simulation study that this is not always the case. We interpret our results in light of recently developed statistical theory for measurement error, and we discuss implications for the design and analysis of epidemiologic research.

  8. Is Three-Dimensional Soft Tissue Prediction by Software Accurate?

    PubMed

    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions.

  9. Towards Accurate Ab Initio Predictions of the Spectrum of Methane

    NASA Technical Reports Server (NTRS)

    Schwenke, David W.; Kwak, Dochan (Technical Monitor)

    2001-01-01

    We have carried out extensive ab initio calculations of the electronic structure of methane, and these results are used to compute vibrational energy levels. We include basis set extrapolations, core-valence correlation, relativistic effects, and Born- Oppenheimer breakdown terms in our calculations. Our ab initio predictions of the lowest lying levels are superb.

  10. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    PubMed Central

    Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Rosén, Ingmar; Åneman, Anders; Erlinge, David; Gasche, Yvan; Hassager, Christian; Hovdenes, Jan; Kjaergaard, Jesper; Kuiper, Michael; Pellis, Tommaso; Stammet, Pascal; Wanscher, Michael; Wetterslev, Jørn; Wise, Matt P.; Cronberg, Tobias

    2016-01-01

    Objective: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. Methods: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3–5 until 180 days. Results: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome. Conclusions: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome. PMID:26865516

  11. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  12. Accurate contact predictions using covariation techniques and machine learning

    PubMed Central

    Kosciolek, Tomasz

    2015-01-01

    ABSTRACT Here we present the results of residue–residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top‐L/5 long‐range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two‐stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple‐sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2016; 84(Suppl 1):145–151. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:26205532

  13. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  14. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

    PubMed Central

    Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

    2016-01-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

  15. A metabolic signature predicts biological age in mice

    PubMed Central

    Tomás-Loba, Antonia; de Jesus, Bruno Bernardes; Mato, Jose M.; Blasco, Maria A.

    2012-01-01

    Our understanding of the mechanisms by which aging is produced is still very limited. Here, we have determined the sera metabolite profile of 117 wild-type mice of different genetic backgrounds ranging from 8-129 weeks of age. This has allowed us to define a robust metabolomic signature and a derived metabolomic score that reliably/accurately predicts the age of wild-type mice. In the case of telomerase-deficient mice, which have a shortened lifespan, their metabolomic score predicts older ages than expected. Conversely, in the case of mice that over-express telomerase, their metabolic score corresponded to younger ages than expected. Importantly, telomerase reactivation late in life by using a TERT based gene therapy recently described by us, significantly reverted the metabolic profile of old mice to that of younger mice, further confirming an anti-aging role for telomerase. Thus, the metabolomic signature associated to natural mouse aging accurately predicts aging produced by telomere shortening, suggesting that natural mouse aging is in part produced by presence of short telomeres. These results indicate that the metabolomic signature is associated to the biological age rather than to the chronological age. This constitutes one of the first aging-associated metabolomic signatures in a mammalian organism. PMID:23107558

  16. Accurate predictions for the production of vaporized water

    SciTech Connect

    Morin, E.; Montel, F.

    1995-12-31

    The production of water vaporized in the gas phase is controlled by the local conditions around the wellbore. The pressure gradient applied to the formation creates a sharp increase of the molar water content in the hydrocarbon phase approaching the well; this leads to a drop in the pore water saturation around the wellbore. The extent of the dehydrated zone which is formed is the key controlling the bottom-hole content of vaporized water. The maximum water content in the hydrocarbon phase at a given pressure, temperature and salinity is corrected by capillarity or adsorption phenomena depending on the actual water saturation. Describing the mass transfer of the water between the hydrocarbon phases and the aqueous phase into the tubing gives a clear idea of vaporization effects on the formation of scales. Field example are presented for gas fields with temperatures ranging between 140{degrees}C and 180{degrees}C, where water vaporization effects are significant. Conditions for salt plugging in the tubing are predicted.

  17. Change in BMI accurately predicted by social exposure to acquaintances.

    PubMed

    Oloritun, Rahman O; Ouarda, Taha B M J; Moturu, Sai; Madan, Anmol; Pentland, Alex Sandy; Khayal, Inas

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

  18. Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering*

    PubMed Central

    Trauchessec, Mathieu; Jaquinod, Michel; Bonvalot, Aline; Brun, Virginie; Bruley, Christophe; Ropers, Delphine; de Jong, Hidde; Garin, Jérôme; Bestel-Corre, Gwenaëlle; Ferro, Myriam

    2014-01-01

    Metabolic engineering aims to design high performance microbial strains producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. However, multi-omics data including genomics, transcriptomics, fluxomics, and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances to quantitative proteomics have made mass spectrometry-based quantitative assays an interesting alternative to more traditional immuno-affinity based approaches. This has improved specificity and multiplexing capabilities. In this study, we developed a quantification workflow to analyze enzymes involved in central metabolism in Escherichia coli (E. coli). This workflow combined full-length isotopically labeled standards with selected reaction monitoring analysis. First, full-length 15N labeled standards were produced and calibrated to ensure accurate measurements. Liquid chromatography conditions were then optimized for reproducibility and multiplexing capabilities over a single 30-min liquid chromatography-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering. PMID:24482123

  19. Metabolic theory predicts whole-ecosystem properties

    PubMed Central

    Schramski, John R.; Dell, Anthony I.; Grady, John M.; Sibly, Richard M.; Brown, James H.

    2015-01-01

    Understanding the effects of individual organisms on material cycles and energy fluxes within ecosystems is central to predicting the impacts of human-caused changes on climate, land use, and biodiversity. Here we present a theory that integrates metabolic (organism-based bottom-up) and systems (ecosystem-based top-down) approaches to characterize how the metabolism of individuals affects the flows and stores of materials and energy in ecosystems. The theory predicts how the average residence time of carbon molecules, total system throughflow (TST), and amount of recycling vary with the body size and temperature of the organisms and with trophic organization. We evaluate the theory by comparing theoretical predictions with outputs of numerical models designed to simulate diverse ecosystem types and with empirical data for real ecosystems. Although residence times within different ecosystems vary by orders of magnitude—from weeks in warm pelagic oceans with minute phytoplankton producers to centuries in cold forests with large tree producers—as predicted, all ecosystems fall along a single line: residence time increases linearly with slope = 1.0 with the ratio of whole-ecosystem biomass to primary productivity (B/P). TST was affected predominantly by primary productivity and recycling by the transfer of energy from microbial decomposers to animal consumers. The theory provides a robust basis for estimating the flux and storage of energy, carbon, and other materials in terrestrial, marine, and freshwater ecosystems and for quantifying the roles of different kinds of organisms and environments at scales from local ecosystems to the biosphere. PMID:25624499

  20. A modified SCBA facepiece for accurate metabolic data collection from firefighters.

    PubMed

    Kesler, Richard M; Hsiao-Wecksler, Elizabeth T; Motl, Robert W; Klaren, Rachel E; Ensari, Ipek; Horn, Gavin P

    2015-01-01

    To better assess the energy expenditure and exertion of firefighters during simulated firefighting activities, a commercial firefighter self-contained breathing apparatus (SCBA) facepiece was modified to interface with a portable metabolic monitoring device (Cosmed K4b(2)) while still functioning as a positive pressure SCBA air supply. To validate the device, standard National Fire Protection Association 1981 SCBA function tests were conducted and 14 subjects performed variable-workload assessments using all combinations of two test devices (Cosmed K4b(2) and metabolic cart) and two masks (modified SCBA facepiece and stock manufacturer-supplied breath collection). Metabolic data collected with the Cosmed K4b(2) via the modified facepiece were found to be accurate when compared to a ParvoMedics Truemax 2400 metabolic cart (average per cent difference: 4.6%). This modified facepiece design is suitable for use in metabolic studies requiring the utilisation of an SCBA system. Furthermore, the well-established overestimation of oxygen consumption from the Cosmed K4b(2) system was replicated. PMID:25323675

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

    PubMed Central

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

    2016-01-01

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

  2. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 inves...

  3. Mind-set and close relationships: when bias leads to (In)accurate predictions.

    PubMed

    Gagné, F M; Lydon, J E

    2001-07-01

    The authors investigated whether mind-set influences the accuracy of relationship predictions. Because people are more biased in their information processing when thinking about implementing an important goal, relationship predictions made in an implemental mind-set were expected to be less accurate than those made in a more impartial deliberative mind-set. In Study 1, open-ended thoughts of students about to leave for university were coded for mind-set. In Study 2, mind-set about a major life goal was assessed using a self-report measure. In Study 3, mind-set was experimentally manipulated. Overall, mind-set interacted with forecasts to predict relationship survival. Forecasts were more accurate in a deliberative mind-set than in an implemental mind-set. This effect was more pronounced for long-term than for short-term relationship survival. Finally, deliberatives were not pessimistic; implementals were unduly optimistic.

  4. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. PMID:27260782

  5. SIFTER search: a web server for accurate phylogeny-based protein function prediction.

    PubMed

    Sahraeian, Sayed M; Luo, Kevin R; Brenner, Steven E

    2015-07-01

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.

  6. Dive behaviour can predict metabolic expenditure in Steller sea lions.

    PubMed

    Goundie, Elizabeth T; Rosen, David A S; Trites, Andrew W

    2015-01-01

    Quantification of costs associated with foraging contributes to understanding the energetic impact that changes in prey availability have on the energy balance of an animal and the fitness of populations. However, estimating the costs of foraging is difficult for breath-hold divers, such as Steller sea lions, that feed underwater. We developed models parameterized with data from free-diving captive Steller sea lions to estimate the costs incurred by wild animals while foraging. We measured diving metabolic rate of trained sea lions performing four types of dives to 10 and 40 m in the open ocean and estimated the separate costs of different dive components: surface time; bottom time; and transiting to and from depth. We found that the sea lions' diving metabolic rates were higher while transiting (20.5 ± 13.0 ml O2 min(-1) kg(-1)) than while swimming at depth (13.5 ± 4.1 ml O2 min(-1) kg(-1)), and both were higher than metabolism at the surface (9.2 ± 1.6 ml O2 min(-1) kg(-1)). These values were incorporated into an energetic model that accurately predicted oxygen consumption for dives only (within 9.5%) and dive cycles (within 7.7%), although it consistently overestimated costs by 5.9% for dives and 21.8% for dive cycles. Differences in the costs of individual components of dives also explained differences in the efficiency of different dive strategies. Single dives were energetically less costly than bout dives; however, sea lions were more efficient at replenishing oxygen stores after bout dives and could therefore spend a greater portion of their time foraging than when undertaking single dives. The metabolic rates we measured for the different behavioural components of diving can be applied to time-depth recordings from wild Steller sea lions to estimate the energy expended while foraging. In turn, this can be used to understand how changes in prey availability affect energy balance and the health of individuals in

  7. Dive behaviour can predict metabolic expenditure in Steller sea lions

    PubMed Central

    Goundie, Elizabeth T.; Rosen, David A. S.; Trites, Andrew W.

    2015-01-01

    Quantification of costs associated with foraging contributes to understanding the energetic impact that changes in prey availability have on the energy balance of an animal and the fitness of populations. However, estimating the costs of foraging is difficult for breath-hold divers, such as Steller sea lions, that feed underwater. We developed models parameterized with data from free-diving captive Steller sea lions to estimate the costs incurred by wild animals while foraging. We measured diving metabolic rate of trained sea lions performing four types of dives to 10 and 40 m in the open ocean and estimated the separate costs of different dive components: surface time; bottom time; and transiting to and from depth. We found that the sea lions' diving metabolic rates were higher while transiting (20.5 ± 13.0 ml O2 min−1 kg−1) than while swimming at depth (13.5 ± 4.1 ml O2 min−1 kg−1), and both were higher than metabolism at the surface (9.2 ± 1.6 ml O2 min−1 kg−1). These values were incorporated into an energetic model that accurately predicted oxygen consumption for dives only (within 9.5%) and dive cycles (within 7.7%), although it consistently overestimated costs by 5.9% for dives and 21.8% for dive cycles. Differences in the costs of individual components of dives also explained differences in the efficiency of different dive strategies. Single dives were energetically less costly than bout dives; however, sea lions were more efficient at replenishing oxygen stores after bout dives and could therefore spend a greater portion of their time foraging than when undertaking single dives. The metabolic rates we measured for the different behavioural components of diving can be applied to time–depth recordings from wild Steller sea lions to estimate the energy expended while foraging. In turn, this can be used to understand how changes in prey availability affect energy balance and the health of individuals in

  8. A Single Linear Prediction Filter that Accurately Predicts the AL Index

    NASA Astrophysics Data System (ADS)

    McPherron, R. L.; Chu, X.

    2015-12-01

    The AL index is a measure of the strength of the westward electrojet flowing along the auroral oval. It has two components: one from the global DP-2 current system and a second from the DP-1 current that is more localized near midnight. It is generally believed that the index a very poor measure of these currents because of its dependence on the distance of stations from the source of the two currents. In fact over season and solar cycle the coupling strength defined as the steady state ratio of the output AL to the input coupling function varies by a factor of four. There are four factors that lead to this variation. First is the equinoctial effect that modulates coupling strength with peaks (strongest coupling) at the equinoxes. Second is the saturation of the polar cap potential which decreases coupling strength as the strength of the driver increases. Since saturation occurs more frequently at solar maximum we obtain the result that maximum coupling strength occurs at equinox at solar minimum. A third factor is ionospheric conductivity with stronger coupling at summer solstice as compared to winter. The fourth factor is the definition of a solar wind coupling function appropriate to a given index. We have developed an optimum coupling function depending on solar wind speed, density, transverse magnetic field, and IMF clock angle which is better than previous functions. Using this we have determined the seasonal variation of coupling strength and developed an inverse function that modulates the optimum coupling function so that all seasonal variation is removed. In a similar manner we have determined the dependence of coupling strength on solar wind driver strength. The inverse of this function is used to scale a linear prediction filter thus eliminating the dependence on driver strength. Our result is a single linear filter that is adjusted in a nonlinear manner by driver strength and an optimum coupling function that is seasonal modulated. Together this

  9. A review of the kinetic detail required for accurate predictions of normal shock waves

    NASA Technical Reports Server (NTRS)

    Muntz, E. P.; Erwin, Daniel A.; Pham-Van-diep, Gerald C.

    1991-01-01

    Several aspects of the kinetic models used in the collision phase of Monte Carlo direct simulations have been studied. Accurate molecular velocity distribution function predictions require a significantly increased number of computational cells in one maximum slope shock thickness, compared to predictions of macroscopic properties. The shape of the highly repulsive portion of the interatomic potential for argon is not well modeled by conventional interatomic potentials; this portion of the potential controls high Mach number shock thickness predictions, indicating that the specification of the energetic repulsive portion of interatomic or intermolecular potentials must be chosen with care for correct modeling of nonequilibrium flows at high temperatures. It has been shown for inverse power potentials that the assumption of variable hard sphere scattering provides accurate predictions of the macroscopic properties in shock waves, by comparison with simulations in which differential scattering is employed in the collision phase. On the other hand, velocity distribution functions are not well predicted by the variable hard sphere scattering model for softer potentials at higher Mach numbers.

  10. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  11. Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma

    PubMed Central

    Özcan, Emrah; Çakır, Tunahan

    2016-01-01

    Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM. PMID:27147948

  12. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. PMID:27272707

  13. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future.

  14. Accurate similarity index based on activity and connectivity of node for link prediction

    NASA Astrophysics Data System (ADS)

    Li, Longjie; Qian, Lvjian; Wang, Xiaoping; Luo, Shishun; Chen, Xiaoyun

    2015-05-01

    Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines.

  15. Accurate prediction of the linear viscoelastic properties of highly entangled mono and bidisperse polymer melts.

    PubMed

    Stephanou, Pavlos S; Mavrantzas, Vlasis G

    2014-06-01

    We present a hierarchical computational methodology which permits the accurate prediction of the linear viscoelastic properties of entangled polymer melts directly from the chemical structure, chemical composition, and molecular architecture of the constituent chains. The method entails three steps: execution of long molecular dynamics simulations with moderately entangled polymer melts, self-consistent mapping of the accumulated trajectories onto a tube model and parameterization or fine-tuning of the model on the basis of detailed simulation data, and use of the modified tube model to predict the linear viscoelastic properties of significantly higher molecular weight (MW) melts of the same polymer. Predictions are reported for the zero-shear-rate viscosity η0 and the spectra of storage G'(ω) and loss G″(ω) moduli for several mono and bidisperse cis- and trans-1,4 polybutadiene melts as well as for their MW dependence, and are found to be in remarkable agreement with experimentally measured rheological data. PMID:24908037

  16. Accurate prediction of the linear viscoelastic properties of highly entangled mono and bidisperse polymer melts

    NASA Astrophysics Data System (ADS)

    Stephanou, Pavlos S.; Mavrantzas, Vlasis G.

    2014-06-01

    We present a hierarchical computational methodology which permits the accurate prediction of the linear viscoelastic properties of entangled polymer melts directly from the chemical structure, chemical composition, and molecular architecture of the constituent chains. The method entails three steps: execution of long molecular dynamics simulations with moderately entangled polymer melts, self-consistent mapping of the accumulated trajectories onto a tube model and parameterization or fine-tuning of the model on the basis of detailed simulation data, and use of the modified tube model to predict the linear viscoelastic properties of significantly higher molecular weight (MW) melts of the same polymer. Predictions are reported for the zero-shear-rate viscosity η0 and the spectra of storage G'(ω) and loss G″(ω) moduli for several mono and bidisperse cis- and trans-1,4 polybutadiene melts as well as for their MW dependence, and are found to be in remarkable agreement with experimentally measured rheological data.

  17. Prediction of Accurate Thermochemistry of Medium and Large Sized Radicals Using Connectivity-Based Hierarchy (CBH).

    PubMed

    Sengupta, Arkajyoti; Raghavachari, Krishnan

    2014-10-14

    Accurate modeling of the chemical reactions in many diverse areas such as combustion, photochemistry, or atmospheric chemistry strongly depends on the availability of thermochemical information of the radicals involved. However, accurate thermochemical investigations of radical systems using state of the art composite methods have mostly been restricted to the study of hydrocarbon radicals of modest size. In an alternative approach, systematic error-canceling thermochemical hierarchy of reaction schemes can be applied to yield accurate results for such systems. In this work, we have extended our connectivity-based hierarchy (CBH) method to the investigation of radical systems. We have calibrated our method using a test set of 30 medium sized radicals to evaluate their heats of formation. The CBH-rad30 test set contains radicals containing diverse functional groups as well as cyclic systems. We demonstrate that the sophisticated error-canceling isoatomic scheme (CBH-2) with modest levels of theory is adequate to provide heats of formation accurate to ∼1.5 kcal/mol. Finally, we predict heats of formation of 19 other large and medium sized radicals for which the accuracy of available heats of formation are less well-known. PMID:26588131

  18. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

    SciTech Connect

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.; Arkin, Adam P.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, and its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.

  19. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.

    PubMed

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O Anatole; Müller, Klaus-Robert; Tkatchenko, Alexandre

    2015-06-18

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

  20. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    SciTech Connect

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

  1. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  2. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  3. Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.).

    PubMed

    Dan, Zhiwu; Hu, Jun; Zhou, Wei; Yao, Guoxin; Zhu, Renshan; Zhu, Yingguo; Huang, Wenchao

    2016-01-01

    Hybrid crops have contributed greatly to improvements in global food and fodder production over the past several decades. Nevertheless, the growing population and changing climate have produced food crises and energy shortages. Breeding new elite hybrid varieties is currently an urgent task, but present breeding procedures are time-consuming and labour-intensive. In this study, parental metabolic information was utilized to predict three polygenic traits in hybrid rice. A complete diallel cross population consisting of eighteen rice inbred lines was constructed, and the hybrids' plant height, heading date and grain yield per plant were predicted using 525 metabolites. Metabolic prediction models were built using the partial least square regression method, with predictive abilities ranging from 0.858 to 0.977 for the hybrid phenotypes, relative heterosis, and specific combining ability. Only slight changes in predictive ability were observed between hybrid populations, and nearly no changes were detected between reciprocal hybrids. The outcomes of prediction of the three highly polygenic traits demonstrated that metabolic prediction was an accurate (high predictive abilities) and efficient (unaffected by population genetic structures) strategy for screening promising superior hybrid rice. Exploitation of this pre-hybridization strategy may contribute to rice production improvement and accelerate breeding programs. PMID:26907211

  4. Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.)

    PubMed Central

    Dan, Zhiwu; Hu, Jun; Zhou, Wei; Yao, Guoxin; Zhu, Renshan; Zhu, Yingguo; Huang, Wenchao

    2016-01-01

    Hybrid crops have contributed greatly to improvements in global food and fodder production over the past several decades. Nevertheless, the growing population and changing climate have produced food crises and energy shortages. Breeding new elite hybrid varieties is currently an urgent task, but present breeding procedures are time-consuming and labour-intensive. In this study, parental metabolic information was utilized to predict three polygenic traits in hybrid rice. A complete diallel cross population consisting of eighteen rice inbred lines was constructed, and the hybrids’ plant height, heading date and grain yield per plant were predicted using 525 metabolites. Metabolic prediction models were built using the partial least square regression method, with predictive abilities ranging from 0.858 to 0.977 for the hybrid phenotypes, relative heterosis, and specific combining ability. Only slight changes in predictive ability were observed between hybrid populations, and nearly no changes were detected between reciprocal hybrids. The outcomes of prediction of the three highly polygenic traits demonstrated that metabolic prediction was an accurate (high predictive abilities) and efficient (unaffected by population genetic structures) strategy for screening promising superior hybrid rice. Exploitation of this pre-hybridization strategy may contribute to rice production improvement and accelerate breeding programs. PMID:26907211

  5. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    NASA Technical Reports Server (NTRS)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  6. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  7. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

  8. SIFTER search: a web server for accurate phylogeny-based protein function prediction.

    PubMed

    Sahraeian, Sayed M; Luo, Kevin R; Brenner, Steven E

    2015-07-01

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded. PMID:25979264

  9. Microstructure-Dependent Gas Adsorption: Accurate Predictions of Methane Uptake in Nanoporous Carbons

    SciTech Connect

    Ihm, Yungok; Cooper, Valentino R; Gallego, Nidia C; Contescu, Cristian I; Morris, James R

    2014-01-01

    We demonstrate a successful, efficient framework for predicting gas adsorption properties in real materials based on first-principles calculations, with a specific comparison of experiment and theory for methane adsorption in activated carbons. These carbon materials have different pore size distributions, leading to a variety of uptake characteristics. Utilizing these distributions, we accurately predict experimental uptakes and heats of adsorption without empirical potentials or lengthy simulations. We demonstrate that materials with smaller pores have higher heats of adsorption, leading to a higher gas density in these pores. This pore-size dependence must be accounted for, in order to predict and understand the adsorption behavior. The theoretical approach combines: (1) ab initio calculations with a van der Waals density functional to determine adsorbent-adsorbate interactions, and (2) a thermodynamic method that predicts equilibrium adsorption densities by directly incorporating the calculated potential energy surface in a slit pore model. The predicted uptake at P=20 bar and T=298 K is in excellent agreement for all five activated carbon materials used. This approach uses only the pore-size distribution as an input, with no fitting parameters or empirical adsorbent-adsorbate interactions, and thus can be easily applied to other adsorbent-adsorbate combinations.

  10. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    SciTech Connect

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.

  11. Change in heat capacity accurately predicts vibrational coupling in enzyme catalyzed reactions.

    PubMed

    Arcus, Vickery L; Pudney, Christopher R

    2015-08-01

    The temperature dependence of kinetic isotope effects (KIEs) have been used to infer the vibrational coupling of the protein and or substrate to the reaction coordinate, particularly in enzyme-catalyzed hydrogen transfer reactions. We find that a new model for the temperature dependence of experimentally determined observed rate constants (macromolecular rate theory, MMRT) is able to accurately predict the occurrence of vibrational coupling, even where the temperature dependence of the KIE fails. This model, that incorporates the change in heat capacity for enzyme catalysis, demonstrates remarkable consistency with both experiment and theory and in many respects is more robust than models used at present.

  12. Accurate verification of the conserved-vector-current and standard-model predictions

    SciTech Connect

    Sirlin, A.; Zucchini, R.

    1986-10-20

    An approximate analytic calculation of O(Z..cap alpha../sup 2/) corrections to Fermi decays is presented. When the analysis of Koslowsky et al. is modified to take into account the new results, it is found that each of the eight accurately studied scrFt values differs from the average by approx. <1sigma, thus significantly improving the comparison of experiments with conserved-vector-current predictions. The new scrFt values are lower than before, which also brings experiments into very good agreement with the three-generation standard model, at the level of its quantum corrections.

  13. Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

    PubMed

    Kruger, Nicholas J; Ratcliffe, R George

    2015-01-01

    Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

  14. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    NASA Astrophysics Data System (ADS)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

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

    PubMed

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

    2016-09-30

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

  16. Cross-validation of resting metabolic rate prediction equations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Background: Knowledge of the resting metabolic rate (RMR) is necessary for determining individual total energy requirements. Measurement of RMR is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, the accuracy of these equations...

  17. Toward an Accurate Prediction of the Arrival Time of Geomagnetic-Effective Coronal Mass Ejections

    NASA Astrophysics Data System (ADS)

    Shi, T.; Wang, Y.; Wan, L.; Cheng, X.; Ding, M.; Zhang, J.

    2015-12-01

    Accurately predicting the arrival of coronal mass ejections (CMEs) to the Earth based on remote images is of critical significance for the study of space weather. Here we make a statistical study of 21 Earth-directed CMEs, specifically exploring the relationship between CME initial speeds and transit times. The initial speed of a CME is obtained by fitting the CME with the Graduated Cylindrical Shell model and is thus free of projection effects. We then use the drag force model to fit results of the transit time versus the initial speed. By adopting different drag regimes, i.e., the viscous, aerodynamics, and hybrid regimes, we get similar results, with a least mean estimation error of the hybrid model of 12.9 hr. CMEs with a propagation angle (the angle between the propagation direction and the Sun-Earth line) larger than their half-angular widths arrive at the Earth with an angular deviation caused by factors other than the radial solar wind drag. The drag force model cannot be reliably applied to such events. If we exclude these events in the sample, the prediction accuracy can be improved, i.e., the estimation error reduces to 6.8 hr. This work suggests that it is viable to predict the arrival time of CMEs to the Earth based on the initial parameters with fairly good accuracy. Thus, it provides a method of forecasting space weather 1-5 days following the occurrence of CMEs.

  18. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    SciTech Connect

    Shvab, I.; Sadus, Richard J.

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.

  19. Direct Pressure Monitoring Accurately Predicts Pulmonary Vein Occlusion During Cryoballoon Ablation

    PubMed Central

    Kosmidou, Ioanna; Wooden, Shannnon; Jones, Brian; Deering, Thomas; Wickliffe, Andrew; Dan, Dan

    2013-01-01

    Cryoballoon ablation (CBA) is an established therapy for atrial fibrillation (AF). Pulmonary vein (PV) occlusion is essential for achieving antral contact and PV isolation and is typically assessed by contrast injection. We present a novel method of direct pressure monitoring for assessment of PV occlusion. Transcatheter pressure is monitored during balloon advancement to the PV antrum. Pressure is recorded via a single pressure transducer connected to the inner lumen of the cryoballoon. Pressure curve characteristics are used to assess occlusion in conjunction with fluoroscopic or intracardiac echocardiography (ICE) guidance. PV occlusion is confirmed when loss of typical left atrial (LA) pressure waveform is observed with recordings of PA pressure characteristics (no A wave and rapid V wave upstroke). Complete pulmonary vein occlusion as assessed with this technique has been confirmed with concurrent contrast utilization during the initial testing of the technique and has been shown to be highly accurate and readily reproducible. We evaluated the efficacy of this novel technique in 35 patients. A total of 128 veins were assessed for occlusion with the cryoballoon utilizing the pressure monitoring technique; occlusive pressure was demonstrated in 113 veins with resultant successful pulmonary vein isolation in 111 veins (98.2%). Occlusion was confirmed with subsequent contrast injection during the initial ten procedures, after which contrast utilization was rapidly reduced or eliminated given the highly accurate identification of occlusive pressure waveform with limited initial training. Verification of PV occlusive pressure during CBA is a novel approach to assessing effective PV occlusion and it accurately predicts electrical isolation. Utilization of this method results in significant decrease in fluoroscopy time and volume of contrast. PMID:23485956

  20. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  1. Distance scaling method for accurate prediction of slowly varying magnetic fields in satellite missions

    NASA Astrophysics Data System (ADS)

    Zacharias, Panagiotis P.; Chatzineofytou, Elpida G.; Spantideas, Sotirios T.; Capsalis, Christos N.

    2016-07-01

    In the present work, the determination of the magnetic behavior of localized magnetic sources from near-field measurements is examined. The distance power law of the magnetic field fall-off is used in various cases to accurately predict the magnetic signature of an equipment under test (EUT) consisting of multiple alternating current (AC) magnetic sources. Therefore, parameters concerning the location of the observation points (magnetometers) are studied towards this scope. The results clearly show that these parameters are independent of the EUT's size and layout. Additionally, the techniques developed in the present study enable the placing of the magnetometers close to the EUT, thus achieving high signal-to-noise ratio (SNR). Finally, the proposed method is verified by real measurements, using a mobile phone as an EUT.

  2. Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus.

    PubMed

    Park, Seoung Hoon; Kim, Seonjin; Kwon, MinHyuk; Christou, Evangelos A

    2016-03-01

    Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus. The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information. The goal was to anticipate the direction of the service (left or right) and the rotational motion of service (topspin, sidespin, or cut). We recorded their responses and quantified the following outcomes: (i) directional accuracy and (ii) rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials. The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.

  3. In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli

    PubMed Central

    2014-01-01

    A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression. A thermodynamic model of the lac repressor binding to operator DNA and effector is used to link DNA occupancy to either normalized in vitro mRNA product or normalized in vivo fluorescence of a regulated gene, YFP. An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor–operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data. We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system. PMID:25097824

  4. Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-05-14

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric RE891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {le} 5:12 [23{sup o}]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal ('AM1GH') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer.

  5. Measuring solar reflectance - Part I: Defining a metric that accurately predicts solar heat gain

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-09-15

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective ''cool colored'' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland US latitudes, this metric R{sub E891BN} can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {<=} 5:12 [23 ]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool roof net energy savings by as much as 23%. We define clear sky air mass one global horizontal (''AM1GH'') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer. (author)

  6. Temperature-based bioclimatic parameters can predict nematode metabolic footprints.

    PubMed

    Bhusal, Daya Ram; Tsiafouli, Maria A; Sgardelis, Stefanos P

    2015-09-01

    Nematode metabolic footprints (MFs) refer to the lifetime amount of metabolized carbon per individual, indicating a connection to soil food web functions and eventually to processes supporting ecosystem services. Estimating and managing these at a convenient scale requires information upscaling from the soil sample to the landscape level. We explore the feasibility of predicting nematode MFs from temperature-based bioclimatic parameters across a landscape. We assume that temperature effects are reflected in MFs, since temperature variations determine life processes ranging from enzyme activities to community structure. We use microclimate data recorded for 1 year from sites differing by orientation, altitude and vegetation cover. At the same sites we estimate MFs for each nematode trophic group. Our models show that bioclimatic parameters, specifically those accounting for temporal variations in temperature and extremities, predict most of the variation in nematode MFs. Higher fungivorous and lower bacterivorous nematode MFs are predicted for sites with high seasonality and low isothermality (sites of low vegetation, mostly at low altitudes), indicating differences in the relative contribution of the corresponding food web channels to the metabolism of carbon across the landscape. Higher plant-parasitic MFs were predicted for sites with high seasonality. The fitted models provide realistic predictions of unknown cases within the range of the predictor's values, allowing for the interpolation of MFs within the sampled region. We conclude that upscaling of the bioindication potential of nematode communities is feasible and can provide new perspectives not only in the field of soil ecology but other research areas as well.

  7. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

    PubMed Central

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. PMID:27571061

  8. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. PMID:27571061

  9. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.

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

    PubMed

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

    2010-10-01

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

  11. Accurate prediction of solvent accessibility using neural networks-based regression.

    PubMed

    Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

    2004-09-01

    Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning-based methods from the literature, we do not impose a classification problem with arbitrary boundaries between the classes. Instead, we seek a continuous approximation of the real-value RSA using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor. A set of 860 protein structures derived from the PFAM database was used for training, whereas validation of the results was carefully performed on several nonredundant control sets comprising a total of 603 structures derived from new Protein Data Bank structures and had no homology to proteins included in the training. Two classes of alternative predictors were developed for comparison with the regression-based approach: one based on the standard classification approach and the other based on a semicontinuous approximation with the so-called thermometer encoding. Furthermore, a weighted approximation, with errors being scaled by the observed levels of variability in RSA for equivalent residues in families of homologous structures, was applied in order to improve the results. The effects of including evolutionary profiles and the growth of sequence databases were assessed. In accord with the observed levels of variability in RSA for different ranges of RSA values, the regression accuracy is higher for buried than for exposed residues, with overall 15.3-15.8% mean absolute errors and correlation coefficients between the predicted and experimental values of 0.64-0.67 on different control sets. The new method outperforms classification-based algorithms when the real value predictions are projected onto two-class classification problems with several commonly

  12. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

    PubMed

    Maturana, Matias I; Apollo, Nicholas V; Hadjinicolaou, Alex E; Garrett, David J; Cloherty, Shaun L; Kameneva, Tatiana; Grayden, David B; Ibbotson, Michael R; Meffin, Hamish

    2016-04-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron's electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  13. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    NASA Astrophysics Data System (ADS)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  14. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  15. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

    SciTech Connect

    Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

  16. Continuous Metabolic Monitoring Based on Multi-Analyte Biomarkers to Predict Exhaustion

    PubMed Central

    Kastellorizios, Michail; Burgess, Diane J.

    2015-01-01

    This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject’s perception. PMID:26028477

  17. Continuous metabolic monitoring based on multi-analyte biomarkers to predict exhaustion.

    PubMed

    Kastellorizios, Michail; Burgess, Diane J

    2015-01-01

    This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject's perception. PMID:26028477

  18. Continuous metabolic monitoring based on multi-analyte biomarkers to predict exhaustion.

    PubMed

    Kastellorizios, Michail; Burgess, Diane J

    2015-06-01

    This work introduces the concept of multi-analyte biomarkers for continuous metabolic monitoring. The importance of using more than one marker lies in the ability to obtain a holistic understanding of the metabolism. This is showcased for the detection and prediction of exhaustion during intense physical exercise. The findings presented here indicate that when glucose and lactate changes over time are combined into multi-analyte biomarkers, their monitoring trends are more sensitive in the subcutaneous tissue, an implantation-friendly peripheral tissue, compared to the blood. This unexpected observation was confirmed in normal as well as type 1 diabetic rats. This study was designed to be of direct value to continuous monitoring biosensor research, where single analytes are typically monitored. These findings can be implemented in new multi-analyte continuous monitoring technologies for more accurate insulin dosing, as well as for exhaustion prediction studies based on objective data rather than the subject's perception.

  19. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  20. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  1. Use of human in vitro skin models for accurate and ethical risk assessment: metabolic considerations.

    PubMed

    Hewitt, Nicola J; Edwards, Robert J; Fritsche, Ellen; Goebel, Carsten; Aeby, Pierre; Scheel, Julia; Reisinger, Kerstin; Ouédraogo, Gladys; Duche, Daniel; Eilstein, Joan; Latil, Alain; Kenny, Julia; Moore, Claire; Kuehnl, Jochen; Barroso, Joao; Fautz, Rolf; Pfuhler, Stefan

    2013-06-01

    Several human skin models employing primary cells and immortalized cell lines used as monocultures or combined to produce reconstituted 3D skin constructs have been developed. Furthermore, these models have been included in European genotoxicity and sensitization/irritation assay validation projects. In order to help interpret data, Cosmetics Europe (formerly COLIPA) facilitated research projects that measured a variety of defined phase I and II enzyme activities and created a complete proteomic profile of xenobiotic metabolizing enzymes (XMEs) in native human skin and compared them with data obtained from a number of in vitro models of human skin. Here, we have summarized our findings on the current knowledge of the metabolic capacity of native human skin and in vitro models and made an overall assessment of the metabolic capacity from gene expression, proteomic expression, and substrate metabolism data. The known low expression and function of phase I enzymes in native whole skin were reflected in the in vitro models. Some XMEs in whole skin were not detected in in vitro models and vice versa, and some major hepatic XMEs such as cytochrome P450-monooxygenases were absent or measured only at very low levels in the skin. Conversely, despite varying mRNA and protein levels of phase II enzymes, functional activity of glutathione S-transferases, N-acetyltransferase 1, and UDP-glucuronosyltransferases were all readily measurable in whole skin and in vitro skin models at activity levels similar to those measured in the liver. These projects have enabled a better understanding of the contribution of XMEs to toxicity endpoints. PMID:23539547

  2. Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

    SciTech Connect

    Wong, Sharon; Back, Michael; Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun; Lu, Jaide Jay

    2012-07-01

    Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio Registered-Sign treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

  3. Predicting Development of the Metabolically Healthy Obese Phenotype

    PubMed Central

    Achilike, Immaculeta; Hazuda, Helen P.; Fowler, Sharon P.; Aung, KoKo; Lorenzo, Carlos

    2014-01-01

    Objective The metabolically healthy (MHO) and unhealthy obese (MUHO) differ in terms of cardiovascular risk. However, little is known about predicting the development of these phenotypes and the future stability of the MHO phenotype. Therefore, we examined these two issues in the San Antonio Heart Study. Design Longitudinal, population-based study of cardiometabolic risk factors among Mexican Americans and non-Hispanic whites in San Antonio. Subjects The study sample included 2,368 participants with neither MUHO nor diabetes at baseline. Median follow-up was 7.8 years. MHO was defined as obesity with ≤1 metabolic abnormality; MUHO, as obesity with ≥2 abnormalities. Results At baseline, 1,595 and 498 individuals were non-obese with ≤1 and ≥2 metabolic abnormalities, respectively; 275 were MHO. Among non-obese individuals, independent predictors of incident MHO (OR for 1-SD change [95% CI]) included body mass index (8.12 [5.66 – 11.7]), triglycerides (0.52 [0.39 – 0.68]), and HDL-C (1.41 [1.11 – 1.81]), whereas independent predictors of incident MUHO included BMI (5.97 [4.58 – 7.77]) and triglycerides (1.26 [1.05 – 1.51]). Among participants with ≤1 metabolic abnormality, obesity was associated with greater odds of developing multiple metabolic abnormalities (OR 2.26 [1.74 – 2.95]). Conclusions Triglycerides and HDL-C may be useful for predicting progression to MHO. MHO may not be a stable condition, because it confers an increased risk of developing multiple metabolic abnormalities. PMID:24984752

  4. Unifying elemental stoichiometry and metabolic theory in predicting species abundances.

    PubMed

    Ott, David; Digel, Christoph; Rall, Björn C; Maraun, Mark; Scheu, Stefan; Brose, Ulrich

    2014-10-01

    While metabolic theory predicts variance in population density within communities depending on population average body masses, the ecological stoichiometry concept relates density variation across communities to varying resource stoichiometry. Using a data set including biomass densities of 4959 populations of soil invertebrates across 48 forest sites we combined these two frameworks. We analyzed how the scaling of biomass densities with population-averaged body masses systematically interacts with stoichiometric variables. Simplified analyses employing either only body masses or only resource stoichiometry are highly context sensitive and yield variable and often misleading results. Our findings provide strong evidence that analyses of ecological state variables should integrate allometric and stoichiometric variables to explain deviations from predicted allometric scaling and avoid erroneous conclusions. In consequence, our study provides an important step towards unifying two prominent ecological theories, metabolic theory and ecological stoichiometry.

  5. Predicting accurate fluorescent spectra for high molecular weight polycyclic aromatic hydrocarbons using density functional theory

    NASA Astrophysics Data System (ADS)

    Powell, Jacob; Heider, Emily C.; Campiglia, Andres; Harper, James K.

    2016-10-01

    The ability of density functional theory (DFT) methods to predict accurate fluorescence spectra for polycyclic aromatic hydrocarbons (PAHs) is explored. Two methods, PBE0 and CAM-B3LYP, are evaluated both in the gas phase and in solution. Spectra for several of the most toxic PAHs are predicted and compared to experiment, including three isomers of C24H14 and a PAH containing heteroatoms. Unusually high-resolution experimental spectra are obtained for comparison by analyzing each PAH at 4.2 K in an n-alkane matrix. All theoretical spectra visually conform to the profiles of the experimental data but are systematically offset by a small amount. Specifically, when solvent is included the PBE0 functional overestimates peaks by 16.1 ± 6.6 nm while CAM-B3LYP underestimates the same transitions by 14.5 ± 7.6 nm. These calculated spectra can be empirically corrected to decrease the uncertainties to 6.5 ± 5.1 and 5.7 ± 5.1 nm for the PBE0 and CAM-B3LYP methods, respectively. A comparison of computed spectra in the gas phase indicates that the inclusion of n-octane shifts peaks by +11 nm on average and this change is roughly equivalent for PBE0 and CAM-B3LYP. An automated approach for comparing spectra is also described that minimizes residuals between a given theoretical spectrum and all available experimental spectra. This approach identifies the correct spectrum in all cases and excludes approximately 80% of the incorrect spectra, demonstrating that an automated search of theoretical libraries of spectra may eventually become feasible.

  6. How accurately can we predict the melting points of drug-like compounds?

    PubMed

    Tetko, Igor V; Sushko, Yurii; Novotarskyi, Sergii; Patiny, Luc; Kondratov, Ivan; Petrenko, Alexander E; Charochkina, Larisa; Asiri, Abdullah M

    2014-12-22

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638.

  7. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  8. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases). PMID:26430979

  9. How accurately can we predict the melting points of drug-like compounds?

    PubMed

    Tetko, Igor V; Sushko, Yurii; Novotarskyi, Sergii; Patiny, Luc; Kondratov, Ivan; Petrenko, Alexander E; Charochkina, Larisa; Asiri, Abdullah M

    2014-12-22

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638. PMID:25489863

  10. A survey of factors contributing to accurate theoretical predictions of atomization energies and molecular structures

    NASA Astrophysics Data System (ADS)

    Feller, David; Peterson, Kirk A.; Dixon, David A.

    2008-11-01

    High level electronic structure predictions of thermochemical properties and molecular structure are capable of accuracy rivaling the very best experimental measurements as a result of rapid advances in hardware, software, and methodology. Despite the progress, real world limitations require practical approaches designed for handling general chemical systems that rely on composite strategies in which a single, intractable calculation is replaced by a series of smaller calculations. As typically implemented, these approaches produce a final, or "best," estimate that is constructed from one major component, fine-tuned by multiple corrections that are assumed to be additive. Though individually much smaller than the original, unmanageable computational problem, these corrections are nonetheless extremely costly. This study presents a survey of the widely varying magnitude of the most important components contributing to the atomization energies and structures of 106 small molecules. It combines large Gaussian basis sets and coupled cluster theory up to quadruple excitations for all systems. In selected cases, the effects of quintuple excitations and/or full configuration interaction were also considered. The availability of reliable experimental data for most of the molecules permits an expanded statistical analysis of the accuracy of the approach. In cases where reliable experimental information is currently unavailable, the present results are expected to provide some of the most accurate benchmark values available.

  11. Accurate prediction of band gaps and optical properties of HfO2

    NASA Astrophysics Data System (ADS)

    Ondračka, Pavel; Holec, David; Nečas, David; Zajíčková, Lenka

    2016-10-01

    We report on optical properties of various polymorphs of hafnia predicted within the framework of density functional theory. The full potential linearised augmented plane wave method was employed together with the Tran-Blaha modified Becke-Johnson potential (TB-mBJ) for exchange and local density approximation for correlation. Unit cells of monoclinic, cubic and tetragonal crystalline, and a simulated annealing-based model of amorphous hafnia were fully relaxed with respect to internal positions and lattice parameters. Electronic structures and band gaps for monoclinic, cubic, tetragonal and amorphous hafnia were calculated using three different TB-mBJ parametrisations and the results were critically compared with the available experimental and theoretical reports. Conceptual differences between a straightforward comparison of experimental measurements to a calculated band gap on the one hand and to a whole electronic structure (density of electronic states) on the other hand, were pointed out, suggesting the latter should be used whenever possible. Finally, dielectric functions were calculated at two levels, using the random phase approximation without local field effects and with a more accurate Bethe-Salpether equation (BSE) to account for excitonic effects. We conclude that a satisfactory agreement with experimental data for HfO2 was obtained only in the latter case.

  12. Accurate prediction of V1 location from cortical folds in a surface coordinate system

    PubMed Central

    Hinds, Oliver P.; Rajendran, Niranjini; Polimeni, Jonathan R.; Augustinack, Jean C.; Wiggins, Graham; Wald, Lawrence L.; Rosas, H. Diana; Potthast, Andreas; Schwartz, Eric L.; Fischl, Bruce

    2008-01-01

    Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities (Amunts et al., 2000). However, other qualitative reports of V1 location (Smith, 1904; Stensaas et al., 1974; Rademacher et al., 1993) suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 μm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration (Fischl et al., 1999b) was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex, and therefore are better

  13. Unilateral Prostate Cancer Cannot be Accurately Predicted in Low-Risk Patients

    SciTech Connect

    Isbarn, Hendrik; Karakiewicz, Pierre I.; Vogel, Susanne

    2010-07-01

    Purpose: Hemiablative therapy (HAT) is increasing in popularity for treatment of patients with low-risk prostate cancer (PCa). The validity of this therapeutic modality, which exclusively treats PCa within a single prostate lobe, rests on accurate staging. We tested the accuracy of unilaterally unremarkable biopsy findings in cases of low-risk PCa patients who are potential candidates for HAT. Methods and Materials: The study population consisted of 243 men with clinical stage {<=}T2a, a prostate-specific antigen (PSA) concentration of <10 ng/ml, a biopsy-proven Gleason sum of {<=}6, and a maximum of 2 ipsilateral positive biopsy results out of 10 or more cores. All men underwent a radical prostatectomy, and pathology stage was used as the gold standard. Univariable and multivariable logistic regression models were tested for significant predictors of unilateral, organ-confined PCa. These predictors consisted of PSA, %fPSA (defined as the quotient of free [uncomplexed] PSA divided by the total PSA), clinical stage (T2a vs. T1c), gland volume, and number of positive biopsy cores (2 vs. 1). Results: Despite unilateral stage at biopsy, bilateral or even non-organ-confined PCa was reported in 64% of all patients. In multivariable analyses, no variable could clearly and independently predict the presence of unilateral PCa. This was reflected in an overall accuracy of 58% (95% confidence interval, 50.6-65.8%). Conclusions: Two-thirds of patients with unilateral low-risk PCa, confirmed by clinical stage and biopsy findings, have bilateral or non-organ-confined PCa at radical prostatectomy. This alarming finding questions the safety and validity of HAT.

  14. Improving DOE-2's RESYS routine: User defined functions to provide more accurate part load energy use and humidity predictions

    SciTech Connect

    Henderson, Hugh I.; Parker, Danny; Huang, Yu J.

    2000-08-04

    In hourly energy simulations, it is important to properly predict the performance of air conditioning systems over a range of full and part load operating conditions. An important component of these calculations is to properly consider the performance of the cycling air conditioner and how it interacts with the building. This paper presents improved approaches to properly account for the part load performance of residential and light commercial air conditioning systems in DOE-2. First, more accurate correlations are given to predict the degradation of system efficiency at part load conditions. In addition, a user-defined function for RESYS is developed that provides improved predictions of air conditioner sensible and latent capacity at part load conditions. The user function also provides more accurate predictions of space humidity by adding ''lumped'' moisture capacitance into the calculations. The improved cooling coil model and the addition of moisture capacitance predicts humidity swings that are more representative of the performance observed in real buildings.

  15. New predictive equations for estimation of basal metabolic rates of adolescents: tropics versus temperate.

    PubMed

    Noor, M I; Poh, B K; Zawiah, H; Henry, C J K

    2003-01-01

    The energy and nutritional requirements of adolescents are influenced primarily by the growth spurt that occurs at puberty, and also by the need to maintain adequate levels of physical activity. Predictions of BMR have gained attention since the publication of the FAO/WHO/UNU (1985) expert consultation report, which adopted the principle of relying on energy expenditure rather than energy intake to derive requirement of individuals. While the report predicts BMR accurately in many individuals from temperate climate, they are said to be less accurate in predicting BMR in populations living in the tropics. The collation of worldwide data on basal metabolism indicated that, relative to adults, there was a paucity of data in other age groups including the adolescents. Although several BMR studies among children had been reported in the 90's, the data in normal weight children are almost exclusively from small control groups in obesity studies. Furthermore, we know little as to whether BMR of children differs in differing climatic conditions. This paper presents predictive equations for estimating BMR from a two-centre study, Bangi (Malaysia) and Oxford (UK) and to compare the results with the currently used predictive equations. PMID:15806886

  16. Plant Interactions Alter the Predictions of Metabolic Scaling Theory

    PubMed Central

    Lin, Yue; Berger, Uta; Grimm, Volker; Huth, Franka; Weiner, Jacob

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of −4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms’ internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than −4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive. PMID:23460884

  17. Accurate Determination of Radionuclidic Purity and Half-Life of Reactor Produced LU-177G for Metabolic Radioimmunotherapy

    NASA Astrophysics Data System (ADS)

    Groppi, F.; Canella, L.; Bonardi, M. L.; Zona, C.; Morzenti, S.; Menapace, E.; Alfassi, Z. B.; Chinol, M.; Papi, S.; Tosi, G.

    2006-04-01

    The accurate determination of radionuclidic purity and half-life of the beta emitter 177gLu used for metabolic radioimmunotherapy is presented. High-resolution gamma spectrometry, as well as deconvolution of beta decay curve measured by spectrometry with liquid scintillation counting, have been adopted for quality control of different samples available on the market. A simple method was developed to distinguish between the different methods available for production of 177gLu: i.e. either direct (n,γ) reactions of enriched 176Lu or indirect (n,γ) activation of enriched 176Yb followed by β- decay. In the first case, the long-lived metastable level 177mLu is present in the radioactive preparation and a low specific activity radionuclide is obtained, in the latter a very high purity and high specific activity 177gLu is produced.

  18. Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

    PubMed Central

    He, Shuai-Bing; Li, Man-Man; Zhang, Bai-Xia; Ye, Xiao-Tong; Du, Ran-Feng; Wang, Yun; Qiao, Yan-Jiang

    2016-01-01

    During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy. PMID:27735849

  19. Computer-aided prediction of xenobiotic metabolism in the human body

    NASA Astrophysics Data System (ADS)

    Bezhentsev, V. M.; Tarasova, O. A.; Dmitriev, A. V.; Rudik, A. V.; Lagunin, A. A.; Filimonov, D. A.; Poroikov, V. V.

    2016-08-01

    The review describes the major databases containing information about the metabolism of xenobiotics, including data on drug metabolism, metabolic enzymes, schemes of biotransformation and the structures of some substrates and metabolites. Computational approaches used to predict the interaction of xenobiotics with metabolic enzymes, prediction of metabolic sites in the molecule, generation of structures of potential metabolites for subsequent evaluation of their properties are considered. The advantages and limitations of various computational methods for metabolism prediction and the prospects for their applications to improve the safety and efficacy of new drugs are discussed. Bibliography — 165 references.

  20. Accurate prediction model of bead geometry in crimping butt of the laser brazing using generalized regression neural network

    NASA Astrophysics Data System (ADS)

    Rong, Y. M.; Chang, Y.; Huang, Y.; Zhang, G. J.; Shao, X. Y.

    2015-12-01

    There are few researches that concentrate on the prediction of the bead geometry for laser brazing with crimping butt. This paper addressed the accurate prediction of the bead profile by developing a generalized regression neural network (GRNN) algorithm. Firstly GRNN model was developed and trained to decrease the prediction error that may be influenced by the sample size. Then the prediction accuracy was demonstrated by comparing with other articles and back propagation artificial neural network (BPNN) algorithm. Eventually the reliability and stability of GRNN model were discussed from the points of average relative error (ARE), mean square error (MSE) and root mean square error (RMSE), while the maximum ARE and MSE were 6.94% and 0.0303 that were clearly less than those (14.28% and 0.0832) predicted by BPNN. Obviously, it was proved that the prediction accuracy was improved at least 2 times, and the stability was also increased much more.

  1. Towards more accurate wind and solar power prediction by improving NWP model physics

    NASA Astrophysics Data System (ADS)

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  2. A simple accurate method to predict time of ponding under variable intensity rainfall

    NASA Astrophysics Data System (ADS)

    Assouline, S.; Selker, J. S.; Parlange, J.-Y.

    2007-03-01

    The prediction of the time to ponding following commencement of rainfall is fundamental to hydrologic prediction of flood, erosion, and infiltration. Most of the studies to date have focused on prediction of ponding resulting from simple rainfall patterns. This approach was suitable to rainfall reported as average values over intervals of up to a day but does not take advantage of knowledge of the complex patterns of actual rainfall now commonly recorded electronically. A straightforward approach to include the instantaneous rainfall record in the prediction of ponding time and excess rainfall using only the infiltration capacity curve is presented. This method is tested against a numerical solution of the Richards equation on the basis of an actual rainfall record. The predicted time to ponding showed mean error ≤7% for a broad range of soils, with and without surface sealing. In contrast, the standard predictions had average errors of 87%, and worst-case errors exceeding a factor of 10. In addition to errors intrinsic in the modeling framework itself, errors that arise from averaging actual rainfall records over reporting intervals were evaluated. Averaging actual rainfall records observed in Israel over periods of as little as 5 min significantly reduced predicted runoff (75% for the sealed sandy loam and 46% for the silty clay loam), while hourly averaging gave complete lack of prediction of ponding in some of the cases.

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

    PubMed

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

    2015-12-01

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

  4. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    NASA Astrophysics Data System (ADS)

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  5. Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

    PubMed

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2016-01-01

    Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

  6. Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments.

    PubMed

    Ding, Tong; Case, Kyle A; Omolo, Morrine A; Reiland, Holly A; Metz, Zachary P; Diao, Xinyu; Baumler, David J

    2016-01-01

    Microorganisms have evolved to occupy certain environmental niches, and the metabolic genes essential for growth in these locations are retained in the genomes. Many microorganisms inhabit niches located in the human body, sometimes causing disease, and may retain genes essential for growth in locations such as the bloodstream and urinary tract, or growth during intracellular invasion of the hosts' macrophage cells. Strains of Escherichia coli (E. coli) and Salmonella spp. are thought to have evolved over 100 million years from a common ancestor, and now cause disease in specific niches within humans. Here we have used a genome scale metabolic model representing the pangenome of E. coli which contains all metabolic reactions encoded by genes from 16 E. coli genomes, and have simulated environmental conditions found in the human bloodstream, urinary tract, and macrophage to determine essential metabolic genes needed for growth in each location. We compared the predicted essential genes for three E. coli strains and one Salmonella strain that cause disease in each host environment, and determined that essential gene retention could be accurately predicted using this approach. This project demonstrated that simulating human body environments such as the bloodstream can successfully lead to accurate computational predictions of essential/important genes.

  7. Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments.

    PubMed

    Ding, Tong; Case, Kyle A; Omolo, Morrine A; Reiland, Holly A; Metz, Zachary P; Diao, Xinyu; Baumler, David J

    2016-01-01

    Microorganisms have evolved to occupy certain environmental niches, and the metabolic genes essential for growth in these locations are retained in the genomes. Many microorganisms inhabit niches located in the human body, sometimes causing disease, and may retain genes essential for growth in locations such as the bloodstream and urinary tract, or growth during intracellular invasion of the hosts' macrophage cells. Strains of Escherichia coli (E. coli) and Salmonella spp. are thought to have evolved over 100 million years from a common ancestor, and now cause disease in specific niches within humans. Here we have used a genome scale metabolic model representing the pangenome of E. coli which contains all metabolic reactions encoded by genes from 16 E. coli genomes, and have simulated environmental conditions found in the human bloodstream, urinary tract, and macrophage to determine essential metabolic genes needed for growth in each location. We compared the predicted essential genes for three E. coli strains and one Salmonella strain that cause disease in each host environment, and determined that essential gene retention could be accurately predicted using this approach. This project demonstrated that simulating human body environments such as the bloodstream can successfully lead to accurate computational predictions of essential/important genes. PMID:26885654

  8. Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments

    PubMed Central

    Baumler, David J.

    2016-01-01

    Microorganisms have evolved to occupy certain environmental niches, and the metabolic genes essential for growth in these locations are retained in the genomes. Many microorganisms inhabit niches located in the human body, sometimes causing disease, and may retain genes essential for growth in locations such as the bloodstream and urinary tract, or growth during intracellular invasion of the hosts’ macrophage cells. Strains of Escherichia coli (E. coli) and Salmonella spp. are thought to have evolved over 100 million years from a common ancestor, and now cause disease in specific niches within humans. Here we have used a genome scale metabolic model representing the pangenome of E. coli which contains all metabolic reactions encoded by genes from 16 E. coli genomes, and have simulated environmental conditions found in the human bloodstream, urinary tract, and macrophage to determine essential metabolic genes needed for growth in each location. We compared the predicted essential genes for three E. coli strains and one Salmonella strain that cause disease in each host environment, and determined that essential gene retention could be accurately predicted using this approach. This project demonstrated that simulating human body environments such as the bloodstream can successfully lead to accurate computational predictions of essential/important genes. PMID:26885654

  9. Prediction of metabolic syndrome in women with polycystic ovary syndrome

    PubMed Central

    Ersan, Fırat; Arslan, Esra; Esmer, Aytül Çorbacıoğlu; Aydın, Serdar; Gedikbaşı, Asuman; Gedikbaşı, Ali; Alkış, İsmet; Ark, Cemal

    2012-01-01

    Objective To identify biochemical factors that serve as predictors for the metabolic syndrome (MetS) in patients with polycystic ovary syndrome (PCOS) and to investigate the value of adipocytokines in the prediction of metabolic syndrome. Material and Methods A total of 91 pre-menopausal women with PCOS diagnosed according to the Rotterdam consensus criteria were recruited as study subjects. Waist circumference, blood pressure, body mass index (BMI), fasting glucose, serum lipids, insulin, FSH, LH, E2, total testosteron, homeostatic model assessment–insulin resistance (HOMA-IR), serum leptin and adiponectin levels were evaluated for all patients. Results Of the 91 women with PCOS, 15 patients met the criteria for MetS. Body weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, fasting glucose, total cholesterol, triglyceride, and VLDL concentrations were significantly higher and HDL was significantly lower in women with PCOS+MetS compared with those with PCOS only. However, the level of LDL, FSH, LH, E2 and total testesterone was not significantly different between these two groups. Women with PCOS+MetS had significantly higher levels of leptin and HOMA-IR, and significantly lower levels of adiponectin compared to the women with PCOS only. In the multiple logistic regression model, the association between HOMA-IR and leptin, and MetS remained statistically significant (p=0.001 and 0.018), while the association between adiponectin and MetS was no longer statistically significant. Conclusion Aside from the biochemical markers such as glucose, cholesterol and triglyceride, adipose tissue factors and insulin resistance are valuable parameters in the prediction of MetS in patients with PCOS. PMID:24592034

  10. Assessing Confidence in Predictions Using Veracity and Utility - A Case Study on the Prediction of Mammalian Metabolism by Meteor Nexus.

    PubMed

    Judson, Philip N; Long, Anthony; Murray, Ernest; Patel, Mukesh

    2015-05-01

    A previous paper1 described new metrics, veracity and utility, for assessing the performance of toxicity prediction systems that report confidence in their predictions. Assessing the performance of systems that predict mammalian metabolism is complicated by the absence of comprehensive sets of negative observations and predictions. This paper presents an approach to assessing the performance of such systems using veracity and utility. PMID:27490274

  11. Assessing Confidence in Predictions Using Veracity and Utility - A Case Study on the Prediction of Mammalian Metabolism by Meteor Nexus.

    PubMed

    Judson, Philip N; Long, Anthony; Murray, Ernest; Patel, Mukesh

    2015-05-01

    A previous paper1 described new metrics, veracity and utility, for assessing the performance of toxicity prediction systems that report confidence in their predictions. Assessing the performance of systems that predict mammalian metabolism is complicated by the absence of comprehensive sets of negative observations and predictions. This paper presents an approach to assessing the performance of such systems using veracity and utility.

  12. Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions

    PubMed Central

    Hosey, Chelsea M; Benet, Leslie Z

    2015-01-01

    The Biopharmaceutics Drug Disposition Classification System (BDDCS) can be utilized to predict drug disposition, including interactions with other drugs and transporter or metabolizing enzyme effects based on the extent of metabolism and solubility of a drug. However, defining the extent of metabolism relies upon clinical data. Drugs exhibiting high passive intestinal permeability rates are extensively metabolized. Therefore, we aimed to determine if in vitro measures of permeability rate or in silico permeability rate predictions could predict the extent of metabolism, to determine a reference compound representing the permeability rate above which compounds would be expected to be extensively metabolized, and to predict the major route of elimination of compounds in a two-tier approach utilizing permeability rate and a previously published model predicting the major route of elimination of parent drug. Twenty-two in vitro permeability rate measurement data sets in Caco-2 and MDCK cell lines and PAMPA were collected from the literature, while in silico permeability rate predictions were calculated using ADMET Predictor™ or VolSurf+. The potential for permeability rate to differentiate between extensively and poorly metabolized compounds was analyzed with receiver operating characteristic curves. Compounds that yielded the highest sensitivity-specificity average were selected as permeability rate reference standards. The major route of elimination of poorly permeable drugs was predicted by our previously published model and the accuracies and predictive values were calculated. The areas under the receiver operating curves were >0.90 for in vitro measures of permeability rate and >0.80 for the VolSurf+ model of permeability rate, indicating they were able to predict the extent of metabolism of compounds. Labetalol and zidovudine predicted greater than 80% of extensively metabolized drugs correctly and greater than 80% of poorly metabolized drugs correctly in Caco

  13. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

    PubMed Central

    Kim, Minseung; Rai, Navneet; Zorraquino, Violeta; Tagkopoulos, Ilias

    2016-01-01

    A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery. PMID:27713404

  14. Empirical approaches to more accurately predict benthic-pelagic coupling in biogeochemical ocean models

    NASA Astrophysics Data System (ADS)

    Dale, Andy; Stolpovsky, Konstantin; Wallmann, Klaus

    2016-04-01

    The recycling and burial of biogenic material in the sea floor plays a key role in the regulation of ocean chemistry. Proper consideration of these processes in ocean biogeochemical models is becoming increasingly recognized as an important step in model validation and prediction. However, the rate of organic matter remineralization in sediments and the benthic flux of redox-sensitive elements are difficult to predict a priori. In this communication, examples of empirical benthic flux models that can be coupled to earth system models to predict sediment-water exchange in the open ocean are presented. Large uncertainties hindering further progress in this field include knowledge of the reactivity of organic carbon reaching the sediment, the importance of episodic variability in bottom water chemistry and particle rain rates (for both the deep-sea and margins) and the role of benthic fauna. How do we meet the challenge?

  15. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    PubMed Central

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  16. Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes

    PubMed Central

    Victora, Andrea; Möller, Heiko M.; Exner, Thomas E.

    2014-01-01

    NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3–0.6 ppm and correlation coefficients (r2) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization. PMID:25404135

  17. Change in body mass accurately and reliably predicts change in body water after endurance exercise.

    PubMed

    Baker, Lindsay B; Lang, James A; Kenney, W Larry

    2009-04-01

    This study tested the hypothesis that the change in body mass (DeltaBM) accurately reflects the change in total body water (DeltaTBW) after prolonged exercise. Subjects (4 men, 4 women; 22-36 year; 66 +/- 10 kg) completed 2 h of interval running (70% VO(2max)) in the heat (30 degrees C), followed by a run to exhaustion (85% VO(2max)), and then sat for a 1 h recovery period. During exercise and recovery, subjects drank fluid or no fluid to maintain their BM, increase BM by 2%, or decrease BM by 2 or 4% in separate trials. Pre- and post-experiment TBW were determined using the deuterium oxide (D(2)O) dilution technique and corrected for D(2)O lost in urine, sweat, breath vapor, and nonaqueous hydrogen exchange. The average difference between DeltaBM and DeltaTBW was 0.07 +/- 1.07 kg (paired t test, P = 0.29). The slope and intercept of the relation between DeltaBM and DeltaTBW were not significantly different from 1 and 0, respectively. The intraclass correlation coefficient between DeltaBM and DeltaTBW was 0.76, which is indicative of excellent reliability between methods. Measuring pre- to post-exercise DeltaBM is an accurate and reliable method to assess the DeltaTBW.

  18. Towards Accurate Residue-Residue Hydrophobic Contact Prediction for Alpha Helical Proteins Via Integer Linear Optimization

    PubMed Central

    Rajgaria, R.; McAllister, S. R.; Floudas, C. A.

    2008-01-01

    A new optimization-based method is presented to predict the hydrophobic residue contacts in α-helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent α-helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was approximately 66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 Å and 14.67 Å respectively. PMID:18767158

  19. Accurate prediction of kidney allograft outcome based on creatinine course in the first 6 months posttransplant.

    PubMed

    Fritsche, L; Hoerstrup, J; Budde, K; Reinke, P; Neumayer, H-H; Frei, U; Schlaefer, A

    2005-03-01

    Most attempts to predict early kidney allograft loss are based on the patient and donor characteristics at baseline. We investigated how the early posttransplant creatinine course compares to baseline information in the prediction of kidney graft failure within the first 4 years after transplantation. Two approaches to create a prediction rule for early graft failure were evaluated. First, the whole data set was analysed using a decision-tree building software. The software, rpart, builds classification or regression models; the resulting models can be represented as binary trees. In the second approach, a Hill-Climbing algorithm was applied to define cut-off values for the median creatinine level and creatinine slope in the period between day 60 and 180 after transplantation. Of the 497 patients available for analysis, 52 (10.5%) experienced an early graft loss (graft loss within the first 4 years after transplantation). From the rpart algorithm, a single decision criterion emerged: Median creatinine value on days 60 to 180 higher than 3.1 mg/dL predicts early graft failure (accuracy 95.2% but sensitivity = 42.3%). In contrast, the Hill-Climbing algorithm delivered a cut-off of 1.8 mg/dL for the median creatinine level and a cut-off of 0.3 mg/dL per month for the creatinine slope (sensitivity = 69.5% and specificity 79.0%). Prediction rules based on median and slope of creatinine levels in the first half year after transplantation allow early identification of patients who are at risk of loosing their graft early after transplantation. These patients may benefit from therapeutic measures tailored for this high-risk setting. PMID:15848516

  20. Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

    PubMed Central

    Luo, Longqiang; Li, Dingfang; Zhang, Wen; Tu, Shikui; Zhu, Xiaopeng; Tian, Gang

    2016-01-01

    Background Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File. PMID:27074043

  1. Significance of reductive metabolism in human intestine and quantitative prediction of intestinal first-pass metabolism by cytosolic reductive enzymes.

    PubMed

    Nishimuta, Haruka; Nakagawa, Tetsuya; Nomura, Naruaki; Yabuki, Masashi

    2013-05-01

    The number of new drug candidates that are cleared via non-cytochrome P450 (P450) enzymes has increased. However, unlike oxidation by P450, the roles of reductive enzymes are less understood. The metabolism in intestine is especially not well known. The purposes of this study were to investigate the significance of reductive metabolism in human intestine, and to establish a quantitative prediction method of intestinal first-pass metabolism by cytosolic reductive enzymes, using haloperidol, mebendazole, and ziprasidone. First, we estimated the metabolic activities for these compounds in intestine and liver using subcellular fractions. Metabolic activities were detected in human intestinal cytosol (HIC) for all three compounds, and the intrinsic clearance values were higher than those in human liver cytosol for haloperidol and mebendazole. These metabolic activities in HIC were NADPH- and/or NADH-dependent. Furthermore, the metabolic activities for all three compounds in HIC were largely inhibited by menadione, which has been used as a carbonyl reductase (CBR)-selective chemical inhibitor. Therefore, considering subcellular location, cofactor requirement, and chemical inhibition, these compounds might be metabolized by CBRs in human intestine. Subsequently, we tried to quantitatively predict intestinal availability (F(g)) for these compounds using human intestinal S9 (HIS9). Our prediction model using apparent permeability of parallel artificial membrane permeability assay and metabolic activities in HIS9 could predict F(g) in humans for the three compounds well. In conclusion, CBRs might have higher metabolic activities in human intestine than in human liver. Furthermore, our prediction method of human F(g) using HIS9 is applicable to substrates of cytosolic reductive enzymes.

  2. Accurate, conformation-dependent predictions of solvent effects on protein ionization constants

    PubMed Central

    Barth, P.; Alber, T.; Harbury, P. B.

    2007-01-01

    Predicting how aqueous solvent modulates the conformational transitions and influences the pKa values that regulate the biological functions of biomolecules remains an unsolved challenge. To address this problem, we developed FDPB_MF, a rotamer repacking method that exhaustively samples side chain conformational space and rigorously calculates multibody protein–solvent interactions. FDPB_MF predicts the effects on pKa values of various solvent exposures, large ionic strength variations, strong energetic couplings, structural reorganizations and sequence mutations. The method achieves high accuracy, with root mean square deviations within 0.3 pH unit of the experimental values measured for turkey ovomucoid third domain, hen lysozyme, Bacillus circulans xylanase, and human and Escherichia coli thioredoxins. FDPB_MF provides a faithful, quantitative assessment of electrostatic interactions in biological macromolecules. PMID:17360348

  3. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues

    PubMed Central

    EL-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

    A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein

  4. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.

    PubMed

    El-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

    A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein

  5. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    PubMed Central

    Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

    2015-01-01

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

  6. Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.

    PubMed

    Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure.

  7. Fast and accurate numerical method for predicting gas chromatography retention time.

    PubMed

    Claumann, Carlos Alberto; Wüst Zibetti, André; Bolzan, Ariovaldo; Machado, Ricardo A F; Pinto, Leonel Teixeira

    2015-08-01

    Predictive modeling for gas chromatography compound retention depends on the retention factor (ki) and on the flow of the mobile phase. Thus, different approaches for determining an analyte ki in column chromatography have been developed. The main one is based on the thermodynamic properties of the component and on the characteristics of the stationary phase. These models can be used to estimate the parameters and to optimize the programming of temperatures, in gas chromatography, for the separation of compounds. Different authors have proposed the use of numerical methods for solving these models, but these methods demand greater computational time. Hence, a new method for solving the predictive modeling of analyte retention time is presented. This algorithm is an alternative to traditional methods because it transforms its attainments into root determination problems within defined intervals. The proposed approach allows for tr calculation, with accuracy determined by the user of the methods, and significant reductions in computational time; it can also be used to evaluate the performance of other prediction methods.

  8. Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

    SciTech Connect

    Park, Min-Sun; Park, Sung Yong; Miller, Keith R.; Collins, Edward J.; Lee, Ha Youn

    2013-11-01

    Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

  9. Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.

    PubMed

    Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure. PMID:19950907

  10. HAAD: A quick algorithm for accurate prediction of hydrogen atoms in protein structures.

    PubMed

    Li, Yunqi; Roy, Ambrish; Zhang, Yang

    2009-08-20

    Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD.

  11. Accurate single-sequence prediction of solvent accessible surface area using local and global features.

    PubMed

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org. PMID:25204636

  12. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    PubMed

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

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

    PubMed

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

    2013-01-01

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

  14. Accurate and Rigorous Prediction of the Changes in Protein Free Energies in a Large-Scale Mutation Scan.

    PubMed

    Gapsys, Vytautas; Michielssens, Servaas; Seeliger, Daniel; de Groot, Bert L

    2016-06-20

    The prediction of mutation-induced free-energy changes in protein thermostability or protein-protein binding is of particular interest in the fields of protein design, biotechnology, and bioengineering. Herein, we achieve remarkable accuracy in a scan of 762 mutations estimating changes in protein thermostability based on the first principles of statistical mechanics. The remaining error in the free-energy estimates appears to be due to three sources in approximately equal parts, namely sampling, force-field inaccuracies, and experimental uncertainty. We propose a consensus force-field approach, which, together with an increased sampling time, leads to a free-energy prediction accuracy that matches those reached in experiments. This versatile approach enables accurate free-energy estimates for diverse proteins, including the prediction of changes in the melting temperature of the membrane protein neurotensin receptor 1. PMID:27122231

  15. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    NASA Astrophysics Data System (ADS)

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-02-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

  16. Cross-validation of recent and longstanding resting metabolic rate prediction equations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence predicted RMR accuracy at the individual lev...

  17. Predicting drug metabolism--an evaluation of the expert system METEOR.

    PubMed

    Testa, Bernard; Balmat, Anne-Loyse; Long, Anthony; Judson, Philip

    2005-07-01

    The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between 'local' and 'global' systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative) predictions. The predictions for four representative substrates are presented in detail (Figs. 1-4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.

  18. PSI: A Comprehensive and Integrative Approach for Accurate Plant Subcellular Localization Prediction

    PubMed Central

    Chen, Ming

    2013-01-01

    Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ∼10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/. PMID:24194827

  19. CRYSpred: accurate sequence-based protein crystallization propensity prediction using sequence-derived structural characteristics.

    PubMed

    Mizianty, Marcin J; Kurgan, Lukasz A

    2012-01-01

    Relatively low success rates of X-ray crystallography, which is the most popular method for solving proteins structures, motivate development of novel methods that support selection of tractable protein targets. This aspect is particularly important in the context of the current structural genomics efforts that allow for a certain degree of flexibility in the target selection. We propose CRYSpred, a novel in-silico crystallization propensity predictor that uses a set of 15 novel features which utilize a broad range of inputs including charge, hydrophobicity, and amino acid composition derived from the protein chain, and the solvent accessibility and disorder predicted from the protein sequence. Our method outperforms seven modern crystallization propensity predictors on three, independent from training dataset, benchmark test datasets. The strong predictive performance offered by the CRYSpred is attributed to the careful design of the features, utilization of the comprehensive set of inputs, and the usage of the Support Vector Machine classifier. The inputs utilized by CRYSpred are well-aligned with the existing rules-of-thumb that are used in the structural genomics studies. PMID:21919861

  20. CRYSpred: accurate sequence-based protein crystallization propensity prediction using sequence-derived structural characteristics.

    PubMed

    Mizianty, Marcin J; Kurgan, Lukasz A

    2012-01-01

    Relatively low success rates of X-ray crystallography, which is the most popular method for solving proteins structures, motivate development of novel methods that support selection of tractable protein targets. This aspect is particularly important in the context of the current structural genomics efforts that allow for a certain degree of flexibility in the target selection. We propose CRYSpred, a novel in-silico crystallization propensity predictor that uses a set of 15 novel features which utilize a broad range of inputs including charge, hydrophobicity, and amino acid composition derived from the protein chain, and the solvent accessibility and disorder predicted from the protein sequence. Our method outperforms seven modern crystallization propensity predictors on three, independent from training dataset, benchmark test datasets. The strong predictive performance offered by the CRYSpred is attributed to the careful design of the features, utilization of the comprehensive set of inputs, and the usage of the Support Vector Machine classifier. The inputs utilized by CRYSpred are well-aligned with the existing rules-of-thumb that are used in the structural genomics studies.

  1. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    SciTech Connect

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-28

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  2. Accurate predictions of dielectrophoretic force and torque on particles with strong mutual field, particle, and wall interactions

    NASA Astrophysics Data System (ADS)

    Liu, Qianlong; Reifsnider, Kenneth

    2012-11-01

    The basis of dielectrophoresis (DEP) is the prediction of the force and torque on particles. The classical approach to the prediction is based on the effective moment method, which, however, is an approximate approach, assumes infinitesimal particles. Therefore, it is well-known that for finite-sized particles, the DEP approximation is inaccurate as the mutual field, particle, wall interactions become strong, a situation presently attracting extensive research for practical significant applications. In the present talk, we provide accurate calculations of the force and torque on the particles from first principles, by directly resolving the local geometry and properties and accurately accounting for the mutual interactions for finite-sized particles with both dielectric polarization and conduction in a sinusoidally steady-state electric field. Since the approach has a significant advantage, compared to other numerical methods, to efficiently simulate many closely packed particles, it provides an important, unique, and accurate technique to investigate complex DEP phenomena, for example heterogeneous mixtures containing particle chains, nanoparticle assembly, biological cells, non-spherical effects, etc. This study was supported by the Department of Energy under funding for an EFRC (the HeteroFoaM Center), grant no. DE-SC0001061.

  3. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    NASA Astrophysics Data System (ADS)

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-01

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  4. The Compensatory Reserve For Early and Accurate Prediction Of Hemodynamic Compromise: A Review of the Underlying Physiology.

    PubMed

    Convertino, Victor A; Wirt, Michael D; Glenn, John F; Lein, Brian C

    2016-06-01

    Shock is deadly and unpredictable if it is not recognized and treated in early stages of hemorrhage. Unfortunately, measurements of standard vital signs that are displayed on current medical monitors fail to provide accurate or early indicators of shock because of physiological mechanisms that effectively compensate for blood loss. As a result of new insights provided by the latest research on the physiology of shock using human experimental models of controlled hemorrhage, it is now recognized that measurement of the body's reserve to compensate for reduced circulating blood volume is the single most important indicator for early and accurate assessment of shock. We have called this function the "compensatory reserve," which can be accurately assessed by real-time measurements of changes in the features of the arterial waveform. In this paper, the physiology underlying the development and evaluation of a new noninvasive technology that allows for real-time measurement of the compensatory reserve will be reviewed, with its clinical implications for earlier and more accurate prediction of shock. PMID:26950588

  5. Predictive value of ceruloplasmin for metabolic syndrome in adolescents.

    PubMed

    González-Jiménez, Emilio; Schmidt-Riovalle, Jacqueline; Sinausía, Laura; Carmen Valenza, Maríe; Perona, Javier S

    2016-01-01

    The metabolic syndrome (MetS) is precisely defined and the cardiovascular risk associated with the clustering of its components has been demonstrated in adults. However, data on children and adolescents are still scarce, in part, because of difficulties in transposing the definition from adults. The identification of risk factors for the development of MetS at an early age is essential for prevention purposes with low-grade inflammation acting as a determinant for the association among the MetS components. The aim of this study was to investigate the associations of the MetS with systemic markers of inflammation and ceruloplasmin in a population of adolescents. The present is a cross-sectional study whose sample population consisted of 976 adolescents, 13.2 ± 1.2 years of age. Interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were determined by ELISA. High-sensitivity C-reactive protein (hs-CRP) was determined by a solid-phase chemiluminiscent immunometric assay. Ceruloplasmin was measured by immunoturbidimetry. MetS adolescents exhibited higher levels of TNF-α, IL-6, CRP, and ceruloplasmin compared to non-MetS individuals. TNF-α, IL-6, and CRP showed strong correlations with the MetS components and insulin resistance but not relevant predictive values according to ROC curves (AUC values 0.544- 0.555). In contrast, ceruloplasmin only showed significant correlations in non-Mets individuals, but exhibited a very high predictive value (AUC=0.941, P < 0.001). The determination of serum ceruloplasmin in adolescents might be a useful tool to identify patients with the highest risk of future cardiovascular disease.

  6. Predictive value of ceruloplasmin for metabolic syndrome in adolescents.

    PubMed

    González-Jiménez, Emilio; Schmidt-Riovalle, Jacqueline; Sinausía, Laura; Carmen Valenza, Maríe; Perona, Javier S

    2016-01-01

    The metabolic syndrome (MetS) is precisely defined and the cardiovascular risk associated with the clustering of its components has been demonstrated in adults. However, data on children and adolescents are still scarce, in part, because of difficulties in transposing the definition from adults. The identification of risk factors for the development of MetS at an early age is essential for prevention purposes with low-grade inflammation acting as a determinant for the association among the MetS components. The aim of this study was to investigate the associations of the MetS with systemic markers of inflammation and ceruloplasmin in a population of adolescents. The present is a cross-sectional study whose sample population consisted of 976 adolescents, 13.2 ± 1.2 years of age. Interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were determined by ELISA. High-sensitivity C-reactive protein (hs-CRP) was determined by a solid-phase chemiluminiscent immunometric assay. Ceruloplasmin was measured by immunoturbidimetry. MetS adolescents exhibited higher levels of TNF-α, IL-6, CRP, and ceruloplasmin compared to non-MetS individuals. TNF-α, IL-6, and CRP showed strong correlations with the MetS components and insulin resistance but not relevant predictive values according to ROC curves (AUC values 0.544- 0.555). In contrast, ceruloplasmin only showed significant correlations in non-Mets individuals, but exhibited a very high predictive value (AUC=0.941, P < 0.001). The determination of serum ceruloplasmin in adolescents might be a useful tool to identify patients with the highest risk of future cardiovascular disease. PMID:27083758

  7. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients

    PubMed Central

    Asaoka, Ryo; Fujino, Yuri; Murata, Hiroshi; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an ‘intraocular pressure (IOP)-integrated VF trend analysis’ was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

  8. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients.

    PubMed

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an 'intraocular pressure (IOP)-integrated VF trend analysis' was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

  10. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    SciTech Connect

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  11. nuMap: a web platform for accurate prediction of nucleosome positioning.

    PubMed

    Alharbi, Bader A; Alshammari, Thamir H; Felton, Nathan L; Zhurkin, Victor B; Cui, Feng

    2014-10-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site. PMID:25220945

  12. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    SciTech Connect

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  13. Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

    PubMed

    Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in <1h compared to >3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required.

  14. Four-protein signature accurately predicts lymph node metastasis and survival in oral squamous cell carcinoma.

    PubMed

    Zanaruddin, Sharifah Nurain Syed; Saleh, Amyza; Yang, Yi-Hsin; Hamid, Sharifah; Mustafa, Wan Mahadzir Wan; Khairul Bariah, A A N; Zain, Rosnah Binti; Lau, Shin Hin; Cheong, Sok Ching

    2013-03-01

    The presence of lymph node (LN) metastasis significantly affects the survival of patients with oral squamous cell carcinoma (OSCC). Successful detection and removal of positive LNs are crucial in the treatment of this disease. Current evaluation methods still have their limitations in detecting the presence of tumor cells in the LNs, where up to a third of clinically diagnosed metastasis-negative (N0) patients actually have metastasis-positive LNs in the neck. We developed a molecular signature in the primary tumor that could predict LN metastasis in OSCC. A total of 211 cores from 55 individuals were included in the study. Eleven proteins were evaluated using immunohistochemical analysis in a tissue microarray. Of the 11 biomarkers evaluated using receiver operating curve analysis, epidermal growth factor receptor (EGFR), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (HER-2/neu), laminin, gamma 2 (LAMC2), and ras homolog family member C (RHOC) were found to be significantly associated with the presence of LN metastasis. Unsupervised hierarchical clustering-demonstrated expression patterns of these 4 proteins could be used to differentiate specimens that have positive LN metastasis from those that are negative for LN metastasis. Collectively, EGFR, HER-2/neu, LAMC2, and RHOC have a specificity of 87.5% and a sensitivity of 70%, with a prognostic accuracy of 83.4% for LN metastasis. We also demonstrated that the LN signature could independently predict disease-specific survival (P = .036). The 4-protein LN signature validated in an independent set of samples strongly suggests that it could reliably distinguish patients with LN metastasis from those who were metastasis-free and therefore could be a prognostic tool for the management of patients with OSCC.

  15. Four-protein signature accurately predicts lymph node metastasis and survival in oral squamous cell carcinoma.

    PubMed

    Zanaruddin, Sharifah Nurain Syed; Saleh, Amyza; Yang, Yi-Hsin; Hamid, Sharifah; Mustafa, Wan Mahadzir Wan; Khairul Bariah, A A N; Zain, Rosnah Binti; Lau, Shin Hin; Cheong, Sok Ching

    2013-03-01

    The presence of lymph node (LN) metastasis significantly affects the survival of patients with oral squamous cell carcinoma (OSCC). Successful detection and removal of positive LNs are crucial in the treatment of this disease. Current evaluation methods still have their limitations in detecting the presence of tumor cells in the LNs, where up to a third of clinically diagnosed metastasis-negative (N0) patients actually have metastasis-positive LNs in the neck. We developed a molecular signature in the primary tumor that could predict LN metastasis in OSCC. A total of 211 cores from 55 individuals were included in the study. Eleven proteins were evaluated using immunohistochemical analysis in a tissue microarray. Of the 11 biomarkers evaluated using receiver operating curve analysis, epidermal growth factor receptor (EGFR), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (HER-2/neu), laminin, gamma 2 (LAMC2), and ras homolog family member C (RHOC) were found to be significantly associated with the presence of LN metastasis. Unsupervised hierarchical clustering-demonstrated expression patterns of these 4 proteins could be used to differentiate specimens that have positive LN metastasis from those that are negative for LN metastasis. Collectively, EGFR, HER-2/neu, LAMC2, and RHOC have a specificity of 87.5% and a sensitivity of 70%, with a prognostic accuracy of 83.4% for LN metastasis. We also demonstrated that the LN signature could independently predict disease-specific survival (P = .036). The 4-protein LN signature validated in an independent set of samples strongly suggests that it could reliably distinguish patients with LN metastasis from those who were metastasis-free and therefore could be a prognostic tool for the management of patients with OSCC. PMID:23026198

  16. Nonempirically Tuned Range-Separated DFT Accurately Predicts Both Fundamental and Excitation Gaps in DNA and RNA Nucleobases

    PubMed Central

    2012-01-01

    Using a nonempirically tuned range-separated DFT approach, we study both the quasiparticle properties (HOMO–LUMO fundamental gaps) and excitation energies of DNA and RNA nucleobases (adenine, thymine, cytosine, guanine, and uracil). Our calculations demonstrate that a physically motivated, first-principles tuned DFT approach accurately reproduces results from both experimental benchmarks and more computationally intensive techniques such as many-body GW theory. Furthermore, in the same set of nucleobases, we show that the nonempirical range-separated procedure also leads to significantly improved results for excitation energies compared to conventional DFT methods. The present results emphasize the importance of a nonempirically tuned range-separation approach for accurately predicting both fundamental and excitation gaps in DNA and RNA nucleobases. PMID:22904693

  17. Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

    PubMed Central

    2012-01-01

    Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for

  18. Lateral impact validation of a geometrically accurate full body finite element model for blunt injury prediction.

    PubMed

    Vavalle, Nicholas A; Moreno, Daniel P; Rhyne, Ashley C; Stitzel, Joel D; Gayzik, F Scott

    2013-03-01

    This study presents four validation cases of a mid-sized male (M50) full human body finite element model-two lateral sled tests at 6.7 m/s, one sled test at 8.9 m/s, and a lateral drop test. Model results were compared to transient force curves, peak force, chest compression, and number of fractures from the studies. For one of the 6.7 m/s impacts (flat wall impact), the peak thoracic, abdominal and pelvic loads were 8.7, 3.1 and 14.9 kN for the model and 5.2 ± 1.1 kN, 3.1 ± 1.1 kN, and 6.3 ± 2.3 kN for the tests. For the same test setup in the 8.9 m/s case, they were 12.6, 6, and 21.9 kN for the model and 9.1 ± 1.5 kN, 4.9 ± 1.1 kN, and 17.4 ± 6.8 kN for the experiments. The combined torso load and the pelvis load simulated in a second rigid wall impact at 6.7 m/s were 11.4 and 15.6 kN, respectively, compared to 8.5 ± 0.2 kN and 8.3 ± 1.8 kN experimentally. The peak thorax load in the drop test was 6.7 kN for the model, within the range in the cadavers, 5.8-7.4 kN. When analyzing rib fractures, the model predicted Abbreviated Injury Scale scores within the reported range in three of four cases. Objective comparison methods were used to quantitatively compare the model results to the literature studies. The results show a good match in the thorax and abdomen regions while the pelvis results over predicted the reaction loads from the literature studies. These results are an important milestone in the development and validation of this globally developed average male FEA model in lateral impact.

  19. Accurate prediction of the refractive index of polymers using first principles and data modeling

    NASA Astrophysics Data System (ADS)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    Organic polymers with a high refractive index (RI) have recently attracted considerable interest due to their potential application in optical and optoelectronic devices. The ability to tailor the molecular structure of polymers is the key to increasing the accessible RI values. Our work concerns the creation of predictive in silico models for the optical properties of organic polymers, the screening of large-scale candidate libraries, and the mining of the resulting data to extract the underlying design principles that govern their performance. This work was set up to guide our experimentalist partners and allow them to target the most promising candidates. Our model is based on the Lorentz-Lorenz equation and thus includes the polarizability and number density values for each candidate. For the former, we performed a detailed benchmark study of different density functionals, basis sets, and the extrapolation scheme towards the polymer limit. For the number density we devised an exceedingly efficient machine learning approach to correlate the polymer structure and the packing fraction in the bulk material. We validated the proposed RI model against the experimentally known RI values of 112 polymers. We could show that the proposed combination of physical and data modeling is both successful and highly economical to characterize a wide range of organic polymers, which is a prerequisite for virtual high-throughput screening.

  20. Accurate predictions of C-SO2R bond dissociation enthalpies using density functional theory methods.

    PubMed

    Yu, Hai-Zhu; Fu, Fang; Zhang, Liang; Fu, Yao; Dang, Zhi-Min; Shi, Jing

    2014-10-14

    The dissociation of the C-SO2R bond is frequently involved in organic and bio-organic reactions, and the C-SO2R bond dissociation enthalpies (BDEs) are potentially important for understanding the related mechanisms. The primary goal of the present study is to provide a reliable calculation method to predict the different C-SO2R bond dissociation enthalpies (BDEs). Comparing the accuracies of 13 different density functional theory (DFT) methods (such as B3LYP, TPSS, and M05 etc.), and different basis sets (such as 6-31G(d) and 6-311++G(2df,2p)), we found that M06-2X/6-31G(d) gives the best performance in reproducing the various C-S BDEs (and especially the C-SO2R BDEs). As an example for understanding the mechanisms with the aid of C-SO2R BDEs, some primary mechanistic studies were carried out on the chemoselective coupling (in the presence of a Cu-catalyst) or desulfinative coupling reactions (in the presence of a Pd-catalyst) between sulfinic acid salts and boryl/sulfinic acid salts.

  1. Towards Accurate Prediction of Turbulent, Three-Dimensional, Recirculating Flows with the NCC

    NASA Technical Reports Server (NTRS)

    Iannetti, A.; Tacina, R.; Jeng, S.-M.; Cai, J.

    2001-01-01

    The National Combustion Code (NCC) was used to calculate the steady state, nonreacting flow field of a prototype Lean Direct Injection (LDI) swirler. This configuration used nine groups of eight holes drilled at a thirty-five degree angle to induce swirl. These nine groups created swirl in the same direction, or a corotating pattern. The static pressure drop across the holes was fixed at approximately four percent. Computations were performed on one quarter of the geometry, because the geometry is considered rotationally periodic every ninety degrees. The final computational grid used was approximately 2.26 million tetrahedral cells, and a cubic nonlinear k - epsilon model was used to model turbulence. The NCC results were then compared to time averaged Laser Doppler Velocimetry (LDV) data. The LDV measurements were performed on the full geometry, but four ninths of the geometry was measured. One-, two-, and three-dimensional representations of both flow fields are presented. The NCC computations compare both qualitatively and quantitatively well to the LDV data, but differences exist downstream. The comparison is encouraging, and shows that NCC can be used for future injector design studies. To improve the flow prediction accuracy of turbulent, three-dimensional, recirculating flow fields with the NCC, recommendations are given.

  2. An improved method for accurate prediction of mass flows through combustor liner holes

    SciTech Connect

    Adkins, R.C.; Gueroui, D.

    1986-01-01

    The objective of this paper is to present a simple approach to the solution of flow through combustor liner holes which can be used by practicing combustor engineers as well as providing the specialist modeler with a convenient boundary condition. For modeling, suppose that all relevant details of the incoming jets can be readily predicted, then the computational boundary can be limited to the inner wall of the liner and to the jets themselves. The scope of this paper is limited to the derivation of a simple analysis, the development of a reliable test technique, and to the correlation of data for plane holes having a diameter which is large when compared to the liner wall thickness. The effect of internal liner flow on the performance of the holes is neglected; this is considered to be justifiable because the analysis terminates at a short distance downstream of the hole and the significantly lower velocities inside the combustor have had little opportunity to have taken any effect. It is intended to extend the procedure to more complex hole forms and flow configurations in later papers.

  3. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

    NASA Astrophysics Data System (ADS)

    Balabin, Roman M.; Lomakina, Ekaterina I.

    2009-08-01

    Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol-1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

  4. Line Shape Parameters for CO_2 Transitions: Accurate Predictions from Complex Robert-Bonamy Calculations

    NASA Astrophysics Data System (ADS)

    Lamouroux, Julien; Gamache, Robert R.

    2013-06-01

    A model for the prediction of the vibrational dependence of CO_2 half-widths and line shifts for several broadeners, based on a modification of the model proposed by Gamache and Hartmann, is presented. This model allows the half-widths and line shifts for a ro-vibrational transition to be expressed in terms of the number of vibrational quanta exchanged in the transition raised to a power p and a reference ro-vibrational transition. Complex Robert-Bonamy calculations were made for 24 bands for lower rotational quantum numbers J'' from 0 to 160 for N_2-, O_2-, air-, and self-collisions with CO_2. In the model a Quantum Coordinate is defined by (c_1 Δν_1 + c_2 Δν_2 + c_3 Δν_3)^p where a linear least-squares fit to the data by the model expression is made. The model allows the determination of the slope and intercept as a function of rotational transition, broadening gas, and temperature. From these fit data, the half-width, line shift, and the temperature dependence of the half-width can be estimated for any ro-vibrational transition, allowing spectroscopic CO_2 databases to have complete information for the line shape parameters. R. R. Gamache, J.-M. Hartmann, J. Quant. Spectrosc. Radiat. Transfer. {{83}} (2004), 119. R. R. Gamache, J. Lamouroux, J. Quant. Spectrosc. Radiat. Transfer. {{117}} (2013), 93.

  5. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    PubMed Central

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

    2015-01-01

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries. PMID:26520735

  6. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    SciTech Connect

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

    2015-11-15

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  7. How Accurate Are the Anthropometry Equations in in Iranian Military Men in Predicting Body Composition?

    PubMed Central

    Shakibaee, Abolfazl; Faghihzadeh, Soghrat; Alishiri, Gholam Hossein; Ebrahimpour, Zeynab; Faradjzadeh, Shahram; Sobhani, Vahid; Asgari, Alireza

    2015-01-01

    Background: The body composition varies according to different life styles (i.e. intake calories and caloric expenditure). Therefore, it is wise to record military personnel’s body composition periodically and encourage those who abide to the regulations. Different methods have been introduced for body composition assessment: invasive and non-invasive. Amongst them, the Jackson and Pollock equation is most popular. Objectives: The recommended anthropometric prediction equations for assessing men’s body composition were compared with dual-energy X-ray absorptiometry (DEXA) gold standard to develop a modified equation to assess body composition and obesity quantitatively among Iranian military men. Patients and Methods: A total of 101 military men aged 23 - 52 years old with a mean age of 35.5 years were recruited and evaluated in the present study (average height, 173.9 cm and weight, 81.5 kg). The body-fat percentages of subjects were assessed both with anthropometric assessment and DEXA scan. The data obtained from these two methods were then compared using multiple regression analysis. Results: The mean and standard deviation of body fat percentage of the DEXA assessment was 21.2 ± 4.3 and body fat percentage obtained from three Jackson and Pollock 3-, 4- and 7-site equations were 21.1 ± 5.8, 22.2 ± 6.0 and 20.9 ± 5.7, respectively. There was a strong correlation between these three equations and DEXA (R² = 0.98). Conclusions: The mean percentage of body fat obtained from the three equations of Jackson and Pollock was very close to that of body fat obtained from DEXA; however, we suggest using a modified Jackson-Pollock 3-site equation for volunteer military men because the 3-site equation analysis method is simpler and faster than other methods. PMID:26715964

  8. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    SciTech Connect

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  9. An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function

    PubMed Central

    Wang, Kui; Gao, Jianzhao; Shen, Shiyi; Tuszynski, Jack A.; Ruan, Jishou

    2013-01-01

    Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery. In the past two decades, a lot of algorithms were present to predict the protein-ligand binding site. In this paper, we introduce statistical depth function to define negative samples and propose an SVM-based method which integrates sequence and structural information to predict binding site. The results show that the present method performs better than the existent ones. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively; on the independent test set, the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively. PMID:24195070

  10. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  11. Environment and feeding change the ability of heart rate to predict metabolism in resting Steller sea lions (Eumetopias jubatus).

    PubMed

    Young, Beth L; Rosen, David A S; Haulena, Martin; Hindle, Allyson G; Trites, Andrew W

    2011-01-01

    The ability to use heart rate (fh) to predict oxygen consumption rates ([Formula: see text]) in Steller sea lions and other pinnipeds has been investigated in fasting animals. However, it is unknown whether established fh:[Formula: see text] relationships hold under more complex physiological situations, such as when animals are feeding or digesting. We assessed whether fh could accurately predict [Formula: see text] in trained Steller sea lions while fasting and after being fed. Using linear mixed-effects models, we derived unique equations to describe the fh:[Formula: see text] relationship for fasted sea lions resting on land and in water. Feeding did not significantly change the fh:[Formula: see text] relationship on land. However, Steller sea lions in water displayed a different fh:[Formula: see text] relationship after consuming a 4-kg meal compared with the fasting condition. Incorporating comparable published fh:[Formula: see text] data from Steller sea lions showed a distinct effect of feeding after a 6-kg meal. Ultimately, our study illustrated that both feeding and physical environment are statistically relevant when deriving [Formula: see text] from telemetered fh, but that only environment affects the practical ability to predict metabolism from fh. Updating current bioenergetic models with data gathered using these predictive fh:[Formula: see text] equations will yield more accurate estimates of metabolic rates of free-ranging Steller sea lions under a variety of physiological, behavioral, and environmental states.

  12. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race.

    PubMed

    Hourihan, Kathleen L; Benjamin, Aaron S; Liu, Xiping

    2012-09-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness's claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness.

  13. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed.

  14. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. PMID:26121186

  15. Why don't we learn to accurately forecast feelings? How misremembering our predictions blinds us to past forecasting errors.

    PubMed

    Meyvis, Tom; Ratner, Rebecca K; Levav, Jonathan

    2010-11-01

    Why do affective forecasting errors persist in the face of repeated disconfirming evidence? Five studies demonstrate that people misremember their forecasts as consistent with their experience and thus fail to perceive the extent of their forecasting error. As a result, people do not learn from past forecasting errors and fail to adjust subsequent forecasts. In the context of a Super Bowl loss (Study 1), a presidential election (Studies 2 and 3), an important purchase (Study 4), and the consumption of candies (Study 5), individuals mispredicted their affective reactions to these experiences and subsequently misremembered their predictions as more accurate than they actually had been. The findings indicate that this recall error results from people's tendency to anchor on their current affective state when trying to recall their affective forecasts. Further, those who showed larger recall errors were less likely to learn to adjust their subsequent forecasts and reminding people of their actual forecasts enhanced learning. These results suggest that a failure to accurately recall one's past predictions contributes to the perpetuation of forecasting errors.

  16. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  17. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding.

    PubMed

    Nissley, Daniel A; Sharma, Ajeet K; Ahmed, Nabeel; Friedrich, Ulrike A; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  18. RS-Predictor models augmented with SMARTCyp reactivities: Robust metabolic regioselectivity predictions for nine CYP isozymes

    PubMed Central

    Zaretzki, Jed; Rydberg, Patrik; Bergeron, Charles; Bennett, Kristin P.; Olsen, Lars

    2012-01-01

    RS-Predictor is a tool for creating pathway-independent, isozyme-specific site of metabolism (SOM) prediction models using any set of known cytochrome P450 substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1 and 3A4, which we believe is the largest publicly accessible collection of P450 ligands and metabolites ever released. A comprehensive investigation into the importance of different descriptor classes for predicting the regioselectivity of each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a DFT reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by StarDrop and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2(83.0%), 2A6(85.7%), 2B6(82.1%), 2C19(86.2%), 2C8(83.8%), 2C9(84.5%), 2D6(85.9%), 2E1(82.8%), 3A4(82.3%) and merged(86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs. PMID:22524152

  19. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

    PubMed Central

    Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

    2014-01-01

    Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely. PMID:23088273

  20. Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures

    PubMed Central

    2013-01-01

    Background A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. Results To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. Conclusions These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures. PMID:23548183

  1. A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans

    PubMed Central

    Bigdeli, T. Bernard; Lee, Donghyung; Webb, Bradley Todd; Riley, Brien P.; Vladimirov, Vladimir I.; Fanous, Ayman H.; Kendler, Kenneth S.; Bacanu, Silviu-Alin

    2016-01-01

    Motivation: For genetic studies, statistically significant variants explain far less trait variance than ‘sub-threshold’ association signals. To dimension follow-up studies, researchers need to accurately estimate ‘true’ effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner’s curse biases, which are reduced only by laborious winner’s curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. Results:WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose FDR Inverse Quantile Transformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. Conclusions: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). Availability and Implementation: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT. Contact: sabacanu@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27187203

  2. Small-scale field experiments accurately scale up to predict density dependence in reef fish populations at large scales.

    PubMed

    Steele, Mark A; Forrester, Graham E

    2005-09-20

    Field experiments provide rigorous tests of ecological hypotheses but are usually limited to small spatial scales. It is thus unclear whether these findings extrapolate to larger scales relevant to conservation and management. We show that the results of experiments detecting density-dependent mortality of reef fish on small habitat patches scale up to have similar effects on much larger entire reefs that are the size of small marine reserves and approach the scale at which some reef fisheries operate. We suggest that accurate scaling is due to the type of species interaction causing local density dependence and the fact that localized events can be aggregated to describe larger-scale interactions with minimal distortion. Careful extrapolation from small-scale experiments identifying species interactions and their effects should improve our ability to predict the outcomes of alternative management strategies for coral reef fishes and their habitats.

  3. Effects of the inlet conditions and blood models on accurate prediction of hemodynamics in the stented coronary arteries

    NASA Astrophysics Data System (ADS)

    Jiang, Yongfei; Zhang, Jun; Zhao, Wanhua

    2015-05-01

    Hemodynamics altered by stent implantation is well-known to be closely related to in-stent restenosis. Computational fluid dynamics (CFD) method has been used to investigate the hemodynamics in stented arteries in detail and help to analyze the performances of stents. In this study, blood models with Newtonian or non-Newtonian properties were numerically investigated for the hemodynamics at steady or pulsatile inlet conditions respectively employing CFD based on the finite volume method. The results showed that the blood model with non-Newtonian property decreased the area of low wall shear stress (WSS) compared with the blood model with Newtonian property and the magnitude of WSS varied with the magnitude and waveform of the inlet velocity. The study indicates that the inlet conditions and blood models are all important for accurately predicting the hemodynamics. This will be beneficial to estimate the performances of stents and also help clinicians to select the proper stents for the patients.

  4. TIMP2•IGFBP7 biomarker panel accurately predicts acute kidney injury in high-risk surgical patients

    PubMed Central

    Gunnerson, Kyle J.; Shaw, Andrew D.; Chawla, Lakhmir S.; Bihorac, Azra; Al-Khafaji, Ali; Kashani, Kianoush; Lissauer, Matthew; Shi, Jing; Walker, Michael G.; Kellum, John A.

    2016-01-01

    BACKGROUND Acute kidney injury (AKI) is an important complication in surgical patients. Existing biomarkers and clinical prediction models underestimate the risk for developing AKI. We recently reported data from two trials of 728 and 408 critically ill adult patients in whom urinary TIMP2•IGFBP7 (NephroCheck, Astute Medical) was used to identify patients at risk of developing AKI. Here we report a preplanned analysis of surgical patients from both trials to assess whether urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor–binding protein 7 (IGFBP7) accurately identify surgical patients at risk of developing AKI. STUDY DESIGN We enrolled adult surgical patients at risk for AKI who were admitted to one of 39 intensive care units across Europe and North America. The primary end point was moderate-severe AKI (equivalent to KDIGO [Kidney Disease Improving Global Outcomes] stages 2–3) within 12 hours of enrollment. Biomarker performance was assessed using the area under the receiver operating characteristic curve, integrated discrimination improvement, and category-free net reclassification improvement. RESULTS A total of 375 patients were included in the final analysis of whom 35 (9%) developed moderate-severe AKI within 12 hours. The area under the receiver operating characteristic curve for [TIMP-2]•[IGFBP7] alone was 0.84 (95% confidence interval, 0.76–0.90; p < 0.0001). Biomarker performance was robust in sensitivity analysis across predefined subgroups (urgency and type of surgery). CONCLUSION For postoperative surgical intensive care unit patients, a single urinary TIMP2•IGFBP7 test accurately identified patients at risk for developing AKI within the ensuing 12 hours and its inclusion in clinical risk prediction models significantly enhances their performance. LEVEL OF EVIDENCE Prognostic study, level I. PMID:26816218

  5. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    2014-01-01

    Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  6. In silico prediction of xenobiotic metabolism in humans

    SciTech Connect

    Mu, Fangping

    2009-01-01

    Xenobiotic metabolism in humans is catalyzed by a few enzymes with broad substrate specificities, which provide the overall broad chemical specificity for nearly all xenobiotics that humans encounter. Xenobiotic metabolism are classified into functional group biotransformations. Based on bona fide reactions and negative examples for each reaction class, support vector machine (SVM) classifiers are built. The input to SVM is a set of atomic and molecular features to define the electrostatic, steric, energetic, geometrical and topological environment of the atoms in the reaction center under the molecule. Results show that the overall sensitivity and specificity of classifiers is around 87%.

  7. Sub-concussive hit characteristics predict deviant brain metabolism in football athletes.

    PubMed

    Poole, Victoria N; Breedlove, Evan L; Shenk, Trey E; Abbas, Kausar; Robinson, Meghan E; Leverenz, Larry J; Nauman, Eric A; Dydak, Ulrike; Talavage, Thomas M

    2015-01-01

    Magnetic resonance spectroscopy and helmet telemetry were used to monitor the neural metabolic response to repetitive head collisions in 25 high school American football athletes. Specific hit characteristics were determined highly predictive of metabolic alterations, suggesting that sub-concussive blows can produce biochemical changes and potentially lead to neurological problems.

  8. A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network

    PubMed Central

    2014-01-01

    Background Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons. Results We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification. Conclusions The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can

  9. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  10. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    PubMed

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate. PMID:22792053

  11. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    PubMed

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

  12. The use of drug metabolism for prediction of intestinal permeability (dagger).

    PubMed

    Chen, Mei-Ling; Yu, Lawrence

    2009-01-01

    The Biopharmaceutics Classification System (BCS), based on the aqueous solubility and intestinal permeability of a drug substance, has been widely used to predict the extent of drug absorption during the course of pharmaceutical development. Combined with product dissolution data, this system has gained a prominent role in regulatory process to determine if a drug formulated in an immediate release solid oral dosage form qualifies for waiver of in vivo bioequivalence studies. In parallel, the Biopharmaceutics Drug Disposition Classification System (BDDCS), using aqueous solubility and drug metabolism, takes on another venue to predict overall drug disposition. It has been suggested that the matrix of drug metabolism in BDDCS can be used to substantiate the classification of permeability by BCS. A total of 51 drugs were compiled in this study to examine the use of drug metabolism for predicting permeability. All compounds were classified as high permeability based on BCS, but only 73% of the compounds were found to exhibit extensive metabolism. Lipophilicity accounts for significant metabolism of many highly permeable drugs. Fourteen (14) out of 51 drugs have poor metabolism, suggesting that high permeability as defined by BCS does not necessarily dictate extensive metabolism. The drugs that have high permeability but poor metabolism are generally hydrophilic molecules with low molecular weight and are likely to be absorbed by active transport mechanisms. Based on the present data and literature information, it seems logical to predict that the extent of absorption is mostly complete (or > or =90%) if the drug is subject to a high degree of metabolism (e.g., > or =90%). The extent of drug metabolism may be useful in supporting permeability classification under certain circumstances.

  13. QSAR and QM/MM approaches applied to drug metabolism prediction.

    PubMed

    Braga, R C; Andrade, C H

    2012-06-01

    In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

  14. SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism.

    PubMed

    Rydberg, Patrik; Gloriam, David E; Zaretzki, Jed; Breneman, Curt; Olsen, Lars

    2010-06-10

    SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. This is a major advantage, because it makes SMARTCyp very fast. Other advantages are that experimental data are not a prerequisite to create the model, and it can easily be integrated with other methods to create models for other cytochrome P450 isoforms. Benchmarking tests on a database of 394 3A4 substrates show that SMARTCyp successfully identifies at least one metabolic site in the top two ranked positions 76% of the time. SMARTCyp is available for download at http://www.farma.ku.dk/p450.

  15. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

  16. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

    PubMed Central

    Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

    2015-01-01

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

  17. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.

  18. Computational finite element bone mechanics accurately predicts mechanical competence in the human radius of an elderly population.

    PubMed

    Mueller, Thomas L; Christen, David; Sandercott, Steve; Boyd, Steven K; van Rietbergen, Bert; Eckstein, Felix; Lochmüller, Eva-Maria; Müller, Ralph; van Lenthe, G Harry

    2011-06-01

    High-resolution peripheral quantitative computed tomography (HR-pQCT) is clinically available today and provides a non-invasive measure of 3D bone geometry and micro-architecture with unprecedented detail. In combination with microarchitectural finite element (μFE) models it can be used to determine bone strength using a strain-based failure criterion. Yet, images from only a relatively small part of the radius are acquired and it is not known whether the region recommended for clinical measurements does predict forearm fracture load best. Furthermore, it is questionable whether the currently used failure criterion is optimal because of improvements in image resolution, changes in the clinically measured volume of interest, and because the failure criterion depends on the amount of bone present. Hence, we hypothesized that bone strength estimates would improve by measuring a region closer to the subchondral plate, and by defining a failure criterion that would be independent of the measured volume of interest. To answer our hypotheses, 20% of the distal forearm length from 100 cadaveric but intact human forearms was measured using HR-pQCT. μFE bone strength was analyzed for different subvolumes, as well as for the entire 20% of the distal radius length. Specifically, failure criteria were developed that provided accurate estimates of bone strength as assessed experimentally. It was shown that distal volumes were better in predicting bone strength than more proximal ones. Clinically speaking, this would argue to move the volume of interest for the HR-pQCT measurements even more distally than currently recommended by the manufacturer. Furthermore, new parameter settings using the strain-based failure criterion are presented providing better accuracy for bone strength estimates.

  19. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  20. Development of gold-immobilized P450 platform for exploring the effect of oligomer formation on P450-mediated metabolism for in vitro to in vivo drug metabolism predictions

    NASA Astrophysics Data System (ADS)

    Kabulski, Jarod L.

    The cytochrome P450 (P450) enzyme family is responsible for the biotransformation of a wide range of endogenous and xenobiotic compounds, as well as being the major metabolic enzyme in first pass drug metabolism. In vivo drug metabolism for P450 enzymes is predicted using in vitro data obtained from a reconstituted expressed P450 system, but these systems have not always been proven to accurately represent in vivo enzyme kinetics, due to interactions caused by oligomer formation. These in vitro systems use soluble P450 enzymes prone to oligomer formation and studies have shown that increased states of protein aggregation directly affect the P450 enzyme kinetics. We have developed an immobilized enzyme system that isolates the enzyme and can be used to elucidate the effect of P450 aggregation on metabolism kinetics. The long term goal of my research is to develop a tool that will help improve the assessment of pharmaceuticals by better predicting in vivo kinetics in an in vitro system. The central hypothesis of this research is that P450-mediated kinetics measured in vitro is dependent on oligomer formation and that the accurate prediction of in vivo P450-mediated kinetics requires elucidation of the effect of oligomer formation. The rationale is that the development of a P450 bound to a Au platform can be used to control the aggregation of enzymes and bonding to Au may also permit replacement of the natural redox partners with an electrode capable of supplying a constant flow of electrons. This dissertation explains the details of the enzyme attachment, monitoring substrate binding, and metabolism using physiological and electrochemical methods, determination of enzyme kinetics, and the development of an immobilized-P450 enzyme bioreactor. This work provides alternative approaches to studying P450-mediated kinetics, a platform for controlling enzyme aggregation, electrochemically-driven P450 metabolism, and for investigating the effect of protein

  1. Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations.

    PubMed

    Pragyan, Preeti; Kesharwani, Siddharth S; Nandekar, Prajwal P; Rathod, Vijay; Sangamwar, Abhay T

    2014-11-01

    Recently, CYP1 enzymes are documented for selective metabolism of anticancer leads in cancer prevention and/or progression. Elucidation of specificity of substrates/inhibitors of CYP1 isoforms plays a vital role in design of more selective and potent anticancer leads. However, an area of concern is the broad range of substrate specificities and planar nature of substrates with limited dataset which makes it difficult to predict their site of metabolism (SOM) accurately. In the present study, various models for prediction of site of metabolism in case of CYP1A1, CYP1A2, and CYP1B1 substrates were developed using MetaSite, molecular docking, and quantum chemical descriptors. The predictive accuracy of MetaSite, molecular docking, and quantum chemical descriptors in identifying experimental site of metabolism was analyzed at three levels; top rank, top three ranks, and top five ranks. Two quantum chemical descriptors, chemical hardness and local nucleophilicity are proposed for the prediction of CYP-mediated SOM for the first time. The predictive accuracy shown by chemical hardness at top three ranks was 83.3, 85.7, and 84.6 % for CYP1A1, CYP1A2 and CYP1B1, respectively, whereas local nucleophilicity gave poor predictions of 50, 42.8, and 46.2 %, respectively. The predictability of chemical hardness descriptor outperformed at all three levels of ranks for CYP1A1, CYP1A2, and CYP1B1. Hence, we propose chemical hardness as an useful quantum chemical descriptor for prediction of metabolically vulnerable prints in CYP1A1, CYP1A2, and CYP1B1 mediated metabolism and support the optimization efforts in drug discovery and development programs.

  2. High IFIT1 expression predicts improved clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

    PubMed

    Zhang, Jin-Feng; Chen, Yao; Lin, Guo-Shi; Zhang, Jian-Dong; Tang, Wen-Long; Huang, Jian-Huang; Chen, Jin-Shou; Wang, Xing-Fu; Lin, Zhi-Xiong

    2016-06-01

    Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma. PMID:26980050

  3. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

    PubMed Central

    Heinken, Almut; Thiele, Ines

    2015-01-01

    The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates “germfree” and “ex-germfree” human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism. PMID:25901891

  4. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework.

    PubMed

    Heinken, Almut; Thiele, Ines

    2015-01-01

    The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "ex-germfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

  5. A time accurate prediction of the viscous flow in a turbine stage including a rotor in motion

    NASA Astrophysics Data System (ADS)

    Shavalikul, Akamol

    accurate flow characteristics in the NGV domain and the rotor domain with less computational time and computer memory requirements. In contrast, the time accurate flow simulation can predict all unsteady flow characteristics occurring in the turbine stage, but with high computational resource requirements. (Abstract shortened by UMI.)

  6. Use of HμREL Human Coculture System for Prediction of Intrinsic Clearance and Metabolite Formation for Slowly Metabolized Compounds.

    PubMed

    Hultman, Ia; Vedin, Charlotta; Abrahamsson, Anna; Winiwarter, Susanne; Darnell, Malin

    2016-08-01

    be applied to accurately estimate intrinsic clearance of slowly metabolized drugs and is now utilized as a standard method for in vivo clearance prediction of such compounds in-house.

  7. A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

    PubMed Central

    Harris, Adam; Harries, Priscilla

    2016-01-01

    overall accuracy being reported. Data were extracted using a standardised tool, by one reviewer, which could have introduced bias. Devising search terms for prognostic studies is challenging. Every attempt was made to devise search terms that were sufficiently sensitive to detect all prognostic studies; however, it remains possible that some studies were not identified. Conclusion Studies of prognostic accuracy in palliative care are heterogeneous, but the evidence suggests that clinicians’ predictions are frequently inaccurate. No sub-group of clinicians was consistently shown to be more accurate than any other. Implications of Key Findings Further research is needed to understand how clinical predictions are formulated and how their accuracy can be improved. PMID:27560380

  8. Prediction of Metabolic Flux Distribution from Gene Expression Data Based on the Flux Minimization Principle

    PubMed Central

    Song, Hyun-Seob; Reifman, Jaques; Wallqvist, Anders

    2014-01-01

    Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts. PMID:25397773

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

    PubMed Central

    Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

    2016-01-01

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

  10. Metabolic Youth in Middle Age: Predicting Aging in Caenorhabditis elegans Using Metabolomics.

    PubMed

    Davies, Sarah K; Bundy, Jacob G; Leroi, Armand M

    2015-11-01

    Many mutations and allelic variants are known that influence the rate at which animals age, but when in life do such variants diverge from normal patterns of aging? Is this divergence visible in their physiologies? To investigate these questions, we have used (1)H NMR spectroscopy to study how the metabolome of the nematode Caenorhabditis elegans changes as it grows older. We identify a series of metabolic changes that, collectively, predict the age of wild-type worms. We then show that long-lived mutant daf-2(m41) worms are metabolically youthful compared to wild-type worms, but that this relative youth only appears in middle age. Finally, we show that metabolic age predicts the timing and magnitude of differences in age-specific mortality between these strains. Thus, the future mortality of these two genotypes can be predicted long before most of the worms die.

  11. Metabolic Youth in Middle Age: Predicting Aging in Caenorhabditis elegans Using Metabolomics.

    PubMed

    Davies, Sarah K; Bundy, Jacob G; Leroi, Armand M

    2015-11-01

    Many mutations and allelic variants are known that influence the rate at which animals age, but when in life do such variants diverge from normal patterns of aging? Is this divergence visible in their physiologies? To investigate these questions, we have used (1)H NMR spectroscopy to study how the metabolome of the nematode Caenorhabditis elegans changes as it grows older. We identify a series of metabolic changes that, collectively, predict the age of wild-type worms. We then show that long-lived mutant daf-2(m41) worms are metabolically youthful compared to wild-type worms, but that this relative youth only appears in middle age. Finally, we show that metabolic age predicts the timing and magnitude of differences in age-specific mortality between these strains. Thus, the future mortality of these two genotypes can be predicted long before most of the worms die. PMID:26381038

  12. [Predicting genetic modification targets based on metabolic network analysis--a review].

    PubMed

    Li, Peishun; Ma, Hongwu; Zhao, Xueming; Chen, Tao

    2016-01-01

    Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results. PMID:27363195

  13. Predicting Performance and Plasticity in the Development of Respiratory Structures and Metabolic Systems

    PubMed Central

    Montooth, Kristi L.; Helm, Bryan R.

    2014-01-01

    The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change. PMID:24812329

  14. Prediction of Metabolic Gene Biomarkers for Neurodegenerative Disease by an Integrated Network-Based Approach

    PubMed Central

    Su, Xianming

    2015-01-01

    Neurodegenerative diseases (NDs), such as Parkinson's disease (PD) and Huntington's disease (HD), have become more and more common among aged people worldwide. One hallmark of NDs is the presence of intracellular accumulation of specific pathogenic proteins that may result from abnormal function of metabolic processes. Previously, we have developed a computational method named Met-express that predicted key enzyme-coding genes in cancer development by integrating cancer gene coexpression network with the metabolic network. Here, we applied Met-express to predict key enzyme-coding genes in both PD and HD. Functional enrichment analysis and literature review of predicted genes suggested that there might be some common pathogenic metabolic pathways for PD and HD. We further found that the predicted genes had significant functional association with known disease genes, with some of them already documented as biomarkers or therapeutic targets for NDs. As such, the predicted metabolic genes may be of use as novel biomarkers not only for ND diagnosis but also for potential therapeutic treatments. PMID:26064912

  15. Prediction of glycated hemoglobin levels at 3 months after metabolic surgery based on the 7-day plasma metabolic profile.

    PubMed

    Kwon, Hyuk Nam; Lee, Yeon Ji; Kang, Ju-Hee; Choi, Ji-Ho; An, Yong Jin; Kang, Sunmi; Lee, Dae Hyun; Suh, Young Ju; Heo, Yoonseok; Park, Sunghyouk

    2014-01-01

    Metabolic surgery has been shown to provide better glycemic control for type 2 diabetes than conventional therapies. Still, the outcomes of the surgery are variable, and prognostic markers reflecting the metabolic changes by the surgery are yet to be established. NMR-based plasma metabolomics followed by multivariate regression was used to test the correlation between the metabolomic profile at 7-days after surgery and glycated hemoglobin (HbA1c) levels at 3-months (and up to 12 months with less patients), and to identify the relevant markers. Metabolomic profiles at 7-days could differentiate the patients according to the HbA1c improvement status at 3-months. The HbA1c values were predicted based on the metabolomics profile with partial least square regression, and found to be correlated with the observed values. Metabolite analysis suggested that 3-Hydroxybutyrate (3-HB) and glucose contributes to this prediction, and the [3-HB]/[glucose] exhibited a modest to good correlation with the HbA1c level at 3-months. The prediction of 3-month HbA1c using 7-day metabolomic profile and the suggested new criterion [3-HB]/[glucose] could augment current prognostic modalities and help clinicians decide if drug therapy is necessary.

  16. Prediction of Glycated Hemoglobin Levels at 3 Months after Metabolic Surgery Based on the 7-Day Plasma Metabolic Profile

    PubMed Central

    Choi, Ji-ho; An, Yong Jin; Kang, Sunmi; Lee, Dae Hyun; Suh, Young Ju; Heo, Yoonseok; Park, Sunghyouk

    2014-01-01

    Metabolic surgery has been shown to provide better glycemic control for type 2 diabetes than conventional therapies. Still, the outcomes of the surgery are variable, and prognostic markers reflecting the metabolic changes by the surgery are yet to be established. NMR-based plasma metabolomics followed by multivariate regression was used to test the correlation between the metabolomic profile at 7-days after surgery and glycated hemoglobin (HbA1c) levels at 3-months (and up to 12 months with less patients), and to identify the relevant markers. Metabolomic profiles at 7-days could differentiate the patients according to the HbA1c improvement status at 3-months. The HbA1c values were predicted based on the metabolomics profile with partial least square regression, and found to be correlated with the observed values. Metabolite analysis suggested that 3-Hydroxybutyrate (3-HB) and glucose contributes to this prediction, and the [3-HB]/[glucose] exhibited a modest to good correlation with the HbA1c level at 3-months. The prediction of 3-month HbA1c using 7-day metabolomic profile and the suggested new criterion [3-HB]/[glucose] could augment current prognostic modalities and help clinicians decide if drug therapy is necessary. PMID:25384027

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

    PubMed

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

    2012-01-01

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

  18. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    NASA Astrophysics Data System (ADS)

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  19. Predicting effects of structural stress in a genome-reduced model bacterial metabolism.

    PubMed

    Güell, Oriol; Sagués, Francesc; Serrano, M Ángeles

    2012-01-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment. PMID:22934134

  20. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

    PubMed Central

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

    2015-01-01

    Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

  1. Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

    PubMed

    MacLeod, Erin L; Hall, Kevin D; McGuire, Peter J

    2016-01-01

    Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

  2. Bioenergetics of Nutrient Reserves and Metabolism in Spiny Lobster Juveniles Sagmariasus verreauxi: Predicting Nutritional Condition from Hemolymph Biochemistry.

    PubMed

    Simon, C J; Fitzgibbon, Q P; Battison, A; Carter, C G; Battaglene, S C

    2015-01-01

    The nutritional condition of cultured Sagmariasus verreauxi juveniles over the molt and during starvation was investigated by studying their metabolism, bioenergetics of nutrient reserves, and hemolymph biochemistry. Juveniles were shown to downregulate standard metabolic rate by as much as 52% within 14 d during starvation. Hepatopancreas (HP) lipid was prioritized as a source of energy, but this reserve represented only between 1% and 13% of the total measured energy reserve and was used quickly during starvation, especially in the immediate postmolt period when as much as 60% was depleted within 3 d. Abdominal muscle (AM) protein represented between 74% and 90% of the total measured energy reserve in juvenile lobsters, and as much as 40% of available AM protein energy was used over 28 d of starvation after the molt. Carbohydrate reserves represented less than 2% of the measured total energy reserve in fed intermolt lobsters and provided negligible energy during starvation. Eighteen hemolymph parameters were measured to identify a nondestructive biomarker of condition that would reflect accurately the state of energy reserves of the lobster. Among these, the hemolymph Brix index was the most accurate and practical method to predict HP lipid and the total energy content of both the HP and the AM in juvenile S. verreauxi. The Brix index was strongly correlated with hemolymph proteins, triglyceride, cholesterol, calcium, and phosphorus concentrations, as well as lipase activity; all were useful in predicting condition. Electrolytes such as chloride, magnesium, and potassium and metabolites such as glucose and lactate were poor indicators of nutritional condition. Uric acid and the "albumin"-to-"globulin" ratio provided complementary information to the Brix index, which may assist in determining nutritional condition of wild juvenile lobsters of unknown intermolt development. This study will greatly assist future ecological studies examining the nutritional condition

  3. Bioenergetics of Nutrient Reserves and Metabolism in Spiny Lobster Juveniles Sagmariasus verreauxi: Predicting Nutritional Condition from Hemolymph Biochemistry.

    PubMed

    Simon, C J; Fitzgibbon, Q P; Battison, A; Carter, C G; Battaglene, S C

    2015-01-01

    The nutritional condition of cultured Sagmariasus verreauxi juveniles over the molt and during starvation was investigated by studying their metabolism, bioenergetics of nutrient reserves, and hemolymph biochemistry. Juveniles were shown to downregulate standard metabolic rate by as much as 52% within 14 d during starvation. Hepatopancreas (HP) lipid was prioritized as a source of energy, but this reserve represented only between 1% and 13% of the total measured energy reserve and was used quickly during starvation, especially in the immediate postmolt period when as much as 60% was depleted within 3 d. Abdominal muscle (AM) protein represented between 74% and 90% of the total measured energy reserve in juvenile lobsters, and as much as 40% of available AM protein energy was used over 28 d of starvation after the molt. Carbohydrate reserves represented less than 2% of the measured total energy reserve in fed intermolt lobsters and provided negligible energy during starvation. Eighteen hemolymph parameters were measured to identify a nondestructive biomarker of condition that would reflect accurately the state of energy reserves of the lobster. Among these, the hemolymph Brix index was the most accurate and practical method to predict HP lipid and the total energy content of both the HP and the AM in juvenile S. verreauxi. The Brix index was strongly correlated with hemolymph proteins, triglyceride, cholesterol, calcium, and phosphorus concentrations, as well as lipase activity; all were useful in predicting condition. Electrolytes such as chloride, magnesium, and potassium and metabolites such as glucose and lactate were poor indicators of nutritional condition. Uric acid and the "albumin"-to-"globulin" ratio provided complementary information to the Brix index, which may assist in determining nutritional condition of wild juvenile lobsters of unknown intermolt development. This study will greatly assist future ecological studies examining the nutritional condition

  4. Predicting metabolic syndrome using decision tree and support vector machine methods

    PubMed Central

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-01-01

    BACKGROUND Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. CONCLUSION The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in

  5. CYP450 phenotyping and metabolite identification of quinine by accurate mass UPLC-MS analysis: a possible metabolic link to blackwater fever

    PubMed Central

    2013-01-01

    Background The naturally occurring alkaloid drug, quinine is commonly used for the treatment of severe malaria. Despite centuries of use, its metabolism is still not fully understood, and may play a role in the haemolytic disorders associated with the drug. Methods Incubations of quinine with CYPs 1A2, 2C9, 2C19, 2D6, and 3A4 were conducted, and the metabolites were characterized by accurate mass UPLC-MSE analysis. Reactive oxygen species generation was also measured in human erythrocytes incubated in the presence of quinine with and without microsomes. Results The metabolites 3-hydroxyquinine, 2’-oxoquininone, and O-desmethylquinine were observed after incubation with CYPs 3A4 (3-hydroxyquinine and 2’-oxoquininone) and 2D6 (O-desmethylquinine). In addition, multiple hydroxylations were observed both on the quinoline core and the quinuclidine ring system. Of the five primary abundance CYPs tested, 3A4, 2D6, 2C9, and 2C19 all demonstrated activity toward quinine, while 1A2 did not. Further, quinine produced robust dose-dependent oxidative stress in human erythrocytes in the presence of microsomes. Conclusions Taken in context, these data suggest a CYP-mediated link between quinine metabolism and the poorly understood haemolytic condition known as blackwater fever, often associated with quinine ingestion. PMID:23800033

  6. Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions

    PubMed Central

    Yuan, Huili; Cheung, C. Y. Maurice; Hilbers, Peter A. J.; van Riel, Natal A. W.

    2016-01-01

    The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models. PMID:27200014

  7. Power and metabolic scope of bird flight: a phylogenetic analysis of biomechanical predictions.

    PubMed

    Hedenström, Anders

    2008-07-01

    For flying animals aerodynamic theory predicts that mechanical power required to fly scales as P proportional, variant m (7/6) in a series of isometric birds, and that the flight metabolic scope (P/BMR; BMR is basal metabolic rate) scales as P (scope) proportional, variant m (5/12). I tested these predictions by using phylogenetic independent contrasts from a set of 20 bird species, where flight metabolic rate was measured during laboratory conditions (mainly in wind tunnels). The body mass scaling exponent for P was 0.90, significantly lower than the predicted 7/6. This is partially due to the fact that real birds show an allometric scaling of wing span, which reduces flight cost. P (scope) was estimated using direct measurements of BMR in combination with allometric equations. The body mass scaling of P (scope) ranged between 0.31 and 0.51 for three data sets, respectively, and none differed significantly from the prediction of 5/12. Body mass scaling exponents of P (scope) differed significantly from 0 in all cases, and so P (scope) showed a positive body mass scaling in birds in accordance with the prediction.

  8. Metabolic profiling of yeast culture using gas chromatography coupled with orthogonal acceleration accurate mass time-of-flight mass spectrometry: application to biomarker discovery.

    PubMed

    Kondo, Elsuida; Marriott, Philip J; Parker, Rhiannon M; Kouremenos, Konstantinos A; Morrison, Paul; Adams, Mike

    2014-01-01

    Yeast and yeast cultures are frequently used as additives in diets of dairy cows. Beneficial effects from the inclusion of yeast culture in diets for dairy mammals have been reported, and the aim of this study was to develop a comprehensive analytical method for the accurate mass identification of the 'global' metabolites in order to differentiate a variety of yeasts at varying growth stages (Diamond V XP, Yea-Sacc and Levucell). Microwave-assisted derivatization for metabolic profiling is demonstrated through the analysis of differing yeast samples developed for cattle feed, which include a wide range of metabolites of interest covering a large range of compound classes. Accurate identification of the components was undertaken using GC-oa-ToFMS (gas chromatography-orthogonal acceleration-time-of-flight mass spectrometry), followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for data reduction and biomarker discovery. Semi-quantification (fold changes in relative peak areas) was reported for metabolites identified as possible discriminative biomarkers (p-value <0.05, fold change >2), including D-ribose (four fold decrease), myo-inositol (five fold increase), L-phenylalanine (three fold increase), glucopyranoside (two fold increase), fructose (three fold increase) and threitol (three fold increase) respectively. PMID:24356230

  9. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems.

    PubMed

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-07-01

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems.

  10. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems

    PubMed Central

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-01-01

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems. PMID:27174940

  11. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates

    PubMed Central

    Glazier, Douglas S.; Hirst, Andrew G.; Atkinson, David

    2015-01-01

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. PMID:25652833

  12. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates.

    PubMed

    Glazier, Douglas S; Hirst, Andrew G; Atkinson, David

    2015-03-01

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants.

  13. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems.

    PubMed

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-07-01

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems. PMID:27174940

  14. The active metabolic rate predicts a male spider's proximity to females and expected fitness.

    PubMed

    Kasumovic, Michael M; Seebacher, Frank

    2013-04-23

    Conspicuous traits, such as weaponry and body size, are often correlated with fitness. By contrast, we understand less about how inconspicuous physiological traits affect fitness. Not only is linking physiology directly to fitness a challenge, but in addition, behavioural studies most often focus on resting or basal metabolic rates, resulting in a poor understanding of how active metabolic rates affect fitness. Here we use the golden orb-web spider (Nephila plumipes), a species for which proximity to a female on the web predicts a male's paternity share, to examine the role of resting and active metabolic rates in fitness. Using a semi-natural experimental set-up, we show that males closer to a female have higher active metabolic rates than males further from females. This higher metabolic activity is paralleled by increased citrate synthase activity, suggesting greater mitochondrial densities. Our results link both higher active metabolic rates and increased citrate synthase activity with fitness. Coupled with the behaviour and life history of N. plumipes, these results provide insight into the evolution of physiological systems.

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

    PubMed Central

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

    2014-01-01

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

  16. PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

    PubMed Central

    Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets. PMID:24675610

  17. Clinical Neurochemistry of Subarachnoid Hemorrhage: Toward Predicting Individual Outcomes via Biomarkers of Brain Energy Metabolism.

    PubMed

    Tholance, Yannick; Barcelos, Gleicy; Dailler, Frederic; Perret-Liaudet, Armand; Renaud, Bernard

    2015-12-16

    The functional outcome of patients with subarachnoid hemorrhage is difficult to predict at the individual level. The monitoring of brain energy metabolism has proven to be useful in improving the pathophysiological understanding of subarachnoid hemorrhage. Nonetheless, brain energy monitoring has not yet clearly been included in official guidelines for the management of subarachnoid hemorrhage patients, likely because previous studies compared only biological data between two groups of patients (unfavorable vs favorable outcomes) and did not determine decision thresholds that could be useful in clinical practice. Therefore, this Viewpoint discusses recent findings suggesting that monitoring biomarkers of brain energy metabolism at the level of individuals can be used to predict the outcomes of subarachnoid hemorrhage patients. Indeed, by taking into account specific neurochemical patterns obtained by local or global monitoring of brain energy metabolism, it may become possible to predict routinely, and with sufficient sensitivity and specificity, the individual outcomes of subarachnoid hemorrhage patients. Moreover, combining both local and global monitoring improves the overall performance of individual outcome prediction. Such a combined neurochemical monitoring approach may become, after prospective clinical validation, an important component in the management of subarachnoid hemorrhage patients to adapt individualized therapeutic interventions. PMID:26595414

  18. Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach.

    PubMed

    Steinfath, Matthias; Strehmel, Nadine; Peters, Rolf; Schauer, Nicolas; Groth, Detlef; Hummel, Jan; Steup, Martin; Selbig, Joachim; Kopka, Joachim; Geigenberger, Peter; Van Dongen, Joost T

    2010-10-01

    Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.

  19. An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1.

    PubMed

    Kosakovsky Pond, Sergei L; Posada, David; Stawiski, Eric; Chappey, Colombe; Poon, Art F Y; Hughes, Gareth; Fearnhill, Esther; Gravenor, Mike B; Leigh Brown, Andrew J; Frost, Simon D W

    2009-11-01

    Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate

  20. Conformations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane: are ab initio quantum chemistry predictions accurate?

    NASA Astrophysics Data System (ADS)

    Smith, Grant D.; Jaffe, Richard L.; Yoon, Do. Y.

    1998-06-01

    High-level ab initio quantum chemistry calculations are shown to predict conformer populations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane that are consistent with gas-phase NMR vicinal coupling constant measurements. The conformational energies of the cyclic ether 5-methoxy-1,3-dioxane are found to be consistent with those predicted by a rotational isomeric state (RIS) model based upon the acyclic analog 1,2-dimethoxypropane. The quantum chemistry and RIS calculations indicate the presence of strong attractive 1,5 C(H 3)⋯O electrostatic interactions in these molecules, similar to those found in 1,2-dimethoxyethane.

  1. Metabolic scaling predicts posthepatectomy liver regeneration after accounting for hepatocyte hypertrophy.

    PubMed

    Young, LeAnne H; Periwal, Vipul

    2016-04-01

    We adapted a mathematical model of posthepatectomy liver regeneration using data from a subset of patients in the Adult-to-Adult Living Donor Liver Transplantation Cohort Study. The original model addressed changes in the number of quiescent, primed, and proliferating cells. Our adapted model takes into account hypertrophy of primed and replicating cells, and it is better able to predict liver volume. In addition, by building off the hypothesis that cell cycle parameters are approximately the same across all mammals, we found that changing only a single parameter characterizing metabolic load could model liver regeneration in 5 species of mammals. In conclusion, we improved a mathematical model of liver regeneration, predicted mammalian liver regeneration based on metabolism, and found correlations between model parameters and physiological measurements from liver donors.

  2. A Maximal Graded Exercise Test to Accurately Predict VO2max in 18-65-Year-Old Adults

    ERIC Educational Resources Information Center

    George, James D.; Bradshaw, Danielle I.; Hyde, Annette; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G.

    2007-01-01

    The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO sub 2 max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18-65 years, reached a maximal level of exertion (mean plus or minus standard deviation [SD]; maximal heart rate [HR sub…

  3. Survival outcomes scores (SOFT, BAR, and Pedi-SOFT) are accurate in predicting post-liver transplant survival in adolescents.

    PubMed

    Conjeevaram Selvakumar, Praveen Kumar; Maksimak, Brian; Hanouneh, Ibrahim; Youssef, Dalia H; Lopez, Rocio; Alkhouri, Naim

    2016-09-01

    SOFT and BAR scores utilize recipient, donor, and graft factors to predict the 3-month survival after LT in adults (≥18 years). Recently, Pedi-SOFT score was developed to predict 3-month survival after LT in young children (≤12 years). These scoring systems have not been studied in adolescent patients (13-17 years). We evaluated the accuracy of these scoring systems in predicting the 3-month post-LT survival in adolescents through a retrospective analysis of data from UNOS of patients aged 13-17 years who received LT between 03/01/2002 and 12/31/2012. Recipients of combined organ transplants, donation after cardiac death, or living donor graft were excluded. A total of 711 adolescent LT recipients were included with a mean age of 15.2±1.4 years. A total of 100 patients died post-LT including 33 within 3 months. SOFT, BAR, and Pedi-SOFT scores were all found to be good predictors of 3-month post-transplant survival outcome with areas under the ROC curve of 0.81, 0.80, and 0.81, respectively. All three scores provided good accuracy for predicting 3-month survival post-LT in adolescents and may help clinical decision making to optimize survival rate and organ utilization. PMID:27478012

  4. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    PubMed

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  5. Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

    PubMed Central

    Gusenleitner, Daniel; Auerbach, Scott S.; Melia, Tisha; Gómez, Harold F.; Sherr, David H.; Monti, Stefano

    2014-01-01

    Background Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity. Results In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors. Conclusion Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure. PMID:25058030

  6. Length of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals

    PubMed Central

    Fleten, Nils; Johnsen, Roar; Førde, Olav Helge

    2004-01-01

    Background The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire the diagnostic accuracy of length of sick leaves predicted in the Norwegian National Insurance Offices, and to compare their predictions with the self-predictions of the sick-listed. Methods Based on medical certificates, two National Insurance medical consultants and two National Insurance officers predicted, at day 14, the length of sick leave in 993 consecutive cases of sick leave, resulting from musculoskeletal or mental disorders, in this 1-year follow-up study. Two months later they reassessed 322 cases based on extended medical certificates. Self-predictions were obtained in 152 sick-listed subjects when their sick leave passed 14 days. Diagnostic accuracy of the predictions was analysed by ROC area, sensitivity, specificity, likelihood ratio, and positive predictive value was included in the analyses of predictive validity. Results The sick-listed identified sick leave lasting 12 weeks or longer with an ROC area of 80.9% (95% CI 73.7–86.8), while the corresponding estimates for medical consultants and officers had ROC areas of 55.6% (95% CI 45.6–65.6%) and 56.0% (95% CI 46.6–65.4%), respectively. The predictions of sick-listed males were significantly better than those of female subjects, and older subjects predicted somewhat better than younger subjects. Neither formal medical competence, nor additional medical information, noticeably improved the diagnostic accuracy based on medical certificates. Conclusion This study demonstrates that the accuracy of a prognosis based on medical documentation in sickness absence forms, is lower than that of one based on direct communication with the sick-listed themselves

  7. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models

    NASA Astrophysics Data System (ADS)

    Pau, George Shu Heng; Shen, Chaopeng; Riley, William J.; Liu, Yaning

    2016-02-01

    The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.

  8. Prognostic models and risk scores: can we accurately predict postoperative nausea and vomiting in children after craniotomy?

    PubMed

    Neufeld, Susan M; Newburn-Cook, Christine V; Drummond, Jane E

    2008-10-01

    Postoperative nausea and vomiting (PONV) is a problem for many children after craniotomy. Prognostic models and risk scores help identify who is at risk for an adverse event such as PONV to help guide clinical care. The purpose of this article is to assess whether an existing prognostic model or risk score can predict PONV in children after craniotomy. The concepts of transportability, calibration, and discrimination are presented to identify what is required to have a valid tool for clinical use. Although previous work may inform clinical practice and guide future research, existing prognostic models and risk scores do not appear to be options for predicting PONV in children undergoing craniotomy. However, until risk factors are further delineated, followed by the development and validation of prognostic models and risk scores that include children after craniotomy, clinical judgment in the context of current research may serve as a guide for clinical care in this population. PMID:18939320

  9. How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements.

    PubMed

    Grassi, Lorenzo; Väänänen, Sami P; Ristinmaa, Matti; Jurvelin, Jukka S; Isaksson, Hanna

    2016-03-21

    Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R(2)=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10-16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical-experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response. PMID:26944687

  10. Twenty-four hour metabolic rate measurements utilized as a reference to evaluate several prediction equations for calculating energy requirements in healthy infants

    PubMed Central

    2011-01-01

    Background To date, only short-duration metabolic rate measurements of less than four hours have been used to evaluate prediction equations for calculating energy requirements in healthy infants. Therefore, the objective of this analysis was to utilize direct 24-hour metabolic rate measurements from a prior study to evaluate the accuracy of several currently used prediction equations for calculating energy expenditure (EE) in healthy infants. Methods Data from 24-hour EE, resting (RMR) and sleeping (SMR) metabolic rates obtained from 10 healthy infants, served as a reference to evaluate 11 length-weight (LWT) and weight (WT) based prediction equations. Six prediction equations have been previously derived from 50 short-term EE measurements in the Enhanced Metabolic Testing Activity Chamber (EMTAC) for assessing 24-hour EE, (EMTACEE-LWT and EMTACEE-WT), RMR (EMTACRMR-LWT and EMTACRMR-WT) and SMR (EMTACSMR-LWT and EMTACSMR-WT). The last five additional prediction equations for calculating RMR consisted of the World Health Organization (WHO), the Schofield (SCH-LWT and SCH-WT) and the Oxford (OXFORD-LWT and OXFORD-WT). Paired t-tests and the Bland & Altman limit analysis were both applied to evaluate the performance of each equation in comparison to the reference data. Results 24-hour EE, RMR and SMR calculated with the EMTACEE-WT, EMTACRMR-WT and both the EMTACSMR-LWT and EMTACSMR-WT prediction equations were similar, p = NS, to that obtained from the reference measurements. However, RMR calculated using the WHO, SCH-LWT, SCH-WT, OXFORD-LWT and OXFORD-WT prediction equations were not comparable to the direct 24-hour metabolic measurements (p < 0.05) obtained in the 10 reference infants. Moreover, the EMTACEE-LWT and EMTACRMR-LWT were also not similar (p < 0.05) to direct 24-hour metabolic measurements. Conclusions Weight based prediction equations, derived from short-duration EE measurements in the EMTAC, were accurate for calculating EE, RMR and SMR in healthy

  11. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    PubMed

    Wang, Ting; He, Quanze; Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing.

  12. Coronary Computed Tomographic Angiography Does Not Accurately Predict the Need of Coronary Revascularization in Patients with Stable Angina

    PubMed Central

    Hong, Sung-Jin; Her, Ae-Young; Suh, Yongsung; Won, Hoyoun; Cho, Deok-Kyu; Cho, Yun-Hyeong; Yoon, Young-Won; Lee, Kyounghoon; Kang, Woong Chol; Kim, Yong Hoon; Kim, Sang-Wook; Shin, Dong-Ho; Kim, Jung-Sun; Kim, Byeong-Keuk; Ko, Young-Guk; Choi, Byoung-Wook; Choi, Donghoon; Jang, Yangsoo

    2016-01-01

    Purpose To evaluate the ability of coronary computed tomographic angiography (CCTA) to predict the need of coronary revascularization in symptomatic patients with stable angina who were referred to a cardiac catheterization laboratory for coronary revascularization. Materials and Methods Pre-angiography CCTA findings were analyzed in 1846 consecutive symptomatic patients with stable angina, who were referred to a cardiac catheterization laboratory at six hospitals and were potential candidates for coronary revascularization between July 2011 and December 2013. The number of patients requiring revascularization was determined based on the severity of coronary stenosis as assessed by CCTA. This was compared to the actual number of revascularization procedures performed in the cardiac catheterization laboratory. Results Based on CCTA findings, coronary revascularization was indicated in 877 (48%) and not indicated in 969 (52%) patients. Of the 877 patients indicated for revascularization by CCTA, only 600 (68%) underwent the procedure, whereas 285 (29%) of the 969 patients not indicated for revascularization, as assessed by CCTA, underwent the procedure. When the coronary arteries were divided into 15 segments using the American Heart Association coronary tree model, the sensitivity, specificity, positive predictive value, and negative predictive value of CCTA for therapeutic decision making on a per-segment analysis were 42%, 96%, 40%, and 96%, respectively. Conclusion CCTA-based assessment of coronary stenosis severity does not sufficiently differentiate between coronary segments requiring revascularization versus those not requiring revascularization. Conventional coronary angiography should be considered to determine the need of revascularization in symptomatic patients with stable angina. PMID:27401637

  13. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    PubMed

    Wang, Ting; He, Quanze; Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  14. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing

    PubMed Central

    Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  15. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.

    PubMed

    Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

  16. SNPs in transporter and metabolizing genes as predictive markers for oxaliplatin treatment in colorectal cancer patients.

    PubMed

    Kap, Elisabeth J; Seibold, Petra; Scherer, Dominique; Habermann, Nina; Balavarca, Yesilda; Jansen, Lina; Zucknick, Manuela; Becker, Natalia; Hoffmeister, Michael; Ulrich, Alexis; Benner, Axel; Ulrich, Cornelia M; Burwinkel, Barbara; Brenner, Hermann; Chang-Claude, Jenny

    2016-06-15

    Oxaliplatin is frequently used as part of a chemotherapeutic regimen with 5-fluorouracil in the treatment of colorectal cancer (CRC). The cellular availability of oxaliplatin is dependent on metabolic and transporter enzymes. Variants in genes encoding these enzymes may cause variation in response to oxaliplatin and could be potential predictive markers. Therefore, we used a two-step procedure to comprehensively investigate 1,444 single nucleotide polymorphisms (SNPs) from these pathways for their potential as predictive markers for oxaliplatin treatment, using 623 stage II-IV CRC patients (of whom 201 patients received oxaliplatin) from a German prospective patient cohort treated with adjuvant or palliative chemotherapy. First, all genes were screened using the global test that evaluated SNP*oxaliplatin interaction terms per gene. Second, one model was created by backward elimination on all SNP*oxaliplatin interactions of the selected genes. The statistical procedure was evaluated using bootstrap analyses. Nine genes differentially associated with overall survival according to oxaliplatin treatment (unadjusted p values < 0.05) were selected. Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. If confirmed, inclusion of these variants in a predictive test could identify patients who are more likely to benefit from treatment with oxaliplatin. PMID:26835885

  17. SNPs in transporter and metabolizing genes as predictive markers for oxaliplatin treatment in colorectal cancer patients.

    PubMed

    Kap, Elisabeth J; Seibold, Petra; Scherer, Dominique; Habermann, Nina; Balavarca, Yesilda; Jansen, Lina; Zucknick, Manuela; Becker, Natalia; Hoffmeister, Michael; Ulrich, Alexis; Benner, Axel; Ulrich, Cornelia M; Burwinkel, Barbara; Brenner, Hermann; Chang-Claude, Jenny

    2016-06-15

    Oxaliplatin is frequently used as part of a chemotherapeutic regimen with 5-fluorouracil in the treatment of colorectal cancer (CRC). The cellular availability of oxaliplatin is dependent on metabolic and transporter enzymes. Variants in genes encoding these enzymes may cause variation in response to oxaliplatin and could be potential predictive markers. Therefore, we used a two-step procedure to comprehensively investigate 1,444 single nucleotide polymorphisms (SNPs) from these pathways for their potential as predictive markers for oxaliplatin treatment, using 623 stage II-IV CRC patients (of whom 201 patients received oxaliplatin) from a German prospective patient cohort treated with adjuvant or palliative chemotherapy. First, all genes were screened using the global test that evaluated SNP*oxaliplatin interaction terms per gene. Second, one model was created by backward elimination on all SNP*oxaliplatin interactions of the selected genes. The statistical procedure was evaluated using bootstrap analyses. Nine genes differentially associated with overall survival according to oxaliplatin treatment (unadjusted p values < 0.05) were selected. Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. If confirmed, inclusion of these variants in a predictive test could identify patients who are more likely to benefit from treatment with oxaliplatin.

  18. Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

    PubMed Central

    Wagner, Allon; Zarecki, Raphy; Reshef, Leah; Gochev, Camelia; Sorek, Rotem; Gophna, Uri; Ruppin, Eytan

    2013-01-01

    Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE’s predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering. PMID:24198337

  19. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events.

    PubMed

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The [Formula: see text] class contains tandem [Formula: see text]-type motif sequences, and the [Formula: see text] class contains alternating [Formula: see text], [Formula: see text] and [Formula: see text] type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a [Formula: see text]-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the [Formula: see text] class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for [Formula: see text]-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

  20. A 3D-CFD code for accurate prediction of fluid flows and fluid forces in seals

    NASA Technical Reports Server (NTRS)

    Athavale, M. M.; Przekwas, A. J.; Hendricks, R. C.

    1994-01-01

    Current and future turbomachinery requires advanced seal configurations to control leakage, inhibit mixing of incompatible fluids and to control the rotodynamic response. In recognition of a deficiency in the existing predictive methodology for seals, a seven year effort was established in 1990 by NASA's Office of Aeronautics Exploration and Technology, under the Earth-to-Orbit Propulsion program, to develop validated Computational Fluid Dynamics (CFD) concepts, codes and analyses for seals. The effort will provide NASA and the U.S. Aerospace Industry with advanced CFD scientific codes and industrial codes for analyzing and designing turbomachinery seals. An advanced 3D CFD cylindrical seal code has been developed, incorporating state-of-the-art computational methodology for flow analysis in straight, tapered and stepped seals. Relevant computational features of the code include: stationary/rotating coordinates, cylindrical and general Body Fitted Coordinates (BFC) systems, high order differencing schemes, colocated variable arrangement, advanced turbulence models, incompressible/compressible flows, and moving grids. This paper presents the current status of code development, code demonstration for predicting rotordynamic coefficients, numerical parametric study of entrance loss coefficients for generic annular seals, and plans for code extensions to labyrinth, damping, and other seal configurations.

  1. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

    PubMed Central

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B.; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The P class contains tandem P-type motif sequences, and the PLS class contains alternating P, L and S type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a PLS-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the PLS class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for PLS-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

  2. Prediction of metabolic syndrome among postmenopausal Ghanaian women using obesity and atherogenic markers

    PubMed Central

    2012-01-01

    Background Metabolic syndrome (MetS) is an important health problem which puts individuals at risk for cardiovascular diseases and type 2 diabetes as well as obesity-related cancers such as colon and renal cell in men, and endometrial and oesophageal in women. Objective This study was aimed at examining how obesity indicators and related determinants influence metabolic syndrome, and how the factors can be used to predict the syndrome and its cut-offs in postmenopausal Ghanaian women. Methods Two hundred and fifty (250) Ghanaian subjects were involved in the study with one hundred and forty-three (143) being premenopausal women and one hundred and seven (107) postmenopausal women. The influence of traditional metabolic risk factors including high blood pressure, dyslipidemia and glucose intolerance on obesity and atherogenic indices i.e. body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), Waist-to-thigh ratio (WTR), waist-to-height ratio (WHtR), high density lipoprotein cholesterol to total cholesterol ratio (HDL-C/TC), high density lipoprotein cholesterol to low density lipoprotein ratio (HDL-C/LDL-C) and triglyceride to high density lipoprotein cholesterol ratio (TG/HDL-C) were identified according to the Harmonization (H_MS) criterion. Results The predominant anthropometric marker that significantly influence metabolic risk factors among the pre- and postmenopausal women was waist-to-hip ratio (premenopausal: p- 0.004, 0.026 and 0.002 for systolic blood pressure (SBP), fasting blood glucose (FBG) and HDL-C; postmenopausal: p-0.012, 0.048, 0.007 and 0.0061 for diastolic blood pressure (DBP), FBG, triglyceride (TG) and high density lipoprotein cholesterol (HDL-C) respectively). Using the receiver operating characteristic (ROC) analysis, the area under the curve for WC, WHR, TG/HDL-C and HDL-C/TC among postmenopausal women were estimated at 0.6, 0.6, 0.8 and 0.8 respectively. The appropriate cut-off values for WC, WHR, TG/HDL-C and HDL

  3. IrisPlex: a sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information.

    PubMed

    Walsh, Susan; Liu, Fan; Ballantyne, Kaye N; van Oven, Mannis; Lao, Oscar; Kayser, Manfred

    2011-06-01

    A new era of 'DNA intelligence' is arriving in forensic biology, due to the impending ability to predict externally visible characteristics (EVCs) from biological material such as those found at crime scenes. EVC prediction from forensic samples, or from body parts, is expected to help concentrate police investigations towards finding unknown individuals, at times when conventional DNA profiling fails to provide informative leads. Here we present a robust and sensitive tool, termed IrisPlex, for the accurate prediction of blue and brown eye colour from DNA in future forensic applications. We used the six currently most eye colour-informative single nucleotide polymorphisms (SNPs) that previously revealed prevalence-adjusted prediction accuracies of over 90% for blue and brown eye colour in 6168 Dutch Europeans. The single multiplex assay, based on SNaPshot chemistry and capillary electrophoresis, both widely used in forensic laboratories, displays high levels of genotyping sensitivity with complete profiles generated from as little as 31pg of DNA, approximately six human diploid cell equivalents. We also present a prediction model to correctly classify an individual's eye colour, via probability estimation solely based on DNA data, and illustrate the accuracy of the developed prediction test on 40 individuals from various geographic origins. Moreover, we obtained insights into the worldwide allele distribution of these six SNPs using the HGDP-CEPH samples of 51 populations. Eye colour prediction analyses from HGDP-CEPH samples provide evidence that the test and model presented here perform reliably without prior ancestry information, although future worldwide genotype and phenotype data shall confirm this notion. As our IrisPlex eye colour prediction test is capable of immediate implementation in forensic casework, it represents one of the first steps forward in the creation of a fully individualised EVC prediction system for future use in forensic DNA intelligence.

  4. Predictive equations for the estimation of basal metabolic rate in Malaysian adults.

    PubMed

    Ismail, M; Chee, S; Roslee, R; Zawiah, H

    1998-12-01

    In the field of human energy expenditure, the measurement of basal metabolic rate (BMR) is an essential element to derive energy requirement estimates for any given population. Besides basic anthropometrics data, this paper reports the generation of predictive equation for basal metabolic rates of healthy Malaysian adult from prospective measurements on 307 male and 349 females aged 18-60 years, using the Douglas bag technique. These new equations based on body-weight reveal that the current FAO/WHO/UNU (1985) predictive equations overestimate BMR of adult Malaysian by an average of 13% in males and 9% in female subjects while differences of between 4-5% were observed when compared to Henry and Rees (1991) equations for tropical people. There is a good reason to believe that the capacity to slow down metabolism amidst the hot and humid climate experience throughout the year as a genuine phenomenon for Malaysians. Similarly, these findings suggest that at equal energy intake recommendation for similar body weight, the lower energy needs of Malaysian could put them at greater risk for developing obesity. These observed deviations must be taken into account in formulating energy requirements of the population.

  5. Accurate ab initio prediction of propagation rate coefficients in free-radical polymerization: Acrylonitrile and vinyl chloride

    NASA Astrophysics Data System (ADS)

    Izgorodina, Ekaterina I.; Coote, Michelle L.

    2006-05-01

    A systematic methodology for calculating accurate propagation rate coefficients in free-radical polymerization was designed and tested for vinyl chloride and acrylonitrile polymerization. For small to medium-sized polymer systems, theoretical reaction barriers are calculated using G3(MP2)-RAD. For larger systems, G3(MP2)-RAD barriers can be approximated (to within 1 kJ mol -1) via an ONIOM-based approach in which the core is studied at G3(MP2)-RAD and the substituent effects are modeled with ROMP2/6-311+G(3df,2p). DFT methods (including BLYP, B3LYP, MPWB195, BB1K and MPWB1K) failed to reproduce the correct trends in the reaction barriers and enthalpies with molecular size, though KMLYP showed some promise as a low cost option for very large systems. Reaction rates are calculated via standard transition state theory in conjunction with the one-dimensional hindered rotor model. The harmonic oscillator approximation was shown to introduce an error of a factor of 2-3, and would be suitable for "order-of-magnitude" estimates. A systematic study of chain length effects indicated that rate coefficients had largely converged to their long chain limit at the dimer radical stage, and the inclusion of the primary substituent of the penultimate unit was sufficient for practical purposes. Solvent effects, as calculated using the COSMO model, were found to be relatively minor. The overall methodology reproduced the available experimental data for both of these monomers within a factor of 2.

  6. Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

    PubMed Central

    Ahn, Song Vogue; Baik, Soon Koo; Cho, Youn zoo; Koh, Sang Baek; Huh, Ji Hye; Chang, Yoosoo; Sung, Ki-Chul; Kim, Jang Young

    2016-01-01

    Aims The ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) is of great interest as a possible novel marker of metabolic syndrome. However, longitudinal studies emphasizing the incremental predictive value of the AST-to-ALT ratio in diagnosing individuals at higher risk of developing metabolic syndrome are very scarce. Therefore, our study aimed to evaluate the AST-to-ALT ratio as an incremental predictor of new onset metabolic syndrome in a population-based cohort study. Material and Methods The population-based cohort study included 2276 adults (903 men and 1373 women) aged 40–70 years, who participated from 2005–2008 (baseline) without metabolic syndrome and were followed up from 2008–2011. Metabolic syndrome was defined according to the harmonized definition of metabolic syndrome. Serum concentrations of AST and ALT were determined by enzymatic methods. Results During an average follow-up period of 2.6-years, 395 individuals (17.4%) developed metabolic syndrome. In a multivariable adjusted model, the odds ratio (95% confidence interval) for new onset of metabolic syndrome, comparing the fourth quartile to the first quartile of the AST-to-ALT ratio, was 0.598 (0.422–0.853). The AST-to-ALT ratio also improved the area under the receiver operating characteristic curve (AUC) for predicting new cases of metabolic syndrome (0.715 vs. 0.732, P = 0.004). The net reclassification improvement of prediction models including the AST-to-ALT ratio was 0.23 (95% CI: 0.124–0.337, P<0.001), and the integrated discrimination improvement was 0.0094 (95% CI: 0.0046–0.0143, P<0.001). Conclusions The AST-to-ALT ratio independently predicted the future development of metabolic syndrome and had incremental predictive value for incident metabolic syndrome. PMID:27560931

  7. Computing with evidence part II: an evidential approach to predicting metabolic drug-drug interactions

    PubMed Central

    Boyce, Richard; Collins, Carol; Horn, John; Kalet, Ira

    2009-01-01

    We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions. PMID:19539050

  8. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems

    SciTech Connect

    Samudrala, Ram; Heffron, Fred; McDermott, Jason E.

    2009-04-24

    The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates, effector proteins, are not. We have used a machine learning approach to identify new secreted effectors. The method integrates evolutionary measures, such as the pattern of homologs in a range of other organisms, and sequence-based features, such as G+C content, amino acid composition and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from Salmonella typhimurium and validated on a corresponding set of effectors from Pseudomonas syringae, after eliminating effectors with detectable sequence similarity. The method was able to identify all of the known effectors in P. syringae with a specificity of 84% and sensitivity of 82%. The reciprocal validation, training on P. syringae and validating on S. typhimurium, gave similar results with a specificity of 86% when the sensitivity level was 87%. These results show that type III effectors in disparate organisms share common features. We found that maximal performance is attained by including an N-terminal sequence of only 30 residues, which agrees with previous studies indicating that this region contains the secretion signal. We then used the method to define the most important residues in this putative secretion signal. Finally, we present novel predictions of secreted effectors in S. typhimurium, some of which have been experimentally validated, and apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis. This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  9. Automatic Earthquake Shear Stress Measurement Method Developed for Accurate Time- Prediction Analysis of Forthcoming Major Earthquakes Along Shallow Active Faults

    NASA Astrophysics Data System (ADS)

    Serata, S.

    2006-12-01

    The Serata Stressmeter has been developed to measure and monitor earthquake shear stress build-up along shallow active faults. The development work made in the past 25 years has established the Stressmeter as an automatic stress measurement system to study timing of forthcoming major earthquakes in support of the current earthquake prediction studies based on statistical analysis of seismological observations. In early 1982, a series of major Man-made earthquakes (magnitude 4.5-5.0) suddenly occurred in an area over deep underground potash mine in Saskatchewan, Canada. By measuring underground stress condition of the mine, the direct cause of the earthquake was disclosed. The cause was successfully eliminated by controlling the stress condition of the mine. The Japanese government was interested in this development and the Stressmeter was introduced to the Japanese government research program for earthquake stress studies. In Japan the Stressmeter was first utilized for direct measurement of the intrinsic lateral tectonic stress gradient G. The measurement, conducted at the Mt. Fuji Underground Research Center of the Japanese government, disclosed the constant natural gradients of maximum and minimum lateral stresses in an excellent agreement with the theoretical value, i.e., G = 0.25. All the conventional methods of overcoring, hydrofracturing and deformation, which were introduced to compete with the Serata method, failed demonstrating the fundamental difficulties of the conventional methods. The intrinsic lateral stress gradient determined by the Stressmeter for the Japanese government was found to be the same with all the other measurements made by the Stressmeter in Japan. The stress measurement results obtained by the major international stress measurement work in the Hot Dry Rock Projects conducted in USA, England and Germany are found to be in good agreement with the Stressmeter results obtained in Japan. Based on this broad agreement, a solid geomechanical

  10. Predicting College Students' First Year Success: Should Soft Skills Be Taken into Consideration to More Accurately Predict the Academic Achievement of College Freshmen?

    ERIC Educational Resources Information Center

    Powell, Erica Dion

    2013-01-01

    This study presents a survey developed to measure the skills of entering college freshmen in the areas of responsibility, motivation, study habits, literacy, and stress management, and explores the predictive power of this survey as a measure of academic performance during the first semester of college. The survey was completed by 334 incoming…

  11. Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

    PubMed Central

    Fortin, Élise; Platt, Robert W.; Fontela, Patricia S.; Buckeridge, David L.; Quach, Caroline

    2015-01-01

    Objective The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy. Methods Retrospective cohort study including all patients admitted to three neonatal (NICU), two pediatric (PICU) and four adult ICUs between April 2006 and March 2010. Ten different resistance / antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE) in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests. Results Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006). Conclusions A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use. PMID:26710322

  12. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages.

    PubMed

    Vlaming, M L H; van Duijn, E; Dillingh, M R; Brands, R; Windhorst, A D; Hendrikse, N H; Bosgra, S; Burggraaf, J; de Koning, M C; Fidder, A; Mocking, J A J; Sandman, H; de Ligt, R A F; Fabriek, B O; Pasman, W J; Seinen, W; Alves, T; Carrondo, M; Peixoto, C; Peeters, P A M; Vaes, W H J

    2015-08-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBEs in humans can be successfully obtained early in the drug development process by the use of microdosing in a small group of healthy subjects combined with ultrasensitive accelerator mass spectrometry (AMS). After only minimal preclinical testing, we performed a first-in-human phase 0/phase 1 trial with a human recombinant therapeutic protein (RESCuing Alkaline Phosphatase, human recombinant placental alkaline phosphatase [hRESCAP]) to assess its safety and kinetics. Pharmacokinetic analysis showed dose linearity from microdose (53 μg) [(14) C]-hRESCAP to therapeutic doses (up to 5.3 mg) of the protein in healthy volunteers. This study demonstrates the value of a microdosing approach in a very small cohort for accelerating the clinical development of NBEs. PMID:25869840

  13. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    NASA Astrophysics Data System (ADS)

    Delahaye, Thibault; Nikitin, Andrei; Rey, Michaël; Szalay, Péter G.; Tyuterev, Vladimir G.

    2014-09-01

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C2H4 obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C2H4 molecule was obtained with a RMS(Obs.-Calc.) deviation of 2.7 cm-1 for fundamental bands centers and 5.9 cm-1 for vibrational bands up to 7800 cm-1. Large scale vibrational and rotational calculations for 12C2H4, 13C2H4, and 12C2D4 isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm-1 are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of 13C2H4 and 12C2D4 and rovibrational levels of 12C2H4.

  14. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    NASA Technical Reports Server (NTRS)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  15. Infectious titres of sheep scrapie and bovine spongiform encephalopathy agents cannot be accurately predicted from quantitative laboratory test results.

    PubMed

    González, Lorenzo; Thorne, Leigh; Jeffrey, Martin; Martin, Stuart; Spiropoulos, John; Beck, Katy E; Lockey, Richard W; Vickery, Christopher M; Holder, Thomas; Terry, Linda

    2012-11-01

    It is widely accepted that abnormal forms of the prion protein (PrP) are the best surrogate marker for the infectious agent of prion diseases and, in practice, the detection of such disease-associated (PrP(d)) and/or protease-resistant (PrP(res)) forms of PrP is the cornerstone of diagnosis and surveillance of the transmissible spongiform encephalopathies (TSEs). Nevertheless, some studies question the consistent association between infectivity and abnormal PrP detection. To address this discrepancy, 11 brain samples of sheep affected with natural scrapie or experimental bovine spongiform encephalopathy were selected on the basis of the magnitude and predominant types of PrP(d) accumulation, as shown by immunohistochemical (IHC) examination; contra-lateral hemi-brain samples were inoculated at three different dilutions into transgenic mice overexpressing ovine PrP and were also subjected to quantitative analysis by three biochemical tests (BCTs). Six samples gave 'low' infectious titres (10⁶·⁵ to 10⁶·⁷ LD₅₀ g⁻¹) and five gave 'high titres' (10⁸·¹ to ≥ 10⁸·⁷ LD₅₀ g⁻¹) and, with the exception of the Western blot analysis, those two groups tended to correspond with samples with lower PrP(d)/PrP(res) results by IHC/BCTs. However, no statistical association could be confirmed due to high individual sample variability. It is concluded that although detection of abnormal forms of PrP by laboratory methods remains useful to confirm TSE infection, infectivity titres cannot be predicted from quantitative test results, at least for the TSE sources and host PRNP genotypes used in this study. Furthermore, the near inverse correlation between infectious titres and Western blot results (high protease pre-treatment) argues for a dissociation between infectivity and PrP(res).

  16. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    SciTech Connect

    Delahaye, Thibault Rey, Michaël Tyuterev, Vladimir G.; Nikitin, Andrei; Szalay, Péter G.

    2014-09-14

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C{sub 2}H{sub 4} obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C{sub 2}H{sub 4} molecule was obtained with a RMS(Obs.–Calc.) deviation of 2.7 cm{sup −1} for fundamental bands centers and 5.9 cm{sup −1} for vibrational bands up to 7800 cm{sup −1}. Large scale vibrational and rotational calculations for {sup 12}C{sub 2}H{sub 4}, {sup 13}C{sub 2}H{sub 4}, and {sup 12}C{sub 2}D{sub 4} isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm{sup −1} are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of {sup 13}C{sub 2}H{sub 4} and {sup 12}C{sub 2}D{sub 4} and rovibrational levels of {sup 12}C{sub 2}H{sub 4}.

  17. Predicting metabolic rate during level and uphill outdoor walking using a low-cost GPS receiver.

    PubMed

    de Müllenheim, Pierre-Yves; Dumond, Rémy; Gernigon, Marie; Mahé, Guillaume; Lavenu, Audrey; Bickert, Sandrine; Prioux, Jacques; Noury-Desvaux, Bénédicte; Le Faucheur, Alexis

    2016-08-01

    The objective of this study was to assess the accuracy of using speed and grade data obtained from a low-cost global positioning system (GPS) receiver to estimate metabolic rate (MR) during level and uphill outdoor walking. Thirty young, healthy adults performed randomized outdoor walking for 6-min periods at 2.0, 3.5, and 5.0 km/h and on three different grades: 1) level walking, 2) uphill walking on a 3.7% mean grade, and 3) uphill walking on a 10.8% mean grade. The reference MR [metabolic equivalents (METs) and oxygen uptake (V̇o2)] values were obtained using a portable metabolic system. The speed and grade were obtained using a low-cost GPS receiver (1-Hz recording). The GPS grade (Δ altitude/distance walked) was calculated using both uncorrected GPS altitude data and GPS altitude data corrected with map projection software. The accuracy of predictions using reference speed and grade (actual[SPEED/GRADE]) data was high [R(2) = 0.85, root-mean-square error (RMSE) = 0.68 MET]. The accuracy decreased when GPS speed and uncorrected grade (GPS[UNCORRECTED]) data were used, although it remained substantial (R(2) = 0.66, RMSE = 1.00 MET). The accuracy was greatly improved when the GPS speed and corrected grade (GPS[CORRECTED]) data were used (R(2) = 0.82, RMSE = 0.79 MET). Published predictive equations for walking MR were also cross-validated using actual or GPS speed and grade data when appropriate. The prediction accuracy was very close when either actual[SPEED/GRADE] values or GPS[CORRECTED] values (for level and uphill combined) or GPS speed values (for level walking only) were used. These results offer promising research and clinical applications related to the assessment of energy expenditure during free-living walking. PMID:27402559

  18. Predicting metabolic rate during level and uphill outdoor walking using a low-cost GPS receiver.

    PubMed

    de Müllenheim, Pierre-Yves; Dumond, Rémy; Gernigon, Marie; Mahé, Guillaume; Lavenu, Audrey; Bickert, Sandrine; Prioux, Jacques; Noury-Desvaux, Bénédicte; Le Faucheur, Alexis

    2016-08-01

    The objective of this study was to assess the accuracy of using speed and grade data obtained from a low-cost global positioning system (GPS) receiver to estimate metabolic rate (MR) during level and uphill outdoor walking. Thirty young, healthy adults performed randomized outdoor walking for 6-min periods at 2.0, 3.5, and 5.0 km/h and on three different grades: 1) level walking, 2) uphill walking on a 3.7% mean grade, and 3) uphill walking on a 10.8% mean grade. The reference MR [metabolic equivalents (METs) and oxygen uptake (V̇o2)] values were obtained using a portable metabolic system. The speed and grade were obtained using a low-cost GPS receiver (1-Hz recording). The GPS grade (Δ altitude/distance walked) was calculated using both uncorrected GPS altitude data and GPS altitude data corrected with map projection software. The accuracy of predictions using reference speed and grade (actual[SPEED/GRADE]) data was high [R(2) = 0.85, root-mean-square error (RMSE) = 0.68 MET]. The accuracy decreased when GPS speed and uncorrected grade (GPS[UNCORRECTED]) data were used, although it remained substantial (R(2) = 0.66, RMSE = 1.00 MET). The accuracy was greatly improved when the GPS speed and corrected grade (GPS[CORRECTED]) data were used (R(2) = 0.82, RMSE = 0.79 MET). Published predictive equations for walking MR were also cross-validated using actual or GPS speed and grade data when appropriate. The prediction accuracy was very close when either actual[SPEED/GRADE] values or GPS[CORRECTED] values (for level and uphill combined) or GPS speed values (for level walking only) were used. These results offer promising research and clinical applications related to the assessment of energy expenditure during free-living walking.

  19. Noncontrast computed tomography can predict the outcome of shockwave lithotripsy via accurate stone measurement and abdominal fat distribution determination.

    PubMed

    Geng, Jiun-Hung; Tu, Hung-Pin; Shih, Paul Ming-Chen; Shen, Jung-Tsung; Jang, Mei-Yu; Wu, Wen-Jen; Li, Ching-Chia; Chou, Yii-Her; Juan, Yung-Shun

    2015-01-01

    Urolithiasis is a common disease of the urinary system. Extracorporeal shockwave lithotripsy (SWL) has become one of the standard treatments for renal and ureteral stones; however, the success rates range widely and failure of stone disintegration may cause additional outlay, alternative procedures, and even complications. We used the data available from noncontrast abdominal computed tomography (NCCT) to evaluate the impact of stone parameters and abdominal fat distribution on calculus-free rates following SWL. We retrospectively reviewed 328 patients who had urinary stones and had undergone SWL from August 2012 to August 2013. All of them received pre-SWL NCCT; 1 month after SWL, radiography was arranged to evaluate the condition of the fragments. These patients were classified into stone-free group and residual stone group. Unenhanced computed tomography variables, including stone attenuation, abdominal fat area, and skin-to-stone distance (SSD) were analyzed. In all, 197 (60%) were classified as stone-free and 132 (40%) as having residual stone. The mean ages were 49.35 ± 13.22 years and 55.32 ± 13.52 years, respectively. On univariate analysis, age, stone size, stone surface area, stone attenuation, SSD, total fat area (TFA), abdominal circumference, serum creatinine, and the severity of hydronephrosis revealed statistical significance between these two groups. From multivariate logistic regression analysis, the independent parameters impacting SWL outcomes were stone size, stone attenuation, TFA, and serum creatinine. [Adjusted odds ratios and (95% confidence intervals): 9.49 (3.72-24.20), 2.25 (1.22-4.14), 2.20 (1.10-4.40), and 2.89 (1.35-6.21) respectively, all p < 0.05]. In the present study, stone size, stone attenuation, TFA and serum creatinine were four independent predictors for stone-free rates after SWL. These findings suggest that pretreatment NCCT may predict the outcomes after SWL. Consequently, we can use these predictors for selecting

  20. The Validity of Body Adiposity Indices in Predicting Metabolic Syndrome and Its Components among Egyptian Women

    PubMed Central

    Zaki, Moushira Erfan; Kamal, Sanaa; Reyad, Hanaa; Yousef, Walaa; Hassan, Naglaa; Helwa, Iman; Kholoussi, Shams

    2016-01-01

    AIM: To assess the associations between the body adiposity indices and risk of metabolic syndrome (MS) and its components in Egyptian women and to evaluate their predictive power. MATERIALS AND METHODS: This was a cross-sectional analysis performed on 180 Egyptian women aged between 25-35 years. They were 90 women with MS diagnosed by International Diabetes Federation (IDF) and 90 healthy age matched controls. Body adiposity index (BAI), body mass index (BMI), waist to hip ratio (WHR) and waist to height ratio (WHtR) were calculated and serum samples were analyzed for metabolic parameters. Receiver operating characteristic curves (ROC) was used to determine the discriminatory capacity of BAI, WHR WHtR and BMI for MS. RESULTS: Area under the curve (AUC) was highest for BIA, followed by WHR, WHtR and then BMI. All adiposity indices were significantly correlated with metabolic components and BAI had the highest correlation coefficients compared to other indices. CONCLUSION: BAI is a practical predictor for MS and has satisfactory diagnostic accuracy for diagnosing MS among Egyptian women and can be used in addition to WHR, WHtR and BMI for identifying MS in the field studies. PMID:27275324

  1. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  2. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    PubMed Central

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  3. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  4. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    PubMed

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  5. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism.

    PubMed

    Vital-Lopez, Francisco G; Reifman, Jaques; Wallqvist, Anders

    2015-10-01

    A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm

  6. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

    PubMed Central

    Vital-Lopez, Francisco G.; Reifman, Jaques; Wallqvist, Anders

    2015-01-01

    A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm

  7. Application of a Micropatterned Cocultured Hepatocyte System To Predict Preclinical and Human-Specific Drug Metabolism.

    PubMed

    Ballard, T Eric; Wang, Shuai; Cox, Loretta M; Moen, Mark A; Krzyzewski, Stacy; Ukairo, Okechukwu; Obach, R Scott

    2016-02-01

    Laboratory animal models are the industry standard for preclinical risk assessment of drug candidates. Thus, it is important that these species possess profiles of drug metabolites that are similar to those anticipated in human, since metabolites also could be responsible for biologic activities or unanticipated toxicity. Under most circumstances, preclinical species reflect human in vivo metabolites well; however, there have been several notable exceptions, and understanding and predicting these exceptions with an in vitro system would be very useful. Human micropatterned cocultured (MPCC) hepatocytes have been shown to recapitulate human in vivo qualitative metabolic profiles, but the same demonstration has not been performed yet for laboratory animal species. In this study, we investigated several compounds that are known to produce human-unique metabolites through CYP2C9, UGT1A4, aldehyde oxidase (AO), or N-acetyltransferase that were poorly covered or not detected at all in the selected preclinical species. To perform our investigation we used 24-well MPCC hepatocyte plates having three individual human donors and a single donor each of monkey, dog, and rat to study drug metabolism at four time points per species. Through the use of the multispecies MPCC hepatocyte system, the metabolite profiles of the selected compounds in human donors effectively captured the qualitative in vivo metabolite profile with respect to the human metabolite of interest. Human-unique metabolites that were not detected in vivo in certain preclinical species (normally dog and rat) were also not generated in the corresponding species in vitro, confirming that the MPCC hepatocytes can provide an assessment of preclinical species metabolism. From these results, we conclude that multispecies MPCC hepatocyte plates could be used as an effective in vitro tool for preclinical understanding of species metabolism relative to humans and aid in the choice of appropriate preclinical models.

  8. Metabolism

    MedlinePlus

    Metabolism refers to all the physical and chemical processes in the body that convert or use energy, ... Tortora GJ, Derrickson BH. Metabolism. In: Tortora GJ, Derrickson BH. Principles of Anatomy and Physiology . 14th ed. Hoboken, NJ: John H Wiley and Sons; 2013: ...

  9. Hyperandrogenemia Predicts Metabolic Phenotype in Polycystic Ovary Syndrome: The Utility of Serum Androstenedione

    PubMed Central

    O'Reilly, Michael W.; Taylor, Angela E.; Crabtree, Nicola J.; Hughes, Beverly A.; Capper, Farfia; Crowley, Rachel K.; Stewart, Paul M.; Tomlinson, Jeremy W.

    2014-01-01

    Context: Polycystic ovary syndrome (PCOS) is a triad of anovulation, insulin resistance, and hyperandrogenism. Androgen excess may correlate with metabolic risk and PCOS consensus criteria define androgen excess on the basis of serum T. Here we studied the utility of the androgen precursor serum androstenedione (A) in conjunction with serum T for predicting metabolic dysfunction in PCOS. Patients and Methods: Eighty-six PCOS patients fulfilling Rotterdam diagnostic consensus criteria and 43 age- and body mass index-matched controls underwent measurement of serum androgens by tandem mass spectrometry and an oral glucose tolerance test with homeostatic model assessment of insulin resistance and insulin sensitivity index calculation. We analyzed 24-hour urine androgen excretion by gas chromatography/mass spectrometry. Results: PCOS patients had higher levels of serum androgens and urinary androgen metabolites than controls (all P < .001). Within the PCOS cohort, both serum A and T were positively correlated with the free androgen index (T × 100/SHBG) and total androgen metabolite excretion (all P < .001). All subjects with T above the normal reference range [high T (HT)] also had high A (HA/HT group, n = 56). However, the remaining 30 patients had normal T levels, either in the presence of HA (HA/NT; n = 20) or normal A (NA/NT; n = 10). The groups did not differ in age or BMI. The HA/HT and HA/NT groups had higher total androgen excretion than NA/NT (P < .01 and P < .05, respectively). Multiple linear regression showed a strong negative association between serum androstenedione and insulin sensitivity. The incidence of dysglycemia according to an oral glucose tolerance test increased with the severity of androgen phenotype (NA/NT, 0%; HA/NT, 14%; HA/HT, 25%, P = .03). Conclusion: Simultaneous measurement of serum T and A represents a useful tool for predicting metabolic risk in PCOS women. HA levels are a sensitive indicator of PCOS-related androgen excess. PMID

  10. How accurately are maximal metabolic equivalents estimated based on the treadmill workload in healthy people and asymptomatic subjects with cardiovascular risk factors?

    PubMed

    Maeder, M T; Muenzer, T; Rickli, H; Brunner-La Rocca, H P; Myers, J; Ammann, P

    2008-08-01

    Maximal exercise capacity expressed as metabolic equivalents (METs) is rarely directly measured (measured METs; mMETs) but estimated from maximal workload (estimated METs; eMETs). We assessed the accuracy of predicting mMETs by eMETs in asymptomatic subjects. Thirty-four healthy volunteers without cardiovascular risk factors (controls) and 90 patients with at least one risk factor underwent cardiopulmonary exercise testing using individualized treadmill ramp protocols. The equation of the American College of Sports Medicine (ACSM) was employed to calculate eMETs. Despite a close correlation between eMETs and mMETs (patients: r = 0.82, controls: r = 0.88; p < 0.001 for both), eMETs were higher than mMETs in both patients [11.7 (8.9 - 13.4) vs. 8.2 (7.0 - 10.6) METs; p < 0.001] and controls [17.0 (16.2 - 18.2) vs. 15.6 (14.2 - 17.0) METs; p < 0.001]. The absolute [2.5 (1.6 - 3.7) vs. 1.3 (0.9 - 2.1) METs; p < 0.001] and the relative [28 (19 - 47) vs. 9 (6 - 14) %; p < 0.001] difference between eMETs and mMETs was higher in patients. In patients, ratio limits of agreement of 1.33 (*/ divided by 1.40) between eMETs and mMETs were obtained, whereas the ratio limits of agreement were 1.09 (*/ divided by 1.13) in controls. The ACSM equation is associated with a significant overestimation of mMETs in young and fit subjects, which is markedly more pronounced in older and less fit subjects with cardiovascular risk factors.

  11. Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models.

    PubMed

    Peters, Sheila Annie; Jones, Christopher R; Ungell, Anna-Lena; Hatley, Oliver J D

    2016-06-01

    Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models-in vivo, in situ and in vitro-have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.

  12. Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

    PubMed Central

    Yin, H; Anders, M W; Korzekwa, K R; Higgins, L; Thummel, K E; Kharasch, E D; Jones, J P

    1995-01-01

    A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts the rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies (delta Hact) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,2,2-tetrachloroethane, 1,1-difluoro-1,2,2-trichloroethane, 1,1,1-trifluro-2,2-dichloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2,-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: In(rate, rat liver microsomes) = 44.99 - 1.79(delta Hact), r2 = 0.86; In(rate, human CYP2E1) = 46.99 - 1.77(delta Hact), r2 = 0.97 (rates are in nmol of product per min per nmol of cytochrome P450 and energies are in kcal/mol). Correlations were also obtained for five inhalation anesthetics (enflurane, sevoflurane, desflurane, methoxyflurane, and isoflurane) for both in vivo and in vitro metabolism by humans: In[F(-)]peak plasma = 42.87 - 1.57(delta Hact), r2 = 0.86. To our knowledge, these are the first in vivo human metabolic rates to be quantitatively predicted. Furthermore, this is one of the first examples where computational predictions and in vivo and in vitro data have been shown to agree in any species. The model presented herein provides an archetype for the methodology that may be used in the future design of safer chemicals, particularly hydrochlorofluorocarbons and inhalation anesthetics. PMID:7479940

  13. Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?

    PubMed

    Christmann-Franck, Serge; van Westen, Gerard J P; Papadatos, George; Beltran Escudie, Fanny; Roberts, Alexander; Overington, John P; Domine, Daniel

    2016-09-26

    Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.

  14. Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

    PubMed Central

    2016-01-01

    Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

  15. Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?

    PubMed

    Christmann-Franck, Serge; van Westen, Gerard J P; Papadatos, George; Beltran Escudie, Fanny; Roberts, Alexander; Overington, John P; Domine, Daniel

    2016-09-26

    Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

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

    PubMed Central

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

    2016-01-01

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

  17. Prediction of nitrogen metabolism-related genes in Anabaena by kernel-based network analysis.

    PubMed

    Okamoto, Shinobu; Yamanishi, Yoshihiro; Ehira, Shigeki; Kawashima, Shuichi; Tonomura, Koichiro; Kanehisa, Minoru

    2007-03-01

    Prediction of molecular interaction networks from large-scale datasets in genomics and other omics experiments is an important task in terms of both developing bioinformatics methods and solving biological problems. We have applied a kernel-based network inference method for extracting functionally related genes to the response of nitrogen deprivation in cyanobacteria Anabaena sp. PCC 7120 integrating three heterogeneous datasets: microarray data, phylogenetic profiles, and gene orders on the chromosome. We obtained 1348 predicted genes that are somehow related to known genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. While this dataset contained previously known genes related to the nitrogen deprivation condition, it also contained additional genes. Thus, we attempted to select any relevant genes using the constraints of Pfam domains and NtcA-binding sites. We found candidates of nitrogen metabolism-related genes, which are depicted as extensions of existing KEGG pathways. The prediction of functional relationships between proteins rather than functions of individual proteins will thus assist the discovery from the large-scale datasets.

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

    PubMed Central

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

    2009-01-01

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

  19. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

    PubMed

    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  20. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

    PubMed Central

    Brezovský, Jan

    2016-01-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations

  1. Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients

    PubMed Central

    Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M.

    2016-01-01

    Background: Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Methods: Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. Results: We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. Conclusion: We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more

  2. Dose Addition Models Based on Biologically Relevant Reductions in Fetal Testosterone Accurately Predict Postnatal Reproductive Tract Alterations by a Phthalate Mixture in Rats.

    PubMed

    Howdeshell, Kembra L; Rider, Cynthia V; Wilson, Vickie S; Furr, Johnathan R; Lambright, Christy R; Gray, L Earl

    2015-12-01

    Challenges in cumulative risk assessment of anti-androgenic phthalate mixtures include a lack of data on all the individual phthalates and difficulty determining the biological relevance of reduction in fetal testosterone (T) on postnatal development. The objectives of the current study were 2-fold: (1) to test whether a mixture model of dose addition based on the fetal T production data of individual phthalates would predict the effects of a 5 phthalate mixture on androgen-sensitive postnatal male reproductive tract development, and (2) to determine the biological relevance of the reductions in fetal T to induce abnormal postnatal reproductive tract development using data from the mixture study. We administered a dose range of the mixture (60, 40, 20, 10, and 5% of the top dose used in the previous fetal T production study consisting of 300 mg/kg per chemical of benzyl butyl (BBP), di(n)butyl (DBP), diethyl hexyl phthalate (DEHP), di-isobutyl phthalate (DiBP), and 100 mg dipentyl (DPP) phthalate/kg; the individual phthalates were present in equipotent doses based on their ability to reduce fetal T production) via gavage to Sprague Dawley rat dams on GD8-postnatal day 3. We compared observed mixture responses to predictions of dose addition based on the previously published potencies of the individual phthalates to reduce fetal T production relative to a reference chemical and published postnatal data for the reference chemical (called DAref). In addition, we predicted DA (called DAall) and response addition (RA) based on logistic regression analysis of all 5 individual phthalates when complete data were available. DA ref and DA all accurately predicted the observed mixture effect for 11 of 14 endpoints. Furthermore, reproductive tract malformations were seen in 17-100% of F1 males when fetal T production was reduced by about 25-72%, respectively. PMID:26350170

  3. Nucleic Acid Content in Crustacean Zooplankton: Bridging Metabolic and Stoichiometric Predictions

    PubMed Central

    Bullejos, Francisco José; Carrillo, Presentación; Gorokhova, Elena; Medina-Sánchez, Juan Manuel; Villar-Argaiz, Manuel

    2014-01-01

    Metabolic and stoichiometric theories of ecology have provided broad complementary principles to understand ecosystem processes across different levels of biological organization. We tested several of their cornerstone hypotheses by measuring the nucleic acid (NA) and phosphorus (P) content of crustacean zooplankton species in 22 high mountain lakes (Sierra Nevada and the Pyrenees mountains, Spain). The P-allocation hypothesis (PAH) proposes that the genome size is smaller in cladocerans than in copepods as a result of selection for fast growth towards P-allocation from DNA to RNA under P limitation. Consistent with the PAH, the RNA:DNA ratio was >8-fold higher in cladocerans than in copepods, although ‘fast-growth’ cladocerans did not always exhibit higher RNA and lower DNA contents in comparison to ‘slow-growth’ copepods. We also showed strong associations among growth rate, RNA, and total P content supporting the growth rate hypothesis, which predicts that fast-growing organisms have high P content because of the preferential allocation to P-rich ribosomal RNA. In addition, we found that ontogenetic variability in NA content of the copepod Mixodiaptomus laciniatus (intra- and interstage variability) was comparable to the interspecific variability across other zooplankton species. Further, according to the metabolic theory of ecology, temperature should enhance growth rate and hence RNA demands. RNA content in zooplankton was correlated with temperature, but the relationships were nutrient-dependent, with a positive correlation in nutrient-rich ecosystems and a negative one in those with scarce nutrients. Overall our results illustrate the mechanistic connections among organismal NA content, growth rate, nutrients and temperature, contributing to the conceptual unification of metabolic and stoichiometric theories. PMID:24466118

  4. Application of Physiologically Based Pharmacokinetic Modeling to Predict Acetaminophen Metabolism and Pharmacokinetics in Children

    PubMed Central

    Jiang, X-L; Zhao, P; Barrett, J S; Lesko, L J; Schmidt, S

    2013-01-01

    Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0–28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12–17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug- and system-specific information of adults and children through PBPK modeling. PMID:24132164

  5. Accuracy of Predictive Equations for Resting Metabolic Rates and Daily Energy Expenditures of Police Officials Doing Shift Work by Type of Work

    PubMed Central

    Lee, Sun Hee

    2012-01-01

    The purpose of this study was to analyze the accuracy of predictive equations for resting metabolic rate (RMR) and daily energy expenditure in policemen on a rotating shift. Subjects were 28 healthy policemen on a rotating shift (males) age of 23-46 years. The participants' RMR was measured by using indirect calorimetry (TrueOne2400) and also calculated from various predicted equations of RMR (Harris-Benedict, Schofield(W)/(WH), FAO/WHO/UNU(W)/(W/H), Cunningham, Mifflin, Liu, Owen, IMNA and Henry(W)/(WH)). The accuracy of these equations were evaluated on basis of accurate prediction (the percentage of subjects whose RMR was predicted within 90% to 110% of the RMR measured), mean difference, root mean squared prediction error, mean % difference, limits of agreement of Bland-Altman method between predicted and measured RMR. The measured RMR value of subjects was 1748 ± 205.9 kcal. Of the predictive equations tested, the Harris-Benedict equation (mean difference: -14.8 kcal/day, RMSPE: 195.8 kcal/day, mean % difference: 0.1%) was the most accurate and precise, but accuracy in prediction of the equation were only 35.7%. The daily energy expenditure at night-duty was 3062 kcal calculated as multiplying RMR by its physical activity level. Subsequently, daily energy expenditure of day-duty was 2647 kcal and the lowest daily energy expenditure was, 2310 kcal at holiday duty. Daily energy intake of all study participants was 2351 kcal at day-duty, 1959 kcal at night-duty and 1796 kcal at holiday-duty in order. The estimated energy requirements for policemen on a rotating shift on day shift, night shift and holiday came to 2743.6 kcal/day, 2998.6 kcal/day and 2576.9 kcal/day, respectively. These results suggest that estimated energy requirements (EER) of policemen on a rotating shift should be differently proposed by a proper equation which can closely reflect their metabolic status at each time shift. PMID:23429979

  6. Accuracy of predictive equations for resting metabolic rates and daily energy expenditures of police officials doing shift work by type of work.

    PubMed

    Lee, Sun Hee; Kim, Eun Kyung

    2012-07-01

    The purpose of this study was to analyze the accuracy of predictive equations for resting metabolic rate (RMR) and daily energy expenditure in policemen on a rotating shift. Subjects were 28 healthy policemen on a rotating shift (males) age of 23-46 years. The participants' RMR was measured by using indirect calorimetry (TrueOne2400) and also calculated from various predicted equations of RMR (Harris-Benedict, Schofield(W)/(WH), FAO/WHO/UNU(W)/(W/H), Cunningham, Mifflin, Liu, Owen, IMNA and Henry(W)/(WH)). The accuracy of these equations were evaluated on basis of accurate prediction (the percentage of subjects whose RMR was predicted within 90% to 110% of the RMR measured), mean difference, root mean squared prediction error, mean % difference, limits of agreement of Bland-Altman method between predicted and measured RMR. The measured RMR value of subjects was 1748 ± 205.9 kcal. Of the predictive equations tested, the Harris-Benedict equation (mean difference: -14.8 kcal/day, RMSPE: 195.8 kcal/day, mean % difference: 0.1%) was the most accurate and precise, but accuracy in prediction of the equation were only 35.7%. The daily energy expenditure at night-duty was 3062 kcal calculated as multiplying RMR by its physical activity level. Subsequently, daily energy expenditure of day-duty was 2647 kcal and the lowest daily energy expenditure was, 2310 kcal at holiday duty. Daily energy intake of all study participants was 2351 kcal at day-duty, 1959 kcal at night-duty and 1796 kcal at holiday-duty in order. The estimated energy requirements for policemen on a rotating shift on day shift, night shift and holiday came to 2743.6 kcal/day, 2998.6 kcal/day and 2576.9 kcal/day, respectively. These results suggest that estimated energy requirements (EER) of policemen on a rotating shift should be differently proposed by a proper equation which can closely reflect their metabolic status at each time shift. PMID:23429979

  7. Lifetime History of Major Depression Predicts the Development of the Metabolic Syndrome in Middle-Aged Women

    PubMed Central

    Goldbacher, Edie M.; Bromberger, Joyce; Matthews, Karen A.

    2010-01-01

    Objective To prospectively examine the association of major depression with incidence of the metabolic syndrome in women. Methods Data were drawn from one of seven sites of the Study of Women’s Health Across the Nation (SWAN), a prospective cohort study of the menopausal transition. Participants were 429 (34.5% African-American) women. Major depression and comorbid diagnoses were assessed via the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition Axis I Disorders at baseline and seven annual follow-up evaluations. The metabolic syndrome was measured at baseline and each follow-up evaluation (except the second) based on National Cholesterol Education Program (NCEP) criteria. Results Longitudinal generalized estimating equations (GEE) models indicated that, in women who were free of the metabolic syndrome at baseline, a lifetime major depression history or current major depressive episode at baseline was significantly associated with the onset and presence of the metabolic syndrome during the follow-up (odds ratio = 1.82; 95% Confidence Interval (CI) = 1.06–3.14). Survival analyses showed that, in women who were free of the metabolic syndrome at baseline, a lifetime major depression history or current major depressive episode at baseline predicted increased risk of developing the metabolic syndrome during the follow-up (hazard ratio = 1.66; 95% CI = 0.99–3.75). Lifetime history of alcohol abuse or dependence predicted incident metabolic syndrome and attenuated the association between depression and the metabolic syndrome in both models. Conclusions This study documents that major depression is a significant predictor of the onset of the metabolic syndrome. Intervention studies targeting depression may prevent the development of the metabolic syndrome in women. PMID:19188528

  8. Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction

    PubMed Central

    Crooke, Philip S.; Justenhoven, Christina; Brauch, Hiltrud; Dawling, Sheila; Roodi, Nady; Higginbotham, Kathryn S. P.; Plummer, W. Dale; Schuyler, Peggy A.; Sanders, Melinda E; Page, David L.; Smith, Jeffrey R.; Dupont, William D.; Parl, Fritz F.

    2012-01-01

    Background Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case-control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI 1.7 – 3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI 1.5 – 2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI 1.3 – 2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI 1.4 – 2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates. PMID:21610218

  9. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

    PubMed

    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  10. Tuning of Strouhal number for high propulsive efficiency accurately predicts how wingbeat frequency and stroke amplitude relate and scale with size and flight speed in birds.

    PubMed Central

    Nudds, Robert L.; Taylor, Graham K.; Thomas, Adrian L. R.

    2004-01-01

    The wing kinematics of birds vary systematically with body size, but we still, after several decades of research, lack a clear mechanistic understanding of the aerodynamic selection pressures that shape them. Swimming and flying animals have recently been shown to cruise at Strouhal numbers (St) corresponding to a regime of vortex growth and shedding in which the propulsive efficiency of flapping foils peaks (St approximately fA/U, where f is wingbeat frequency, U is cruising speed and A approximately bsin(theta/2) is stroke amplitude, in which b is wingspan and theta is stroke angle). We show that St is a simple and accurate predictor of wingbeat frequency in birds. The Strouhal numbers of cruising birds have converged on the lower end of the range 0.2 < St < 0.4 associated with high propulsive efficiency. Stroke angle scales as theta approximately 67b-0.24, so wingbeat frequency can be predicted as f approximately St.U/bsin(33.5b-0.24), with St0.21 and St0.25 for direct and intermittent fliers, respectively. This simple aerodynamic model predicts wingbeat frequency better than any other relationship proposed to date, explaining 90% of the observed variance in a sample of 60 bird species. Avian wing kinematics therefore appear to have been tuned by natural selection for high aerodynamic efficiency: physical and physiological constraints upon wing kinematics must be reconsidered in this light. PMID:15451698

  11. Predictive markers of capecitabine sensitivity identified from the expression profile of pyrimidine nucleoside-metabolizing enzymes.

    PubMed

    Yasuno, Hideyuki; Kurasawa, Mitsue; Yanagisawa, Mieko; Sato, Yasuko; Harada, Naoki; Mori, Kazushige

    2013-02-01

    Molecular markers predicting sensitivity to anticancer drugs are important and useful not only for selecting potential responders but also for developing new combinations. In the present study, we analyzed the difference in the sensitivity of xenograft models to capecitabine (Xeloda®), 5'-deoxy-5-fluorouridine (5'-DFUR, doxifluridine, Furtulon®) and 5-FU by comparing the mRNA levels of 12 pyrimidine nucleoside-metabolizing enzymes. Amounts of mRNA in the tumor tissues of 80 xenograft models were determined by real-time RT-PCR and mutual correlations were examined. A clustering analysis revealed that the 12 enzymes were divided into two groups; one group consisted of 8 enzymes, including orotate phosphoribosyl transferase (OPRT), TMP kinase (TMPK) and UMP kinase (UMPK), and was related to the de novo synthesis pathway for nucleotides, with mRNA expression levels showing significant mutual correlation. In the other group, 4 enzymes, including thymidine phosphorylase (TP) and dihydropyrimidine dehydrogenase (DPD), were involved in the salvage/degradation pathway of the nucleotides, and the mRNA levels of this group were dispersed more widely than that of the de novo group. Antitumor activity was assessed in 24 xenograft models for each drug. The antitumor activity of capecitabine and 5'-DFUR correlated significantly with the mRNA levels of TP and with the TP/DPD ratio, whereas the activity of 5-FU correlated significantly with OPRT, TMPK, UMPK and CD. In a stepwise regression analysis, TP and DPD were found to be independent predictive factors of sensitivity to capecitabine and 5'-DFUR, and UMPK was predictive of sensitivity to 5-FU. These results indicate that the predictive factors for sensitivity to capecitabine and 5'-DFUR in xenograft models may be different from those for 5-FU, suggesting that these drugs may have different responders in clinical usage. PMID:23229803

  12. The Model for End-stage Liver Disease accurately predicts 90-day liver transplant wait-list mortality in Atlantic Canada

    PubMed Central

    Renfrew, Paul Douglas; Quan, Hude; Doig, Christopher James; Dixon, Elijah; Molinari, Michele

    2011-01-01

    OBJECTIVE: To determine the generalizability of the predictions for 90-day mortality generated by Model for End-stage Liver Disease (MELD) and the serum sodium augmented MELD (MELDNa) to Atlantic Canadian adults with end-stage liver disease awaiting liver transplantation (LT). METHODS: The predictive accuracy of the MELD and the MELDNa was evaluated by measurement of the discrimination and calibration of the respective models’ estimates for the occurrence of 90-day mortality in a consecutive cohort of LT candidates accrued over a five-year period. Accuracy of discrimination was measured by the area under the ROC curves. Calibration accuracy was evaluated by comparing the observed and model-estimated incidences of 90-day wait-list failure for the total cohort and within quantiles of risk. RESULTS: The area under the ROC curve for the MELD was 0.887 (95% CI 0.705 to 0.978) – consistent with very good accuracy of discrimination. The area under the ROC curve for the MELDNa was 0.848 (95% CI 0.681 to 0.965). The observed incidence of 90-day wait-list mortality in the validation cohort was 7.9%, which was not significantly different from the MELD estimate of 6.6% (95% CI 4.9% to 8.4%; P=0.177) or the MELDNa estimate of 5.8% (95% CI 3.5% to 8.0%; P=0.065). Global goodness-of-fit testing found no evidence of significant lack of fit for either model (Hosmer-Lemeshow χ2 [df=3] for MELD 2.941, P=0.401; for MELDNa 2.895, P=0.414). CONCLUSION: Both the MELD and the MELDNa accurately predicted the occurrence of 90-day wait-list mortality in the study cohort and, therefore, are generalizable to Atlantic Canadians with end-stage liver disease awaiting LT. PMID:21876856

  13. The VACS Index Accurately Predicts Mortality and Treatment Response among Multi-Drug Resistant HIV Infected Patients Participating in the Options in Management with Antiretrovirals (OPTIMA) Study

    PubMed Central

    Brown, Sheldon T.; Tate, Janet P.; Kyriakides, Tassos C.; Kirkwood, Katherine A.; Holodniy, Mark; Goulet, Joseph L.; Angus, Brian J.; Cameron, D. William; Justice, Amy C.

    2014-01-01

    Objectives The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Methods Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel’s C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Results Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14–0.49) and 0.39(95% CI 0.22–0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27–3.38) and 1.51 (95%CI 0.90–2.53) for the 25% least improved scores. Conclusions The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research. PMID:24667813

  14. Hybrid metabolic flux analysis and recombinant protein prediction in Pichia pastoris X-33 cultures expressing a single-chain antibody fragment.

    PubMed

    Isidro, Inês A; Portela, Rui M; Clemente, João J; Cunha, António E; Oliveira, Rui

    2016-09-01

    Despite the growing importance of the Pichia pastoris expression system as industrial workhorse, the literature is almost absent in systematic studies on how culture medium composition affects central carbon fluxes and heterologous protein expression. In this study we investigate how 26 variations of the BSM+PTM1 medium impact central carbon fluxes and protein expression in a P. pastoris X-33 strain expressing a single-chain antibody fragment. To achieve this goal, we adopted a hybrid metabolic flux analysis (MFA) methodology, which is a modification of standard MFA to predict the rate of synthesis of recombinant proteins. Hybrid MFA combines the traditional parametric estimation of central carbon fluxes with non-parametric statistical modeling of product-related quantitative or qualitative measurements as a function of central carbon fluxes. It was observed that protein yield variability was 53.6 % (relative standard deviation) among the different experiments. Protein yield is much more sensitive to medium composition than biomass growth, which is mainly determined by the carbon source availability and main salts. Hybrid MFA was able to describe accurately the protein yield with normalized RMSE of 6.3 % over 5 independent experiments. The metabolic state that promotes high protein yields is characterized by high overall metabolic rates through main central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy generating pathways. PMID:27129458

  15. Metabolic syndrome definitions and components in predicting major adverse cardiovascular events after kidney transplantation.

    PubMed

    Prasad, G V Ramesh; Huang, Michael; Silver, Samuel A; Al-Lawati, Ali I; Rapi, Lindita; Nash, Michelle M; Zaltzman, Jeffrey S

    2015-01-01

    Metabolic syndrome (MetS) associates with cardiovascular risk post-kidney transplantation, but its ambiguity impairs understanding of its diagnostic utility relative to components. We compared five MetS definitions and the predictive value of constituent components of significant definitions for major adverse cardiovascular events (MACE) in a cohort of 1182 kidney transplant recipients. MetS definitions were adjusted for noncomponent traditional Framingham risk factors and relevant transplant-related variables. Kaplan-Meier, logistic regression, and Cox proportional hazards analysis were utilized. There were 143 MACE over 7447 patient-years of follow-up. Only the World Health Organization (WHO) 1998 definition predicted MACE (25.3 vs 15.5 events/1000 patient-years, P = 0.019). Time-to-MACE was 5.5 ± 3.5 years with MetS and 6.8 ± 3.9 years without MetS (P < 0.0001). MetS was independent of pertinent MACE risk factors except age and previous cardiac disease. Among MetS components, dysglycemia provided greatest hazard ratio (HR) for MACE (1.814 [95% confidence interval 1.26-2.60]), increased successively by microalbuminuria (HR 1.946 [1.37-2.75]), dyslipidemia (3.284 [1.72-6.26]), hypertension (4.127 [2.16-7.86]), and central obesity (4.282 [2.09-8.76]). MetS did not affect graft survival. In summary, although the WHO 1998 definition provides greatest predictive value for post-transplant MACE, most of this is conferred by dysglycemia and is overshadowed by age and previous cardiac disease. PMID:25207680

  16. Prognostic Value of Metabolic Tumor Volume and Velocity in Predicting Head-and-Neck Cancer Outcomes

    SciTech Connect

    Chu, Karen P.; Murphy, James D.; La, Trang H.; Krakow, Trevor E.; Iagaru, Andrei; Graves, Edward E.; Hsu, Annie; Maxim, Peter G.; Loo, Billy; Chang, Daniel T.; Le, Quynh-Thu

    2012-08-01

    Purpose: We previously showed that metabolic tumor volume (MTV) on positron emission tomography-computed tomography (PET-CT) predicts for disease recurrence and death in head-and-neck cancer (HNC). We hypothesized that increases in MTV over time would correlate with tumor growth and biology, and would predict outcome. We sought to examine tumor growth over time in serial pretreatment PET-CT scans. Methods and Materials: From 2006 to 2009, 51 patients had two PET-CT scans before receiving HNC treatment. MTV was defined as the tumor volume {>=}50% of maximum SUV (SUV{sub max}). MTV was calculated for the primary tumor, nodal disease, and composite (primary tumor + nodes). MTV and SUV velocity were defined as the change in MTV or SUV{sub max} over time, respectively. Cox regression analyses were used to examine correlations between SUV, MTV velocity, and outcome (disease progression and overall survival). Results: The median follow-up time was 17.5 months. The median time between PET-CT scans was 3 weeks. Unexpectedly, 51% of cases demonstrated a decrease in SUV{sub max} (average, -0.1 cc/week) and MTV (average, -0.3 cc/week) over time. Despite the variability in MTV, primary tumor MTV velocity predicted disease progression (hazard ratio 2.94; p = 0.01) and overall survival (hazard ratio 1.85; p = 0.03). Conclusions: Primary tumor MTV velocity appears to be a better prognostic indicator of disease progression and survival in comparison to nodal MTV velocity. However, substantial variability was found in PET-CT biomarkers between serial scans. Caution should be used when PET-CT biomarkers are integrated into clinical protocols for HNC.

  17. Lower than predicted resting metabolic rate is associated with severely impaired cardiorespiratory fitness in obese individuals.

    PubMed

    Miller, Wendy M; Spring, Thomas J; Zalesin, Kerstyn C; Kaeding, Kaylee R; Nori Janosz, Katherine E; McCullough, Peter A; Franklin, Barry A

    2012-03-01

    Obese individuals have reduced cardiorespiratory fitness as compared with leaner counterparts. Regular exercise maintains or increases fitness and lean body mass. Lean body mass, in turn, has a direct impact on resting metabolic rate (RMR). Given these relationships, we sought to evaluate the association between RMR and cardiorespiratory fitness in obese individuals. We evaluated 64 obese individuals (78% female) with direct assessment of RMR and cardiorespiratory fitness via breath-by-breath measurement of oxygen consumption and carbon dioxide production at rest and during exercise. The mean age and BMI were 47.4 ± 12.2 years and 47.2 ± 9.2 kg/m(2), respectively. The majority of subjects, 69%, had a measured RMR above that predicted by the Harris-Benedict equation. Compared with the higher RMR group, those with a lower than predicted RMR had increased BMI, with values of 52.9 vs. 44.7 kg/m(2), P = 0.001, respectively. Analysis of those demonstrating significant effort during cardiopulmonary exercise testing (peak respiratory exchange ratio ≥1.10) revealed a significantly higher peak oxygen uptake (VO(2) peak) in the higher RMR group (17.3 ± 3.5 ml/min/kg) compared with the lower RMR group (13.6 ± 1.9 ml/min/kg), P = 0.003. In summary, a lower than predicted RMR was associated with a severely reduced VO(2) peak and a higher BMI in this cohort. These data suggest that morbid obesity may be a vicious cycle of increasing BMI, reduced cardiorespiratory fitness, muscle deconditioning, and lower RMR. Collectively, these responses may, over time, exacerbate the imbalance between energy intake and expenditure, resulting in progressive increases in body weight and fat stores.

  18. A Semi-Mechanistic Metabolism Model of CYP3A Substrates in Pregnancy: Predicting Changes in Midazolam and Nifedipine Pharmacokinetics

    PubMed Central

    Quinney, S K; Mohamed, A N; Hebert, M F; Haas, D M; Clark, S; Umans, J G; Caritis, S N; Li, L

    2012-01-01

    Physiological changes in pregnancy, including changes in body composition and metabolic enzyme activity, can alter drug pharmacokinetics. A semi-mechanistic metabolism model was developed to describe the pharmacokinetics of two cytochrome P450 3A (CYP3A) substrates, midazolam and nifedipine, in obstetrics patients. The model parameters were optimized to fit the data of oral midazolam pharmacokinetics in pregnant women, by increasing CYP3A-induced hepatic metabolism 1.6-fold in the model with no change in gut wall metabolism. Fetal metabolism had a negligible effect on maternal plasma drug concentrations. Validation of the model was performed by applying changes in volume of distribution and metabolism, consistent with those observed for midazolam, to the pharmacokinetics parameters of immediate-release nifedipine in healthy volunteers. The predicted steady-state areas under the concentration–time curve (AUCs) for nifedipine were within 15% of the data observed in pregnant women undergoing treatment for preterm labor. This model predicts the pharmacokinetics of two CYP3A substrates in pregnancy, and may be applicable to other CYP3A substrates as well. PMID:23835882

  19. Deviation from symmetrically self-similar branching in trees predicts altered hydraulics, mechanics, light interception and metabolic scaling.

    PubMed

    Smith, Duncan D; Sperry, John S; Enquist, Brian J; Savage, Van M; McCulloh, Katherine A; Bentley, Lisa P

    2014-01-01

    The West, Brown, Enquist (WBE) model derives symmetrically self-similar branching to predict metabolic scaling from hydraulic conductance, K, (a metabolism proxy) and tree mass (or volume, V). The original prediction was Kα V(0.75). We ask whether trees differ from WBE symmetry and if it matters for plant function and scaling. We measure tree branching and model how architecture influences K, V, mechanical stability, light interception and metabolic scaling. We quantified branching architecture by measuring the path fraction, Pf : mean/maximum trunk-to-twig pathlength. WBE symmetry produces the maximum, Pf = 1.0. We explored tree morphospace using a probability-based numerical model constrained only by biomechanical principles. Real tree Pf ranged from 0.930 (nearly symmetric) to 0.357 (very asymmetric). At each modeled tree size, a reduction in Pf led to: increased K; decreased V; increased mechanical stability; and decreased light absorption. When Pf was ontogenetically constant, strong asymmetry only slightly steepened metabolic scaling. The Pf ontogeny of real trees, however, was 'U' shaped, resulting in size-dependent metabolic scaling that exceeded 0.75 in small trees before falling below 0.65. Architectural diversity appears to matter considerably for whole-tree hydraulics, mechanics, photosynthesis and potentially metabolic scaling. Optimal architectures likely exist that maximize carbon gain per structural investment.

  20. Validation that Metabolic Tumor Volume Predicts Outcome in Head-and-Neck Cancer

    SciTech Connect

    Tang, Chad; Murphy, James D.; Khong, Brian; La, Trang H.; Kong, Christina; Fischbein, Nancy J.; Colevas, A. Dimitrios; Iagaru, Andrei H.; Graves, Edward E.; Loo, Billy W.; Le, Quynh-Thu

    2012-08-01

    Purpose: We have previously reported that metabolic tumor volume (MTV) obtained from pretreatment {sup 18}F-fluorodeoxydeglucose positron emission tomography (FDG PET)/ computed tomography (CT) predicted outcome in patients with head-and-neck cancer (HNC). The purpose of this study was to validate these results on an independent dataset, determine whether the primary tumor or nodal MTV drives this correlation, and explore the interaction with p16{sup INK4a} status as a surrogate marker for human papillomavirus (HPV). Methods and Materials: The validation dataset in this study included 83 patients with squamous cell HNC who had a FDG PET/CT scan before receiving definitive radiotherapy. MTV and maximum standardized uptake value (SUV{sub max}) were calculated for the primary tumor, the involved nodes, and the combination of both. The primary endpoint was to validate that MTV predicted progression-free survival and overall survival. Secondary analyses included determining the prognostic utility of primary tumor vs. nodal MTV. Results: Similarly to our prior findings, an increase in total MTV of 17 cm{sup 3} (difference between the 75th and 25th percentiles) was associated with a 2.1-fold increase in the risk of disease progression (p = 0.0002) and a 2.0-fold increase in the risk of death (p = 0.0048). SUV{sub max} was not associated with either outcome. Primary tumor MTV predicted progression-free (hazard ratio [HR] = 1.94; p < 0.0001) and overall (HR = 1.57; p < 0.0001) survival, whereas nodal MTV did not. In addition, MTV predicted progression-free (HR = 4.23; p < 0.0001) and overall (HR = 3.21; p = 0.0029) survival in patients with p16{sup INK4a}-positive oropharyngeal cancer. Conclusions: This study validates our previous findings that MTV independently predicts outcomes in HNC. MTV should be considered as a potential risk-stratifying biomarker in future studies of HNC.

  1. Toward Relatively General and Accurate Quantum Chemical Predictions of Solid-State 17O NMR Chemical Shifts in Various Biologically Relevant Oxygen-containing Compounds

    PubMed Central

    Rorick, Amber; Michael, Matthew A.; Yang, Liu; Zhang, Yong

    2015-01-01

    Oxygen is an important element in most biologically significant molecules and experimental solid-state 17O NMR studies have provided numerous useful structural probes to study these systems. However, computational predictions of solid-state 17O NMR chemical shift tensor properties are still challenging in many cases and in particular each of the prior computational work is basically limited to one type of oxygen-containing systems. This work provides the first systematic study of the effects of geometry refinement, method and basis sets for metal and non-metal elements in both geometry optimization and NMR property calculations of some biologically relevant oxygen-containing compounds with a good variety of XO bonding groups, X= H, C, N, P, and metal. The experimental range studied is of 1455 ppm, a major part of the reported 17O NMR chemical shifts in organic and organometallic compounds. A number of computational factors towards relatively general and accurate predictions of 17O NMR chemical shifts were studied to provide helpful and detailed suggestions for future work. For the studied various kinds of oxygen-containing compounds, the best computational approach results in a theory-versus-experiment correlation coefficient R2 of 0.9880 and mean absolute deviation of 13 ppm (1.9% of the experimental range) for isotropic NMR shifts and R2 of 0.9926 for all shift tensor properties. These results shall facilitate future computational studies of 17O NMR chemical shifts in many biologically relevant systems, and the high accuracy may also help refinement and determination of active-site structures of some oxygen-containing substrate bound proteins. PMID:26274812

  2. Toward Relatively General and Accurate Quantum Chemical Predictions of Solid-State (17)O NMR Chemical Shifts in Various Biologically Relevant Oxygen-Containing Compounds.

    PubMed

    Rorick, Amber; Michael, Matthew A; Yang, Liu; Zhang, Yong

    2015-09-01

    Oxygen is an important element in most biologically significant molecules, and experimental solid-state (17)O NMR studies have provided numerous useful structural probes to study these systems. However, computational predictions of solid-state (17)O NMR chemical shift tensor properties are still challenging in many cases, and in particular, each of the prior computational works is basically limited to one type of oxygen-containing system. This work provides the first systematic study of the effects of geometry refinement, method, and basis sets for metal and nonmetal elements in both geometry optimization and NMR property calculations of some biologically relevant oxygen-containing compounds with a good variety of XO bonding groups (X = H, C, N, P, and metal). The experimental range studied is of 1455 ppm, a major part of the reported (17)O NMR chemical shifts in organic and organometallic compounds. A number of computational factors toward relatively general and accurate predictions of (17)O NMR chemical shifts were studied to provide helpful and detailed suggestions for future work. For the studied kinds of oxygen-containing compounds, the best computational approach results in a theory-versus-experiment correlation coefficient (R(2)) value of 0.9880 and a mean absolute deviation of 13 ppm (1.9% of the experimental range) for isotropic NMR shifts and an R(2) value of 0.9926 for all shift-tensor properties. These results shall facilitate future computational studies of (17)O NMR chemical shifts in many biologically relevant systems, and the high accuracy may also help the refinement and determination of active-site structures of some oxygen-containing substrate-bound proteins.

  3. A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4.

    PubMed

    Dai, Zi-Ru; Ai, Chun-Zhi; Ge, Guang-Bo; He, Yu-Qi; Wu, Jing-Jing; Wang, Jia-Yue; Man, Hui-Zi; Jia, Yan; Yang, Ling

    2015-06-30

    Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.

  4. A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4

    PubMed Central

    Dai, Zi-Ru; Ai, Chun-Zhi; Ge, Guang-Bo; He, Yu-Qi; Wu, Jing-Jing; Wang, Jia-Yue; Man, Hui-Zi; Jia, Yan; Yang, Ling

    2015-01-01

    Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy. PMID:26133240

  5. APPLICATION OF ADVANCED IN VITRO TECHNIQUES TO MEASURE, UNDERSTAND AND PREDICT THE KINETICS AND MECHANISMS OF XENOBIOTIC METABOLISM

    EPA Science Inventory

    We have developed a research program in metabolism that involves numerous collaborators across EPA as well as other federal and academic labs. A primary goal is to develop and apply advanced in vitro techniques to measure, understand and predict the kinetics and mechanisms of xen...

  6. Insulin-Related Biomarkers to Predict the Risk of Metabolic Syndrome

    PubMed Central

    Kawada, Tomoyuki

    2013-01-01

    Background: The predictive ability of insulin resistance or insulin sensitivity, in combination with traditional cardiovascular risk factors for metabolic syndrome (MetS), has not yet been clearly evaluated in Japanese male subjects. Objectives: A one-year follow-up study was conducted to determine the ability of the insulin-related biomarkers to predict the risk of MetS development. Patients and Methods: A total of 2642 male workers of a Japanese company free from MetS at the baseline were monitored. The homeostasis model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) were selected as the insulin-related markers. Results: The incidence of metabolic syndrome after one year was 8.8%. A multiple logistic regression analysis identified regular physical activity, age (≥ 45 years old), serum uric acid (≥ 7 mg/dL), serum alanine aminotransferase (≥ 45 IU/L), serum C-reactive protein (≥ 0.1 mg/L) and HOMA-IR (≥ 2.5) as significant risk factors for the development of MetS, with odds ratios (95% confidence intervals) of 0.68 (0.50 – 0.92), 2.0 (1.5 – 2.6), 2.2 (1.6 – 3.0), 1.5 (1.02 – 2.2), 1.4 (1.01 – 2.0), and 2.3 (1.6 – 3.3), respectively. When QUICKI was used instead of HOMA-IR, age (≥ 45 years old), serum uric acid (≥ 7 mg/dL), serum gamma-glutamyl transferase (≥ 50 IU/L), and QUICKI (≤ 0.33) were identified as significant contributors to the risk of MetS, with odds ratios (95% confidence intervals) of 0.68 (0.51 – 0.93), 2.0 (1.5 – 2.6), 2.2 (1.6 – 3.0), 1.4 (1.01 – 2.0), and 2.5 (1.7 – 3.6), respectively. Conclusions: The mathematical meaning of the two insulin-related biomarkers examined was the same, and the odds ratios of the two biomarkers were almost the same after adjustments for other independent variables. PMID:24719625

  7. Deep vein thrombosis is accurately predicted by comprehensive analysis of the levels of microRNA-96 and plasma D-dimer

    PubMed Central

    Xie, Xuesheng; Liu, Changpeng; Lin, Wei; Zhan, Baoming; Dong, Changjun; Song, Zhen; Wang, Shilei; Qi, Yingguo; Wang, Jiali; Gu, Zengquan

    2016-01-01

    The aim of the present study was to investigate the association between platelet microRNA-96 (miR-96) expression levels and the occurrence of deep vein thrombosis (DVT) in orthopedic patients. A total of consecutive 69 orthopedic patients with DVT and 30 healthy individuals were enrolled. Ultrasonic color Doppler imaging was performed on lower limb veins after orthopedic surgery to determine the occurrence of DVT. An enzyme-linked fluorescent assay was performed to detect the levels of D-dimer in plasma. A quantitative polymerase chain reaction assay was performed to determine the expression levels of miR-96. Expression levels of platelet miR-96 were significantly increased in orthopedic patients after orthopedic surgery. miR-96 expression levels in orthopedic patients with DVT at days 1, 3 and 7 after orthopedic surgery were significantly increased when compared with those in the control group. The increased miR-96 expression levels were correlated with plasma D-dimer levels in orthopedic patients with DVT. However, for the orthopedic patients in the non-DVT group following surgery, miR-96 expression levels were correlated with plasma D-dimer levels. In summary, the present results suggest that the expression levels of miR-96 may be associated with the occurrence of DVT. The occurrence of DVT may be accurately predicted by comprehensive analysis of the levels of miR-96 and plasma D-dimer. PMID:27588107

  8. An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis Software.

    PubMed

    Shah, Pranav; Kerns, Edward; Nguyen, Dac-Trung; Obach, R Scott; Wang, Amy Q; Zakharov, Alexey; McKew, John; Simeonov, Anton; Hop, Cornelis E C A; Xu, Xin

    2016-10-01

    Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (P450) isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets. The substrate depletion method [in vitro half-life (t1/2) method] was chosen to determine CLint The assay (384-well format) consisted of three parts: 1) a robotic system for incubation and sample cleanup; 2) two different integrated, ultraperformance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and 3) an automated data analysis system. The CYP3A4 assay was evaluated using two long t1/2 compounds, carbamazepine and antipyrine (t1/2 > 30 minutes); one moderate t1/2 compound, ketoconazole (10 < t1/2 < 30 minutes); and two short t1/2 compounds, loperamide and buspirone (t½ < 10 minutes). Interday and intraday precision and accuracy of the assay were within acceptable range (∼12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human P450 isozymes. PMID:27417180

  9. Metabolic changes assessed by MRS accurately reflect brain function during drug-induced epilepsy in mice in contrast to fMRI-based hemodynamic readouts.

    PubMed

    Seuwen, Aline; Schroeter, Aileen; Grandjean, Joanes; Rudin, Markus

    2015-10-15

    Functional proton magnetic resonance spectroscopy (1H-MRS) enables the non-invasive assessment of neural activity by measuring signals arising from endogenous metabolites in a time resolved manner. Proof-of-principle of this approach has been demonstrated in humans and rats; yet functional 1H-MRS has not been applied in mice so far, although it would be of considerable interest given the many genetically engineered models of neurological disorders established in this species only. Mouse 1H-MRS is challenging as the high demands on spatial resolution typically result in long data acquisition times not commensurable with functional studies. Here, we propose an approach based on spectroscopic imaging in combination with the acquisition of the free induction decay to maximize signal intensity. Highly resolved metabolite maps have been recorded from mouse brain with 12 min temporal resolution. This enabled monitoring of metabolic changes following the administration of bicuculline, a GABA-A receptor antagonist. Changes in levels of metabolites involved in energy metabolism (lactate and phosphocreatine) and neurotransmitters (glutamate) were investigated in a region-dependent manner and shown to scale with the bicuculline dose. GABAergic inhibition induced spectral changes characteristic for increased neurotransmitter turnover and oxidative stress. In contrast to metabolic readouts, BOLD and CBV fMRI responses did not scale with the bicuculline dose indicative of the failure of neurovascular coupling. Nevertheless fMRI measurements supported the notion of increased oxidative stress revealed by functional MRS. Hence, the combined analysis of metabolic and hemodynamic changes in response to stimulation provides complementary insight into processes associated with neural activity.

  10. Metabolic changes assessed by MRS accurately reflect brain function during drug-induced epilepsy in mice in contrast to fMRI-based hemodynamic readouts.

    PubMed

    Seuwen, Aline; Schroeter, Aileen; Grandjean, Joanes; Rudin, Markus

    2015-10-15

    Functional proton magnetic resonance spectroscopy (1H-MRS) enables the non-invasive assessment of neural activity by measuring signals arising from endogenous metabolites in a time resolved manner. Proof-of-principle of this approach has been demonstrated in humans and rats; yet functional 1H-MRS has not been applied in mice so far, although it would be of considerable interest given the many genetically engineered models of neurological disorders established in this species only. Mouse 1H-MRS is challenging as the high demands on spatial resolution typically result in long data acquisition times not commensurable with functional studies. Here, we propose an approach based on spectroscopic imaging in combination with the acquisition of the free induction decay to maximize signal intensity. Highly resolved metabolite maps have been recorded from mouse brain with 12 min temporal resolution. This enabled monitoring of metabolic changes following the administration of bicuculline, a GABA-A receptor antagonist. Changes in levels of metabolites involved in energy metabolism (lactate and phosphocreatine) and neurotransmitters (glutamate) were investigated in a region-dependent manner and shown to scale with the bicuculline dose. GABAergic inhibition induced spectral changes characteristic for increased neurotransmitter turnover and oxidative stress. In contrast to metabolic readouts, BOLD and CBV fMRI responses did not scale with the bicuculline dose indicative of the failure of neurovascular coupling. Nevertheless fMRI measurements supported the notion of increased oxidative stress revealed by functional MRS. Hence, the combined analysis of metabolic and hemodynamic changes in response to stimulation provides complementary insight into processes associated with neural activity. PMID:26166624

  11. An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis Software

    PubMed Central

    Shah, Pranav; Kerns, Edward; Nguyen, Dac-Trung; Obach, R. Scott; Wang, Amy Q.; Zakharov, Alexey; McKew, John; Simeonov, Anton; Hop, Cornelis E. C. A.

    2016-01-01

    Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (P450) isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets. The substrate depletion method [in vitro half-life (t1/2) method] was chosen to determine CLint. The assay (384-well format) consisted of three parts: 1) a robotic system for incubation and sample cleanup; 2) two different integrated, ultraperformance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and 3) an automated data analysis system. The CYP3A4 assay was evaluated using two long t1/2 compounds, carbamazepine and antipyrine (t1/2 > 30 minutes); one moderate t1/2 compound, ketoconazole (10 < t1/2 < 30 minutes); and two short t1/2 compounds, loperamide and buspirone (t½ < 10 minutes). Interday and intraday precision and accuracy of the assay were within acceptable range (∼12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human P450 isozymes. PMID:27417180

  12. Metabolic characterization of AH-7921, a synthetic opioid designer drug: in vitro metabolic stability assessment and metabolite identification, evaluation of in silico prediction, and in vivo confirmation.

    PubMed

    Wohlfarth, Ariane; Scheidweiler, Karl B; Pang, Shaokun; Zhu, Mingshe; Castaneto, Marisol; Kronstrand, Robert; Huestis, Marilyn A

    2016-08-01

    AH-7921 (3,4-dichloro-N-[(1-dimethylamino)cyclohexylmethyl]benzamide) is a new synthetic opioid and has led to multiple non-fatal and fatal intoxications. To comprehensively study AH-7921 metabolism, we assessed human liver microsome (HLM) metabolic stability, determined AH-7921's metabolic profile after human hepatocytes incubation, confirmed our findings in a urine case specimen, and compared results to in silico predictions. For metabolic stability, 1 µmol/L AH-7921 was incubated with HLM for up to 1 h; for metabolite profiling, 10 µmol/L was incubated with pooled human hepatocytes for up to 3 h. Hepatocyte samples were analyzed by liquid chromatography quadrupole/time-of-flight high-resolution mass spectrometry (MS). High-resolution full scan MS and information-dependent acquisition MS/MS data were analyzed with MetabolitePilot™ (SCIEX) using multiple data processing algorithms. The presence of AH-7921 and metabolites was confirmed in the urine case specimen. In silico prediction of metabolite structures was performed with MetaSite™ (Molecular Discovery). AH-7921 in vitro half-life was 13.5 ± 0.4 min. We identified 12 AH-7921 metabolites after hepatocyte incubation, predominantly generated by demethylation, less dominantly by hydroxylation, and combinations of different biotransformations. Eleven of 12 metabolites identified in hepatocytes were found in the urine case specimen. One metabolite, proposed to be di-demethylated, N-hydroxylated and glucuronidated, eluted after AH-7921 and was the most abundant metabolite in non-hydrolyzed urine. MetaSite™ correctly predicted the two most abundant metabolites and the majority of observed biotransformations. The two most dominant metabolites after hepatocyte incubation (also identified in the urine case specimen) were desmethyl and di-desmethyl AH-7921. Together with the glucuronidated metabolites, these are likely suitable analytical targets for documenting AH-7921 intake. Copyright © 2015

  13. Metabolic theory and taxonomic identity predict nutrient recycling in a diverse food web.

    PubMed

    Allgeier, Jacob Edward; Wenger, Seth J; Rosemond, Amy D; Schindler, Daniel E; Layman, Craig A

    2015-05-19

    Reconciling the degree to which ecological processes are generalizable among taxa and ecosystems, or contingent on the identity of interacting species, remains a critical challenge in ecology. Ecological stoichiometry (EST) and metabolic theory of ecology (MTE) are theoretical approaches used to evaluate how consumers mediate nutrient dynamics and energy flow through ecosystems. Recent theoretical work has explored the utility of these theories, but empirical tests in species-rich ecological communities remain scarce. Here we use an unprecedented dataset collected from fishes and dominant invertebrates (n = 900) in a diverse subtropical coastal marine community (50 families, 72 genera, 102 species; body mass range: 0.04-2,597 g) to test the utility of EST and MTE in predicting excretion rates of nitrogen (E(N)), phosphorus (E(P)), and their ratio (E(NP)). Body mass explained a large amount of the variation in EN and EP but not E(NP). Strong evidence in support of the MTE 3/4 allometric scaling coefficient was found for E(P), and for E(N) only after accounting for variation in excretion rates among taxa. In all cases, including taxonomy in models substantially improved model performance, highlighting the importance of species identity for this ecosystem function. Body nutrient content and trophic position explained little of the variation in E(N), E(P), or E(NP), indicating limited applicability of basic predictors of EST. These results highlight the overriding importance of MTE for predicting nutrient flow through organisms, but emphasize that these relationships still fall short of explaining the unique effects certain species can have on ecological processes. PMID:25877152

  14. Metabolic theory and taxonomic identity predict nutrient recycling in a diverse food web.

    PubMed

    Allgeier, Jacob Edward; Wenger, Seth J; Rosemond, Amy D; Schindler, Daniel E; Layman, Craig A

    2015-05-19

    Reconciling the degree to which ecological processes are generalizable among taxa and ecosystems, or contingent on the identity of interacting species, remains a critical challenge in ecology. Ecological stoichiometry (EST) and metabolic theory of ecology (MTE) are theoretical approaches used to evaluate how consumers mediate nutrient dynamics and energy flow through ecosystems. Recent theoretical work has explored the utility of these theories, but empirical tests in species-rich ecological communities remain scarce. Here we use an unprecedented dataset collected from fishes and dominant invertebrates (n = 900) in a diverse subtropical coastal marine community (50 families, 72 genera, 102 species; body mass range: 0.04-2,597 g) to test the utility of EST and MTE in predicting excretion rates of nitrogen (E(N)), phosphorus (E(P)), and their ratio (E(NP)). Body mass explained a large amount of the variation in EN and EP but not E(NP). Strong evidence in support of the MTE 3/4 allometric scaling coefficient was found for E(P), and for E(N) only after accounting for variation in excretion rates among taxa. In all cases, including taxonomy in models substantially improved model performance, highlighting the importance of species identity for this ecosystem function. Body nutrient content and trophic position explained little of the variation in E(N), E(P), or E(NP), indicating limited applicability of basic predictors of EST. These results highlight the overriding importance of MTE for predicting nutrient flow through organisms, but emphasize that these relationships still fall short of explaining the unique effects certain species can have on ecological processes.

  15. Metabolic theory and taxonomic identity predict nutrient recycling in a diverse food web

    PubMed Central

    Allgeier, Jacob Edward; Wenger, Seth J.; Schindler, Daniel E.; Layman, Craig A.

    2015-01-01

    Reconciling the degree to which ecological processes are generalizable among taxa and ecosystems, or contingent on the identity of interacting species, remains a critical challenge in ecology. Ecological stoichiometry (EST) and metabolic theory of ecology (MTE) are theoretical approaches used to evaluate how consumers mediate nutrient dynamics and energy flow through ecosystems. Recent theoretical work has explored the utility of these theories, but empirical tests in species-rich ecological communities remain scarce. Here we use an unprecedented dataset collected from fishes and dominant invertebrates (n = 900) in a diverse subtropical coastal marine community (50 families, 72 genera, 102 species; body mass range: 0.04–2,597 g) to test the utility of EST and MTE in predicting excretion rates of nitrogen (EN), phosphorus (EP), and their ratio (ENP). Body mass explained a large amount of the variation in EN and EP but not ENP. Strong evidence in support of the MTE 3/4 allometric scaling coefficient was found for EP, and for EN only after accounting for variation in excretion rates among taxa. In all cases, including taxonomy in models substantially improved model performance, highlighting the importance of species identity for this ecosystem function. Body nutrient content and trophic position explained little of the variation in EN, EP, or ENP, indicating limited applicability of basic predictors of EST. These results highlight the overriding importance of MTE for predicting nutrient flow through organisms, but emphasize that these relationships still fall short of explaining the unique effects certain species can have on ecological processes. PMID:25877152

  16. Microbial community composition and in silico predicted metabolic potential reflect biogeochemical gradients between distinct peatland types.

    PubMed

    Urbanová, Zuzana; Bárta, Jiří

    2014-12-01

    It is not well understood how the ecological status and microbial community composition of spruce swamp forests (SSF) relate to those found in bogs and fens. To clarify this, we investigated biogeochemical parameters and microbial community composition in a bog, a fen and two SSF using high throughput barcoded sequencing of the small ribosomal subunit (SSU) variable region V4. The results demonstrated that the microbial community of SSF is positioned between those of bogs and fens, and this was confirmed by in silico predicted metabolic potentials. This corresponds well with the position of SSF on the trophic gradient and reflects distinct responses of microbial communities to environmental variables. Species richness and microbial diversity increased significantly from bog to fen, with SSF in between, reflecting the variation in pH, nutrient availability and peat decomposability. The archaeal community, dominated by hydrogenotrophic methanogens, was more similar in SSF and the bog compared with the fen. The composition of the bacterial community of SSF was intermediate between those of bog and fen. However, the production of CO2 (an indicator of peat decomposability) did not differ between SSF and bog, suggesting the limiting effect of low pH and poor litter quality on the functioning of the bacterial community in SSF. These results help to clarify the transitional position of SSF between bogs and fens and showed the strong effect of environmental conditions on microbial community composition and functioning.

  17. Temperature dependences of growth rates and carrying capacities of marine bacteria depart from metabolic theoretical predictions.

    PubMed

    Huete-Stauffer, Tamara Megan; Arandia-Gorostidi, Nestor; Díaz-Pérez, Laura; Morán, Xosé Anxelu G

    2015-10-01

    Using the metabolic theory of ecology (MTE) framework, we evaluated over a whole annual cycle the monthly responses to temperature of the growth rates (μ) and carrying capacities (K) of heterotrophic bacterioplankton at a temperate coastal site. We used experimental incubations spanning 6ºC with bacterial physiological groups identified by flow cytometry according to membrane integrity (live), nucleic acid content (HNA and LNA) and respiratory activity (CTC+). The temperature dependence of μ at the exponential phase of growth was summarized by the activation energy (E), which was variable (-0.52 to 0.72 eV) but followed a seasonal pattern, only reaching the hypothesized value for aerobic heterotrophs of 0.65 eV during the spring bloom for the most active bacterial groups (live, HNA, CTC+). K (i.e. maximum experimental abundance) peaked at 4 × 10(6) cells mL(-1) and generally covaried with μ but, contrary to MTE predictions, it did not decrease consistently with temperature. In the case of live cells, the responses of μ and K to temperature were positively correlated and related to seasonal changes in substrate availability, indicating that the responses of bacteria to warming are far from homogeneous and poorly explained by MTE at our site. PMID:26362925

  18. In vitro enantioselective human liver microsomal metabolism and prediction of in vivo pharmacokinetic parameters of tetrabenazine by DLLME-CE.

    PubMed

    Bocato, Mariana Zuccherato; de Lima Moreira, Fernanda; de Albuquerque, Nayara Cristina Perez; de Gaitani, Cristiane Masetto; de Oliveira, Anderson Rodrigo Moraes

    2016-09-01

    A new capillary electrophoresis method for the enantioselective analysis of cis- and trans- dihydrotetrabenazine (diHTBZ) after in vitro metabolism by human liver microsomes (HLMs) was developed. The chiral electrophoretic separations were performed by using tris-phosphate buffer (pH 2.5) containing 1% (w/v) carboxymethyl-β-CD as background electrolyte with an applied voltage of +15kV and capillary temperature kept at 15°C. Dispersive liquid-liquid microextraction was employed to extract the analytes from HLMs. Dichloromethane was used as extraction solvent (75μL) and acetone as disperser solvent (150μL). The method was validated according to official guidelines and showed to be linear over the concentration range of 0.29-19.57μmolL(-1) (r=0.9955) for each metabolite enantiomer. Within- and between-day precision and accuracy evaluated by relative standard deviation and relative error were lower than 15% for all enantiomers. The stability assay showed that the analytes kept stable under handling, storage and in metabolism conditions. After method validation, an enantioselective in vitro metabolism and in vivo pharmacokinetic prediction was carried out. This study showed a stereoselective metabolism and the observed kinetic profile indicated a substrate inhibition behavior. DiHTBZ enantiomers were catalyzed mainly by CYP2C19 and the predicted clearance suggests that liver metabolism is the main route for TBZ elimination which supports the literature data. PMID:27381871

  19. In vitro enantioselective human liver microsomal metabolism and prediction of in vivo pharmacokinetic parameters of tetrabenazine by DLLME-CE.

    PubMed

    Bocato, Mariana Zuccherato; de Lima Moreira, Fernanda; de Albuquerque, Nayara Cristina Perez; de Gaitani, Cristiane Masetto; de Oliveira, Anderson Rodrigo Moraes

    2016-09-01

    A new capillary electrophoresis method for the enantioselective analysis of cis- and trans- dihydrotetrabenazine (diHTBZ) after in vitro metabolism by human liver microsomes (HLMs) was developed. The chiral electrophoretic separations were performed by using tris-phosphate buffer (pH 2.5) containing 1% (w/v) carboxymethyl-β-CD as background electrolyte with an applied voltage of +15kV and capillary temperature kept at 15°C. Dispersive liquid-liquid microextraction was employed to extract the analytes from HLMs. Dichloromethane was used as extraction solvent (75μL) and acetone as disperser solvent (150μL). The method was validated according to official guidelines and showed to be linear over the concentration range of 0.29-19.57μmolL(-1) (r=0.9955) for each metabolite enantiomer. Within- and between-day precision and accuracy evaluated by relative standard deviation and relative error were lower than 15% for all enantiomers. The stability assay showed that the analytes kept stable under handling, storage and in metabolism conditions. After method validation, an enantioselective in vitro metabolism and in vivo pharmacokinetic prediction was carried out. This study showed a stereoselective metabolism and the observed kinetic profile indicated a substrate inhibition behavior. DiHTBZ enantiomers were catalyzed mainly by CYP2C19 and the predicted clearance suggests that liver metabolism is the main route for TBZ elimination which supports the literature data.

  20. Interethnic variation of CYP2C19 alleles, 'predicted' phenotypes and 'measured' metabolic phenotypes across world populations.

    PubMed

    Fricke-Galindo, I; Céspedes-Garro, C; Rodrigues-Soares, F; Naranjo, M E G; Delgado, Á; de Andrés, F; López-López, M; Peñas-Lledó, E; LLerena, A

    2016-04-01

    The present study evaluates the worldwide frequency distribution of CYP2C19 alleles and CYP2C19 metabolic phenotypes ('predicted' from genotypes and 'measured' with a probe drug) among healthy volunteers from different ethnic groups and geographic regions, as well as the relationship between the 'predicted' and 'measured' CYP2C19 metabolic phenotypes. A total of 52 181 healthy volunteers were studied within 138 selected original research papers. CYP2C19*17 was 42- and 24-fold more frequent in Mediterranean-South Europeans and Middle Easterns than in East Asians (P<0.001, in both cases). Contrarily, CYP2C19*2 and CYP2C19*3 alleles were more frequent in East Asians (30.26% and 6.89%, respectively), and even a twofold higher frequency of these alleles was found in Native populations from Oceania (61.30% and 14.42%, respectively; P<0.001, in all cases), which may be a consequence of genetic drift process in the Pacific Islands. Regarding CYP2C19 metabolic phenotype, poor metabolizers (PMs) were more frequent among Asians than in Europeans, contrarily to the phenomenon reported for CYP2D6. A correlation has been found between the frequencies of CYP2C19 poor metabolism 'predicted' from CYP2C19 genotypes (gPMs) and the poor metabolic phenotype 'measured' with a probe drug (mPMs) when subjects are either classified by ethnicity (r=0.94, P<0.001) or geographic region (r=0.99, P=0.002). Nevertheless, further research is needed in African and Asian populations, which are under-represented, and additional CYP2C19 variants and the 'measured' phenotype should be studied. PMID:26503820

  1. Interethnic variation of CYP2C19 alleles, 'predicted' phenotypes and 'measured' metabolic phenotypes across world populations.

    PubMed

    Fricke-Galindo, I; Céspedes-Garro, C; Rodrigues-Soares, F; Naranjo, M E G; Delgado, Á; de Andrés, F; López-López, M; Peñas-Lledó, E; LLerena, A

    2016-04-01

    The present study evaluates the worldwide frequency distribution of CYP2C19 alleles and CYP2C19 metabolic phenotypes ('predicted' from genotypes and 'measured' with a probe drug) among healthy volunteers from different ethnic groups and geographic regions, as well as the relationship between the 'predicted' and 'measured' CYP2C19 metabolic phenotypes. A total of 52 181 healthy volunteers were studied within 138 selected original research papers. CYP2C19*17 was 42- and 24-fold more frequent in Mediterranean-South Europeans and Middle Easterns than in East Asians (P<0.001, in both cases). Contrarily, CYP2C19*2 and CYP2C19*3 alleles were more frequent in East Asians (30.26% and 6.89%, respectively), and even a twofold higher frequency of these alleles was found in Native populations from Oceania (61.30% and 14.42%, respectively; P<0.001, in all cases), which may be a consequence of genetic drift process in the Pacific Islands. Regarding CYP2C19 metabolic phenotype, poor metabolizers (PMs) were more frequent among Asians than in Europeans, contrarily to the phenomenon reported for CYP2D6. A correlation has been found between the frequencies of CYP2C19 poor metabolism 'predicted' from CYP2C19 genotypes (gPMs) and the poor metabolic phenotype 'measured' with a probe drug (mPMs) when subjects are either classified by ethnicity (r=0.94, P<0.001) or geographic region (r=0.99, P=0.002). Nevertheless, further research is needed in African and Asian populations, which are under-represented, and additional CYP2C19 variants and the 'measured' phenotype should be studied.

  2. Metabolic and hemodynamic events following changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

    PubMed Central

    Mangia, Silvia; Giove, Federico; Tkáč, Ivan; Logothetis, Nikos K.; Henry, Pierre-Gilles; Olman, Cheryl A.; Maraviglia, Bruno; Di Salle, Francesco; Uğurbil, Kâmil

    2009-01-01

    Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the non-invasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal micro-circuitry which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by 1H MRS, 13C MRS and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission, will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI. PMID:19002199

  3. Accurate Prediction of Hyperfine Coupling Constants in Muoniated and Hydrogenated Ethyl Radicals: Ab Initio Path Integral Simulation Study with Density Functional Theory Method.

    PubMed

    Yamada, Kenta; Kawashima, Yukio; Tachikawa, Masanori

    2014-05-13

    We performed ab initio path integral molecular dynamics (PIMD) simulations with a density functional theory (DFT) method to accurately predict hyperfine coupling constants (HFCCs) in the ethyl radical (CβH3-CαH2) and its Mu-substituted (muoniated) compound (CβH2Mu-CαH2). The substitution of a Mu atom, an ultralight isotope of the H atom, with larger nuclear quantum effect is expected to strongly affect the nature of the ethyl radical. The static conventional DFT calculations of CβH3-CαH2 find that the elongation of one Cβ-H bond causes a change in the shape of potential energy curve along the rotational angle via the imbalance of attractive and repulsive interactions between the methyl and methylene groups. Investigation of the methyl-group behavior including the nuclear quantum and thermal effects shows that an unbalanced CβH2Mu group with the elongated Cβ-Mu bond rotates around the Cβ-Cα bond in a muoniated ethyl radical, quite differently from the CβH3 group with the three equivalent Cβ-H bonds in the ethyl radical. These rotations couple with other molecular motions such as the methylene-group rocking motion (inversion), leading to difficulties in reproducing the corresponding barrier heights. Our PIMD simulations successfully predict the barrier heights to be close to the experimental values and provide a significant improvement in muon and proton HFCCs given by the static conventional DFT method. Further investigation reveals that the Cβ-Mu/H stretching motion, methyl-group rotation, methylene-group rocking motion, and HFCC values deeply intertwine with each other. Because these motions are different between the radicals, a proper description of the structural fluctuations reflecting the nuclear quantum and thermal effects is vital to evaluate HFCC values in theory to be comparable to the experimental ones. Accordingly, a fundamental difference in HFCC between the radicals arises from their intrinsic molecular motions at a finite temperature, in

  4. Proteomics of Genetically Engineered Mouse Mammary Tumors Identifies Fatty Acid Metabolism Members as Potential Predictive Markers for Cisplatin Resistance*

    PubMed Central

    Warmoes, Marc; Jaspers, Janneke E.; Xu, Guotai; Sampadi, Bharath K.; Pham, Thang V.; Knol, Jaco C.; Piersma, Sander R.; Boven, Epie; Jonkers, Jos; Rottenberg, Sven; Jimenez, Connie R.

    2013-01-01

    In contrast to various signatures that predict the prognosis of breast cancer patients, markers that predict chemotherapy response are still elusive. To detect such predictive biomarkers, we investigated early changes in protein expression using two mouse models for distinct breast cancer subtypes who have a differential knock-out status for the breast cancer 1, early onset (Brca1) gene. The proteome of cisplatin-sensitive BRCA1-deficient mammary tumors was compared with that of cisplatin-resistant mammary tumors resembling pleomorphic invasive lobular carcinoma. The analyses were performed 24 h after administration of the maximum tolerable dose of cisplatin. At this time point, drug-sensitive BRCA1-deficient tumors showed DNA damage, but cells were largely viable. By applying paired statistics and quantitative filtering, we identified highly discriminatory markers for the sensitive and resistant model. Proteins up-regulated in the sensitive model are involved in centrosome organization, chromosome condensation, homology-directed DNA repair, and nucleotide metabolism. Major discriminatory markers that were up-regulated in the resistant model were predominantly involved in fatty acid metabolism, such as fatty-acid synthase. Specific inhibition of fatty-acid synthase sensitized resistant cells to cisplatin. Our data suggest that exploring the functional link between the DNA damage response and cancer metabolism shortly after the initial treatment may be a useful strategy to predict the efficacy of cisplatin. PMID:23397111

  5. Impaired Cross-Talk between Mesolimbic Food Reward Processing and Metabolic Signaling Predicts Body Mass Index

    PubMed Central

    Simon, Joe J.; Skunde, Mandy; Hamze Sinno, Maria; Brockmeyer, Timo; Herpertz, Sabine C.; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

    2014-01-01

    The anticipation of the pleasure derived from food intake drives the motivation to eat, and hence facilitate overconsumption of food, which ultimately results in obesity. Brain imaging studies provide evidence that mesolimbic brain regions underlie both general as well as food-related anticipatory reward processing. In light of this knowledge, the present study examined the neural responsiveness of the ventral striatum (VS) in participants with a broad BMI spectrum. The study differentiated between general (i.e., monetary) and food-related anticipatory reward processing. We recruited a sample of volunteers with greatly varying body weights, ranging from a low BMI (below 20 kg/m2) over a normal (20–25 kg/m2) and overweight (25–30 kg/m2) BMI, to class I (30–35 kg/m2) and class II (35–40 kg/m2) obesity. A total of 24 participants underwent functional magnetic resonance imaging while performing both a food and monetary incentive delay task, which allows to measure neural activation during the anticipation of rewards. After the presentation of a cue indicating the amount of food or money to be won, participants had to react correctly in order to earn “snack points” or “money coins,” which could then be exchanged for real food or money, respectively, at the end of the experiment. During the anticipation of both types of rewards, participants displayed activity in the VS, a region that plays a pivotal role in the anticipation of rewards. Additionally, we observed that specifically anticipatory food reward processing predicted the individual BMI (current and maximum lifetime). This relation was found to be mediated by impaired hormonal satiety signaling, i.e., increased leptin levels and insulin resistance. These findings suggest that heightened food reward motivation contributes to obesity through impaired metabolic signaling. PMID:25368558

  6. MELANCHOLIC DEPRESSION PREDICTION BY IDENTIFYING REPRESENTATIVE FEATURES IN METABOLIC AND MICROARRAY PROFILES WITH MISSING VALUES

    PubMed Central

    Nie, Zhi; Yang, Tao; Liu, Yashu; Lin, Binbin; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

    2014-01-01

    Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

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

    PubMed

    de Souza, Robson Francisco; Aravind, L

    2012-06-01

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

  8. The role of flexibility and optimality in the prediction of intracellular fluxes of microbial central carbon metabolism.

    PubMed

    Tarlak, Fatih; Sadıkoğlu, Hasan; Çakır, Tunahan

    2014-07-29

    Prediction of intracellular metabolic fluxes based on optimal biomass assumption is a well-known computational approach. While there has been a significant emphasis on the optimality, cellular flexibility, the co-occurrence of suboptimal flux distributions in a microbial population, has hardly been considered in the related computational methods. We have implemented a flexibility-incorporated optimization framework to calculate intracellular fluxes based on a few extracellular measurement constraints. Taking into account slightly suboptimal flux distributions together with a dual-optimality framework (maximization of the growth rate followed by the minimization of the total enzyme amount) we were able to show the positive effect of incorporating flexibility and minimal enzyme consumption on the better prediction of intracellular fluxes of central carbon metabolism of two microorganisms: E. coli and S. cerevisiae.

  9. Absorption, Distribution, Metabolism, Excretion, and Toxicity Evaluation in Drug Discovery. 14. Prediction of Human Pregnane X Receptor Activators by Using Naive Bayesian Classification Technique.

    PubMed

    Shi, Huali; Tian, Sheng; Li, Youyong; Li, Dan; Yu, Huidong; Zhen, Xuechu; Hou, Tingjun

    2015-01-20

    The activation of pregnane X receptor (PXR), a member of the nuclear receptor (NR) superfamily, can mediate potential drug-drug interactions, and therefore, prediction of PXR activation is of great importance for evaluating drug metabolism and toxicity. In this study, based on 532 structurally diverse compounds, we present a comprehensive analysis with the aim to build accurate classification models for distinguishing PXR activators from nonactivators by using a naive Bayesian classification technique. First, the distributions of eight important molecular physicochemical properties of PXR activators versus nonactivators were compared, illustrating that the hydrophobicity-related molecular descriptors (AlogP and log D) show slightly better capability to discriminate PXR activators from nonactivators than the others. Then, based on molecular physicochemical properties, VolSurf descriptors, and molecular fingerprints, naive Bayesian classifiers were developed to separate PXR activators from nonactivators. The results demonstrate that the introduction of molecular fingerprints is quite essential to enhance the prediction accuracy of the classifiers. The best Bayesian classifier based on the 21 physicochemical properties, VolSurf descriptors, and LCFC_10 fingerprints descriptors yields a prediction accuracy of 92.7% for the training set based on leave-one-out (LOO) cross-validation and of 85.2% for the test set. Moreover, by exploring the important structural fragments derived from the best Bayesian classifier, we observed that flexibility is an important structural pattern for PXR activation. In addition, chemical compounds containing more halogen atoms, unsaturated alkanes chains relevant to π-π stacking, and fewer nitrogen atoms tend to be PXR activators. We believe that the naive Bayesian classifier can be used as a reliable virtual screening tool to predict PXR activation in the drug design and discovery pipeline.

  10. Electrochemical simulation of cocaine metabolism-a step toward predictive toxicology for drugs of abuse.

    PubMed

    Mielczarek, Przemystaw; Raoof, Hana; Kotlinska, Joltanta H; Stefanowicz, Piotr; Szewczuk, Zbigniew; Suder, Piotr; Silberring, Jerzy

    2014-01-01

    Knowledge of the metabolic pathways and biotransformation of the most popular drugs, such as cocaine, amphetamine, morphine and others, is crucial for the elucidation of their possible toxicity and mechanism of action in the human body. In vitro studies on metabolism are mainly based on the incubation of drugs with liver celL homogenate and utilizing Living animals. These methods need to be followed by isolation and detection of metabolic products, which makes these techniques time-consuming and technically demanding. We show here that the oxidative metabolism that occurs in the liver cells and is mainly caused by cytochrome P450 can be successfully mimicked with the electrochemical system [EC] connected on-line with electrospray ionization mass spectrometry. Cocaine was chosen as a model drug for these studies and was analyzed with a previously described system under various conditions using the boron-doped diamond working electrode. The results were compared with the number of metabolites generated by a standard procedure based on the reaction with the rat Liver microsomes. Two electrochemical products of cocaine oxidation were created, of which one was a natural metabolite of cocaine in the human body-norcocaine. The EC provides a promising platform for the screening of the addictive drug phase I metabolism. The metabolites can be directly analyzed by mass spectrometry or collected and separated by Liquid chromatog- raphy. No Liver cell homogenate or microsome is necessary to generate these metabolites, which simplifies separation of the mixtures and reduces time and costs of all experiments.

  11. Does individual variation in metabolic phenotype predict fish behaviour and performance?

    PubMed

    Metcalfe, N B; Van Leeuwen, T E; Killen, S S

    2016-01-01

    There is increasing interest in documenting and explaining the existence of marked intraspecific variation in metabolic rate in animals, with fishes providing some of the best-studied examples. After accounting for variation due to other factors, there can typically be a two to three-fold variation among individual fishes for both standard and maximum metabolic rate (SMR and MMR). This variation is reasonably consistent over time (provided that conditions remain stable), and its underlying causes may be influenced by both genes and developmental conditions. In this paper, current knowledge of the extent and causes of individual variation in SMR, MMR and aerobic scope (AS), collectively its metabolic phenotype, is reviewed and potential links among metabolism, behaviour and performance are described. Intraspecific variation in metabolism has been found to be related to other traits: fishes with a relatively high SMR tend to be more dominant and grow faster in high food environments, but may lose their advantage and are more prone to risk-taking when conditions deteriorate. In contrast to the wide body of research examining links between SMR and behavioural traits, very little work has been directed towards understanding the ecological consequences of individual variation in MMR and AS. Although AS can differ among populations of the same species in response to performance demands, virtually nothing is known about the effects of AS on individual behaviours such as those associated with foraging or predator avoidance. Further, while factors such as food availability, temperature, hypoxia and the fish's social environment are known to alter resting and MMRs in fishes, there is a paucity of studies examining how these effects vary among individuals, and how this variation relates to behaviour. Given the observed links between metabolism and measures of performance, understanding the metabolic responses of individuals to changing environments will be a key area for

  12. Does individual variation in metabolic phenotype predict fish behaviour and performance?

    PubMed

    Metcalfe, N B; Van Leeuwen, T E; Killen, S S

    2016-01-01

    There is increasing interest in documenting and explaining the existence of marked intraspecific variation in metabolic rate in animals, with fishes providing some of the best-studied examples. After accounting for variation due to other factors, there can typically be a two to three-fold variation among individual fishes for both standard and maximum metabolic rate (SMR and MMR). This variation is reasonably consistent over time (provided that conditions remain stable), and its underlying causes may be influenced by both genes and developmental conditions. In this paper, current knowledge of the extent and causes of individual variation in SMR, MMR and aerobic scope (AS), collectively its metabolic phenotype, is reviewed and potential links among metabolism, behaviour and performance are described. Intraspecific variation in metabolism has been found to be related to other traits: fishes with a relatively high SMR tend to be more dominant and grow faster in high food environments, but may lose their advantage and are more prone to risk-taking when conditions deteriorate. In contrast to the wide body of research examining links between SMR and behavioural traits, very little work has been directed towards understanding the ecological consequences of individual variation in MMR and AS. Although AS can differ among populations of the same species in response to performance demands, virtually nothing is known about the effects of AS on individual behaviours such as those associated with foraging or predator avoidance. Further, while factors such as food availability, temperature, hypoxia and the fish's social environment are known to alter resting and MMRs in fishes, there is a paucity of studies examining how these effects vary among individuals, and how this variation relates to behaviour. Given the observed links between metabolism and measures of performance, understanding the metabolic responses of individuals to changing environments will be a key area for

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

    EPA Science Inventory

    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...

  14. Accurate prediction of explicit solvent atom distribution in HIV-1 protease and F-ATP synthase by statistical theory of liquids

    NASA Astrophysics Data System (ADS)

    Sindhikara, Daniel; Yoshida, Norio; Hirata, Fumio

    2012-02-01

    We have created a simple algorithm for automatically predicting the explicit solvent atom distribution of biomolecules. The explicit distribution is coerced from the 3D continuous distribution resulting from a 3D-RISM calculation. This procedure predicts optimal location of solvent molecules and ions given a rigid biomolecular structure. We show examples of predicting water molecules near KNI-275 bound form of HIV-1 protease and predicting both sodium ions and water molecules near the rotor ring of F-ATP synthase. Our results give excellent agreement with experimental structure with an average prediction error of 0.45-0.65 angstroms. Further, unlike experimental methods, this method does not suffer from the partial occupancy limit. Our method can be performed directly on 3D-RISM output within minutes. It is useful not only as a location predictor but also as a convenient method for generating initial structures for MD calculations.

  15. Metabolic model of Synechococcus sp. PCC 7002: Prediction of flux distribution and network modification for enhanced biofuel production.

    PubMed

    Hendry, John I; Prasannan, Charulata B; Joshi, Aditi; Dasgupta, Santanu; Wangikar, Pramod P

    2016-08-01

    Flux Balance Analysis was performed with the Genome Scale Metabolic Model of a fast growing cyanobacterium Synechococcus sp. PCC 7002 to gain insights that would help in engineering the organism as a production host. Gene essentiality and synthetic lethality analysis revealed a reduced metabolic robustness under genetic perturbation compared to the heterotrophic bacteria Escherichia coli. Under glycerol heterotrophy the reducing equivalents were generated from tricarboxylic acid cycle rather than the oxidative pentose phosphate pathway. During mixotrophic growth in glycerol the photosynthetic electron transport chain was predominantly used for ATP synthesis with a photosystem I/photosystem II flux ratio higher than that observed under autotrophy. An exhaustive analysis of all possible double reaction knock outs was performed to reroute fixed carbon towards ethanol and butanol production. It was predicted that only ∼10% of fixed carbon could be diverted for ethanol and butanol production.

  16. Neck circumference as a new anthropometric indicator for prediction of insulin resistance and components of metabolic syndrome in adolescents: Brazilian Metabolic Syndrome Study

    PubMed Central

    da Silva, Cleliani de Cassia; Zambon, Mariana Porto; Vasques, Ana Carolina J.; Rodrigues, Ana Maria de B.; Camilo, Daniella Fernandes; Antonio, Maria Ângela R. de G. M.; Cassani, Roberta Soares L.; Geloneze, Bruno

    2014-01-01

    OBJECTIVE: To evaluate the correlation between neck circumference and insulin resistance and components of metabolic syndrome in adolescents with different adiposity levels and pubertal stages, as well as to determine the usefulness of neck circumference to predict insulin resistance in adolescents. METHODS: Cross-sectional study with 388 adolescents of both genders from ten to 19 years old. The adolescents underwent anthropometric and body composition assessment, including neck and waist circumferences, and biochemical evaluation. The pubertal stage was obtained by self-assessment, and the blood pressure, by auscultation. Insulin resistance was evaluated by the Homeostasis Model Assessment-Insulin Resistance. The correlation between two variables was evaluated by partial correlation coefficient adjusted for the percentage of body fat and pubertal stage. The performance of neck circumference to identify insulin resistance was tested by Receiver Operating Characteristic Curve. RESULTS: After the adjustment for percentage body fat and pubertal stage, neck circumference correlated with waist circumference, blood pressure, triglycerides and markers of insulin resistance in both genders. CONCLUSIONS: The results showed that the neck circumference is a useful tool for the detection of insulin resistance and changes in the indicators of metabolic syndrome in adolescents. The easiness of application and low cost of this measure may allow its use in Public Health services. PMID:25119754

  17. Scaling up from traits to communities to ecosystems across broad climate gradients: Testing Metabolic Scaling Theories predictions for forests

    NASA Astrophysics Data System (ADS)

    Enquist, B. J.; Michaletz, S. T.; Buzzard, V.

    2015-12-01

    Key insights in global ecology will come from mechanistically linking pattern and process across scales. Macrosystems ecology specifically attempts to link ecological processes across spatiotemporal scales. The goal s to link the processing of energy and nutrients from cells all the way ecosystems and to understand how shifting climate influences ecosystem processes. Using new data collected from NSF funded Macrosystems project we report on new findings from forests sites across a broad temperature gradient. Our study sites span tropical, temperate, and high elevation forests we assess several key predictions and assumptions of Metabolic Scaling Theory (MST) as well as several other competing hypotheses for the role of climate, light, and plant traits on influencing forest demography and forest ecosystems. Specifically, we assess the importance of plant size, light limitation, size structure, and various climatic factors on forest growth, demography, and ecosystem functioning. We provide some of the first systematic tests of several key predictions from MST. We show that MST predictions are largely upheld and that new insights from assessing theories predictions yields new observations and findings that help modify and extend MST's predictions and applicability. We discuss how theory is critically needed to further our understanding of how to scale pattern and process in ecology - from traits to ecosystems - in order to develop a more predictive global change biology.

  18. Can we predict the intracellular metabolic state of a cell based on extracellular metabolite data?

    PubMed

    Granucci, Ninna; Pinu, Farhana R; Han, Ting-Li; Villas-Boas, Silas G

    2015-12-01

    The analysis of extracellular metabolites presents many technical advantages over the analysis of intracellular compounds, which made this approach very popular in recent years as a high-throughput tool to assess the metabolic state of microbial cells. However, very little effort has been made to determine the actual relationship between intracellular and extracellular metabolite levels. The secretion of intracellular metabolites has been traditionally interpreted as a consequence of an intracellular metabolic overflow, which is based on the premise that for a metabolite to be secreted, it must be over-produced inside the cell. Therefore, we expect to find a secreted metabolite at increased levels inside the cells. Here we present a time-series metabolomics study of Saccharomyces cerevisiae growing on a glucose-limited chemostat with parallel measurements of intra- and extracellular metabolites. Although most of the extracellular metabolites were also detected in the intracellular samples and showed a typical metabolic overflow behaviour, we demonstrate that the secretion of many metabolites could not be explained by the metabolic overflow theory. PMID:26400772

  19. A Data Repository and Visualization Toolbox for Metabolic Pathways and PBPK parameter prediction

    EPA Science Inventory

    NHANES is an extensive, well-structured collection of data about hundreds chemicals products of human metabolism and their concentration in human biomarkers, which includes parent to product mapping where known. Together, these data can be used to test the efficacy of application...

  20. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

    PubMed

    Lundegaard, Claus; Lamberth, Kasper; Harndahl, Mikkel; Buus, Søren; Lund, Ole; Nielsen, Morten

    2008-07-01

    NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8-11 for all 122 alleles. artificial neural network predictions are given as actual IC(50) values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75-80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

  1. Unifying ecological stoichiometry and metabolic theory to predict production and trophic transfer in a marine planktonic food web.

    PubMed

    Moorthi, Stefanie D; Schmitt, Jennifer A; Ryabov, Alexey; Tsakalakis, Ioannis; Blasius, Bernd; Prelle, Lara; Tiedemann, Marc; Hodapp, Dorothee

    2016-05-19

    Two ecological frameworks have been used to explain multitrophic interactions, but rarely in combination: (i) ecological stoichiometry (ES), explaining consumption rates in response to consumers' demand and prey's nutrient content; and (ii) metabolic theory of ecology (MTE), proposing that temperature and body mass affect metabolic rates, growth and consumption rates. Here we combined both, ES and MTE to investigate interactive effects of phytoplankton prey stoichiometry, temperature and zooplankton consumer body mass on consumer grazing rates and production in a microcosm experiment. A simple model integrating parameters from both frameworks was used to predict interactive effects of temperature and nutrient conditions on consumer performance. Overall, model predictions reflected experimental patterns well: consumer grazing rates and production increased with temperature, as could be expected based on MTE. With decreasing algal food quality, grazing rates increased due to compensatory feeding, while consumer growth rates and final biovolume decreased. Nutrient effects on consumer biovolume increased with increasing temperature, while nutrient effects on grazing rates decreased. Highly interactive effects of temperature and nutrient supply indicate that combining the frameworks of ES and MTE is highly important to enhance our ability to predict ecosystem functioning in the context of global change.

  2. Evaluation of comprehensive two-dimensional gas chromatography with accurate mass time-of-flight mass spectrometry for the metabolic profiling of plant-fungus interaction in Aquilaria malaccensis.

    PubMed

    Wong, Yong Foo; Chin, Sung-Tong; Perlmutter, Patrick; Marriott, Philip J

    2015-03-27

    To explore the possible obligate interactions between the phytopathogenic fungus and Aquilaria malaccensis which result in generation of a complex array of secondary metabolites, we describe a comprehensive two-dimensional gas chromatography (GC × GC) method, coupled to accurate mass time-of-flight mass spectrometry (TOFMS) for the untargeted and comprehensive metabolic profiling of essential oils from naturally infected A. malaccensis trees. A polar/non-polar column configuration was employed, offering an improved separation pattern of components when compared to other column sets. Four different grades of the oils displayed quite different metabolic patterns, suggesting the evolution of a signalling relationship between the host tree (emergence of various phytoalexins) and fungi (activation of biotransformation). In total, ca. 550 peaks/metabolites were detected, of which tentative identification of 155 of these compounds was reported, representing between 20.1% and 53.0% of the total ion count. These are distributed over the chemical families of monoterpenic and sesquiterpenic hydrocarbons, oxygenated monoterpenes and sesquiterpenes (comprised of ketone, aldehyde, oxide, alcohol, lactone, keto-alcohol and diol), norterpenoids, diterpenoids, short chain glycols, carboxylic acids and others. The large number of metabolites detected, combined with the ease with which they are located in the 2D separation space, emphasises the importance of a comprehensive analytical approach for the phytochemical analysis of plant metabolomes. Furthermore, the potential of this methodology in grading agarwood oils by comparing the obtained metabolic profiles (pattern recognition for unique metabolite chemical families) is discussed. The phytocomplexity of the agarwood oils signified the production of a multitude of plant-fungus mediated secondary metabolites as chemical signals for natural ecological communication. To the best of our knowledge, this is the most complete

  3. Electrochemical simulation of cocaine metabolism-a step toward predictive toxicology for drugs of abuse.

    PubMed

    Mietczarek, Przemystaw; Raoof, Hana; Kottinska, Joltanta H; Stefanowicz, Piotr; Szewczuk, Zbigniew; Sudera, Piotr; Silberringb, Jerzy

    2014-01-01

    Knowledge of the metabolic pathways and biotransformation of the most popular drugs, such as cocaine, amphetamine, morphine and others, is crucial for the elucidation of their possible toxicity and mechanism of action in the human body. In vitro studies on metabolism are mainly based on the incubation of drugs with liver celL homogenate and utilizing Living animals. These methods need to be followed by isolation and detection of metabolic products, which makes these techniques time-consuming and technically demanding. We show here that the oxidative metabolism that occurs in the liver cells and is mainly caused by cytochrome P450 can be successfully mimicked with the electrochemical system [EC] connected on-line with electrospray ionization mass spectrometry. Cocaine was chosen as a model drug for these studies and was analyzed with a previously described system under various conditions using the boron-doped diamond working electrode. The results were compared with the number of metabolites generated by a standard procedure based on the reaction with the rat Liver microsomes. Two electrochemical products of cocaine oxidation were created, of which one was a natural metabolite of cocaine in the human body-norcocaine. The EC provides a promising platform for the screening of the addictive drug phase I metabolism. The metabolites can be directly analyzed by mass spectrometry or collected and separated by Liquid chromatog- raphy. No Liver cell homogenate or microsome is necessary to generate these metabolites, which simplifies separation of the mixtures and reduces time and costs of all experiments. PMID:25507324

  4. Methodologies for investigating drug metabolism at the early drug discovery stage: prediction of hepatic drug clearance and P450 contribution.

    PubMed

    Emoto, Chie; Murayama, Norie; Rostami-Hodjegan, Amin; Yamazaki, Hiroshi

    2010-10-01

    The attrition rate in drug development is being reduced by continuous advances in science and technology introduced by various academic institutions and pharmaceutical companies. This has been certainly noticeable in reducing the frequency with which unfavorable absorption, distribution, metabolism, and elimination (ADME) characteristics of any candidate drug causes failure in clinical development. Nonetheless, it is important that the objectives in reducing attrition during later stages of development are matched by information generated in the earliest stage of discovery. In this review, we summarize the methodologies employed during the early stages of drug discovery and discuss new findings in the areas of (1) drug metabolism enzymes, (2) the contribution of cytochrome P450 enzymes (P450, CYP) to hepatic metabolism, (3) prediction of hepatic intrinsic clearance, (4) reaction phenotyping, and (5) the metabolic differences between highly homologous enzymes such as CYP3A4 and CYP3A5. The total contribution of P450 and UDP-glucuronosyltransferases to drug metabolism is reported to be more than 80%; therefore, glucuronidation is increasingly recognized as an important clearance pathway in addition to that of P450 enzymes. When estimating the contribution of P450, interpreting the results of inhibition studies using a single P450 inhibitor can lead to false conclusions. For instance, 1-aminobenzotriazole and SKF-525A have a varying range of IC(50) values for inhibition of drug exidation-reaction by different CYP450 enzymes. There are disparities between methodologies at early stage drug discovery and late stage development. For example, although the drug depletion approach for the prediction of hepatic intrinsic clearance may not be desirable at late stages of development, it is suitable at the early drug discovery stage since kinetic characterization and measurement of specific drug metabolites are not required. Data from protein binding assays in plasma and

  5. Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach.

    PubMed

    Ebbels, Timothy M D; Keun, Hector C; Beckonert, Olaf P; Bollard, Mary E; Lindon, John C; Holmes, Elaine; Nicholson, Jeremy K

    2007-11-01

    Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The

  6. Basal Metabolic Rate of Adolescent Modern Pentathlon Athletes: Agreement between Indirect Calorimetry and Predictive Equations and the Correlation with Body Parameters

    PubMed Central

    Loureiro, Luiz Lannes; Fonseca, Sidnei; Castro, Natalia Gomes Casanova de Oliveira e; dos Passos, Renata Baratta; Porto, Cristiana Pedrosa Melo; Pierucci, Anna Paola Trindade Rocha

    2015-01-01

    Purpose The accurate estimative of energy needs is crucial for an optimal physical performance among athletes and the basal metabolic rate (BMR) equations often are not well adjusted for adolescent athletes requiring the use of specific methods, such as the golden standard indirect calorimetry (IC). Therefore, we had the aim to analyse the agreement between the BMR of adolescents pentathletes measured by IC and estimated by commonly used predictive equations. Methods Twenty-eight athletes (17 males and 11 females) were evaluated for BMR, using IC and the predictive equations Harris and Benedict (HB), Cunningham (CUN), Henry and Rees (HR) and FAO/WHO/UNU (FAO). Body composition was obtained using DXA and sexual maturity data were retrieved through validated questionnaires. The correlations among anthropometric variables an IC were analysed by T-student test and ICC, while the agreement between IC and the predictive equations was analysed according to Bland and Altman and by survival-agreement plotting. Results The whole sample average BMR measured by IC was significantly different from the estimated by FAO (p<0.05). Adjusting data by gender FAO and HR equations were statistically different from IC (p <0.05) among males, while female differed only for the HR equation (p <0.05). Conclusion The FAO equation underestimated athletes’ BMR when compared with IC (T Test). When compared to the golden standard IC, using Bland and Altman, ICC and Survival-Agreement, the equations underestimated the energy needs of adolescent pentathlon athletes up to 300kcal/day. Therefore, they should be used with caution when estimating individual energy requirements in such populations. PMID:26569101

  7. Predictive and prognostic value of metabolic tumour volume and total lesion glycolysis in solid tumours.

    PubMed

    Van de Wiele, Christophe; Kruse, Vibeke; Smeets, Peter; Sathekge, Mike; Maes, Alex

    2013-01-01

    Data available in patients suffering from squamous cell carcinoma of the head and neck, lung carcinoma, oesophageal carcinoma and gynaecological malignancies suggest that metabolic tumour volume and to a lesser extent total lesion glycolysis have the potential to become valuable in the imaging of human solid tumours as prognostic biomarkers for short- to intermediate-term survival outcomes, adding value to clinical staging, for assessment of response to treatment with neoadjuvant and concurrent chemotherapy, and for treatment optimization; for example, based on early treatment response assessment using changes in metabolic tumour volume over time, it might be possible to select patients who require a more aggressive treatment to improve their outcome. Prospective studies enrolling consecutive patients, adopting standardized protocols for FDG PET acquisition and processing, adjusting for potential confounders in the analysis (tumour size and origin) and determining the optimal methodology for determination of these novel markers are mandatory.

  8. Predicting chronic copper and nickel reproductive toxicity to Daphnia pulex-pulicaria from whole-animal metabolic profiles.

    PubMed

    Taylor, Nadine S; Kirwan, Jennifer A; Johnson, Craig; Yan, Norman D; Viant, Mark R; Gunn, John M; McGeer, James C

    2016-05-01

    The emergence of omics approaches in environmental research has enhanced our understanding of the mechanisms underlying toxicity; however, extrapolation from molecular effects to whole-organism and population level outcomes remains a considerable challenge. Using environmentally relevant, sublethal, concentrations of two metals (Cu and Ni), both singly and in binary mixtures, we integrated data from traditional chronic, partial life-cycle toxicity testing and metabolomics to generate a statistical model that was predictive of reproductive impairment in a Daphnia pulex-pulicaria hybrid that was isolated from an historically metal-stressed lake. Furthermore, we determined that the metabolic profiles of organisms exposed in a separate acute assay were also predictive of impaired reproduction following metal exposure. Thus we were able to directly associate molecular profiles to a key population response - reproduction, a key step towards improving environmental risk assessment and management. PMID:26854702

  9. Predicting chronic copper and nickel reproductive toxicity to Daphnia pulex-pulicaria from whole-animal metabolic profiles.

    PubMed

    Taylor, Nadine S; Kirwan, Jennifer A; Johnson, Craig; Yan, Norman D; Viant, Mark R; Gunn, John M; McGeer, James C

    2016-05-01

    The emergence of omics approaches in environmental research has enhanced our understanding of the mechanisms underlying toxicity; however, extrapolation from molecular effects to whole-organism and population level outcomes remains a considerable challenge. Using environmentally relevant, sublethal, concentrations of two metals (Cu and Ni), both singly and in binary mixtures, we integrated data from traditional chronic, partial life-cycle toxicity testing and metabolomics to generate a statistical model that was predictive of reproductive impairment in a Daphnia pulex-pulicaria hybrid that was isolated from an historically metal-stressed lake. Furthermore, we determined that the metabolic profiles of organisms exposed in a separate acute assay were also predictive of impaired reproduction following metal exposure. Thus we were able to directly associate molecular profiles to a key population response - reproduction, a key step towards improving environmental risk assessment and management.

  10. Quantitative prediction of intestinal metabolism in humans from a simplified intestinal availability model and empirical scaling factor.

    PubMed

    Kadono, Keitaro; Akabane, Takafumi; Tabata, Kenji; Gato, Katsuhiko; Terashita, Shigeyuki; Teramura, Toshio

    2010-07-01

    This study aimed to establish a practical and convenient method of predicting intestinal availability (F(g)) in humans for highly permeable compounds at the drug discovery stage, with a focus on CYP3A4-mediated metabolism. We constructed a "simplified F(g) model," described using only metabolic parameters, assuming that passive diffusion is dominant when permeability is high and that the effect of transporters in epithelial cells is negligible. Five substrates for CYP3A4 (alprazolam, amlodipine, clonazepam, midazolam, and nifedipine) and four for both CYP3A4 and P-glycoprotein (P-gp) (nicardipine, quinidine, tacrolimus, and verapamil) were used as model compounds. Observed fraction of drug absorbed (F(a)F(g)) values for these compounds were calculated from in vivo pharmacokinetic (PK) parameters, whereas in vitro intestinal intrinsic clearance (CL(int,intestine)) was determined using human intestinal microsomes. The CL(int,intestine) for the model compounds corrected with that of midazolam was defined as CL(m,index) and incorporated into a simplified F(g) model with empirical scaling factor. Regardless of whether the compound was a P-gp substrate, the F(a)F(g) could be reasonably fitted by the simplified F(g) model, and the value of the empirical scaling factor was well estimated. These results suggest that the effects of P-gp on F(a) and F(g) are substantially minor, at least in the case of highly permeable compounds. Furthermore, liver intrinsic clearance (CL(int,liver)) can be used as a surrogate index of intestinal metabolism based on the relationship between CL(int,liver) and CL(m,index). F(g) can be easily predicted using a simplified F(g) model with the empirical scaling factor, enabling more confident selection of drug candidates with desirable PK profiles in humans. PMID:20354105

  11. Migrant Asian Indians in New Zealand; prediction of metabolic syndrome using body weights and measures.

    PubMed

    Jowitt, Ljiljana M; Lu, Louise Weiwei; Rush, Elaine C

    2014-01-01

    The aim of this study of Asian Indian migrants in New Zealand was to determine cut-off points for body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio that best discriminate for increased risk of type 2 diabetes and cardiovascular disease. One hundred and seventy-five (90F, 85M) Asian Indian volunteers (aged >50 y) were recruited from urban Auckland, New Zealand. Body weight, height and waist and hip circumferences were measured using standard techniques. Waist-to-hip ratio, waist-to-height ratio and body mass index were derived. Total and percent body fat by dual energy X-ray absorptiometry, and fasting glucose, insulin and lipids were measured. Three measures of metabolic risk were determined: the homeostasis model assessment of insulin resistance, the McAuley score for insulin sensitivity and metabolic syndrome by International Diabetes Federation criteria. Body mass index, percent body fat and anthropometric measurements of central adiposity generally did not perform well as indicators of metabolic risk in this high risk population of Asian Indian migrants. Our data support the use of lower ethnic specific body mass index and waist circumference for Asian Indian women and men. The discriminatory power of waist-to-height ratio was similar to that of body mass index. Hence, waist-to-height ratio could be used as a simple screening tool. A recommendation, of a waist-to- height ratio of less than 0.5 that would underpin the simple public health message of "your waist circumference should be less than half your height". PMID:25164448

  12. Migrant Asian Indians in New Zealand; prediction of metabolic syndrome using body weights and measures.

    PubMed

    Jowitt, Ljiljana M; Lu, Louise Weiwei; Rush, Elaine C

    2014-01-01

    The aim of this study of Asian Indian migrants in New Zealand was to determine cut-off points for body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio that best discriminate for increased risk of type 2 diabetes and cardiovascular disease. One hundred and seventy-five (90F, 85M) Asian Indian volunteers (aged >50 y) were recruited from urban Auckland, New Zealand. Body weight, height and waist and hip circumferences were measured using standard techniques. Waist-to-hip ratio, waist-to-height ratio and body mass index were derived. Total and percent body fat by dual energy X-ray absorptiometry, and fasting glucose, insulin and lipids were measured. Three measures of metabolic risk were determined: the homeostasis model assessment of insulin resistance, the McAuley score for insulin sensitivity and metabolic syndrome by International Diabetes Federation criteria. Body mass index, percent body fat and anthropometric measurements of central adiposity generally did not perform well as indicators of metabolic risk in this high risk population of Asian Indian migrants. Our data support the use of lower ethnic specific body mass index and waist circumference for Asian Indian women and men. The discriminatory power of waist-to-height ratio was similar to that of body mass index. Hence, waist-to-height ratio could be used as a simple screening tool. A recommendation, of a waist-to- height ratio of less than 0.5 that would underpin the simple public health message of "your waist circumference should be less than half your height".

  13. RS-Predictor: A new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4

    PubMed Central

    Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P.; Breneman, Curt M.

    2011-01-01

    This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as “metabolophores”: A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent,i isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared. PMID:21528931

  14. Use of quantitative shape-activity relationships to model the photoinduced toxicity of polycyclic aromatic hydrocarbons: Electron density shape features accurately predict toxicity

    SciTech Connect

    Mezey, P.G.; Zimpel, Z.; Warburton, P.; Walker, P.D.; Irvine, D.G.; Huang, X.D.; Dixon, D.G.; Greenberg, B.M.

    1998-07-01

    The quantitative shape-activity relationship (QShAR) methodology, based on accurate three-dimensional electron densities and detailed shape analysis methods, has been applied to a Lemna gibba photoinduced toxicity data set of 16 polycyclic aromatic hydrocarbon (PAH) molecules. In the first phase of the studies, a shape fragment QShAR database of PAHs was developed. The results provide a very good match to toxicity based on a combination of the local shape features of single rings in comparison to the central ring of anthracene and a more global shape feature involving larger molecular fragments. The local shape feature appears as a descriptor of the susceptibility of PAHs to photomodification and the global shape feature is probably related to photosensitization activity.

  15. A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs

    PubMed Central

    2014-01-01

    Background We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV. Results For the previously identified candidate proteins, we used the reciprocal best BLAST hits method followed by manual alignment and pathway comparison to identify 6 human proteins with yeast orthologs which were suitable for flux balance analysis (FBA). 5 of these proteins are known to be associated with diseases, including ribose 5-phosphate isomerase deficiency, myopathy with lactic acidosis and sideroblastic anaemia, anemia due to disorders of glutathione metabolism, and two porphyrias, and we suspect the sixth enzyme to have disease associations which are not yet classified or understood based on the work described herein. Conclusions Preliminary findings using the Yeast 7.0 FBA model show lack of growth for only one enzyme, but augmentation of the Yeast 7.0 biomass function to better simulate knockout of certain genes suggested physiological relevance of variations in three additional proteins. Thus, we suggest the following four proteins for laboratory validation: delta-aminolevulinic acid dehydratase, ferrochelatase, ribose-5 phosphate isomerase and mitochondrial tyrosyl-tRNA synthetase. This study indicates that the predictive ability of this method will improve as more advanced, comprehensive models are developed. Moreover, these findings will be useful in the development

  16. Use of dose-dependent absorption into target tissues to more accurately predict cancer risk at low oral doses of hexavalent chromium.

    PubMed

    Haney, J

    2015-02-01

    The mouse dose at the lowest water concentration used in the National Toxicology Program hexavalent chromium (CrVI) drinking water study (NTP, 2008) is about 74,500 times higher than the approximate human dose corresponding to the 35-city geometric mean reported in EWG (2010) and over 1000 times higher than that based on the highest reported tap water concentration. With experimental and environmental doses differing greatly, it is a regulatory challenge to extrapolate high-dose results to environmental doses orders of magnitude lower in a meaningful and toxicologically predictive manner. This seems particularly true for the low-dose extrapolation of results for oral CrVI-induced carcinogenesis since dose-dependent differences in the dose fraction absorbed by mouse target tissues are apparent (Kirman et al., 2012). These data can be used for a straightforward adjustment of the USEPA (2010) draft oral slope factor (SFo) to be more predictive of risk at environmentally-relevant doses. More specifically, the evaluation of observed and modeled differences in the fraction of dose absorbed by target tissues at the point-of-departure for the draft SFo calculation versus lower doses suggests that the draft SFo be divided by a dose-specific adjustment factor of at least an order of magnitude to be less over-predictive of risk at more environmentally-relevant doses.

  17. Taxonomic and predicted metabolic profiles of the human gut microbiome in pre-Columbian mummies.

    PubMed

    Santiago-Rodriguez, Tasha M; Fornaciari, Gino; Luciani, Stefania; Dowd, Scot E; Toranzos, Gary A; Marota, Isolina; Cano, Raul J

    2016-11-01

    Characterization of naturally mummified human gut remains could potentially provide insights into the preservation and evolution of commensal and pathogenic microorganisms, and metabolic profiles. We characterized the gut microbiome of two pre-Columbian Andean mummies dating to the 10-15th centuries using 16S rRNA gene high-throughput sequencing and metagenomics, and compared them to a previously characterized gut microbiome of an 11th century AD pre-Columbian Andean mummy. Our previous study showed that the Clostridiales represented the majority of the bacterial communities in the mummified gut remains, but that other microbial communities were also preserved during the process of natural mummification, as shown with the metagenomics analyses. The gut microbiome of the other two mummies were mainly comprised by Clostridiales or Bacillales, as demonstrated with 16S rRNA gene amplicon sequencing, many of which are facultative anaerobes, possibly consistent with the process of natural mummification requiring low oxygen levels. Metagenome analyses showed the presence of other microbial groups that were positively or negatively correlated with specific metabolic profiles. The presence of sequences similar to both Trypanosoma cruzi and Leishmania donovani could suggest that these pathogens were prevalent in pre-Columbian individuals. Taxonomic and functional profiling of mummified human gut remains will aid in the understanding of the microbial ecology of the process of natural mummification. PMID:27559027

  18. PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

    PubMed Central

    Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

    2014-01-01

    X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed ‘PredPPCrys’ using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of

  19. Normal Tissue Complication Probability Estimation by the Lyman-Kutcher-Burman Method Does Not Accurately Predict Spinal Cord Tolerance to Stereotactic Radiosurgery

    SciTech Connect

    Daly, Megan E.; Luxton, Gary; Choi, Clara Y.H.; Gibbs, Iris C.; Chang, Steven D.; Adler, John R.; Soltys, Scott G.

    2012-04-01

    Purpose: To determine whether normal tissue complication probability (NTCP) analyses of the human spinal cord by use of the Lyman-Kutcher-Burman (LKB) model, supplemented by linear-quadratic modeling to account for the effect of fractionation, predict the risk of myelopathy from stereotactic radiosurgery (SRS). Methods and Materials: From November 2001 to July 2008, 24 spinal hemangioblastomas in 17 patients were treated with SRS. Of the tumors, 17 received 1 fraction with a median dose of 20 Gy (range, 18-30 Gy) and 7 received 20 to 25 Gy in 2 or 3 sessions, with cord maximum doses of 22.7 Gy (range, 17.8-30.9 Gy) and 22.0 Gy (range, 20.2-26.6 Gy), respectively. By use of conventional values for {alpha}/{beta}, volume parameter n, 50% complication probability dose TD{sub 50}, and inverse slope parameter m, a computationally simplified implementation of the LKB model was used to calculate the biologically equivalent uniform dose and NTCP for each treatment. Exploratory calculations were performed with alternate values of {alpha}/{beta} and n. Results: In this study 1 case (4%) of myelopathy occurred. The LKB model using radiobiological parameters from Emami and the logistic model with parameters from Schultheiss overestimated complication rates, predicting 13 complications (54%) and 18 complications (75%), respectively. An increase in the volume parameter (n), to assume greater parallel organization, improved the predictive value of the models. Maximum-likelihood LKB fitting of {alpha}/{beta} and n yielded better predictions (0.7 complications), with n = 0.023 and {alpha}/{beta} = 17.8 Gy. Conclusions: The spinal cord tolerance to the dosimetry of SRS is higher than predicted by the LKB model using any set of accepted parameters. Only a high {alpha}/{beta} value in the LKB model and only a large volume effect in the logistic model with Schultheiss data could explain the low number of complications observed. This finding emphasizes that radiobiological models

  20. In Vivo Validation of In Silico Predicted Metabolic Engineering Strategies in Yeast: Disruption of α-Ketoglutarate Dehydrogenase and Expression of ATP-Citrate Lyase for Terpenoid Production

    PubMed Central

    Gruchattka, Evamaria; Kayser, Oliver

    2015-01-01

    Background Engineering of the central carbon metabolism of Saccharomyces cerevisiae to redirect metabolic flux towards cytosolic acetyl-CoA has become a central topic in yeast biotechnology. A cell factory with increased flux into acetyl-CoA can be used for heterologous production of terpenoids for pharmaceuticals, biofuels, fragrances, or other acetyl-CoA derived compounds. In a previous study, we identified promising metabolic engineering targets in S. cerevisiae using an in silico stoichiometric metabolic network analysis. Here, we validate selected in silico strategies in vivo. Results Patchoulol was produced by yeast via a heterologous patchoulol synthase of Pogostemon cablin. To increase the metabolic flux from acetyl-CoA towards patchoulol, a truncated HMG-CoA reductase was overexpressed and farnesyl diphosphate synthase was fused with patchoulol synthase. The highest increase in production could be achieved by modifying the carbon source; sesquiterpenoid titer increased from glucose to ethanol by a factor of 8.4. Two strategies predicted in silico were chosen for validation in this work. Disruption of α-ketoglutarate dehydrogenase gene (KGD1) was predicted to redirect the metabolic flux via the pyruvate dehydrogenase bypass towards acetyl-CoA. The metabolic flux was redirected as predicted, however, the effect was dependent on cultivation conditions and the flux was interrupted at the level of acetate. High amounts of acetate were produced. As an alternative pathway to synthesize cytosolic acetyl-CoA, ATP-citrate lyase was expressed as a polycistronic construct, however, in vivo performance of the enzyme needs to be optimized to increase terpenoid production. Conclusions Stoichiometric metabolic network analysis can be used successfully as a metabolic prediction tool. However, this study highlights that kinetics, regulation and cultivation conditions may interfere, resulting in poor in vivo performance. Main sites of regulation need to be released and

  1. The CUPIC algorithm: an accurate model for the prediction of sustained viral response under telaprevir or boceprevir triple therapy in cirrhotic patients.

    PubMed

    Boursier, J; Ducancelle, A; Vergniol, J; Veillon, P; Moal, V; Dufour, C; Bronowicki, J-P; Larrey, D; Hézode, C; Zoulim, F; Fontaine, H; Canva, V; Poynard, T; Allam, S; De Lédinghen, V

    2015-12-01

    Triple therapy using boceprevir or telaprevir remains the reference treatment for genotype 1 chronic hepatitis C in countries where new interferon-free regimens have not yet become available. Antiviral treatment is highly required in cirrhotic patients, but they represent a difficult-to-treat population. We aimed to develop a simple algorithm for the prediction of sustained viral response (SVR) in cirrhotic patients treated with triple therapy. A total of 484 cirrhotic patients from the ANRS CO20 CUPIC cohort treated with triple therapy were randomly distributed into derivation and validation sets. A total of 52.1% of patients achieved SVR. In the derivation set, a D0 score for the prediction of SVR before treatment initiation included the following independent predictors collected at day 0: prior treatment response, gamma-GT, platelets, telaprevir treatment, viral load. To refine the prediction at the early phase of the treatment, a W4 score included as additional parameter the viral load collected at week 4. The D0 and W4 scores were combined in the CUPIC algorithm defining three subgroups: 'no treatment initiation or early stop at week 4', 'undetermined' and 'SVR highly probable'. In the validation set, the rates of SVR in these three subgroups were, respectively, 11.1%, 50.0% and 82.2% (P < 0.001). By replacing the variable 'prior treatment response' with 'IL28B genotype', another algorithm was derived for treatment-naïve patients with similar results. The CUPIC algorithm is an easy-to-use tool that helps physicians weigh their decision between immediately treating cirrhotic patients using boceprevir/telaprevir triple therapy or waiting for new drugs to become available in their country. PMID:26216230

  2. Predicting metabolic rate across walking speed: One fit for all body sizes?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We formulated a "one-size-fits-all" model that predicts the energy requirements of level human walking from height, weight, and walking speed. Our three-component model theorizes that the energy expended per kilogram per stride is independent of stature at mechanically equivalent walking speeds. We ...

  3. Ab initio molecular dynamics of liquid water using embedded-fragment second-order many-body perturbation theory towards its accurate property prediction

    PubMed Central

    Willow, Soohaeng Yoo; Salim, Michael A.; Kim, Kwang S.; Hirata, So

    2015-01-01

    A direct, simultaneous calculation of properties of a liquid using an ab initio electron-correlated theory has long been unthinkable. Here we present structural, dynamical, and response properties of liquid water calculated by ab initio molecular dynamics using the embedded-fragment spin-component-scaled second-order many-body perturbation method with the aug-cc-pVDZ basis set. This level of theory is chosen as it accurately and inexpensively reproduces the water dimer potential energy surface from the coupled-cluster singles, doubles, and noniterative triples with the aug-cc-pVQZ basis set, which is nearly exact. The calculated radial distribution function, self-diffusion coefficient, coordinate number, and dipole moment, as well as the infrared and Raman spectra are in excellent agreement with experimental results. The shapes and widths of the OH stretching bands in the infrared and Raman spectra and their isotropic-anisotropic Raman noncoincidence, which reflect the diverse local hydrogen-bond environment, are also reproduced computationally. The simulation also reveals intriguing dynamic features of the environment, which are difficult to probe experimentally, such as a surprisingly large fluctuation in the coordination number and the detailed mechanism by which the hydrogen donating water molecules move across the first and second shells, thereby causing this fluctuation. PMID:26400690

  4. Ab initio molecular dynamics of liquid water using embedded-fragment second-order many-body perturbation theory towards its accurate property prediction.

    PubMed

    Willow, Soohaeng Yoo; Salim, Michael A; Kim, Kwang S; Hirata, So

    2015-01-01

    A direct, simultaneous calculation of properties of a liquid using an ab initio electron-correlated theory has long been unthinkable. Here we present structural, dynamical, and response properties of liquid water calculated by ab initio molecular dynamics using the embedded-fragment spin-component-scaled second-order many-body perturbation method with the aug-cc-pVDZ basis set. This level of theory is chosen as it accurately and inexpensively reproduces the water dimer potential energy surface from the coupled-cluster singles, doubles, and noniterative triples with the aug-cc-pVQZ basis set, which is nearly exact. The calculated radial distribution function, self-diffusion coefficient, coordinate number, and dipole moment, as well as the infrared and Raman spectra are in excellent agreement with experimental results. The shapes and widths of the OH stretching bands in the infrared and Raman spectra and their isotropic-anisotropic Raman noncoincidence, which reflect the diverse local hydrogen-bond environment, are also reproduced computationally. The simulation also reveals intriguing dynamic features of the environment, which are difficult to probe experimentally, such as a surprisingly large fluctuation in the coordination number and the detailed mechanism by which the hydrogen donating water molecules move across the first and second shells, thereby causing this fluctuation.

  5. Stable, high-order SBP-SAT finite difference operators to enable accurate simulation of compressible turbulent flows on curvilinear grids, with application to predicting turbulent jet noise

    NASA Astrophysics Data System (ADS)

    Byun, Jaeseung; Bodony, Daniel; Pantano, Carlos

    2014-11-01

    Improved order-of-accuracy discretizations often require careful consideration of their numerical stability. We report on new high-order finite difference schemes using Summation-By-Parts (SBP) operators along with the Simultaneous-Approximation-Terms (SAT) boundary condition treatment for first and second-order spatial derivatives with variable coefficients. In particular, we present a highly accurate operator for SBP-SAT-based approximations of second-order derivatives with variable coefficients for Dirichlet and Neumann boundary conditions. These terms are responsible for approximating the physical dissipation of kinetic and thermal energy in a simulation, and contain grid metrics when the grid is curvilinear. Analysis using the Laplace transform method shows that strong stability is ensured with Dirichlet boundary conditions while weaker stability is obtained for Neumann boundary conditions. Furthermore, the benefits of the scheme is shown in the direct numerical simulation (DNS) of a Mach 1.5 compressible turbulent supersonic jet using curvilinear grids and skew-symmetric discretization. Particularly, we show that the improved methods allow minimization of the numerical filter often employed in these simulations and we discuss the qualities of the simulation.

  6. Accurate prediction of diradical chemistry from a single-reference density-matrix method: Model application to the bicyclobutane to gauche-1,3-butadiene isomerization

    SciTech Connect

    Bertels, Luke W.; Mazziotti, David A.

    2014-07-28

    Multireference correlation in diradical molecules can be captured by a single-reference 2-electron reduced-density-matrix (2-RDM) calculation with only single and double excitations in the 2-RDM parametrization. The 2-RDM parametrization is determined by N-representability conditions that are non-perturbative in their treatment of the electron correlation. Conventional single-reference wave function methods cannot describe the entanglement within diradical molecules without employing triple- and potentially even higher-order excitations of the mean-field determinant. In the isomerization of bicyclobutane to gauche-1,3-butadiene the parametric 2-RDM (p2-RDM) method predicts that the diradical disrotatory transition state is 58.9 kcal/mol above bicyclobutane. This barrier is in agreement with previous multireference calculations as well as recent Monte Carlo and higher-order coupled cluster calculations. The p2-RDM method predicts the Nth natural-orbital occupation number of the transition state to be 0.635, revealing its diradical character. The optimized geometry from the p2-RDM method differs in important details from the complete-active-space self-consistent-field geometry used in many previous studies including the Monte Carlo calculation.

  7. Dietary Intake Can Predict and Protect Against Changes in Bone Metabolism during Spaceflight and Recovery (Pro K)

    NASA Technical Reports Server (NTRS)

    Smith, Scott M.; Zwart, S. R.; Shackelford, L.; Heer, M.

    2009-01-01

    Bone loss is not only a well-documented effect of spaceflight on astronauts, but also a condition that affects millions of men and women on Earth each year. Many countermeasures aimed at preventing bone loss during spaceflight have been proposed, and many have been evaluated to some degree. To date, those showing potential have focused on either exercise or pharmacological interventions, but none have targeted dietary intake alone as a factor to predict or minimize bone loss during spaceflight. The "Dietary Intake Can Predict and Protect against Changes in Bone Metabolism during Spaceflight and Recovery" investigation ("Pro K") is one of the first inflight evaluations of a dietary countermeasure to lessen bone loss of astronauts. This protocol will test the hypothesis that the ratio of acid precursors to base precursors (specifically animal protein to potassium) in the diet can predict directional changes in bone mineral during spaceflight and recovery. The ratio of animal protein to potassium in the diet will be controlled for multiple short (4-day) periods before and during flight. Based on multiple sets of bed rest data, we hypothesize that a higher ratio of the intake of animal protein to the intake of potassium will yield higher concentrations of markers of bone resorption and urinary calcium excretion during flight and during recovery from bone mineral loss after long-duration spaceflight.

  8. SNP development from RNA-seq data in a nonmodel fish: how many individuals are needed for accurate allele frequency prediction?

    PubMed

    Schunter, C; Garza, J C; Macpherson, E; Pascual, M

    2014-01-01

    Single nucleotide polymorphisms (SNPs) are rapidly becoming the marker of choice in population genetics due to a variety of advantages relative to other markers, including higher genomic density, data quality, reproducibility and genotyping efficiency, as well as ease of portability between laboratories. Advances in sequencing technology and methodologies to reduce genomic representation have made the isolation of SNPs feasible for nonmodel organisms. RNA-seq is one such technique for the discovery of SNPs and development of markers for large-scale genotyping. Here, we report the development of 192 validated SNP markers for parentage analysis in Tripterygion delaisi (the black-faced blenny), a small rocky-shore fish from the Mediterranean Sea. RNA-seq data for 15 individual samples were used for SNP discovery by applying a series of selection criteria. Genotypes were then collected from 1599 individuals from the same population with the resulting loci. Differences in heterozygosity and allele frequencies were found between the two data sets. Heterozygosity was lower, on average, in the population sample, and the mean difference between the frequencies of particular alleles in the two data sets was 0.135 ± 0.100. We used bootstrap resampling of the sequence data to predict appropriate sample sizes for SNP discovery. As cDNA library production is time-consuming and expensive, we suggest that using seven individuals for RNA sequencing reduces the probability of discarding highly informative SNP loci, due to lack of observed polymorphism, whereas use of more than 12 samples does not considerably improve prediction of true allele frequencies.

  9. Is scoring system of computed tomography based metric parameters can accurately predicts shock wave lithotripsy stone-free rates and aid in the development of treatment strategies?

    PubMed Central

    Badran, Yasser Ali; Abdelaziz, Alsayed Saad; Shehab, Mohamed Ahmed; Mohamed, Hazem Abdelsabour Dief; Emara, Absel-Aziz Ali; Elnabtity, Ali Mohamed Ali; Ghanem, Maged Mohammed; ELHelaly, Hesham Abdel Azim

    2016-01-01

    Objective: The objective was to determine the predicting success of shock wave lithotripsy (SWL) using a combination of computed tomography based metric parameters to improve the treatment plan. Patients and Methods: Consecutive 180 patients with symptomatic upper urinary tract calculi 20 mm or less were enrolled in our study underwent extracorporeal SWL were divided into two main groups, according to the stone size, Group A (92 patients with stone ≤10 mm) and Group B (88 patients with stone >10 mm). Both groups were evaluated, according to the skin to stone distance (SSD) and Hounsfield units (≤500, 500–1000 and >1000 HU). Results: Both groups were comparable in baseline data and stone characteristics. About 92.3% of Group A rendered stone-free, whereas 77.2% were stone-free in Group B (P = 0.001). Furthermore, in both group SWL success rates was a significantly higher for stones with lower attenuation <830 HU than with stones >830 HU (P < 0.034). SSD were statistically differences in SWL outcome (P < 0.02). Simultaneous consideration of three parameters stone size, stone attenuation value, and SSD; we found that stone-free rate (SFR) was 100% for stone attenuation value <830 HU for stone <10 mm or >10 mm but total number SWL sessions and shock waves required for the larger stone group were higher than in the smaller group (P < 0.01). Furthermore, SFR was 83.3% and 37.5% for stone <10 mm, mean HU >830, SSD 90 mm and SSD >120 mm, respectively. On the other hand, SFR was 52.6% and 28.57% for stone >10 mm, mean HU >830, SSD <90 mm and SSD >120 mm, respectively. Conclusion: Stone size, stone density (HU), and SSD is simple to calculate and can be reported by radiologists to applying combined score help to augment predictive power of SWL, reduce cost, and improving of treatment strategies. PMID:27141192

  10. Metabolic syndrome independently predicts future diabetes in women with a history of gestational diabetes mellitus.

    PubMed

    Cho, Nam H; Ahn, Chang Ho; Moon, Joon Ho; Kwak, Soo Heon; Choi, Sung Hee; Lim, Soo; Park, Kyong Soo; Metzger, Boyd E; Jang, Hak C

    2016-08-01

    Metabolic syndrome (MetS) is an established predisposing condition for type 2 diabetes mellitus (T2DM). However, it is not thoroughly evaluated whether MetS increases the risk of T2DM in women with a previous history of gestational diabetes mellitus (GDM) who already at high risk of T2DM compared with the general population. We investigated the impact of MetS on the development of postpartum diabetes in women with a history of GDM.This was a multicenter, prospective cohort study of women diagnosed with GDM. The follow-up evaluations, including the oral glucose tolerance test, were completed at 6 weeks postpartum and annually thereafter. MetS was diagnosed at the initial postpartum evaluation according to the revised criteria of the National Cholesterol Education Program-Adult Treatment Panel III. The risk of developing type 2 diabetes (T2DM) in the follow-up period was analyzed based on the presence of MetS, and the adjusted risk was calculated using a Cox proportional hazards model.A total of 412 women without diabetes at the initial postpartum evaluation participated in the annual follow-up for median 3.8 years. MetS was prevalent in 66 (19.2%) women at the initial postpartum evaluation. The incidences of diabetes in women with and without MetS were 825 and 227 per 10,000 person-years, respectively (P < 0.001). The presence of MetS was an independent risk factor for T2DM, with a hazard ratio (HR) of 2.23 (95% confidence interval 1.04-5.08) in multivariate analysis after adjustment for clinical and metabolic parameters. When we considered MetS and impaired fasting glucose (IFG) separately, women with MetS, IFG, or both had an increased risk of T2DM, with HRs of 4.17, 4.36, and 6.98, respectively.The presence of MetS during the early postpartum period is an independent risk factor for the development of T2DM in women with a previous history of GDM. PMID:27583868

  11. Metabolic syndrome independently predicts future diabetes in women with a history of gestational diabetes mellitus

    PubMed Central

    Cho, Nam H.; Ahn, Chang Ho; Moon, Joon Ho; Kwak, Soo Heon; Choi, Sung Hee; Lim, Soo; Park, Kyong Soo; Metzger, Boyd E.; Jang, Hak C.

    2016-01-01

    Abstract Metabolic syndrome (MetS) is an established predisposing condition for type 2 diabetes mellitus (T2DM). However, it is not thoroughly evaluated whether MetS increases the risk of T2DM in women with a previous history of gestational diabetes mellitus (GDM) who already at high risk of T2DM compared with the general population. We investigated the impact of MetS on the development of postpartum diabetes in women with a history of GDM. This was a multicenter, prospective cohort study of women diagnosed with GDM. The follow-up evaluations, including the oral glucose tolerance test, were completed at 6 weeks postpartum and annually thereafter. MetS was diagnosed at the initial postpartum evaluation according to the revised criteria of the National Cholesterol Education Program-Adult Treatment Panel III. The risk of developing type 2 diabetes (T2DM) in the follow-up period was analyzed based on the presence of MetS, and the adjusted risk was calculated using a Cox proportional hazards model. A total of 412 women without diabetes at the initial postpartum evaluation participated in the annual follow-up for median 3.8 years. MetS was prevalent in 66 (19.2%) women at the initial postpartum evaluation. The incidences of diabetes in women with and without MetS were 825 and 227 per 10,000 person-years, respectively (P < 0.001). The presence of MetS was an independent risk factor for T2DM, with a hazard ratio (HR) of 2.23 (95% confidence interval 1.04–5.08) in multivariate analysis after adjustment for clinical and metabolic parameters. When we considered MetS and impaired fasting glucose (IFG) separately, women with MetS, IFG, or both had an increased risk of T2DM, with HRs of 4.17, 4.36, and 6.98, respectively. The presence of MetS during the early postpartum period is an independent risk factor for the development of T2DM in women with a previous history of GDM. PMID:27583868

  12. Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

    PubMed Central

    Buick, Julie K.; Moffat, Ivy; Williams, Andrew; Swartz, Carol D.; Recio, Leslie; Hyduke, Daniel R.; Li, Heng‐Hong; Fornace, Albert J.; Aubrecht, Jiri

    2015-01-01

    The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid‐ and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells. Environ. Mol. Mutagen. 56:520–534, 2015. © 2015 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc. PMID:25733247

  13. Predicting future thermal habitat suitability of competing native and invasive fish species: from metabolic scope to oceanographic modelling.

    PubMed

    Marras, Stefano; Cucco, Andrea; Antognarelli, Fabio; Azzurro, Ernesto; Milazzo, Marco; Bariche, Michel; Butenschön, Momme; Kay, Susan; Di Bitetto, Massimiliano; Quattrocchi, Giovanni; Sinerchia, Matteo; Domenici, Paolo

    2015-01-01

    Global increase in sea temperatures has been suggested to facilitate the incoming and spread of tropical invaders. The increasing success of these species may be related to their higher physiological performance compared with indigenous ones. Here, we determined the effect of temperature on the aerobic metabolic scope (MS) of two herbivorous fish species that occupy a similar ecological niche in the Mediterranean Sea: the native salema (Sarpa salpa) and the invasive marbled spinefoot (Siganus rivulatus). Our results demonstrate a large difference in the optimal temperature for aerobic scope between the salema (21.8°C) and the marbled spinefoot (29.1°C), highlighting the importance of temperature in determining the energy availability and, potentially, the distribution patterns of the two species. A modelling approach based on a present-day projection and a future scenario for oceanographic conditions was used to make predictions about the thermal habitat suitability (THS, an index based on the relationship between MS and temperature) of the two species, both at the basin level (the whole Mediterranean Sea) and at the regional level (the Sicilian Channel, a key area for the inflow of invasive species from the Eastern to the Western Mediterranean Sea). For the present-day projection, our basin-scale model shows higher THS of the marbled spinefoot than the salema in the Eastern compared with the Western Mediterranean Sea. However, by 2050, the THS of the marbled spinefoot is predicted to increase throughout the whole Mediterranean Sea, causing its westward expansion. Nevertheless, the regional-scale model suggests that the future thermal conditions of Western Sicily will remain relatively unsuitable for the invasive species and could act as a barrier for its spread westward. We suggest that metabolic scope can be used as a tool to evaluate the potential invasiveness of alien species and the resilience to global warming of native species.

  14. Predicting future thermal habitat suitability of competing native and invasive fish species: from metabolic scope to oceanographic modelling

    PubMed Central

    Marras, Stefano; Cucco, Andrea; Antognarelli, Fabio; Azzurro, Ernesto; Milazzo, Marco; Bariche, Michel; Butenschön, Momme; Kay, Susan; Di Bitetto, Massimiliano; Quattrocchi, Giovanni; Sinerchia, Matteo; Domenici, Paolo

    2015-01-01

    Global increase in sea temperatures has been suggested to facilitate the incoming and spread of tropical invaders. The increasing success of these species may be related to their higher physiological performance compared with indigenous ones. Here, we determined the effect of temperature on the aerobic metabolic scope (MS) of two herbivorous fish species that occupy a similar ecological niche in the Mediterranean Sea: the native salema (Sarpa salpa) and the invasive marbled spinefoot (Siganus rivulatus). Our results demonstrate a large difference in the optimal temperature for aerobic scope between the salema (21.8°C) and the marbled spinefoot (29.1°C), highlighting the importance of temperature in determining the energy availability and, potentially, the distribution patterns of the two species. A modelling approach based on a present-day projection and a future scenario for oceanographic conditions was used to make predictions about the thermal habitat suitability (THS, an index based on the relationship between MS and temperature) of the two species, both at the basin level (the whole Mediterranean Sea) and at the regional level (the Sicilian Channel, a key area for the inflow of invasive species from the Eastern to the Western Mediterranean Sea). For the present-day projection, our basin-scale model shows higher THS of the marbled spinefoot than the salema in the Eastern compared with the Western Mediterranean Sea. However, by 2050, the THS of the marbled spinefoot is predicted to increase throughout the whole Mediterranean Sea, causing its westward expansion. Nevertheless, the regional-scale model suggests that the future thermal conditions of Western Sicily will remain relatively unsuitable for the invasive species and could act as a barrier for its spread westward. We suggest that metabolic scope can be used as a tool to evaluate the potential invasiveness of alien species and the resilience to global warming of native species. PMID:27293680

  15. A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes.

    PubMed

    Allalou, Amina; Nalla, Amarnadh; Prentice, Kacey J; Liu, Ying; Zhang, Ming; Dai, Feihan F; Ning, Xian; Osborne, Lucy R; Cox, Brian J; Gunderson, Erica P; Wheeler, Michael B

    2016-09-01

    Gestational diabetes mellitus (GDM) affects 3-14% of pregnancies, with 20-50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6-9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non-case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions. PMID:27338739

  16. A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes.

    PubMed

    Allalou, Amina; Nalla, Amarnadh; Prentice, Kacey J; Liu, Ying; Zhang, Ming; Dai, Feihan F; Ning, Xian; Osborne, Lucy R; Cox, Brian J; Gunderson, Erica P; Wheeler, Michael B

    2016-09-01

    Gestational diabetes mellitus (GDM) affects 3-14% of pregnancies, with 20-50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6-9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non-case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions.

  17. Accurate prediction of hard-sphere virial coefficients B6 to B12 from a compressibility-based equation of state

    NASA Astrophysics Data System (ADS)

    Hansen-Goos, Hendrik

    2016-04-01

    We derive an analytical equation of state for the hard-sphere fluid that is within 0.01% of computer simulations for the whole range of the stable fluid phase. In contrast, the commonly used Carnahan-Starling equation of state deviates by up to 0.3% from simulations. The derivation uses the functional form of the isothermal compressibility from the Percus-Yevick closure of the Ornstein-Zernike relation as a starting point. Two additional degrees of freedom are introduced, which are constrained by requiring the equation of state to (i) recover the exact fourth virial coefficient B4 and (ii) involve only integer coefficients on the level of the ideal gas, while providing best possible agreement with the numerical result for B5. Virial coefficients B6 to B10 obtained from the equation of state are within 0.5% of numerical computations, and coefficients B11 and B12 are within the error of numerical results. We conjecture that even higher virial coefficients are reliably predicted.

  18. Accurate predictions of spectroscopic and molecular properties of 27 Λ-S and 73 Ω states of AsS radical.

    PubMed

    Shi, Deheng; Song, Ziyue; Niu, Xianghong; Sun, Jinfeng; Zhu, Zunlue

    2016-01-15

    The PECs are calculated for the 27 Λ-S states and their corresponding 73 Ω states of AsS radical. Of these Λ-S states, only the 2(2)Δ and 5(4)Π states are replulsive. The 1(2)Σ(+), 2(2)Σ(+), 4(2)Π, 3(4)Δ, 3(4)Σ(+), and 4(4)Π states possess double wells. The 3(2)Σ(+) state possesses three wells. The A(2)Π, 3(2)Π, 1(2)Φ, 2(4)Π, 3(4)Π, 2(4)Δ, 3(4)Δ, 1(6)Σ(+), and 1(6)Π states are inverted with the SO coupling effect included. The 1(4)Σ(+), 2(4)Σ(+), 2(4)Σ(-), 2(4)Δ, 1(4)Φ, 1(6)Σ(+), and 1(6)Π states, the second wells of 1(2)Σ(+), 3(4)Σ(+), 4(2)Π, 4(4)Π, and 3(4)Δ states, and the third well of 3(2)Σ(+) state are very weakly-bound states. The PECs are extrapolated to the CBS limit. The effect of SO coupling on the PECs is discussed. The spectroscopic parameters are evaluated, and compared with available measurements and other theoretical ones. The vibrational properties of several weakly-bound states are determined. The spectroscopic properties reported here can be expected to be reliably predicted ones.

  19. Accurate predictions of spectroscopic and molecular properties of 27 Λ-S and 73 Ω states of AsS radical

    NASA Astrophysics Data System (ADS)

    Shi, Deheng; Song, Ziyue; Niu, Xianghong; Sun, Jinfeng; Zhu, Zunlue

    2016-01-01

    The PECs are calculated for the 27 Λ-S states and their corresponding 73 Ω states of AsS radical. Of these Λ-S states, only the 22Δ and 54Π states are replulsive. The 12Σ+, 22Σ+, 42Π, 34Δ, 34Σ+, and 44Π states possess double wells. The 32Σ+ state possesses three wells. The A2Π, 32Π, 12Φ, 24Π, 34Π, 24Δ, 34Δ, 16Σ+, and 16Π states are inverted with the SO coupling effect included. The 14Σ+, 24Σ+, 24Σ-, 24Δ, 14Φ, 16Σ+, and 16Π states, the second wells of 12Σ+, 34Σ+, 42Π, 44Π, and 34Δ states, and the third well of 32Σ+ state are very weakly-bound states. The PECs are extrapolated to the CBS limit. The effect of SO coupling on the PECs is discussed. The spectroscopic parameters are evaluated, and compared with available measurements and other theoretical ones. The vibrational properties of several weakly-bound states are determined. The spectroscopic properties reported here can be expected to be reliably predicted ones.

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

    PubMed Central

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

    2016-01-01

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

  1. The Cohort Study on Prediction of Incidence of All-Cause Mortality by Metabolic Syndrome

    PubMed Central

    Li, Zhixia; Yang, Xinghua; Yang, Jun; Yang, Zhirong; Wang, Shengfeng

    2016-01-01

    Aim The aim was to evaluate the impact of metabolic syndrome (MS), MS individual components and 32 kinds of MS specific component combinations on all-cause mortality risk in a fixed cohort of MJ check-up population. Methods We observed the events of death in a fixed cohort, where the population was composed of 45,542 individuals aged 35–74 who were examined at MJ Health check-up Center in 1997 as baseline examination, and were followed up to 2005. Median duration of follow-up was 7.44 years. MS was defined according to the National Cholesterol Educational Program (the revised NCEP-ATPIII for Asian in 2004), the prevalence of MS was standardized according to China’s fifth census data. We constructed common Cox regression model, simultaneously adjusting the classic risk factors (such as age, sex, smoking, alcohol drinking, physical activity, family history, etc.) to examine the relationship between MS, MS individual components and 32 kinds of MS specific component combinations on the occurrence of death with the fixed cohort. Results The standardized prevalence of MS was 29.75% (male: 30.36%, female: 29.51%). There were 1,749 persons who died during the median 7.44-years follow-up, the mortality rate was 46 per 10,000 person years. The mortality rates were 71 and 35 per 10,000 person years for those with and without MS, respectively. After adjustment for age, sex and classical risk factors, compared with subjects without MS, the hazard ratio of all-cause mortality was 1.26 (95% CI: 1.14–1.40). The all-cause mortality were more highly significant than other combinations (P <0.05) when the following combinations exist: “elevated blood pressure”, “elevated fasting plasma glucose + low high-density lipoprotein cholesterol”, “elevated blood pressure + elevated triglyceride + elevated fasting plasma glucose”, “elevated fasting plasma glucose + low high-density lipoprotein cholesterol + elevated blood pressure + elevated triglyceride”. After adjusting

  2. The Value of Accurate Magnetic Resonance Characterization of Posterior Cruciate Ligament Tears in the Setting of Multiligament Knee Injury: Imaging Features Predictive of Early Repair vs Reconstruction.

    PubMed

    Goiney, Christoper C; Porrino, Jack; Twaddle, Bruce; Richardson, Michael L; Mulcahy, Hyojeong; Chew, Felix S

    2016-01-01

    Multiligament knee injury (MLKI) represents a complex set of pathologies treated with a wide variety of surgical approaches. If early surgical intervention is performed, the disrupted posterior cruciate ligament (PCL) can be treated with primary repair or reconstruction. The purpose of our study was to retrospectively identify a critical length of the distal component of the torn PCL on magnetic resonance imaging (MRI) that may predict the ability to perform early proximal femoral repair of the ligament, as opposed to reconstruction. A total of 50 MLKIs were managed at Harborview Medical Center from May 1, 2013, through July 15, 2014, by an orthopedic surgeon. Following exclusions, there were 27 knees with complete disruption of the PCL that underwent either early reattachment to the femoral insertion or reconstruction and were evaluated using preoperative MRI. In a consensus fashion, 2 radiologists measured the proximal and distal fragments of each disrupted PCL using preoperative MRI in multiple planes, as needed. MRI findings were correlated with what was performed at surgery. Those knees with a distal fragment PCL length of ≥41mm were capable of, and underwent, early proximal femoral repair. With repair, the distal stump was attached to the distal femur. Alternatively, those with a distal PCL length of ≤32mm could not undergo repair because of insufficient length and as such, were reconstructed. If early surgical intervention for an MLKI involving disruption of the PCL is considered, attention should be given to the length of the distal PCL fragment on MRI to plan appropriately for proximal femoral reattachment vs reconstruction. If the distal PCL fragment measures ≥41mm, surgical repair is achievable and can be considered as a surgical option.

  3. Obstructive sleep apnea predicts risk of metabolic syndrome independently of obesity: a meta-analysis

    PubMed Central

    Qian, Yingjun; Xu, Huajun; Wang, Yuyu; Yi, Hongliang; Yin, Shankai

    2016-01-01

    Introduction Obstructive sleep apnea (OSA) has been suggested to be associated with a high risk of metabolic syndrome (MS). However, results on whether the association between OSA and risk of MS is independent of obesity, and the effect of nocturnal intermittent hypoxia (IH) on MS, are conflicting. Our purpose was to estimate the magnitude of the independent association between OSA and risk of MS and further explore whether nocturnal IH in OSA plays a role in MS risk. Material and methods The PubMed and EMBASE databases were systematically searched (until January 21, 2015) for available observational evidence. Unadjusted and body mass index (BMI)-adjusted pooled odds ratios (ORs) for MS in OSA or higher nocturnal IH were calculated using fixed or random models. Tests of homogeneity, publication bias, and robustness of the results were performed. Results A total of 13 independent studies (involving 857 participants in 3 case-control studies and 7077 participants in 10 cross-sectional studies) were included. The OSA was significantly associated with an increased risk of MS in a meta-analysis of 10 studies (pooled OR = 1.72, 95% CI: 1.31–2.26, p < 0.001), with a BMI-adjusted pooled OR of 1.97 (95% CI: 1.34–2.88, p < 0.001). Pooled results from 3 studies on the oxygen desaturation index (ODI) and MS risk (OR = 1.96, 95% CI: 1.73–2.22, p < 0.001) and 3 studies on the cumulative percentage of sleep time with SpO2 below 90% (CT90) and MS risk (OR = 1.05, 95% CI: 1.02–1.07, p < 0.001) were also significant. Conclusions Our findings demonstrated a significant association between OSA and increased MS risk independent of BMI, and further indicated a role of nocturnal IH in this association.

  4. Obstructive sleep apnea predicts risk of metabolic syndrome independently of obesity: a meta-analysis

    PubMed Central

    Qian, Yingjun; Xu, Huajun; Wang, Yuyu; Yi, Hongliang; Yin, Shankai

    2016-01-01

    Introduction Obstructive sleep apnea (OSA) has been suggested to be associated with a high risk of metabolic syndrome (MS). However, results on whether the association between OSA and risk of MS is independent of obesity, and the effect of nocturnal intermittent hypoxia (IH) on MS, are conflicting. Our purpose was to estimate the magnitude of the independent association between OSA and risk of MS and further explore whether nocturnal IH in OSA plays a role in MS risk. Material and methods The PubMed and EMBASE databases were systematically searched (until January 21, 2015) for available observational evidence. Unadjusted and body mass index (BMI)-adjusted pooled odds ratios (ORs) for MS in OSA or higher nocturnal IH were calculated using fixed or random models. Tests of homogeneity, publication bias, and robustness of the results were performed. Results A total of 13 independent studies (involving 857 participants in 3 case-control studies and 7077 participants in 10 cross-sectional studies) were included. The OSA was significantly associated with an increased risk of MS in a meta-analysis of 10 studies (pooled OR = 1.72, 95% CI: 1.31–2.26, p < 0.001), with a BMI-adjusted pooled OR of 1.97 (95% CI: 1.34–2.88, p < 0.001). Pooled results from 3 studies on the oxygen desaturation index (ODI) and MS risk (OR = 1.96, 95% CI: 1.73–2.22, p < 0.001) and 3 studies on the cumulative percentage of sleep time with SpO2 below 90% (CT90) and MS risk (OR = 1.05, 95% CI: 1.02–1.07, p < 0.001) were also significant. Conclusions Our findings demonstrated a significant association between OSA and increased MS risk independent of BMI, and further indicated a role of nocturnal IH in this association. PMID:27695500

  5. Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction.

    PubMed

    Trivedi, Pankaj; Anderson, Ian C; Singh, Brajesh K

    2013-12-01

    Soil organic carbon performs a number of functions in ecosystems and it is clear that microbial communities play important roles in land-atmosphere carbon (C) exchange and soil C storage. In this review, we discuss microbial modulators of soil C storage, 'omics'-based approaches to characterize microbial system interactions impacting terrestrial C sequestration, and how data related to microbial composition and activities can be incorporated into mechanistic and predictive models. We argue that although making direct linkage of genomes to global phenomena is a significant challenge, many connections at intermediate scales are viable with integrated application of new systems biology approaches and powerful analytical and modelling techniques. This integration could enhance our capability to develop and evaluate microbial strategies for capturing and sequestering atmospheric CO2.

  6. Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity

    PubMed Central

    Sun, Jinchun; Slavov, Svetoslav; Schnackenberg, Laura K.; Ando, Yosuke; Greenhaw, James; Yang, Xi; Salminen, William; Mendrick, Donna L.; Beger, Richard

    2014-01-01

    It has been estimated that 10% of acute liver failure is due to “idiosyncratic hepatotoxicity”. The inability to identify such compounds with classical preclinical markers of hepatotoxicity has driven the need to discover a mechanism-based biomarker panel for hepatotoxicity. Seven compounds were included in this study: two overt hepatotoxicants (acetaminophen and carbon tetrachloride), two idiosyncratic hepatotoxicants (felbamate and dantrolene), and three non-hepatotoxicants (meloxicam, penicillin and metformin). Male Sprague–Dawley rats were orally gavaged with a single dose of vehicle, low dose or high dose of the compounds. At 6 h and 24 h post-dosing, blood was collected for metabolomics and clinical chemistry analyses, while organs were collected for histopathology analysis. Forty-one metabolites from previous hepatotoxicity studies were semi-quantified and were used to build models to predict hepatotoxicity. The selected metabolites were involved in various pathways, which have been noted to be linked to the underlying mechanisms of hepatotoxicity. PLS models based on all 41 metabolite or smaller subsets of 6 (6 h), 7 (24 h) and 20 (6 h and 24 h) metabolites resulted in models with an accuracy of at least 97.4% for the hold-out test set and 100% for training sets. When applied to the external test sets, the PLS models predicted that 1 of 9 rats at both 6 h and 24 h treated with idiosyncratic liver toxicants was exposed to a hepatotoxic chemical. In conclusion, the biomarker panel might provide information that along with other endpoint data (e.g., transcriptomics and proteomics) may diagnose acute and idiosyncratic hepatotoxicity in a clinical setting. PMID:25379137

  7. Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity.

    PubMed

    Sun, Jinchun; Slavov, Svetoslav; Schnackenberg, Laura K; Ando, Yosuke; Greenhaw, James; Yang, Xi; Salminen, William; Mendrick, Donna L; Beger, Richard

    2014-07-01

    It has been estimated that 10% of acute liver failure is due to "idiosyncratic hepatotoxicity". The inability to identify such compounds with classical preclinical markers of hepatotoxicity has driven the need to discover a mechanism-based biomarker panel for hepatotoxicity. Seven compounds were included in this study: two overt hepatotoxicants (acetaminophen and carbon tetrachloride), two idiosyncratic hepatotoxicants (felbamate and dantrolene), and three non-hepatotoxicants (meloxicam, penicillin and metformin). Male Sprague-Dawley rats were orally gavaged with a single dose of vehicle, low dose or high dose of the compounds. At 6 h and 24 h post-dosing, blood was collected for metabolomics and clinical chemistry analyses, while organs were collected for histopathology analysis. Forty-one metabolites from previous hepatotoxicity studies were semi-quantified and were used to build models to predict hepatotoxicity. The selected metabolites were involved in various pathways, which have been noted to be linked to the underlying mechanisms of hepatotoxicity. PLS models based on all 41 metabolite or smaller subsets of 6 (6 h), 7 (24 h) and 20 (6 h and 24 h) metabolites resulted in models with an accuracy of at least 97.4% for the hold-out test set and 100% for training sets. When applied to the external test sets, the PLS models predicted that 1 of 9 rats at both 6 h and 24 h treated with idiosyncratic liver toxicants was exposed to a hepatotoxic chemical. In conclusion, the biomarker panel might provide information that along with other endpoint data (e.g., transcriptomics and proteomics) may diagnose acute and idiosyncratic hepatotoxicity in a clinical setting.

  8. Network-based function prediction and interactomics: the case for metabolic enzymes.

    PubMed

    Janga, S C; Díaz-Mejía, J Javier; Moreno-Hagelsieb, G

    2011-01-01

    As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases.

  9. Resting heart rate predicts metabolic syndrome in apparently healthy non-obese Japanese men.

    PubMed

    Oda, Eiji; Aizawa, Yoshifusa

    2014-02-01

    Autonomic nervous dysfunction is considered to be one of the mechanisms of metabolic syndrome (MetS). The aim of this study is to investigate whether resting heart rate, a marker of autonomic nervous dysfunction, is a predictor of MetS in apparently healthy non-obese [body mass index (BMI) <25 kg/m(2)] Japanese men. This is an observational study through 3 years in apparently healthy Japanese 1,265 men and 793 women without MetS and with no history of cardiovascular disease and no use of antihypertensive, antidiabetic, or antihyperlipidemic medication at baseline. Hazard ratios (HRs) of incident MetS were calculated for each 1 SD increase in heart rate stratified by gender and obesity. Incidence of MetS for each tertile of heart rate and HRs of MetS for the highest tertile (T3) compared with the lowest tertile (T1) were calculated stratified by gender and obesity. The HRs [95% confidence intervals (CIs)] of MetS for each 1 SD increase in heart rate were 1.319 (1.035-1.681) (p = 0.025) in non-obese men, 1.172 (0.825-1.665) (p = 0.377) in obese men, 1.115 (0.773-1.608) (p = 0.560) in non-obese women, and 1.401 (0.944-2.078) (p = 0.094) in obese women adjusted for BMI, age, smoking, alcohol drinking, and physical activity. The HRs (95% CIs) of MetS for T3 were 2.138 (1.071-4.269) (p = 0.031) in non-obese men and 1.341 (0.565-3.180) (p = 0.506) in obese men adjusted for pre-existing five components of MetS, age, smoking, alcohol drinking, and physical activity. In conclusion, an increase in resting heart rate was a significant predictor of MetS in non-obese Japanese men.

  10. Acanthosis nigricans predicts the clustering of metabolic syndrome components in Hispanic elementary school-aged children

    PubMed Central

    Vanderbloemen, Laura; Skipper, Betty; Leggott, John; Sebesta, Emilie; Glew, Robert; Burge, Mark R.

    2013-01-01

    Background Acanthosis nigricans (AN) is a dermatologic condition associated with hyperinsulinemia, a marker of insulin resistance that is the principal abnormality in metabolic syndrome (MetS). We examined the association of AN with the clustering of MetS components. Methods A cross-sectional study was conducted in an urban school-based health center in New Mexico. Students without diabetes were evaluated for AN, a family history of type 2 diabetes, body mass index (BMI), and MetS components. The clustering of MetS components by BMI category and AN status was assessed by comparing the group means of summed average z-scores of fasting insulin, triglycerides, high-density lipoprotein- cholesterol, and systolic blood pressure among the students. A multivariate model with BMI category and AN status controlling for Tanner stage was performed to identify the variables associated with the clustering of MetS components. Results Complete data were available for 90 children (age, 9.7 ± 1.4 years; 94 % Hispanic; 60 % female). In multivariate modeling of MetS cluster z-score, significant differences were found between the students with BMI < 85th percentile [−0.27; 95 % confidence interval (95 % CI) = − 0.42 to − 0.11] and (a) the students with BMI 85th – 94.9th percentile with AN (0.74; 95 % CI = 0.17 – 1.31) and (b) the students with BMI ≥ 95th percentile with AN (0.86; 95 % CI = 0.54 – 1.18). No significant differences in the MetS cluster z-score were seen between the students with BMI < 85th percentile and those with BMI 85th – 94.9th percentile without AN (0.24; 95 % CI = − 0.33 to 0.81) or those with BMI ≥ 95th percentile without AN (0.31; 95 % CI = − 0.13 to 0.75). Conclusions Overweight/obese Hispanic elementary school-aged children with AN exhibit clustering of MetS components and could benefit from early intervention. PMID:23329755

  11. Accurate quantum chemical calculations

    NASA Technical Reports Server (NTRS)

    Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

    1989-01-01

    An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

  12. Designing safer chemicals: Predicting the rates of metabolism of halogenated alkanes

    SciTech Connect

    Yin, H.; Anders, M.W.; Higgins, L.

    1995-11-21

    A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts that rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies ({Delta}H{sub act}) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,1,2,2-tetrachloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: (rate, human CYP2E1) = 44.99 - 1.79 ({Delta}H{sub act}), r{sup 2} = 0.86; In (rate, human Cyp2E1)= 46399 -1.77 ({Delta}H{sub act}), r{sup 2} = 0.97 (rates are in nmol of product per min per nmol of cytochrome P450 and energies are in kcal/mol). Correlations were also obtained for five inhalation anesthetics (enflurane, sevoflurane, desflurane, methoxyflurane, and isoflurane) for both in vivo and in vitro data have been shown to agree in any species. The model presented herein provides an archetype for the methodology that may be used in the future design of safer chemicals, particularly hydrochlorofluorocarbons and inhalation anesthetics. 41 refs., 1 fig., 2 tabs.

  13. Urinary Acid Excretion Can Predict Changes in Bone Metabolism During Space Flight

    NASA Technical Reports Server (NTRS)

    Zwart, Sara R.; Smith, Scott M.

    2011-01-01

    Mitigating space flight-induced bone loss is critical for space exploration, and a dietary countermeasure would be ideal. We present here preliminary data from a study where we examined the role of dietary intake patterns as one factor that can influence bone mineral loss in astronauts during space flight. Crewmembers (n=5) were asked to consume a prescribed diet with either a low (0.3-0.6) or high (1.0-1.3) ratio of animal protein to potassium (APro:K) before and during space flight for 4-d periods. Diets were controlled for energy, total protein, calcium, and sodium. 24-h urine samples were collected on the last day of each of the 4-d controlled diet sessions. 24-h urinary acid excretion, which was predicted by dietary potential renal acid load, was correlated with urinary n-telopeptide (NTX, Pearson R = 0.99 and 0.80 for the high and low APro:K sessions, respectively, p<0.001). The amount of protein when expressed as the percentage of total energy (but not as total grams) was also correlated with urinary NTX (R = 0.66, p<0.01). These results, from healthy individuals in a unique environment, will be important to better understand diet and bone interrelationships during space flight as well as on Earth. The study was funded by the NASA Human Research Program.

  14. A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions.

    PubMed

    Cheung, C Y Maurice; Williams, Thomas C R; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

    2013-09-01

    Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.

  15. Composition and Predicted Metabolic Capacity of Upper and Lower Airway Microbiota of Healthy Dogs in Relation to the Fecal Microbiota

    PubMed Central

    Personett, Alexa R.; Grobman, Megan E.; Rindt, Hansjorg; Reinero, Carol R.

    2016-01-01

    The upper and lower airways of healthy humans are reported to harbor stable and consistent bacterial populations, and the composition of these communities is altered in individuals affected with several respiratory diseases. Data regarding the presence of airway microbiota in other animals are scant and a better understanding of the composition and metabolic function of such bacterial populations is essential for the development of novel therapeutic and diagnostic modalities for use in both veterinary and human medicine. Based on targeted next-generation sequencing of feces and samples collected at multiple levels of the airways from 16 healthy female dogs, we demonstrate that canine airways harbor a topographically continuous microbiota with increasing relative abundance of proteobacterial species from the upper to lower airways. The lung-associated microbiota, as assessed via bronchoalveolar lavage fluid (BALF), was the most consistent between dogs and was dominated by three distinct taxa, two of which were resolved to the species level and one to the level of family. The gene content of the nasal, oropharyngeal, and lung-associated microbiota, predicted using the Phylogenetic Investigations into Communities by Reconstruction of Unobserved States (PICRUSt) software, provided information regarding the glyoxylate and citrate cycle metabolic pathways utilized by these bacterial populations to colonize such nutrient-poor, low-throughput environments. These data generated in healthy subjects provide context for future analysis of diseased canine airways. Moreover, as dogs have similar respiratory anatomy, physiology, and immune systems as humans, are exposed to many of the same environmental stimuli, and spontaneously develop similar respiratory diseases, these data support the use of dogs as a model species for prospective studies of the airway microbiota, with findings translatable to the human condition. PMID:27136381

  16. Metabolic rates of ATP transfer through creatine kinase (CK Flux) predict clinical heart failure events and death.

    PubMed

    Bottomley, Paul A; Panjrath, Gurusher S; Lai, Shenghan; Hirsch, Glenn A; Wu, Katherine; Najjar, Samer S; Steinberg, Angela; Gerstenblith, Gary; Weiss, Robert G

    2013-12-11

    Morbidity and mortality from heart failure (HF) are high, and current risk stratification approaches for predicting HF progression are imperfect. Adenosine triphosphate (ATP) is required for normal cardiac contraction, and abnormalities in creatine kinase (CK) energy metabolism, the primary myocardial energy reserve reaction, have been observed in experimental and clinical HF. However, the prognostic value of abnormalities in ATP production rates through CK in human HF has not been investigated. Fifty-eight HF patients with nonischemic cardiomyopathy underwent ³¹P magnetic resonance spectroscopy (MRS) to quantify cardiac high-energy phosphates and the rate of ATP synthesis through CK (CK flux) and were prospectively followed for a median of 4.7 years. Multiple-event analysis (MEA) was performed for HF-related events including all-cause and cardiac death, HF hospitalization, cardiac transplantation, and ventricular-assist device placement. Among baseline demographic, clinical, and metabolic parameters, MEA identified four independent predictors of HF events: New York Heart Association (NYHA) class, left ventricular ejection fraction (LVEF), African-American race, and CK flux. Reduced myocardial CK flux was a significant predictor of HF outcomes, even after correction for NYHA class, LVEF, and race. For each increase in CK flux of 1 μmol g⁻¹ s⁻¹, risk of HF-related composite outcomes decreased by 32 to 39%. These findings suggest that reduced CK flux may be a potential HF treatment target. Newer imaging strategies, including noninvasive ³¹P MRS that detect altered ATP kinetics, could thus complement risk stratification in HF and add value in conditions involving other tissues with high energy demands, including skeletal muscle and brain.

  17. Value of volume-based metabolic parameters for predicting survival in breast cancer patients treated with neoadjuvant chemotherapy

    PubMed Central

    Kim, Tae Hee; Yoon, Joon-Kee; Kang, Doo Kyoung; Kang, Seok Yun; Jung, Yong Sik; Han, Sehwan; Kim, Ji Young; Yim, Hyunee; An, Young-Sil

    2016-01-01

    Abstract We evaluated the role of metabolic parameters in the prediction of disease recurrence in operable invasive ductal breast cancer patients treated with neoadjuvant chemotherapy (NAC). We retrospectively evaluated 139 female patients (mean age, 46.5 years; range: 27–72 years) with invasive ductal breast cancer, treated with NAC followed by surgery. All patients underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomography and magnetic resonance imaging at baseline and after completion of NAC before surgery. The prognostic significance of clinicopathological and imaging parameters for disease-free survival (DFS) was evaluated. Recurrence of cancer was detected in 31 of 139 patients (22.3%; follow-up period: 6–82 months). Baseline maximum standardized uptake value, metabolic tumor volume (MTV), and reduction rate (RR) of MTV after NAC were significant independent prognostic factors for DFS in a multivariate analysis (all P < 0.05). The survival functions differed significantly between low and high histological grades (P < 0.001). DFS of the patients with high baseline MTV (≥5.23 cm3) was significantly poorer than that of low MTV patients (P = 0.019). The survival function of the group with low RR of MTV after NAC (≤90.72%) was poorer than the higher RR of the MTV group (P = 0.008). Our findings suggest that breast cancer patients who have a high histological grade, large baseline MTV, or a small RR of MTV after NAC should receive great attention to check for possible recurrence. PMID:27741099

  18. Genome-Enabled Modeling of Biogeochemical Processes Predicts Metabolic Dependencies that Connect the Relative Fitness of Microbial Functional Guilds

    NASA Astrophysics Data System (ADS)

    Brodie, E.; King, E.; Molins, S.; Karaoz, U.; Steefel, C. I.; Banfield, J. F.; Beller, H. R.; Anantharaman, K.; Ligocki, T. J.; Trebotich, D.

    2015-12-01

    Pore-scale processes mediated by microorganisms underlie a range of critical ecosystem services, regulating carbon stability, nutrient flux, and the purification of water. Advances in cultivation-independent approaches now provide us with the ability to reconstruct thousands of genomes from microbial populations from which functional roles may be assigned. With this capability to reveal microbial metabolic potential, the next step is to put these microbes back where they belong to interact with their natural environment, i.e. the pore scale. At this scale, microorganisms communicate, cooperate and compete across their fitness landscapes with communities emerging that feedback on the physical and chemical properties of their environment, ultimately altering the fitness landscape and selecting for new microbial communities with new properties and so on. We have developed a trait-based model of microbial activity that simulates coupled functional guilds that are parameterized with unique combinations of traits that govern fitness under dynamic conditions. Using a reactive transport framework, we simulate the thermodynamics of coupled electron donor-acceptor reactions to predict energy available for cellular maintenance, respiration, biomass development, and enzyme production. From metagenomics, we directly estimate some trait values related to growth and identify the linkage of key traits associated with respiration and fermentation, macromolecule depolymerizing enzymes, and other key functions such as nitrogen fixation. Our simulations were carried out to explore abiotic controls on community emergence such as seasonally fluctuating water table regimes across floodplain organic matter hotspots. Simulations and metagenomic/metatranscriptomic observations highlighted the many dependencies connecting the relative fitness of functional guilds and the importance of chemolithoautotrophic lifestyles. Using an X-Ray microCT-derived soil microaggregate physical model combined

  19. Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks.

    PubMed

    Ehret, A; Hochstuhl, D; Krattenmacher, N; Tetens, J; Klein, M S; Gronwald, W; Thaller, G

    2015-01-01

    Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information.

  20. Towards improved models for predicting bioconcentration of well-metabolized compounds by rainbow trout using measured rates of in vitro intrinsic clearance

    EPA Science Inventory

    Consensus models were developed to predict the bioconcentration of well-metabolized chemicals by rainbow trout. The models employ intrinsic clearance data from in vitro studies with liver S9 fractions or isolated hepatocytes to estimate a liver clearance rate which is extrapolat...

  1. Can Selforganizing Maps Accurately Predict Photometric Redshifts?

    NASA Technical Reports Server (NTRS)

    Way, Michael J.; Klose, Christian

    2012-01-01

    We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods