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Sample records for accurately predicting future

  1. Predicting Future Citation Behavior.

    ERIC Educational Resources Information Center

    Burrell, Quentin L.

    2003-01-01

    Develops the theory for a stochastic model for the citation process in the presence of obsolescence to predict the future citation pattern of individual papers in a collection. Shows that the expected number of future citations is a linear function of the current number, interpreted as an example of a success-breeds-success phenomenon. (Author/LRW)

  2. New model accurately predicts reformate composition

    SciTech Connect

    Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. )

    1994-01-31

    Although naphtha reforming is a well-known process, the evolution of catalyst formulation, as well as new trends in gasoline specifications, have led to rapid evolution of the process, including: reactor design, regeneration mode, and operating conditions. Mathematical modeling of the reforming process is an increasingly important tool. It is fundamental to the proper design of new reactors and revamp of existing ones. Modeling can be used to optimize operating conditions, analyze the effects of process variables, and enhance unit performance. Instituto Mexicano del Petroleo has developed a model of the catalytic reforming process that accurately predicts reformate composition at the higher-severity conditions at which new reformers are being designed. The new AA model is more accurate than previous proposals because it takes into account the effects of temperature and pressure on the rate constants of each chemical reaction.

  3. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  4. You Can Accurately Predict Land Acquisition Costs.

    ERIC Educational Resources Information Center

    Garrigan, Richard

    1967-01-01

    Land acquisition costs were tested for predictability based upon the 1962 assessed valuations of privately held land acquired for campus expansion by the University of Wisconsin from 1963-1965. By correlating the land acquisition costs of 108 properties acquired during the 3 year period with--(1) the assessed value of the land, (2) the assessed…

  5. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  6. Predicting Future Improvements in Footracing.

    ERIC Educational Resources Information Center

    Browne, Joseph

    1980-01-01

    Three models to predict future world records for footraces are reviewed. The records for the mile run are presented with time and year given in linear, hyperbolic, and experiential relationships. (MP)

  7. A predictable and accurate technique with elastomeric impression materials.

    PubMed

    Barghi, N; Ontiveros, J C

    1999-08-01

    A method for obtaining more predictable and accurate final impressions with polyvinylsiloxane impression materials in conjunction with stock trays is proposed and tested. Heavy impression material is used in advance for construction of a modified custom tray, while extra-light material is used for obtaining a more accurate final impression.

  8. Accurate torque-speed performance prediction for brushless dc motors

    NASA Astrophysics Data System (ADS)

    Gipper, Patrick D.

    Desirable characteristics of the brushless dc motor (BLDCM) have resulted in their application for electrohydrostatic (EH) and electromechanical (EM) actuation systems. But to effectively apply the BLDCM requires accurate prediction of performance. The minimum necessary performance characteristics are motor torque versus speed, peak and average supply current and efficiency. BLDCM nonlinear simulation software specifically adapted for torque-speed prediction is presented. The capability of the software to quickly and accurately predict performance has been verified on fractional to integral HP motor sizes, and is presented. Additionally, the capability of torque-speed prediction with commutation angle advance is demonstrated.

  9. 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.

  10. 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

  11. Inverter Modeling For Accurate Energy Predictions Of Tracking HCPV Installations

    NASA Astrophysics Data System (ADS)

    Bowman, J.; Jensen, S.; McDonald, Mark

    2010-10-01

    High efficiency high concentration photovoltaic (HCPV) solar plants of megawatt scale are now operational, and opportunities for expanded adoption are plentiful. However, effective bidding for sites requires reliable prediction of energy production. HCPV module nameplate power is rated for specific test conditions; however, instantaneous HCPV power varies due to site specific irradiance and operating temperature, and is degraded by soiling, protective stowing, shading, and electrical connectivity. These factors interact with the selection of equipment typically supplied by third parties, e.g., wire gauge and inverters. We describe a time sequence model accurately accounting for these effects that predicts annual energy production, with specific reference to the impact of the inverter on energy output and interactions between system-level design decisions and the inverter. We will also show two examples, based on an actual field design, of inverter efficiency calculations and the interaction between string arrangements and inverter selection.

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

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    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

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

    PubMed Central

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

    2013-01-01

    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. PMID:23499924

  15. 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

  16. Mouse models of human AML accurately predict chemotherapy response.

    PubMed

    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

    2009-04-01

    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.

  17. Turbulence Models for Accurate Aerothermal Prediction in Hypersonic Flows

    NASA Astrophysics Data System (ADS)

    Zhang, Xiang-Hong; Wu, Yi-Zao; Wang, Jiang-Feng

    Accurate description of the aerodynamic and aerothermal environment is crucial to the integrated design and optimization for high performance hypersonic vehicles. In the simulation of aerothermal environment, the effect of viscosity is crucial. The turbulence modeling remains a major source of uncertainty in the computational prediction of aerodynamic forces and heating. In this paper, three turbulent models were studied: the one-equation eddy viscosity transport model of Spalart-Allmaras, the Wilcox k-ω model and the Menter SST model. For the k-ω model and SST model, the compressibility correction, press dilatation and low Reynolds number correction were considered. The influence of these corrections for flow properties were discussed by comparing with the results without corrections. In this paper the emphasis is on the assessment and evaluation of the turbulence models in prediction of heat transfer as applied to a range of hypersonic flows with comparison to experimental data. This will enable establishing factor of safety for the design of thermal protection systems of hypersonic vehicle.

  18. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

  19. Pathogenesis and Prediction of Future Rheumatoid Arthritis

    DTIC Science & Technology

    2014-10-01

    AWARD NUMBER: W81XWH-13-1-0408 TITLE: Pathogenesis and Prediction of Future Rheumatoid Arthritis ...5a. CONTRACT NUMBER Pathogenesis and Prediction of Future Rheumatoid Arthritis 5b. GRANT NUMBER W81XWH-13-1-0408 5c...SUPPLEMENTARY NOTES 14. ABSTRACT It is now well established that there is a preclinical period of rheumatoid arthritis (RA) development that is

  20. Predicting the Future of ESL.

    ERIC Educational Resources Information Center

    Ashworth, Mary

    Influences in the classroom of English as a second language (ESL) are briefly reviewed as a preface to a discussion of the past, present, and future of ESL instruction in Canada. Ten influences on ESL's past are examined in terms of their effects on ESL teachers: international, national, social, political, economic, commercial, media,…

  1. 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

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

    PubMed

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

    2016-05-07

    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

  3. 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.

  4. IRIS: Towards an Accurate and Fast Stage Weight Prediction Method

    NASA Astrophysics Data System (ADS)

    Taponier, V.; Balu, A.

    2002-01-01

    The knowledge of the structural mass fraction (or the mass ratio) of a given stage, which affects the performance of a rocket, is essential for the analysis of new or upgraded launchers or stages, whose need is increased by the quick evolution of the space programs and by the necessity of their adaptation to the market needs. The availability of this highly scattered variable, ranging between 0.05 and 0.15, is of primary importance at the early steps of the preliminary design studies. At the start of the staging and performance studies, the lack of frozen weight data (to be obtained later on from propulsion, trajectory and sizing studies) leads to rely on rough estimates, generally derived from printed sources and adapted. When needed, a consolidation can be acquired trough a specific analysis activity involving several techniques and implying additional effort and time. The present empirical approach allows thus to get approximated values (i.e. not necessarily accurate or consistent), inducing some result inaccuracy as well as, consequently, difficulties of performance ranking for a multiple option analysis, and an increase of the processing duration. This forms a classical harsh fact of the preliminary design system studies, insufficiently discussed to date. It appears therefore highly desirable to have, for all the evaluation activities, a reliable, fast and easy-to-use weight or mass fraction prediction method. Additionally, the latter should allow for a pre selection of the alternative preliminary configurations, making possible a global system approach. For that purpose, an attempt at modeling has been undertaken, whose objective was the determination of a parametric formulation of the mass fraction, to be expressed from a limited number of parameters available at the early steps of the project. It is based on the innovative use of a statistical method applicable to a variable as a function of several independent parameters. A specific polynomial generator

  5. Predicting the future trend of popularity by network diffusion

    NASA Astrophysics Data System (ADS)

    Zeng, An; Yeung, Chi Ho

    2016-06-01

    Conventional approaches to predict the future popularity of products are mainly based on extrapolation of their current popularity, which overlooks the hidden microscopic information under the macroscopic trend. Here, we study diffusion processes on consumer-product and citation networks to exploit the hidden microscopic information and connect consumers to their potential purchase, publications to their potential citers to obtain a prediction for future item popularity. By using the data obtained from the largest online retailers including Netflix and Amazon as well as the American Physical Society citation networks, we found that our method outperforms the accurate short-term extrapolation and identifies the potentially popular items long before they become prominent.

  6. Predicting the future trend of popularity by network diffusion.

    PubMed

    Zeng, An; Yeung, Chi Ho

    2016-06-01

    Conventional approaches to predict the future popularity of products are mainly based on extrapolation of their current popularity, which overlooks the hidden microscopic information under the macroscopic trend. Here, we study diffusion processes on consumer-product and citation networks to exploit the hidden microscopic information and connect consumers to their potential purchase, publications to their potential citers to obtain a prediction for future item popularity. By using the data obtained from the largest online retailers including Netflix and Amazon as well as the American Physical Society citation networks, we found that our method outperforms the accurate short-term extrapolation and identifies the potentially popular items long before they become prominent.

  7. Helicopter noise prediction - The current status and future direction

    NASA Technical Reports Server (NTRS)

    Brentner, Kenneth S.; Farassat, F.

    1992-01-01

    The paper takes stock of the progress, assesses the current prediction capabilities, and forecasts the direction of future helicopter noise prediction research. The acoustic analogy approach, specifically, theories based on the Ffowcs Williams-Hawkings equations, are the most widely used for deterministic noise sources. Thickness and loading noise can be routinely predicted given good plane motion and blade loading inputs. Blade-vortex interaction noise can also be predicted well with measured input data, but prediction of airloads with the high spatial and temporal resolution required for BVI is still difficult. Current semiempirical broadband noise predictions are useful and reasonably accurate. New prediction methods based on a Kirchhoff formula and direct computation appear to be very promising, but are currently very demanding computationally.

  8. 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

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

    PubMed

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

    2015-07-07

    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.

  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. Prediction of Preoperative Anxiety in Children: Who is Most Accurate?

    PubMed Central

    MacLaren, Jill E.; Thompson, Caitlin; Weinberg, Megan; Fortier, Michelle A.; Morrison, Debra E.; Perret, Danielle; Kain, Zeev N.

    2009-01-01

    Background In this investigation, we sought to assess the ability of pediatric attending anesthesiologists, resident anesthesiologists and mothers to predict anxiety during induction of anesthesia in 2 to 16-year-old children (n=125). Methods Anesthesiologists and mothers provided predictions using a visual analog scale and children's anxiety was assessed using a valid behavior observation tool the Modified Yale Preoperative Anxiety Scale (mYPAS). All mothers were present during anesthetic induction and no child received sedative premedication. Correlational analyses were conducted. Results A total of 125 children aged 2 to 16 years, their mothers, and their attending pediatric anesthesiologists and resident anesthesiologists were studied. Correlational analyses revealed significant associations between attending predictions and child anxiety at induction (rs= 0.38, p<0.001). Resident anesthesiologist and mother predictions were not significantly related to children's anxiety during induction (rs = 0.01 and 0.001, respectively). In terms of accuracy of prediction, 47.2% of predictions made by attending anesthesiologists were within one standard deviation of the observed anxiety exhibited by the child, and 70.4% of predictions were within 2 standard deviations. Conclusions We conclude that attending anesthesiologists who practice in pediatric settings are better than mothers in predicting the anxiety of children during induction of anesthesia. While this finding has significant clinical implications, it is unclear if it can be extended to attending anesthesiologists whose practice is not mostly pediatric anesthesia. PMID:19448201

  12. Memory, Imagination, and Predicting the Future

    PubMed Central

    Mullally, Sinéad L.

    2014-01-01

    On the face of it, memory, imagination, and prediction seem to be distinct cognitive functions. However, metacognitive, cognitive, neuropsychological, and neuroimaging evidence is emerging that they are not, suggesting intimate links in their underlying processes. Here, we explore these empirical findings and the evolving theoretical frameworks that seek to explain how a common neural system supports our recollection of times past, imagination, and our attempts to predict the future. PMID:23846418

  13. 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.

  14. Fast and accurate automatic structure prediction with HHpred.

    PubMed

    Hildebrand, Andrea; Remmert, Michael; Biegert, Andreas; Söding, Johannes

    2009-01-01

    Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

  15. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration.

  16. 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.

  17. Accurate Theoretical Prediction of the Properties of Energetic Materials

    DTIC Science & Technology

    2007-11-02

    calculations (e.g. Cheetah ). 8. Sensitivity. The structure prediction and lattice potential work will serve as a platform to examine impact/shock...nitromethane molecules. (In an extension of the present work, we will freeze the internal coordinates of the molecules and assess the extent to which the

  18. Learning regulatory programs that accurately predict differential expression with MEDUSA.

    PubMed

    Kundaje, Anshul; Lianoglou, Steve; Li, Xuejing; Quigley, David; Arias, Marta; Wiggins, Chris H; Zhang, Li; Leslie, Christina

    2007-12-01

    Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration.

  19. 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

  20. 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?

  1. Predictive rendering for accurate material perception: modeling and rendering fabrics

    NASA Astrophysics Data System (ADS)

    Bala, Kavita

    2012-03-01

    In computer graphics, rendering algorithms are used to simulate the appearance of objects and materials in a wide range of applications. Designers and manufacturers rely entirely on these rendered images to previsualize scenes and products before manufacturing them. They need to differentiate between different types of fabrics, paint finishes, plastics, and metals, often with subtle differences, for example, between silk and nylon, formaica and wood. Thus, these applications need predictive algorithms that can produce high-fidelity images that enable such subtle material discrimination.

  2. Can numerical simulations accurately predict hydrodynamic instabilities in liquid films?

    NASA Astrophysics Data System (ADS)

    Denner, Fabian; Charogiannis, Alexandros; Pradas, Marc; van Wachem, Berend G. M.; Markides, Christos N.; Kalliadasis, Serafim

    2014-11-01

    Understanding the dynamics of hydrodynamic instabilities in liquid film flows is an active field of research in fluid dynamics and non-linear science in general. Numerical simulations offer a powerful tool to study hydrodynamic instabilities in film flows and can provide deep insights into the underlying physical phenomena. However, the direct comparison of numerical results and experimental results is often hampered by several reasons. For instance, in numerical simulations the interface representation is problematic and the governing equations and boundary conditions may be oversimplified, whereas in experiments it is often difficult to extract accurate information on the fluid and its behavior, e.g. determine the fluid properties when the liquid contains particles for PIV measurements. In this contribution we present the latest results of our on-going, extensive study on hydrodynamic instabilities in liquid film flows, which includes direct numerical simulations, low-dimensional modelling as well as experiments. The major focus is on wave regimes, wave height and wave celerity as a function of Reynolds number and forcing frequency of a falling liquid film. Specific attention is paid to the differences in numerical and experimental results and the reasons for these differences. The authors are grateful to the EPSRC for their financial support (Grant EP/K008595/1).

  3. Objective criteria accurately predict amputation following lower extremity trauma.

    PubMed

    Johansen, K; Daines, M; Howey, T; Helfet, D; Hansen, S T

    1990-05-01

    MESS (Mangled Extremity Severity Score) is a simple rating scale for lower extremity trauma, based on skeletal/soft-tissue damage, limb ischemia, shock, and age. Retrospective analysis of severe lower extremity injuries in 25 trauma victims demonstrated a significant difference between MESS values for 17 limbs ultimately salvaged (mean, 4.88 +/- 0.27) and nine requiring amputation (mean, 9.11 +/- 0.51) (p less than 0.01). A prospective trial of MESS in lower extremity injuries managed at two trauma centers again demonstrated a significant difference between MESS values of 14 salvaged (mean, 4.00 +/- 0.28) and 12 doomed (mean, 8.83 +/- 0.53) limbs (p less than 0.01). In both the retrospective survey and the prospective trial, a MESS value greater than or equal to 7 predicted amputation with 100% accuracy. MESS may be useful in selecting trauma victims whose irretrievably injured lower extremities warrant primary amputation.

  4. Improved Ecosystem Predictions of the California Current System via Accurate Light Calculations

    DTIC Science & Technology

    2011-09-30

    System via Accurate Light Calculations Curtis D. Mobley Sequoia Scientific, Inc. 2700 Richards Road, Suite 107 Bellevue, WA 98005 phone: 425...incorporate extremely fast but accurate light calculations into coupled physical-biological-optical ocean ecosystem models as used for operational three...dimensional ecosystem predictions. Improvements in light calculations lead to improvements in predictions of chlorophyll concentrations and other

  5. Generating highly accurate prediction hypotheses through collaborative ensemble learning

    PubMed Central

    Arsov, Nino; Pavlovski, Martin; Basnarkov, Lasko; Kocarev, Ljupco

    2017-01-01

    Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model’s constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination. PMID:28304378

  6. 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.

  7. Generating highly accurate prediction hypotheses through collaborative ensemble learning

    NASA Astrophysics Data System (ADS)

    Arsov, Nino; Pavlovski, Martin; Basnarkov, Lasko; Kocarev, Ljupco

    2017-03-01

    Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model’s constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination.

  8. Change in BMI Accurately Predicted by Social Exposure to Acquaintances

    PubMed Central

    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 R2. 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. PMID

  9. Predicting Future Reconviction in Offenders with Intellectual Disabilities: The Predictive Efficacy of VRAG, PCL-SV, and the HCR-20

    ERIC Educational Resources Information Center

    Gray, Nicola S.; Fitzgerald, Suzanne; Taylor, John; MacCulloch, Malcolm J.; Snowden, Robert J.

    2007-01-01

    Accurate predictions of future reconviction, including those for violent crimes, have been shown to be greatly aided by the use of formal risk assessment instruments. However, it is unclear as to whether these instruments would also be predictive in a sample of offenders with intellectual disabilities. In this study, the authors have shown that…

  10. Premenstrual depression predicts future major depressive disorder.

    PubMed

    Graze, K K; Nee, J; Endicott, J

    1990-02-01

    To assess the power of premenstrual changes as a risk factor for future major depressive disorder (MDD), we conducted a follow-up study of 36 women who had volunteered for menstrual cycle studies. Scores on the depressive subscale of the Premenstrual Assessment Form (PAF) at initial evaluation were found to be significantly correlated (r = 0.35) with the occurrence of MDD during the follow-up period. Moreover, multiple regression analysis indicated that the PAF scores had predictive value above and beyond 2 known risk factors for MDD, family history of depression and prior personal history of depression. The Premenstrual Change Index, a score derived from prospective daily self-ratings of severity of dysphoric symptoms, was also correlated with interval MDD, but did not enhance the predictive power of the PAF score. We conclude that the assessment of premenstrual depression has validity in identifying women at risk for future MDD, even when a retrospective instrument, PAF, is utilized for such assessment.

  11. 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

  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.

  13. 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

  14. Phylogeny Predicts Future Habitat Shifts Due to Climate Change

    PubMed Central

    Kuntner, Matjaž; Năpăruş, Magdalena; Li, Daiqin; Coddington, Jonathan A.

    2014-01-01

    Background Taxa may respond differently to climatic changes, depending on phylogenetic or ecological effects, but studies that discern among these alternatives are scarce. Here, we use two species pairs from globally distributed spider clades, each pair representing two lifestyles (generalist, specialist) to test the relative importance of phylogeny versus ecology in predicted responses to climate change. Methodology We used a recent phylogenetic hypothesis for nephilid spiders to select four species from two genera (Nephilingis and Nephilengys) that match the above criteria, are fully allopatric but combined occupy all subtropical-tropical regions. Based on their records, we modeled each species niche spaces and predicted their ecological shifts 20, 40, 60, and 80 years into the future using customized GIS tools and projected climatic changes. Conclusions Phylogeny better predicts the species current ecological preferences than do lifestyles. By 2080 all species face dramatic reductions in suitable habitat (54.8–77.1%) and adapt by moving towards higher altitudes and latitudes, although at different tempos. Phylogeny and life style explain simulated habitat shifts in altitude, but phylogeny is the sole best predictor of latitudinal shifts. Models incorporating phylogenetic relatedness are an important additional tool to predict accurately biotic responses to global change. PMID:24892737

  15. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.

    PubMed

    Faraggi, Eshel; Kloczkowski, Andrzej

    2017-01-01

    Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary structure as well as accessible surface area and dihedral angles ϕ and ψ. For secondary structure SPINE-X achieves an accuracy of between 81 and 84 % depending on the dataset and choice of tests. The Pearson correlation coefficient for accessible surface area prediction is 0.75 and the mean absolute error from the ϕ and ψ dihedral angles are 20(∘) and 33(∘), respectively. The source code and a Linux executables for SPINE-X are available from Research and Information Systems at http://mamiris.com .

  16. Accurate prediction of adsorption energies on graphene, using a dispersion-corrected semiempirical method including solvation.

    PubMed

    Vincent, Mark A; Hillier, Ian H

    2014-08-25

    The accurate prediction of the adsorption energies of unsaturated molecules on graphene in the presence of water is essential for the design of molecules that can modify its properties and that can aid its processability. We here show that a semiempirical MO method corrected for dispersive interactions (PM6-DH2) can predict the adsorption energies of unsaturated hydrocarbons and the effect of substitution on these values to an accuracy comparable to DFT values and in good agreement with the experiment. The adsorption energies of TCNE, TCNQ, and a number of sulfonated pyrenes are also predicted, along with the effect of hydration using the COSMO model.

  17. Accurately predicting copper interconnect topographies in foundry design for manufacturability flows

    NASA Astrophysics Data System (ADS)

    Lu, Daniel; Fan, Zhong; Tak, Ki Duk; Chang, Li-Fu; Zou, Elain; Jiang, Jenny; Yang, Josh; Zhuang, Linda; Chen, Kuang Han; Hurat, Philippe; Ding, Hua

    2011-04-01

    This paper presents a model-based Chemical Mechanical Polishing (CMP) Design for Manufacturability (DFM) () methodology that includes an accurate prediction of post-CMP copper interconnect topographies at the advanced process technology nodes. Using procedures of extensive model calibration and validation, the CMP process model accurately predicts post-CMP dimensions, such as erosion, dishing, and copper thickness with excellent correlation to silicon measurements. This methodology provides an efficient DFM flow to detect and fix physical manufacturing hotspots related to copper pooling and Depth of Focus (DOF) failures at both block- and full chip level designs. Moreover, the predicted thickness output is used in the CMP-aware RC extraction and Timing analysis flows for better understanding of performance yield and timing impact. In addition, the CMP model can be applied to the verification of model-based dummy fill flows.

  18. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

    PubMed Central

    Singh, Ritambhara; Kuscu, Cem; Quinlan, Aaron; Qi, Yanjun; Adli, Mazhar

    2015-01-01

    The CRISPR system has become a powerful biological tool with a wide range of applications. However, improving targeting specificity and accurately predicting potential off-targets remains a significant goal. Here, we introduce a web-based CRISPR/Cas9 Off-target Prediction and Identification Tool (CROP-IT) that performs improved off-target binding and cleavage site predictions. Unlike existing prediction programs that solely use DNA sequence information; CROP-IT integrates whole genome level biological information from existing Cas9 binding and cleavage data sets. Utilizing whole-genome chromatin state information from 125 human cell types further enhances its computational prediction power. Comparative analyses on experimentally validated datasets show that CROP-IT outperforms existing computational algorithms in predicting both Cas9 binding as well as cleavage sites. With a user-friendly web-interface, CROP-IT outputs scored and ranked list of potential off-targets that enables improved guide RNA design and more accurate prediction of Cas9 binding or cleavage sites. PMID:26032770

  19. An effective method for accurate prediction of the first hyperpolarizability of alkalides.

    PubMed

    Wang, Jia-Nan; Xu, Hong-Liang; Sun, Shi-Ling; Gao, Ting; Li, Hong-Zhi; Li, Hui; Su, Zhong-Min

    2012-01-15

    The proper theoretical calculation method for nonlinear optical (NLO) properties is a key factor to design the excellent NLO materials. Yet it is a difficult task to obatin the accurate NLO property of large scale molecule. In present work, an effective intelligent computing method, as called extreme learning machine-neural network (ELM-NN), is proposed to predict accurately the first hyperpolarizability (β(0)) of alkalides from low-accuracy first hyperpolarizability. Compared with neural network (NN) and genetic algorithm neural network (GANN), the root-mean-square deviations of the predicted values obtained by ELM-NN, GANN, and NN with their MP2 counterpart are 0.02, 0.08, and 0.17 a.u., respectively. It suggests that the predicted values obtained by ELM-NN are more accurate than those calculated by NN and GANN methods. Another excellent point of ELM-NN is the ability to obtain the high accuracy level calculated values with less computing cost. Experimental results show that the computing time of MP2 is 2.4-4 times of the computing time of ELM-NN. Thus, the proposed method is a potentially powerful tool in computational chemistry, and it may predict β(0) of the large scale molecules, which is difficult to obtain by high-accuracy theoretical method due to dramatic increasing computational cost.

  20. Predicting Future Clinical Adjustment from Treatment Outcome and Process Variables.

    ERIC Educational Resources Information Center

    Patterson, G. R.; Forgatch, Marion S.

    1995-01-01

    Issues related to the use of outcome and process data from the treatment of antisocial children to predict future childhood adjustment were examined through a study of 69 children. Data supported the hypothesis that measures of processes thought to produce changes in child behavior would serve to predict future adjustment. (SLD)

  1. Hash: a Program to Accurately Predict Protein Hα Shifts from Neighboring Backbone Shifts3

    PubMed Central

    Zeng, Jianyang; Zhou, Pei; Donald, Bruce Randall

    2012-01-01

    Chemical shifts provide not only peak identities for analyzing NMR data, but also an important source of conformational information for studying protein structures. Current structural studies requiring Hα chemical shifts suffer from the following limitations. (1) For large proteins, the Hα chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of Cα that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict Hα chemical shifts. Predicting accurate Hα chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called Hash, to predict Hα chemical shifts. Hash combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate Hα chemical shifts. Our testing results on different possible combinations of input data indicate that Hash has a wide rage of potential NMR applications in structural and biological studies of proteins. PMID:23242797

  2. Consistent Predictions of Future Forest Mortality

    NASA Astrophysics Data System (ADS)

    McDowell, N. G.

    2014-12-01

    We examined empirical and model based estimates of current and future forest mortality of conifers in the northern hemisphere. Consistent water potential thresholds were found that resulted in mortality of our case study species, pinon pine and one-seed juniper. Extending these results with IPCC climate scenarios suggests that most existing trees in this region (SW USA) will be dead by 2050. Further, independent estimates of future mortality for the entire coniferous biome suggest widespread mortality by 2100. The validity and assumptions and implications of these results are discussed.

  3. Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

    PubMed Central

    Samsudin, Firdaus; Parker, Joanne L.; Sansom, Mark S.P.; Newstead, Simon; Fowler, Philip W.

    2016-01-01

    Summary Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the β-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepTSt, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families. PMID:27028887

  4. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  5. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2016-12-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  6. "Megatrends" and Knowledge Gaps: Future Predictions.

    ERIC Educational Resources Information Center

    Gaziano, Cecilie

    The distribution of knowledge in society tends to parallel the distribution of other social and economic resources. Currently four major socioeconomic trends point not only to widened knowledge gaps in the future but also to greater divisions between higher and lower socioeconomic status (SES) groups. First, a long-term trend toward a more…

  7. Rapid and Highly Accurate Prediction of Poor Loop Diuretic Natriuretic Response in Patients With Heart Failure

    PubMed Central

    Testani, Jeffrey M.; Hanberg, Jennifer S.; Cheng, Susan; Rao, Veena; Onyebeke, Chukwuma; Laur, Olga; Kula, Alexander; Chen, Michael; Wilson, F. Perry; Darlington, Andrew; Bellumkonda, Lavanya; Jacoby, Daniel; Tang, W. H. Wilson; Parikh, Chirag R.

    2015-01-01

    Background Removal of excess sodium and fluid is a primary therapeutic objective in acute decompensated heart failure (ADHF) and commonly monitored with fluid balance and weight loss. However, these parameters are frequently inaccurate or not collected and require a delay of several hours after diuretic administration before they are available. Accessible tools for rapid and accurate prediction of diuretic response are needed. Methods and Results Based on well-established renal physiologic principles an equation was derived to predict net sodium output using a spot urine sample obtained one or two hours following loop diuretic administration. This equation was then prospectively validated in 50 ADHF patients using meticulously obtained timed 6-hour urine collections to quantitate loop diuretic induced cumulative sodium output. Poor natriuretic response was defined as a cumulative sodium output of <50 mmol, a threshold that would result in a positive sodium balance with twice-daily diuretic dosing. Following a median dose of 3 mg (2–4 mg) of intravenous bumetanide, 40% of the population had a poor natriuretic response. The correlation between measured and predicted sodium output was excellent (r=0.91, p<0.0001). Poor natriuretic response could be accurately predicted with the sodium prediction equation (AUC=0.95, 95% CI 0.89–1.0, p<0.0001). Clinically recorded net fluid output had a weaker correlation (r=0.66, p<0.001) and lesser ability to predict poor natriuretic response (AUC=0.76, 95% CI 0.63–0.89, p=0.002). Conclusions In patients being treated for ADHF, poor natriuretic response can be predicted soon after diuretic administration with excellent accuracy using a spot urine sample. PMID:26721915

  8. Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions

    PubMed Central

    2015-01-01

    Background Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster. Results Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct. Conclusions We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release. PMID:25881257

  9. Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism.

    PubMed

    Kieslich, Chris A; Tamamis, Phanourios; Guzman, Yannis A; Onel, Melis; Floudas, Christodoulos A

    2016-01-01

    HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

  10. 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.

  11. Predicting the Future at Yucca Mountain

    SciTech Connect

    J. R. Wilson

    1999-07-01

    This paper summarizes a climate-prediction model funded by the DOE for the Yucca Mountain nuclear waste repository. Several articles in the open literature attest to the effects of the Global Ocean Conveyor upon paleoclimate, specifically entrance and exit from the ice age. The data shows that these millennial-scale effects are duplicated on the microscale of years to decades. This work also identifies how man may have influenced the Conveyor, affecting global cooling and warming for 2,000 years.

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

    PubMed

    Stephanou, Pavlos S; Mavrantzas, Vlasis G

    2014-06-07

    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.

  13. 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.

  14. Planar Near-Field Phase Retrieval Using GPUs for Accurate THz Far-Field Prediction

    NASA Astrophysics Data System (ADS)

    Junkin, Gary

    2013-04-01

    With a view to using Phase Retrieval to accurately predict Terahertz antenna far-field from near-field intensity measurements, this paper reports on three fundamental advances that achieve very low algorithmic error penalties. The first is a new Gaussian beam analysis that provides accurate initial complex aperture estimates including defocus and astigmatic phase errors, based only on first and second moment calculations. The second is a powerful noise tolerant near-field Phase Retrieval algorithm that combines Anderson's Plane-to-Plane (PTP) with Fienup's Hybrid-Input-Output (HIO) and Successive Over-Relaxation (SOR) to achieve increased accuracy at reduced scan separations. The third advance employs teraflop Graphical Processing Units (GPUs) to achieve practically real time near-field phase retrieval and to obtain the optimum aperture constraint without any a priori information.

  15. 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.

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

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    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

  17. 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.

  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. 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.

  20. Predicting future discoveries from current scientific literature.

    PubMed

    Petrič, Ingrid; Cestnik, Bojan

    2014-01-01

    Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

  1. Predicting UV sky for future UV missions

    NASA Astrophysics Data System (ADS)

    Safonova, M.; Mohan, R.; Sreejith, A. G.; Murthy, Jayant

    2013-02-01

    Software simulators are now widely used in all areas of science, especially in application to astronomical missions: from instrument design to mission planning, and to data interpretation. We present a simulator to model the diffuse ultraviolet sky, where the different contributors are separately calculated and added together to produce a sky image of the size specified by the instrument requirements. Each of the contributors to the background, instrumental dark current, airglow, zodiacal light and diffuse Galactic light, depends on different factors. Airglow is dependent on the time of day; zodiacal light depends on the time of year, angle from the Sun and from the ecliptic; diffuse UV emission depends on the line of sight. To provide a full description of the sky along any line of sight, we have also added stars. The UV background light can dominate in many areas of the sky and severely limit viewing directions due to overbrightness. The simulator, available as a downloadable package and as a web-based tool, can be applied to preparation of real space missions and instruments. For demonstration, we present the example use for the two near-future UV missions: UVIT instrument on the Indian Astrosat mission and a new proposed wide-field (∼1000 square degrees) transient explorer satellite.

  2. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    PubMed

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; 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.

  3. 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.

  4. 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

  5. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

    NASA Astrophysics Data System (ADS)

    Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

    2015-05-01

    Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

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

    PubMed Central

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

    2015-01-01

    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

  7. 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.

  8. 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

  9. 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.

  10. Special purpose hybrid transfinite elements and unified computational methodology for accurately predicting thermoelastic stress waves

    NASA Technical Reports Server (NTRS)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1988-01-01

    This paper represents an attempt to apply extensions of a hybrid transfinite element computational approach for accurately predicting thermoelastic stress waves. The applicability of the present formulations for capturing the thermal stress waves induced by boundary heating for the well known Danilovskaya problems is demonstrated. A unique feature of the proposed formulations for applicability to the Danilovskaya problem of thermal stress waves in elastic solids lies in the hybrid nature of the unified formulations and the development of special purpose transfinite elements in conjunction with the classical Galerkin techniques and transformation concepts. Numerical test cases validate the applicability and superior capability to capture the thermal stress waves induced due to boundary heating.

  11. The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

    SciTech Connect

    Jorgensen, S.

    2016-03-02

    Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

  12. Prediction of Solar Storms in Future

    NASA Technical Reports Server (NTRS)

    1990-01-01

    Using the Solar Vector Magnetograph, a solar observation facility at NASA's Marshall Space Flight Center (MSFC), scientists from the National Space Science and Technology Center (NSSTC) in Huntsville, Alabama, are monitoring the explosive potential of magnetic areas of the Sun. This effort could someday lead to better prediction of severe space weather, a phenomenon that occurs when blasts of particles and magnetic fields from the Sun impact the magnetosphere, the magnetic bubble around the Earth. When massive solar explosions, known as coronal mass ejections, blast through the Sun's outer atmosphere and plow toward Earth at speeds of thousands of miles per second, the resulting effects can be harmful to communication satellites and astronauts outside the Earth's magnetosphere. Like severe weather on Earth, severe space weather can be costly. On the ground, the magnetic storm wrought by these solar particles can knock out electric power. The researchers from MSFC and NSSTC's solar physics group develop instruments for measuring magnetic fields on the Sun. With these instruments, the group studies the origin, structure, and evolution of the solar magnetic field and the impact it has on Earth's space environment. This photograph shows the Solar Vector Magnetograph and Dr. Mona Hagyard of MSFC, the director of the observatory who leads the development, operation and research program of the Solar Vector Magnetograph.

  13. Prediction of Solar Storms in Future

    NASA Technical Reports Server (NTRS)

    2002-01-01

    Using the Solar Vector Magnetograph, a solar observation facility at NASA's Marshall Space Flight Center (MSFC), scientists from the National Space Science and Technology Center (NSSTC) in Huntsville, Alabama, are monitoring the explosive potential of magnetic areas of the Sun. This effort could someday lead to better prediction of severe space weather, a phenomenon that occurs when blasts of particles and magnetic fields from the Sun impact the magnetosphere, the magnetic bubble around the Earth. When massive solar explosions, known as coronal mass ejections, blast through the Sun's outer atmosphere and plow toward Earth at speeds of thousands of miles per second, the resulting effects can be harmful to communication satellites and astronauts outside the Earth's magnetosphere. Like severe weather on Earth, severe space weather can be costly. On the ground, magnetic storms wrought by these solar particles can knock out electric power. Photographed are a group of contributing researchers in front of the Solar Vector Magnetograph at MSFC. The researchers are part of NSSTC's solar physics group, which develops instruments for measuring magnetic fields on the Sun. With these instruments, the group studies the origin, structure, and evolution of the solar magnetic fields and the impact they have on Earth's space environment.

  14. Prediction of Solar Storms in Future

    NASA Technical Reports Server (NTRS)

    2002-01-01

    NASA's Marshall Space Flight Center (MSFC) and university scientists from the National Space Science and Technology Center (NSSTC) in Huntsville, Alabama, are watching the Sun in an effort to better predict space weather - blasts of particles and magnetic fields from the Sun that impact the magnetosphere, the magnetic bubble around the Earth. Filled by charged particles trapped in the Earth's magnetic field, the spherical comet-shaped magnetosphere extends out 40,000 miles from Earth's surface in the sunward direction and more in other directions. This image illustrates the Sun-Earth cornection. When massive solar explosions, known as coronal mass ejections, blast through the Sun's outer atmosphere and plow toward Earth at speeds of thousands of miles per second, the resulting effects can be harmful to communication satellites and astronauts outside the Earth's magnetosphere. Like severe weather on Earth, severe space weather can be costly. On the ground, magnetic storms wrought by these solar particles can knock out electric power. By using the Solar Vector Magnetograph, a solar observation facility at MSFC, scientists are learning what signs to look for as indicators of potential severe space weather.

  15. Accurate prediction of human drug toxicity: a major challenge in drug development.

    PubMed

    Li, Albert P

    2004-11-01

    Over the past decades, a number of drugs have been withdrawn or have required special labeling due to adverse effects observed post-marketing. Species differences in drug toxicity in preclinical safety tests and the lack of sensitive biomarkers and nonrepresentative patient population in clinical trials are probable reasons for the failures in predicting human drug toxicity. It is proposed that toxicology should evolve from an empirical practice to an investigative discipline. Accurate prediction of human drug toxicity requires resources and time to be spent in clearly defining key toxic pathways and corresponding risk factors, which hopefully, will be compensated by the benefits of a lower percentage of clinical failure due to toxicity and a decreased frequency of market withdrawal due to unacceptable adverse drug effects.

  16. Using Human Capital Planning to Predict Future Talent Needs

    ERIC Educational Resources Information Center

    Ruse, Donald; Jansen, Karen

    2008-01-01

    Human capital planning is an important tool in predicting future talent needs and sustaining organizational excellence over the long term. This article examines the concept of human capital planning and outlines how institutions can use HCP to identify the type and number of talent needed both now and in the future, recognize and prioritize talent…

  17. Accurate prediction of the response of freshwater fish to a mixture of estrogenic chemicals.

    PubMed

    Brian, Jayne V; Harris, Catherine A; Scholze, Martin; Backhaus, Thomas; Booy, Petra; Lamoree, Marja; Pojana, Giulio; Jonkers, Niels; Runnalls, Tamsin; Bonfà, Angela; Marcomini, Antonio; Sumpter, John P

    2005-06-01

    Existing environmental risk assessment procedures are limited in their ability to evaluate the combined effects of chemical mixtures. We investigated the implications of this by analyzing the combined effects of a multicomponent mixture of five estrogenic chemicals using vitellogenin induction in male fathead minnows as an end point. The mixture consisted of estradiol, ethynylestradiol, nonylphenol, octylphenol, and bisphenol A. We determined concentration-response curves for each of the chemicals individually. The chemicals were then combined at equipotent concentrations and the mixture tested using fixed-ratio design. The effects of the mixture were compared with those predicted by the model of concentration addition using biomathematical methods, which revealed that there was no deviation between the observed and predicted effects of the mixture. These findings demonstrate that estrogenic chemicals have the capacity to act together in an additive manner and that their combined effects can be accurately predicted by concentration addition. We also explored the potential for mixture effects at low concentrations by exposing the fish to each chemical at one-fifth of its median effective concentration (EC50). Individually, the chemicals did not induce a significant response, although their combined effects were consistent with the predictions of concentration addition. This demonstrates the potential for estrogenic chemicals to act additively at environmentally relevant concentrations. These findings highlight the potential for existing environmental risk assessment procedures to underestimate the hazard posed by mixtures of chemicals that act via a similar mode of action, thereby leading to erroneous conclusions of absence of risk.

  18. Accurate Prediction of the Response of Freshwater Fish to a Mixture of Estrogenic Chemicals

    PubMed Central

    Brian, Jayne V.; Harris, Catherine A.; Scholze, Martin; Backhaus, Thomas; Booy, Petra; Lamoree, Marja; Pojana, Giulio; Jonkers, Niels; Runnalls, Tamsin; Bonfà, Angela; Marcomini, Antonio; Sumpter, John P.

    2005-01-01

    Existing environmental risk assessment procedures are limited in their ability to evaluate the combined effects of chemical mixtures. We investigated the implications of this by analyzing the combined effects of a multicomponent mixture of five estrogenic chemicals using vitellogenin induction in male fathead minnows as an end point. The mixture consisted of estradiol, ethynylestradiol, nonylphenol, octylphenol, and bisphenol A. We determined concentration–response curves for each of the chemicals individually. The chemicals were then combined at equipotent concentrations and the mixture tested using fixed-ratio design. The effects of the mixture were compared with those predicted by the model of concentration addition using biomathematical methods, which revealed that there was no deviation between the observed and predicted effects of the mixture. These findings demonstrate that estrogenic chemicals have the capacity to act together in an additive manner and that their combined effects can be accurately predicted by concentration addition. We also explored the potential for mixture effects at low concentrations by exposing the fish to each chemical at one-fifth of its median effective concentration (EC50). Individually, the chemicals did not induce a significant response, although their combined effects were consistent with the predictions of concentration addition. This demonstrates the potential for estrogenic chemicals to act additively at environmentally relevant concentrations. These findings highlight the potential for existing environmental risk assessment procedures to underestimate the hazard posed by mixtures of chemicals that act via a similar mode of action, thereby leading to erroneous conclusions of absence of risk. PMID:15929895

  19. IDSite: An accurate approach to predict P450-mediated drug metabolism

    PubMed Central

    Li, Jianing; Schneebeli, Severin T.; Bylund, Joseph; Farid, Ramy; Friesner, Richard A.

    2011-01-01

    Accurate prediction of drug metabolism is crucial for drug design. Since a large majority of drugs metabolism involves P450 enzymes, we herein describe a computational approach, IDSite, to predict P450-mediated drug metabolism. To model induced-fit effects, IDSite samples the conformational space with flexible docking in Glide followed by two refinement stages using the Protein Local Optimization Program (PLOP). Sites of metabolism (SOMs) are predicted according to a physical-based score that evaluates the potential of atoms to react with the catalytic iron center. As a preliminary test, we present in this paper the prediction of hydroxylation and O-dealkylation sites mediated by CYP2D6 using two different models: a physical-based simulation model, and a modification of this model in which a small number of parameters are fit to a training set. Without fitting any parameters to experimental data, the Physical IDSite scoring recovers 83% of the experimental observations for 56 compounds with a very low false positive rate. With only 4 fitted parameters, the Fitted IDSite was trained with the subset of 36 compounds and successfully applied to the other 20 compounds, recovering 94% of the experimental observations with high sensitivity and specificity for both sets. PMID:22247702

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

    PubMed

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

    2017-01-09

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

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

    PubMed Central

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

    2017-01-01

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

  2. 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

  3. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    PubMed Central

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  4. A hierarchical approach to accurate predictions of macroscopic thermodynamic behavior from quantum mechanics and molecular simulations

    NASA Astrophysics Data System (ADS)

    Garrison, Stephen L.

    2005-07-01

    The combination of molecular simulations and potentials obtained from quantum chemistry is shown to be able to provide reasonably accurate thermodynamic property predictions. Gibbs ensemble Monte Carlo simulations are used to understand the effects of small perturbations to various regions of the model Lennard-Jones 12-6 potential. However, when the phase behavior and second virial coefficient are scaled by the critical properties calculated for each potential, the results obey a corresponding states relation suggesting a non-uniqueness problem for interaction potentials fit to experimental phase behavior. Several variations of a procedure collectively referred to as quantum mechanical Hybrid Methods for Interaction Energies (HM-IE) are developed and used to accurately estimate interaction energies from CCSD(T) calculations with a large basis set in a computationally efficient manner for the neon-neon, acetylene-acetylene, and nitrogen-benzene systems. Using these results and methods, an ab initio, pairwise-additive, site-site potential for acetylene is determined and then improved using results from molecular simulations using this initial potential. The initial simulation results also indicate that a limited range of energies important for accurate phase behavior predictions. Second virial coefficients calculated from the improved potential indicate that one set of experimental data in the literature is likely erroneous. This prescription is then applied to methanethiol. Difficulties in modeling the effects of the lone pair electrons suggest that charges on the lone pair sites negatively impact the ability of the intermolecular potential to describe certain orientations, but that the lone pair sites may be necessary to reasonably duplicate the interaction energies for several orientations. Two possible methods for incorporating the effects of three-body interactions into simulations within the pairwise-additivity formulation are also developed. A low density

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

    NASA Astrophysics Data System (ADS)

    Shvab, I.; Sadus, Richard J.

    2013-11-01

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm3 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.

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

    PubMed

    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(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.

  7. 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.

  8. Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.

    1991-01-01

    K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

  9. 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.

  10. 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.

  11. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  12. 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.

  13. Accurate prediction of wall shear stress in a stented artery: newtonian versus non-newtonian models.

    PubMed

    Mejia, Juan; Mongrain, Rosaire; Bertrand, Olivier F

    2011-07-01

    A significant amount of evidence linking wall shear stress to neointimal hyperplasia has been reported in the literature. As a result, numerical and experimental models have been created to study the influence of stent design on wall shear stress. Traditionally, blood has been assumed to behave as a Newtonian fluid, but recently that assumption has been challenged. The use of a linear model; however, can reduce computational cost, and allow the use of Newtonian fluids (e.g., glycerine and water) instead of a blood analog fluid in an experimental setup. Therefore, it is of interest whether a linear model can be used to accurately predict the wall shear stress caused by a non-Newtonian fluid such as blood within a stented arterial segment. The present work compares the resulting wall shear stress obtained using two linear and one nonlinear model under the same flow waveform. All numerical models are fully three-dimensional, transient, and incorporate a realistic stent geometry. It is shown that traditional linear models (based on blood's lowest viscosity limit, 3.5 Pa s) underestimate the wall shear stress within a stented arterial segment, which can lead to an overestimation of the risk of restenosis. The second linear model, which uses a characteristic viscosity (based on an average strain rate, 4.7 Pa s), results in higher wall shear stress levels, but which are still substantially below those of the nonlinear model. It is therefore shown that nonlinear models result in more accurate predictions of wall shear stress within a stented arterial segment.

  14. Point-of-care cardiac troponin test accurately predicts heat stroke severity in rats.

    PubMed

    Audet, Gerald N; Quinn, Carrie M; Leon, Lisa R

    2015-11-15

    Heat stroke (HS) remains a significant public health concern. Despite the substantial threat posed by HS, there is still no field or clinical test of HS severity. We suggested previously that circulating cardiac troponin (cTnI) could serve as a robust biomarker of HS severity after heating. In the present study, we hypothesized that (cTnI) point-of-care test (ctPOC) could be used to predict severity and organ damage at the onset of HS. Conscious male Fischer 344 rats (n = 16) continuously monitored for heart rate (HR), blood pressure (BP), and core temperature (Tc) (radiotelemetry) were heated to maximum Tc (Tc,Max) of 41.9 ± 0.1°C and recovered undisturbed for 24 h at an ambient temperature of 20°C. Blood samples were taken at Tc,Max and 24 h after heat via submandibular bleed and analyzed on ctPOC test. POC cTnI band intensity was ranked using a simple four-point scale via two blinded observers and compared with cTnI levels measured by a clinical blood analyzer. Blood was also analyzed for biomarkers of systemic organ damage. HS severity, as previously defined using HR, BP, and recovery Tc profile during heat exposure, correlated strongly with cTnI (R(2) = 0.69) at Tc,Max. POC cTnI band intensity ranking accurately predicted cTnI levels (R(2) = 0.64) and HS severity (R(2) = 0.83). Five markers of systemic organ damage also correlated with ctPOC score (albumin, alanine aminotransferase, blood urea nitrogen, cholesterol, and total bilirubin; R(2) > 0.4). This suggests that cTnI POC tests can accurately determine HS severity and could serve as simple, portable, cost-effective HS field tests.

  15. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    PubMed

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  16. 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.

  17. Fast and accurate pressure-drop prediction in straightened atherosclerotic coronary arteries.

    PubMed

    Schrauwen, Jelle T C; Koeze, Dion J; Wentzel, Jolanda J; van de Vosse, Frans N; van der Steen, Anton F W; Gijsen, Frank J H

    2015-01-01

    Atherosclerotic disease progression in coronary arteries is influenced by wall shear stress. To compute patient-specific wall shear stress, computational fluid dynamics (CFD) is required. In this study we propose a method for computing the pressure-drop in regions proximal and distal to a plaque, which can serve as a boundary condition in CFD. As a first step towards exploring the proposed method we investigated ten straightened coronary arteries. First, the flow fields were calculated with CFD and velocity profiles were fitted on the results. Second, the Navier-Stokes equation was simplified and solved with the found velocity profiles to obtain a pressure-drop estimate (Δp (1)). Next, Δp (1) was compared to the pressure-drop from CFD (Δp CFD) as a validation step. Finally, the velocity profiles, and thus the pressure-drop were predicted based on geometry and flow, resulting in Δp geom. We found that Δp (1) adequately estimated Δp CFD with velocity profiles that have one free parameter β. This β was successfully related to geometry and flow, resulting in an excellent agreement between Δp CFD and Δp geom: 3.9 ± 4.9% difference at Re = 150. We showed that this method can quickly and accurately predict pressure-drop on the basis of geometry and flow in straightened coronary arteries that are mildly diseased.

  18. 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.

  19. Accurate prediction of drug-induced liver injury using stem cell-derived populations.

    PubMed

    Szkolnicka, Dagmara; Farnworth, Sarah L; Lucendo-Villarin, Baltasar; Storck, Christopher; Zhou, Wenli; Iredale, John P; Flint, Oliver; Hay, David C

    2014-02-01

    Despite major progress in the knowledge and management of human liver injury, there are millions of people suffering from chronic liver disease. Currently, the only cure for end-stage liver disease is orthotopic liver transplantation; however, this approach is severely limited by organ donation. Alternative approaches to restoring liver function have therefore been pursued, including the use of somatic and stem cell populations. Although such approaches are essential in developing scalable treatments, there is also an imperative to develop predictive human systems that more effectively study and/or prevent the onset of liver disease and decompensated organ function. We used a renewable human stem cell resource, from defined genetic backgrounds, and drove them through developmental intermediates to yield highly active, drug-inducible, and predictive human hepatocyte populations. Most importantly, stem cell-derived hepatocytes displayed equivalence to primary adult hepatocytes, following incubation with known hepatotoxins. In summary, we have developed a serum-free, scalable, and shippable cell-based model that faithfully predicts the potential for human liver injury. Such a resource has direct application in human modeling and, in the future, could play an important role in developing renewable cell-based therapies.

  20. 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

  1. Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning

    PubMed Central

    Abraham, Gad; Tye-Din, Jason A.; Bhalala, Oneil G.; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-01-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87–0.89) and in independent replication across cohorts (AUC of 0.86–0.9), despite differences in ethnicity. The models explained 30–35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases. PMID:24550740

  2. Energy expenditure during level human walking: seeking a simple and accurate predictive solution.

    PubMed

    Ludlow, Lindsay W; Weyand, Peter G

    2016-03-01

    Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n = 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o2 rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O2·kg(-1)·min(-1), respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o2-speed model types: linear (∝V(1.0)), exponential (∝V(2.0)), and exponential/height (∝V(2.0)/Ht). We found that the proportion of variance (R(2)) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O2·kg(-1)·min(-1)). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o2 total = V̇o2 rest + 3.85 + 5.97·V(2)/Ht (where V is measured in m/s, Ht in meters, and V̇o2 in ml O2·kg(-1)·min(-1)).

  3. 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.

  4. The Future of Seizure Prediction and Intervention: Closing the loop

    PubMed Central

    Nagaraj, Vivek; Lee, Steven; Krook-Magnuson, Esther; Soltesz, Ivan; Benquet, Pascal; Irazoqui, Pedro; Netoff, Theoden

    2014-01-01

    The ultimate goal of epilepsy therapies is to provide seizure control for all patients while eliminating side effects. Improved specificity of intervention through on-demand approaches may overcome many of the limitations of current intervention strategies. This article reviews progress in seizure prediction and detection, potential new therapies to provide improved specificity, and devices to achieve these ends. Specifically, we discuss 1) potential signal modalities and algorithms for seizure detection and prediction, 2) closed-loop intervention approaches, and 3) hardware for implementing these algorithms and interventions. Seizure prediction and therapies maximize efficacy while minimizing side-effects through improved specificity may represent the future of epilepsy treatments. PMID:26035672

  5. Children's Predictions of Future Perceptual Experiences: Temporal Reasoning and Phenomenology

    ERIC Educational Resources Information Center

    Burns, Patrick; Russell, James

    2016-01-01

    We investigated the development and cognitive correlates of envisioning future experiences in 3.5- to 6.5-year old children across 2 experiments, both of which involved toy trains traveling along a track. In the first, children were asked to predict the direction of train travel and color of train side, as it would be seen through an arch.…

  6. Structure-based constitutive model can accurately predict planar biaxial properties of aortic wall tissue.

    PubMed

    Polzer, S; Gasser, T C; Novak, K; Man, V; Tichy, M; Skacel, P; Bursa, J

    2015-03-01

    Structure-based constitutive models might help in exploring mechanisms by which arterial wall histology is linked to wall mechanics. This study aims to validate a recently proposed structure-based constitutive model. Specifically, the model's ability to predict mechanical biaxial response of porcine aortic tissue with predefined collagen structure was tested. Histological slices from porcine thoracic aorta wall (n=9) were automatically processed to quantify the collagen fiber organization, and mechanical testing identified the non-linear properties of the wall samples (n=18) over a wide range of biaxial stretches. Histological and mechanical experimental data were used to identify the model parameters of a recently proposed multi-scale constitutive description for arterial layers. The model predictive capability was tested with respect to interpolation and extrapolation. Collagen in the media was predominantly aligned in circumferential direction (planar von Mises distribution with concentration parameter bM=1.03 ± 0.23), and its coherence decreased gradually from the luminal to the abluminal tissue layers (inner media, b=1.54 ± 0.40; outer media, b=0.72 ± 0.20). In contrast, the collagen in the adventitia was aligned almost isotropically (bA=0.27 ± 0.11), and no features, such as families of coherent fibers, were identified. The applied constitutive model captured the aorta biaxial properties accurately (coefficient of determination R(2)=0.95 ± 0.03) over the entire range of biaxial deformations and with physically meaningful model parameters. Good predictive properties, well outside the parameter identification space, were observed (R(2)=0.92 ± 0.04). Multi-scale constitutive models equipped with realistic micro-histological data can predict macroscopic non-linear aorta wall properties. Collagen largely defines already low strain properties of media, which explains the origin of wall anisotropy seen at this strain level. The structure and mechanical

  7. Predicting Future Years of Life, Health, and Functional Ability

    PubMed Central

    Diehr, Paula; Diehr, Michael; Arnold, Alice; Yee, Laura M.; Odden, Michelle C.; Hirsch, Calvin H; Thielke, Stephen; Psaty, Bruce M.; Johnson, W. Craig; Kizer, MD, Jorge R.; Newman, Anne

    2015-01-01

    Objective: To create personalized estimates of future health and ability status for older adults. Method: Data came from the Cardiovascular Health Study (CHS), a large longitudinal study. Outcomes included years of life, years of healthy life (based on self-rated health), years of able life (based on activities of daily living), and years of healthy and able life. We developed regression estimates using the demographic and health characteristics that best predicted the four outcomes. Internal and external validity were assessed. Results: A prediction equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. The resulting CHS Healthy Life Calculator (CHSHLC) is available at http://healthylifecalculator.org. Conclusion: CHSHLC provides a well-documented estimate of future years of healthy and able life for older adults, who may use it in planning for the future. PMID:28138467

  8. 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.

  9. New consensus definition for acute kidney injury accurately predicts 30-day mortality in cirrhosis with infection

    PubMed Central

    Wong, Florence; O’Leary, Jacqueline G; Reddy, K Rajender; Patton, Heather; Kamath, Patrick S; Fallon, Michael B; Garcia-Tsao, Guadalupe; Subramanian, Ram M.; Malik, Raza; Maliakkal, Benedict; Thacker, Leroy R; Bajaj, Jasmohan S

    2015-01-01

    Background & Aims A consensus conference proposed that cirrhosis-associated acute kidney injury (AKI) be defined as an increase in serum creatinine by >50% from the stable baseline value in <6 months or by ≥0.3mg/dL in <48 hrs. We prospectively evaluated the ability of these criteria to predict mortality within 30 days among hospitalized patients with cirrhosis and infection. Methods 337 patients with cirrhosis admitted with or developed an infection in hospital (56% men; 56±10 y old; model for end-stage liver disease score, 20±8) were followed. We compared data on 30-day mortality, hospital length-of-stay, and organ failure between patients with and without AKI. Results 166 (49%) developed AKI during hospitalization, based on the consensus criteria. Patients who developed AKI had higher admission Child-Pugh (11.0±2.1 vs 9.6±2.1; P<.0001), and MELD scores (23±8 vs17±7; P<.0001), and lower mean arterial pressure (81±16mmHg vs 85±15mmHg; P<.01) than those who did not. Also higher amongst patients with AKI were mortality in ≤30 days (34% vs 7%), intensive care unit transfer (46% vs 20%), ventilation requirement (27% vs 6%), and shock (31% vs 8%); AKI patients also had longer hospital stays (17.8±19.8 days vs 13.3±31.8 days) (all P<.001). 56% of AKI episodes were transient, 28% persistent, and 16% resulted in dialysis. Mortality was 80% among those without renal recovery, higher compared to partial (40%) or complete recovery (15%), or AKI-free patients (7%; P<.0001). Conclusions 30-day mortality is 10-fold higher among infected hospitalized cirrhotic patients with irreversible AKI than those without AKI. The consensus definition of AKI accurately predicts 30-day mortality, length of hospital stay, and organ failure. PMID:23999172

  10. 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).

  11. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  12. Cluster abundance in chameleon f(R) gravity I: toward an accurate halo mass function prediction

    NASA Astrophysics Data System (ADS)

    Cataneo, Matteo; Rapetti, David; Lombriser, Lucas; Li, Baojiu

    2016-12-01

    We refine the mass and environment dependent spherical collapse model of chameleon f(R) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N-body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f(R) halo abundance with respect to that of General Relativity (GR) within a precision of lesssim 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f(R) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

  13. 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.

  14. 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

  15. Past makes future: role of pFC in prediction.

    PubMed

    Fuster, Joaquín M; Bressler, Steven L

    2015-04-01

    The pFC enables the essential human capacities for predicting future events and preadapting to them. These capacities rest on both the structure and dynamics of the human pFC. Structurally, pFC, together with posterior association cortex, is at the highest hierarchical level of cortical organization, harboring neural networks that represent complex goal-directed actions. Dynamically, pFC is at the highest level of the perception-action cycle, the circular processing loop through the cortex that interfaces the organism with the environment in the pursuit of goals. In its predictive and preadaptive roles, pFC supports cognitive functions that are critical for the temporal organization of future behavior, including planning, attentional set, working memory, decision-making, and error monitoring. These functions have a common future perspective and are dynamically intertwined in goal-directed action. They all utilize the same neural infrastructure: a vast array of widely distributed, overlapping, and interactive cortical networks of personal memory and semantic knowledge, named cognits, which are formed by synaptic reinforcement in learning and memory acquisition. From this cortex-wide reservoir of memory and knowledge, pFC generates purposeful, goal-directed actions that are preadapted to predicted future events.

  16. Impact of hierarchies of clinical codes on predicting future days in hospital.

    PubMed

    Yang Xie; Neubauer, Sandra; Schreier, Gunter; Redmond, Stephen J; Lovell, Nigel H

    2015-01-01

    Health insurance claims contain valuable information for predicting the future health of a population. Nowadays, with many mature machine learning algorithms, models can be implemented to predict future medical costs and hospitalizations. However, it is well-known that the way in which the data are represented significantly affects the performance of machine learning algorithms. In health insurance claims, key clinical information mainly comes from the associated clinical codes, such as diagnosis codes and procedure codes, which are hierarchically structured. In this study, it is investigated whether the hierarchies of such clinical codes can be utilized to improve predictive performance in the context of predicting future days in hospital. Empirical investigations were done on data sets of different sizes, considering that the frequency of the appearance of lower-level (more specific) clinical codes could vary significantly in populations of different sizes. The use of bagged trees with feature sets that include only basic demographic features, low-level, medium-level, high-level clinical codes, and a full feature set were compared. The main finding from this study is that different hierarchies of clinical codes do not have a significant impact on the predictive power. Some other findings include: 1) Sample size greatly affects the predictive outcome (more observations result in more stable and more accurate outcomes); 2) Combined use of enriched demographic features and clinical features give better performance as compared to using them separately.

  17. Gastric ESD may be useful as accurate staging and decision of future therapeutic strategy

    PubMed Central

    Fujimoto, Ai; Goto, Osamu; Nishizawa, Toshihiro; Ochiai, Yasutoshi; Horii, Joichiro; Maehata, Tadateru; Akimoto, Teppei; Kinoshita, Satoshi; Sagara, Seiji; Sasaki, Motoki; Uraoka, Toshio; Yahagi, Naohisa

    2017-01-01

    Background and study aims We sometimes perform gastric endoscopic submucosal dissection (ESD) for total pathologic diagnosis when preoperative diagnosis is difficult. In the present study we analyzed the treatment outcomes and adverse events of diagnostic ESD for early gastric cancer (EGC). Patients and methods We conducted a retrospective analysis of 18 consecutive cases of EGC in 18 patients with a suspected out-of-indication diagnosis who underwent diagnostic ESD, between June 2010 and November 2014. The following parameters were examined: the average length of the longer axis of the lesion; the procedure time; the rates of en bloc resection (ER), complete en bloc resection (CER), and curative resection (CR) as treatment outcomes; and the rates of perforation, delayed bleeding, aspiration pneumonia, disease-related death, and emergency surgery as adverse events. Results The treatment outcomes were as follows: average length of the longer axis of the lesion, 27.4 ± 10.0 mm; procedure time, 87.0 ± 43.1 minutes; ER rate, 18/18 (100.0 %); CER rate, 13/18 (72.2 %); CR rate, 4/18 (22.2 %). CR rate was achieved 37.5 % for the lesions which preoperative diagnosis was more than 30 mm (> 30 mm) in diameter differentiated type with mucosal layer/submucosal layer 1 invasion and ulceration positive. The adverse events (AEs) were perforation in 1 of 18 (5.5 %) patients and delayed bleeding in 1 of 18 (5.5 %). There were no other AEs. Conclusions Diagnostic ESD may be acceptable for future therapeutic strategy when we unconfirmed the pre ESD diagnosis because of lower rate of adverse events and high rate of ER. PMID:28210705

  18. Raoult’s law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments

    PubMed Central

    Bowler, Michael G.

    2017-01-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F68, 111–114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult’s law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult’s law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult’s law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample. PMID:28381983

  19. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F68, 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  20. 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.

  1. Predicting future uncertainty constraints on global warming projections

    PubMed Central

    Shiogama, H.; Stone, D.; Emori, S.; Takahashi, K.; Mori, S.; Maeda, A.; Ishizaki, Y.; Allen, M. R.

    2016-01-01

    Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change. PMID:26750491

  2. Predicting future uncertainty constraints on global warming projections

    NASA Astrophysics Data System (ADS)

    Shiogama, H.; Stone, D.; Emori, S.; Takahashi, K.; Mori, S.; Maeda, A.; Ishizaki, Y.; Allen, M. R.

    2016-01-01

    Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.

  3. Predicting future uncertainty constraints on global warming projections.

    PubMed

    Shiogama, H; Stone, D; Emori, S; Takahashi, K; Mori, S; Maeda, A; Ishizaki, Y; Allen, M R

    2016-01-11

    Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.

  4. 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.

  5. Evolving networks-Using past structure to predict the future

    NASA Astrophysics Data System (ADS)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  6. Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods

    NASA Astrophysics Data System (ADS)

    Davis, A. D.

    2015-12-01

    The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity

  7. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  8. Past Realities Versus Hypothetical Futures: Bridging Accurate Perceptions and Individual Expectations Gaps in Relation to Future Space Exploration at Entertainment Attractions

    NASA Astrophysics Data System (ADS)

    Charania, A.; Bradford, J.; Shkirenko, A.

    2002-01-01

    Past Realities Versus Hypothetical Futures: Bridging Accurate Perceptions and Individual Expectation Gaps in Relation to It has been more than forty years since the dawn of the space age and the notion of human space flight has settled comfortably into the human psyche. Yet there is disconnect between the cinematic representations of space exploration and long-term program plans of national space agencies. For entertainment attractions, too often these cinematic representations cloud public perceptions of the art of the possible in space exploration. The forecasts of personal hover mobiles, ubiquitous robots, and luxury cruises to the moon that were to be available to society at the end of the last century have turned out to be grossly exaggerated. This results in continued frustration and subsequent ambivalence of the public towards space. Eventually, these misperceptions have a direct relationship to the level of support shown by legislative bodies towards public outlays for space exploration. The value proposition to society of space has changed, from one of transformational change (Apollo) to transactional apathy (the current Space Shuttle). The past realities of the space program and the potential futures enabled by the current generation of space scientists and engineers will not be equivalent. Yet there is an opportunity to showcase the best of the upcoming future without defrauding the public's imagination. At the start of this century, new visions of the future are being prepared by various entertainment entities (e.g. for movies, them park attractions). This examination consists of a review of previous paradigms of translating space visions to the public. Given the background of the authors in conceptual space engineering, recommendations are made as to more scientifically credible attractions while maintaining the entertainment proposition. Different scenarios are presented as to potential futures and impact of these on entertainment attractions

  9. Fluid reasoning predicts future mathematical performance among children and adolescents.

    PubMed

    Green, Chloe T; Bunge, Silvia A; Briones Chiongbian, Victoria; Barrow, Maia; Ferrer, Emilio

    2017-05-01

    The aim of this longitudinal study was to determine whether fluid reasoning (FR) plays a significant role in the acquisition of mathematics skills above and beyond the effects of other cognitive and numerical abilities. Using a longitudinal cohort sequential design, we examined how FR measured at three assessment occasions, spaced approximately 1.5years apart, predicted math outcomes for a group of 69 participants between ages 6 and 21years across all three assessment occasions. We used structural equation modeling (SEM) to examine the direct and indirect relations between children's previous cognitive abilities and their future math achievement. A model including age, FR, vocabulary, and spatial skills accounted for 90% of the variance in future math achievement. In this model, FR was the only significant predictor of future math achievement; age, vocabulary, and spatial skills were not significant predictors. Thus, FR was the only predictor of future math achievement across a wide age range that spanned primary school and secondary school. These findings build on Cattell's conceptualization of FR as a scaffold for learning, showing that this domain-general ability supports the acquisition of rudimentary math skills as well as the ability to solve more complex mathematical problems.

  10. 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

  11. Predicting current and future global distributions of whale sharks.

    PubMed

    Sequeira, Ana M M; Mellin, Camille; Fordham, Damien A; Meekan, Mark G; Bradshaw, Corey J A

    2014-03-01

    The Vulnerable (IUCN) whale shark spans warm and temperate waters around the globe. However, their present-day and possible future global distribution has never been predicted. Using 30 years (1980-2010) of whale shark observations recorded by tuna purse-seiners fishing in the Atlantic, Indian and Pacific Oceans, we applied generalized linear mixed-effects models to test the hypothesis that similar environmental covariates predict whale shark occurrence in all major ocean basins. We derived global predictors from satellite images for chlorophyll a and sea surface temperature, and bathymetric charts for depth, bottom slope and distance to shore. We randomly generated pseudo-absences within the area covered by the fisheries, and included fishing effort as an offset to account for potential sampling bias. We predicted sea surface temperatures for 2070 using an ensemble of five global circulation models under a no climate-policy reference scenario, and used these to predict changes in distribution. The full model (excluding standard deviation of sea surface temperature) had the highest relative statistical support (wAICc  = 0.99) and explained ca. 60% of the deviance. Habitat suitability was mainly driven by spatial variation in bathymetry and sea surface temperature among oceans, although these effects differed slightly among oceans. Predicted changes in sea surface temperature resulted in a slight shift of suitable habitat towards the poles in both the Atlantic and Indian Oceans (ca. 5°N and 3-8°S, respectively) accompanied by an overall range contraction (2.5-7.4% and 1.1-6.3%, respectively). Predicted changes in the Pacific Ocean were small. Assuming that whale shark environmental requirements and human disturbances (i.e. no stabilization of greenhouse gas emissions) remain similar, we show that warming sea surface temperatures might promote a net retreat from current aggregation areas and an overall redistribution of the species.

  12. Accurately Predicting Future Reading Difficulty for Bilingual Latino Children at Risk for Language Impairment

    ERIC Educational Resources Information Center

    Petersen, Douglas B.; Gillam, Ronald B.

    2013-01-01

    Sixty-three bilingual Latino children who were at risk for language impairment were administered reading-related measures in English and Spanish (letter identification, phonological awareness, rapid automatized naming, and sentence repetition) and descriptive measures including English language proficiency (ELP), language ability (LA),…

  13. 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

  14. Tryptophan Predicts the Risk for Future Type 2 Diabetes

    PubMed Central

    Chen, Tianlu; Zheng, Xiaojiao; Ma, Xiaojing; Bao, Yuqian; Ni, Yan; Hu, Cheng; Rajani, Cynthia; Huang, Fengjie; Zhao, Aihua; Jia, Weiping; Jia, Wei

    2016-01-01

    Recently, 5 amino acids were identified and verified as important metabolites highly associated with type 2 diabetes (T2D) development. This report aims to assess the association of tryptophan with the development of T2D and to evaluate its performance with existing amino acid markers. A total of 213 participants selected from a ten-year longitudinal Shanghai Diabetes Study (SHDS) were examined in two ways: 1) 51 subjects who developed diabetes and 162 individuals who remained metabolically healthy in 10 years; 2) the same 51 future diabetes and 23 strictly matched ones selected from the 162 healthy individuals. Baseline fasting serum tryptophan concentrations were quantitatively measured using ultra-performance liquid chromatography triple quadruple mass spectrometry. First, serum tryptophan level was found significantly higher in future T2D and was positively and independently associated with diabetes onset risk. Patients with higher tryptophan level tended to present higher degree of insulin resistance and secretion, triglyceride and blood pressure. Second, the prediction potential of tryptophan is non-inferior to the 5 existing amino acids. The predictive performance of the combined score improved after taking tryptophan into account. Our findings unveiled the potential of tryptophan as a new marker associated with diabetes risk in Chinese populations. The addition of tryptophan provided complementary value to the existing amino acid predictors. PMID:27598004

  15. An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes.

    PubMed

    Wang, Jia-Nan; Jin, Jun-Ling; Geng, Yun; Sun, Shi-Ling; Xu, Hong-Liang; Lu, Ying-Hua; Su, Zhong-Min

    2013-03-15

    Recently, the extreme learning machine neural network (ELMNN) as a valid computing method has been proposed to predict the nonlinear optical property successfully (Wang et al., J. Comput. Chem. 2012, 33, 231). In this work, first, we follow this line of work to predict the electronic excitation energies using the ELMNN method. Significantly, the root mean square deviation of the predicted electronic excitation energies of 90 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) derivatives between the predicted and experimental values has been reduced to 0.13 eV. Second, four groups of molecule descriptors are considered when building the computing models. The results show that the quantum chemical descriptions have the closest intrinsic relation with the electronic excitation energy values. Finally, a user-friendly web server (EEEBPre: Prediction of electronic excitation energies for BODIPY dyes), which is freely accessible to public at the web site: http://202.198.129.218, has been built for prediction. This web server can return the predicted electronic excitation energy values of BODIPY dyes that are high consistent with the experimental values. We hope that this web server would be helpful to theoretical and experimental chemists in related research.

  16. Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task

    PubMed Central

    Diaz, Gabriel; Cooper, Joseph; Rothkopf, Constantin; Hayhoe, Mary

    2013-01-01

    Despite general agreement that prediction is a central aspect of perception, there is relatively little evidence concerning the basis on which visual predictions are made. Although both saccadic and pursuit eye-movements reveal knowledge of the future position of a moving visual target, in many of these studies targets move along simple trajectories through a fronto-parallel plane. Here, using a naturalistic and racquet-based interception task in a virtual environment, we demonstrate that subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions and the head and body are unrestrained. Furthermore, we found that, following a change in ball elasticity, subjects were able to accurately adjust their prebounce predictions of the ball's post-bounce trajectory. This suggests that prediction is guided by experience-based models of how information in the visual image will change over time. PMID:23325347

  17. Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

    PubMed

    Li, Jiaming; Luo, Suhuai; Jin, Jesse S

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  18. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.

    PubMed

    Zhang, Daoqiang; Shen, Dinggang

    2012-01-01

    Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.

  19. Using unknown knowns to predict coastal response to future climate

    NASA Astrophysics Data System (ADS)

    Plant, N. G.; Lentz, E. E.; Gutierrez, B.; Thieler, E. R.; Passeri, D. L.

    2015-12-01

    The coastal zone, including its bathymetry, topography, ecosystem, and communities, depends on and responds to a wide array of natural and engineered processes associated with climate variability. Climate affects the frequency of coastal storms, which are only resolved probabilistically for future conditions, as well as setting the pace for persistent processes (e.g., waves driving daily alongshore transport; beach nourishment). It is not clear whether persistent processes or extreme events contribute most to the integrated evolution of the coast. Yet, observations of coastal change record the integration of persistent and extreme processes. When these observations span a large spatial domain and/or temporal range they may reflect a wide range of forcing and boundary conditions that include different levels of sea-level rise, storminess, sediment input, engineering activities, and elevation distributions. We have been using a statistical approach to characterize the interrelationships between oceanographic, ecological, and geomorphic processes—including the role played by human activities via coastal protection, beach nourishment, and other forms of coastal management. The statistical approach, Bayesian networks, incorporates existing information to establish underlying prior expectations for the distributions and inter-correlations of variables most relevant to coastal geomorphic evolution. This underlying information can then be used to make predictions. We demonstrate several examples of the utility of this approach using data as constraints and then propagating the constraints and uncertainty to make predictions of unobserved variables that include changes in shorelines, dunes, and overwash deposits. We draw on data from the Gulf and Atlantic Coasts of the United States, resolving time scales of years to a century. The examples include both short-term storm impacts and long-term evolution associated with sea-level rise. We show that the Bayesian network can

  20. Identifying Future Scientists: Predicting Persistence into Research Training

    PubMed Central

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8–12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers. PMID:18056303

  1. Identifying future scientists: predicting persistence into research training.

    PubMed

    McGee, Richard; Keller, Jill L

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8-12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers.

  2. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    PubMed

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy.

  3. NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles.

    PubMed

    Kosugi, Shunichi; Yanagawa, Hiroshi; Terauchi, Ryohei; Tabata, Satoshi

    2014-09-01

    The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs) in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS). Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.

  4. 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

  5. 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?

  6. 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

  7. 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

  8. Dynamics of Flexible MLI-type Debris for Accurate Orbit Prediction

    DTIC Science & Technology

    2014-09-01

    SUBJECT TERMS EOARD, orbital debris , HAMR objects, multi-layered insulation, orbital dynamics, orbit predictions, orbital propagation 16. SECURITY...illustration are orbital debris [Souce: NASA...piece of space junk (a paint fleck) during the STS-7 mission (Photo: NASA Orbital Debris Program Office

  9. Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression.

    PubMed

    Stempler, Shiri; Waldman, Yedael Y; Wolf, Lior; Ruppin, Eytan

    2012-09-01

    Numerous metabolic alterations are associated with the impairment of brain cells in Alzheimer's disease (AD). Here we use gene expression microarrays of both whole hippocampus tissue and hippocampal neurons of AD patients to investigate the ability of metabolic gene expression to predict AD progression and its cognitive decline. We find that the prediction accuracy of different AD stages is markedly higher when using neuronal expression data (0.9) than when using whole tissue expression (0.76). Furthermore, the metabolic genes' expression is shown to be as effective in predicting AD severity as the entire gene list. Remarkably, a regression model from hippocampal metabolic gene expression leads to a marked correlation of 0.57 with the Mini-Mental State Examination cognitive score. Notably, the expression of top predictive neuronal genes in AD is significantly higher than that of other metabolic genes in the brains of healthy subjects. All together, the analyses point to a subset of metabolic genes that is strongly associated with normal brain functioning and whose disruption plays a major role in AD.

  10. Predicting repeat self-harm in children--how accurate can we expect to be?

    PubMed

    Chitsabesan, Prathiba; Harrington, Richard; Harrington, Valerie; Tomenson, Barbara

    2003-01-01

    The main objective of the study was to find which variables predict repetition of deliberate self-harm in children. The study is based on a group of children who took part in a randomized control trial investigating the effects of a home-based family intervention for children who had deliberately poisoned themselves. These children had a range of baseline and outcome measures collected on two occasions (two and six months follow-up). Outcome data were collected from 149 (92 %) of the initial 162 children over the six months. Twenty-three children made a further deliberate self-harm attempt within the follow-up period. A number of variables at baseline were found to be significantly associated with repeat self-harm. Parental mental health and a history of previous attempts were the strongest predictors. A model of prediction of further deliberate self-harm combining these significant individual variables produced a high positive predictive value (86 %) but had low sensitivity (28 %). Predicting repeat self-harm in children is difficult, even with a comprehensive series of assessments over multiple time points, and we need to adapt services with this in mind. We propose a model of service provision which takes these findings into account.

  11. Does future-oriented thinking predict adolescent decision making?

    PubMed

    Eskritt, Michelle; Doucette, Jesslyn; Robitaille, Lori

    2014-01-01

    A number of theorists, as well as plain common sense, suggest that future-oriented thinking (FOT) should be involved in decision making; therefore, the development of FOT should be related to better quality decision making. FOT and quality of the decision making were measured in adolescents as well as adults in 2 different experiments. Though the results of the first experiment revealed an increase in quality of decision making across adolescence into adulthood, there was no relationship between FOT and decision making. In the second experiment, FOT predicted performance on a more deliberative decision-making task independent of age, but not performance on the Iowa Gambling Task (IGT). Performance on the IGT was instead related to emotion regulation. The study's findings suggest that FOT can be related to reflective decision making but not necessarily decision making that is more intuitive.

  12. Developing neuronal networks: self-organized criticality predicts the future.

    PubMed

    Pu, Jiangbo; Gong, Hui; Li, Xiangning; Luo, Qingming

    2013-01-01

    Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and "aging" process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

  13. Should we believe model predictions of future climate change? (Invited)

    NASA Astrophysics Data System (ADS)

    Knutti, R.

    2009-12-01

    As computers get faster and our understanding of the climate system improves, climate models to predict the future are getting more complex by including more and more processes, and they are run at higher and higher resolution to resolve more of the small scale processes. As a result, some of the simulated features and structures, e.g. ocean eddies or tropical cyclones look surprisingly real. But are these deceptive? A pattern can look perfectly real but be in the wrong place. So can the current global models really provide the kind of information on local scales and on the quantities (e.g. extreme events) that the decision maker would need to know to invest for example in adaptation? A closer look indicates that evaluating skill of climate models and quantifying uncertainties in predictions is very difficult. This presentation shows that while models are improving in simulating the climate features we observe (e.g. the present day mean state, or the El Nino Southern Oscillation), the spread from multiple models in predicting future changes is often not decreasing. The main problem is that (unlike with weather forecasts for example) we cannot evaluate the model on a prediction (for example for the year 2100) and we have to use the present, or past changes as metrics of skills. But there are infinite ways of testing a model, and many metrics used to test models do not clearly relate to the prediction. Therefore there is little agreement in the community on metrics to separate ‘good’ and ‘bad’ models, and there is a concern that model development, evaluation and posterior weighting or ranking of models are all using the same datasets. While models are continuously improving in representing what we believe to be the key processes, many models also share ideas, parameterizations or even pieces of model code. The current models can therefore not be considered independent. Robustness of a model simulated result is often interpreted as increasing the confidence

  14. Self-Fitting Hearing Aids: Status Quo and Future Predictions.

    PubMed

    Keidser, Gitte; Convery, Elizabeth

    2016-04-12

    A self-contained, self-fitting hearing aid (SFHA) is a device that enables the user to perform both threshold measurements leading to a prescribed hearing aid setting and fine-tuning, without the need for audiological support or access to other equipment. The SFHA has been proposed as a potential solution to address unmet hearing health care in developing countries and remote locations in the developed world and is considered a means to lower cost and increase uptake of hearing aids in developed countries. This article reviews the status of the SFHA and the evidence for its feasibility and challenges and predicts where it is heading. Devices that can be considered partly or fully self-fitting without audiological support were identified in the direct-to-consumer market. None of these devices are considered self-contained as they require access to other hardware such as a proprietary interface, computer, smartphone, or tablet for manipulation. While there is evidence that self-administered fitting processes can provide valid and reliable results, their success relies on user-friendly device designs and interfaces and easy-to-interpret instructions. Until these issues have been sufficiently addressed, optional assistance with the self-fitting process and on-going use of SFHAs is recommended. Affordability and a sustainable delivery system remain additional challenges for the SFHA in developing countries. Future predictions include a growth in self-fitting products, with most future SFHAs consisting of earpieces that connect wirelessly with a smartphone and providers offering assistance through a telehealth infrastructure, and the integration of SFHAs into the traditional hearing health-care model.

  15. Accurate prediction of the optical rotation and NMR properties for highly flexible chiral natural products.

    PubMed

    Hashmi, Muhammad Ali; Andreassend, Sarah K; Keyzers, Robert A; Lein, Matthias

    2016-09-21

    Despite advances in electronic structure theory the theoretical prediction of spectroscopic properties remains a computational challenge. This is especially true for natural products that exhibit very large conformational freedom and hence need to be sampled over many different accessible conformations. We report a strategy, which is able to predict NMR chemical shifts and more elusive properties like the optical rotation with great precision, through step-wise incremental increases of the conformational degrees of freedom. The application of this method is demonstrated for 3-epi-xestoaminol C, a chiral natural compound with a long, linear alkyl chain of 14 carbon atoms. Experimental NMR and [α]D values are reported to validate the results of the density functional theory calculations.

  16. 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

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

    PubMed

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

    2015-06-30

    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.

  18. 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.

  19. 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

    2015-08-07

    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.

  20. 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.

  1. 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

  2. Predicting the future by explaining the past: constraining carbon-climate feedback using contemporary observations

    NASA Astrophysics Data System (ADS)

    Denning, S.

    2014-12-01

    The carbon-climate community has an historic opportunity to make a step-function improvement in climate prediction by using regional constraints to improve mechanistic model representation of carbon cycle processes. Interactions among atmospheric CO2, global biogeochemistry, and physical climate constitute leading sources of uncertainty in future climate. First-order differences among leading models of these processes produce differences in climate as large as differences in aerosol-cloud-radiation interactions and fossil fuel combustion. Emergent constraints based on global observations of interannual variations provide powerful constraints on model parameterizations. Additional constraints can be defined at regional scales. Organized intercomparison experiments have shown that uncertainties in future carbon-climate feedback arise primarily from model representations of the dependence of photosynthesis on CO2 and drought stress and the dependence of decomposition on temperature. Just as representations of net carbon fluxes have benefited from eddy flux, ecosystem manipulations, and atmospheric CO2, component carbon fluxes (photosynthesis, respiration, decomposition, disturbance) can be constrained at regional scales using new observations. Examples include biogeochemical tracers such as isotopes and carbonyl sulfide as well as remotely-sensed parameters such as chlorophyll fluorescence and biomass. Innovative model evaluation experiments will be needed to leverage the information content of new observations to improve process representations as well as to provide accurate initial conditions for coupled climate model simulations. Successful implementation of a comprehensive benchmarking program could have a huge impact on understanding and predicting future climate change.

  3. 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.

  4. Uncertain future soil carbon dynamics under global change predicted by models constrained by total carbon measurements.

    PubMed

    Luo, Zhongkui; Wang, Enli; Sun, Osbert J

    2017-01-23

    Pool-based carbon (C) models are widely applied to predict soil C dynamics under global change and infer underlying mechanisms. However, it is unclear about the credibility of model-predicted C pool size, decay rate (k) and/or microbial C use efficiency (e) as only data on bulked total C is usually available for model-constraining. Using observing system simulation experiments (OSSE), we constrained a two-pool model using simulated datasets of total soil C dynamics under topical hypotheses on responses of soil C dynamics to warming and elevated CO2 (i.e., global change scenarios). The results indicated that the model predicted great uncertainties in C pool size, k and e under all global change scenarios, resulting in the difficulty to correctly infer the presupposed "real" values of those parameters that are used to generate the simulated total soil C for constraining the model. Furthermore, the model using the constrained parameters generated divergent future soil C dynamics. Compared with the predictions using the presupposed real parameters (i.e., the real future C dynamics), the percentage uncertainty in 100-year predictions using the constrained parameters was up to 45% depending on global change scenarios and data availability for model-constraining. Such great uncertainty was mainly due to the high collinearity among the model parameters. Using pool-based models, we argue that soil C pool size, k and/or e and their responses to global change have to be estimated explicitly and empirically, rather than through model-fitting, in order to accurately predict C dynamics and infer underlying mechanisms. The OSSE approach provides a powerful way to identify data requirement for the new generation of model development and test model performance. This article is protected by copyright. All rights reserved.

  5. Can tritiated water-dilution space accurately predict total body water in chukar partridges

    SciTech Connect

    Crum, B.G.; Williams, J.B.; Nagy, K.A.

    1985-11-01

    Total body water (TBW) volumes determined from the dilution space of injected tritiated water have consistently overestimated actual water volumes (determined by desiccation to constant mass) in reptiles and mammals, but results for birds are controversial. We investigated potential errors in both the dilution method and the desiccation method in an attempt to resolve this controversy. Tritiated water dilution yielded an accurate measurement of water mass in vitro. However, in vivo, this method yielded a 4.6% overestimate of the amount of water (3.1% of live body mass) in chukar partridges, apparently largely because of loss of tritium from body water to sites of dissociable hydrogens on body solids. An additional source of overestimation (approximately 2% of body mass) was loss of tritium to the solids in blood samples during distillation of blood to obtain pure water for tritium analysis. Measuring tritium activity in plasma samples avoided this problem but required measurement of, and correction for, the dry matter content in plasma. Desiccation to constant mass by lyophilization or oven-drying also overestimated the amount of water actually in the bodies of chukar partridges by 1.4% of body mass, because these values included water adsorbed onto the outside of feathers. When desiccating defeathered carcasses, oven-drying at 70 degrees C yielded TBW values identical to those obtained from lyophilization, but TBW was overestimated (0.5% of body mass) by drying at 100 degrees C due to loss of organic substances as well as water.

  6. Does preoperative cross-sectional imaging accurately predict main duct involvement in intraductal papillary mucinous neoplasm?

    PubMed

    Barron, M R; Roch, A M; Waters, J A; Parikh, J A; DeWitt, J M; Al-Haddad, M A; Ceppa, E P; House, M G; Zyromski, N J; Nakeeb, A; Pitt, H A; Schmidt, C Max

    2014-03-01

    Main pancreatic duct (MPD) involvement is a well-demonstrated risk factor for malignancy in intraductal papillary mucinous neoplasm (IPMN). Preoperative radiographic determination of IPMN type is heavily relied upon in oncologic risk stratification. We hypothesized that radiographic assessment of MPD involvement in IPMN is an accurate predictor of pathological MPD involvement. Data regarding all patients undergoing resection for IPMN at a single academic institution between 1992 and 2012 were gathered prospectively. Retrospective analysis of imaging and pathologic data was undertaken. Preoperative classification of IPMN type was based on cross-sectional imaging (MRI/magnetic resonance cholangiopancreatography (MRCP) and/or CT). Three hundred sixty-two patients underwent resection for IPMN. Of these, 334 had complete data for analysis. Of 164 suspected branch duct (BD) IPMN, 34 (20.7%) demonstrated MPD involvement on final pathology. Of 170 patients with suspicion of MPD involvement, 50 (29.4%) demonstrated no MPD involvement. Of 34 patients with suspected BD-IPMN who were found to have MPD involvement on pathology, 10 (29.4%) had invasive carcinoma. Alternatively, 2/50 (4%) of the patients with suspected MPD involvement who ultimately had isolated BD-IPMN demonstrated invasive carcinoma. Preoperative radiographic IPMN type did not correlate with final pathology in 25% of the patients. In addition, risk of invasive carcinoma correlates with pathologic presence of MPD involvement.

  7. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

    PubMed Central

    Wang, Zhiheng; Yang, Qianqian; Li, Tonghua; Cong, Peisheng

    2015-01-01

    The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database. Availability The DisoMCS is available at http://cal.tongji.edu.cn/disorder/. PMID:26090958

  8. 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.

  9. 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.

  10. Computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models.

    PubMed

    Krokhotin, Andrey; Dokholyan, Nikolay V

    2015-01-01

    Computational methods can provide significant insights into RNA structure and dynamics, bridging the gap in our understanding of the relationship between structure and biological function. Simulations enrich and enhance our understanding of data derived on the bench, as well as provide feasible alternatives to costly or technically challenging experiments. Coarse-grained computational models of RNA are especially important in this regard, as they allow analysis of events occurring in timescales relevant to RNA biological function, which are inaccessible through experimental methods alone. We have developed a three-bead coarse-grained model of RNA for discrete molecular dynamics simulations. This model is efficient in de novo prediction of short RNA tertiary structure, starting from RNA primary sequences of less than 50 nucleotides. To complement this model, we have incorporated additional base-pairing constraints and have developed a bias potential reliant on data obtained from hydroxyl probing experiments that guide RNA folding to its correct state. By introducing experimentally derived constraints to our computer simulations, we are able to make reliable predictions of RNA tertiary structures up to a few hundred nucleotides. Our refined model exemplifies a valuable benefit achieved through integration of computation and experimental methods.

  11. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    PubMed

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.

  12. Accurate Prediction of the Dynamical Changes within the Second PDZ Domain of PTP1e

    PubMed Central

    Cilia, Elisa; Vuister, Geerten W.; Lenaerts, Tom

    2012-01-01

    Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the collection of residues involved in long-range communication. Even though different computational approaches have identified subsets of residues that were qualitatively comparable, no quantitative analysis of the accuracy of these predictions was thus far determined. Here, we show that our information theoretical method produces quantitatively better results with respect to the experimental data than some of these earlier methods. Moreover, it provides a global network perspective on the effect experienced by the different residues involved in the process. We also show that these predictions are consistent within both the human and mouse variants of this domain. Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation of thses kinds of methods. PMID:23209399

  13. 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

  14. 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.

  15. How Accurate Is the Prediction of Maximal Oxygen Uptake with Treadmill Testing?

    PubMed Central

    Wicks, John R.; Oldridge, Neil B.

    2016-01-01

    Background Cardiorespiratory fitness measured by treadmill testing has prognostic significance in determining mortality with cardiovascular and other chronic disease states. The accuracy of a recently developed method for estimating maximal oxygen uptake (VO2peak), the heart rate index (HRI), is dependent only on heart rate (HR) and was tested against oxygen uptake (VO2), either measured or predicted from conventional treadmill parameters (speed, incline, protocol time). Methods The HRI equation, METs = 6 x HRI– 5, where HRI = maximal HR/resting HR, provides a surrogate measure of VO2peak. Forty large scale treadmill studies were identified through a systematic search using MEDLINE, Google Scholar and Web of Science in which VO2peak was either measured (TM-VO2meas; n = 20) or predicted (TM-VO2pred; n = 20) based on treadmill parameters. All studies were required to have reported group mean data of both resting and maximal HRs for determination of HR index-derived oxygen uptake (HRI-VO2). Results The 20 studies with measured VO2 (TM-VO2meas), involved 11,477 participants (median 337) with a total of 105,044 participants (median 3,736) in the 20 studies with predicted VO2 (TM-VO2pred). A difference of only 0.4% was seen between mean (±SD) VO2peak for TM- VO2meas and HRI-VO2 (6.51±2.25 METs and 6.54±2.28, respectively; p = 0.84). In contrast, there was a highly significant 21.1% difference between mean (±SD) TM-VO2pred and HRI-VO2 (8.12±1.85 METs and 6.71±1.92, respectively; p<0.001). Conclusion Although mean TM-VO2meas and HRI-VO2 were almost identical, mean TM-VO2pred was more than 20% greater than mean HRI-VO2. PMID:27875547

  16. 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.

  17. Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.

    PubMed

    Faraggi, Eshel; Kouza, Maksim; Zhou, Yaoqi; Kloczkowski, Andrzej

    2017-01-01

    A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence 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 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 ASAquick are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org .

  18. Sequence features accurately predict genome-wide MeCP2 binding in vivo

    PubMed Central

    Rube, H. Tomas; Lee, Wooje; Hejna, Miroslav; Chen, Huaiyang; Yasui, Dag H.; Hess, John F.; LaSalle, Janine M.; Song, Jun S.; Gong, Qizhi

    2016-01-01

    Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2's affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons. PMID:27008915

  19. 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.

  20. Development of a method to accurately calculate the Dpb and quickly predict the strength of a chemical bond

    NASA Astrophysics Data System (ADS)

    Du, Xia; Zhao, Dong-Xia; Yang, Zhong-Zhi

    2013-02-01

    A new approach to characterize and measure bond strength has been developed. First, we propose a method to accurately calculate the potential acting on an electron in a molecule (PAEM) at the saddle point along a chemical bond in situ, denoted by Dpb. Then, a direct method to quickly evaluate bond strength is established. We choose some familiar molecules as models for benchmarking this method. As a practical application, the Dpb of base pairs in DNA along C-H and N-H bonds are obtained for the first time. All results show that C7-H of A-T and C8-H of G-C are the relatively weak bonds that are the injured positions in DNA damage. The significance of this work is twofold: (i) A method is developed to calculate Dpb of various sizable molecules in situ quickly and accurately; (ii) This work demonstrates the feasibility to quickly predict the bond strength in macromolecules.

  1. Fast and accurate prediction for aerodynamic forces and moments acting on satellites flying in Low-Earth Orbit

    NASA Astrophysics Data System (ADS)

    Jin, Xuhon; Huang, Fei; Hu, Pengju; Cheng, Xiaoli

    2016-11-01

    A fundamental prerequisite for satellites operating in a Low Earth Orbit (LEO) is the availability of fast and accurate prediction of non-gravitational aerodynamic forces, which is characterised by the free molecular flow regime. However, conventional computational methods like the analytical integral method and direct simulation Monte Carlo (DSMC) technique are found failing to deal with flow shadowing and multiple reflections or computationally expensive. This work develops a general computer program for the accurate calculation of aerodynamic forces in the free molecular flow regime using the test particle Monte Carlo (TPMC) method, and non-gravitational aerodynamic forces actiong on the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite is calculated for different freestream conditions and gas-surface interaction models by the computer program.

  2. Ensemble predictions of future streamflow drought in Europe

    NASA Astrophysics Data System (ADS)

    Forzieri, Giovanni; Feyen, Luc; Rojas, Rodrigo

    2013-04-01

    Recent developments in climate modeling suggest that global warming and growing human water use are likely to favor conditions for the development of streamflow droughts in several parts of Europe by the end of this century. In this study, we quantify how future drought hazard in Europe may develop in view of these drivers by comparing low-flow predictions of the LISFLOOD hydrological model coupled to a water consumption module and driven by an ensemble of climate projections. This ensemble consists of 12 bias-corrected climate simulations conducted within the ENSEMBLES project, forced by the A1B emission scenario for the period 1961-2100. For time slices of 30 years, low-flow characteristics - quantified in terms of minimum flows, environmental flows and deficits - are derived from the simulated streamflow series and further analyzed using extreme value theory. Changes in extreme river conditions are then analyzed with respect to the 1961-1990 control period. Two main domains with opposite signal of change in drought characteristics can be identified in Europe, as well as a transition zone between them. Southern parts of Europe - from the Iberian to Balkan Peninsula- but also France, Belgium and British Isles are expected to be more prone to severe and persistent low-flow conditions. In contrast, the Scandinavia Peninsula and Northeast Europe show a robust decrease in future drought hazard. In a transition zone between these two regions, climate-induced changes are projected to be marginal. Water use under an A1B-consistent scenario will further aggravate drought conditions in the south as well as in the transition zone. In the regions with a clear pattern of change in streamflow drought, indices derived from the hydrological simulations for different climate experiments are highly consistent, whereas in the transition zone between North and South Europe the consistency in changes amongst the ensemble members is lower.

  3. Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention.

    PubMed

    Resnic, F S; Ohno-Machado, L; Selwyn, A; Simon, D I; Popma, J J

    2001-07-01

    The objectives of this analysis were to develop and validate simplified risk score models for predicting the risk of major in-hospital complications after percutaneous coronary intervention (PCI) in the era of widespread stenting and use of glycoprotein IIb/IIIa antagonists. We then sought to compare the performance of these simplified models with those of full logistic regression and neural network models. From January 1, 1997 to December 31, 1999, data were collected on 4,264 consecutive interventional procedures at a single center. Risk score models were derived from multiple logistic regression models using the first 2,804 cases and then validated on the final 1,460 cases. The area under the receiver operating characteristic (ROC) curve for the risk score model that predicted death was 0.86 compared with 0.85 for the multiple logistic model and 0.83 for the neural network model (validation set). For the combined end points of death, myocardial infarction, or bypass surgery, the corresponding areas under the ROC curves were 0.74, 0.78, and 0.81, respectively. Previously identified risk factors were confirmed in this analysis. The use of stents was associated with a decreased risk of in-hospital complications. Thus, risk score models can accurately predict the risk of major in-hospital complications after PCI. Their discriminatory power is comparable to those of logistic models and neural network models. Accurate bedside risk stratification may be achieved with these simple models.

  4. Forest tree seedlings may suffer from predicted future winters

    NASA Astrophysics Data System (ADS)

    Domisch, Timo; Repo, Tapani; Martz, Françoise; Rautio, Pasi

    2016-04-01

    Future climate scenarios predict increased precipitation and air temperatures, particularly at high latitudes, and especially so during winter, spring and autumn. However, soil temperatures are more difficult to predict, since they depend strongly on the insulating snow cover. Warm periods during winter can lead to thaw-freeze cycles and flooding, which again can result in the formation of ice layers, affecting soil properties, soil gas concentrations and the survival of tree seedlings. We conducted two laboratory experiments of 20 weeks duration each, simulating winter, spring and early summer, and imposed Scots pine (Pinus sylvestris L.) or downy birch (Betula pubescens Ehrh.) seedlings to four different winter scenarios: (1) ambient snow cover, (2) compressed snow and ice encasement, (3) frozen flood and (4) no snow. We estimated the stress that the seedlings experienced by means of gas exchange, chlorophyll fluorescence and determining above- and belowground biomass and carbohydrate contents, as well as measuring soil oxygen and carbon dioxide concentrations. The seedlings in the snow and compressed snow treatments survived until the end of the experiments, although only those covered with an ambient snow cover showed normal height growth and typical carbohydrate contents. The seedlings in the other treatments showed symptoms of dieback already during early spring and had almost completely died at the end of the experiment. Our results suggest the crucial significance of the protective snow cover, and that a missing soil cover or soil hypoxia and anoxia during winter can be lethal for seedlings, and that respiratory losses and winter desiccation of aboveground organs can further lead to the death of tree seedlings.

  5. Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes

    PubMed Central

    Zhao, Li; Chen, Yiyun; Bajaj, Amol Onkar; Eblimit, Aiden; Xu, Mingchu; Soens, Zachry T.; Wang, Feng; Ge, Zhongqi; Jung, Sung Yun; He, Feng; Li, Yumei; Wensel, Theodore G.; Qin, Jun; Chen, Rui

    2016-01-01

    Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases. PMID:26912414

  6. 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.

  7. The human skin/chick chorioallantoic membrane model accurately predicts the potency of cosmetic allergens.

    PubMed

    Slodownik, Dan; Grinberg, Igor; Spira, Ram M; Skornik, Yehuda; Goldstein, Ronald S

    2009-04-01

    The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin.

  8. Searching for Computational Strategies to Accurately Predict pKas of Large Phenolic Derivatives.

    PubMed

    Rebollar-Zepeda, Aida Mariana; Campos-Hernández, Tania; Ramírez-Silva, María Teresa; Rojas-Hernández, Alberto; Galano, Annia

    2011-08-09

    Twenty-two reaction schemes have been tested, within the cluster-continuum model including up to seven explicit water molecules. They have been used in conjunction with nine different methods, within the density functional theory and with second-order Møller-Plesset. The quality of the pKa predictions was found to be strongly dependent on the chosen scheme, while only moderately influenced by the method of calculation. We recommend the E1 reaction scheme [HA + OH(-) (3H2O) ↔ A(-) (H2O) + 3H2O], since it yields mean unsigned errors (MUE) lower than 1 unit of pKa for most of the tested functionals. The best pKa values obtained from this reaction scheme are those involving calculations with PBE0 (MUE = 0.77), TPSS (MUE = 0.82), BHandHLYP (MUE = 0.82), and B3LYP (MUE = 0.86) functionals. This scheme has the additional advantage, compared to the proton exchange method, which also gives very small values of MUE, of being experiment independent. It should be kept in mind, however, that these recommendations are valid within the cluster-continuum model, using the polarizable continuum model in conjunction with the united atom Hartree-Fock cavity and the strategy based on thermodynamic cycles. Changes in any of these aspects of the used methodology may lead to different outcomes.

  9. Towards Relaxing the Spherical Solar Radiation Pressure Model for Accurate Orbit Predictions

    NASA Astrophysics Data System (ADS)

    Lachut, M.; Bennett, J.

    2016-09-01

    The well-known cannonball model has been used ubiquitously to capture the effects of atmospheric drag and solar radiation pressure on satellites and/or space debris for decades. While it lends itself naturally to spherical objects, its validity in the case of non-spherical objects has been debated heavily for years throughout the space situational awareness community. One of the leading motivations to improve orbit predictions by relaxing the spherical assumption, is the ongoing demand for more robust and reliable conjunction assessments. In this study, we explore the orbit propagation of a flat plate in a near-GEO orbit under the influence of solar radiation pressure, using a Lambertian BRDF model. Consequently, this approach will account for the spin rate and orientation of the object, which is typically determined in practice using a light curve analysis. Here, simulations will be performed which systematically reduces the spin rate to demonstrate the point at which the spherical model no longer describes the orbital elements of the spinning plate. Further understanding of this threshold would provide insight into when a higher fidelity model should be used, thus resulting in improved orbit propagations. Therefore, the work presented here is of particular interest to organizations and researchers that maintain their own catalog, and/or perform conjunction analyses.

  10. Learning to predict: Exposure to temporal sequences facilitates prediction of future events

    PubMed Central

    Baker, Rosalind; Dexter, Matthew; Hardwicke, Tom E.; Goldstone, Aimee; Kourtzi, Zoe

    2014-01-01

    Previous experience is thought to facilitate our ability to extract spatial and temporal regularities from cluttered scenes. However, little is known about how we may use this knowledge to predict future events. Here we test whether exposure to temporal sequences facilitates the visual recognition of upcoming stimuli. We presented observers with a sequence of leftwards and rightwards oriented gratings that was interrupted by a test stimulus. Observers were asked to indicate whether the orientation of the test stimulus matched their expectation based on the preceding sequence. Our results demonstrate that exposure to temporal sequences without feedback facilitates our ability to predict an upcoming stimulus. In particular, observers’ performance improved following exposure to structured but not random sequences. Improved performance lasted for a prolonged period and generalized to untrained stimulus orientations rather than sequences of different global structure, suggesting that observers acquire knowledge of the sequence structure rather than its items. Further, this learning was compromised when observers performed a dual task resulting in increased attentional load. These findings suggest that exposure to temporal regularities in a scene allows us to accumulate knowledge about its global structure and predict future events. PMID:24231115

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?

    PubMed

    Ballester, Pedro J; Schreyer, Adrian; Blundell, Tom L

    2014-03-24

    Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein-ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein-ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.

  17. Easy-to-use, general, and accurate multi-Kinect calibration and its application to gait monitoring for fall prediction.

    PubMed

    Staranowicz, Aaron N; Ray, Christopher; Mariottini, Gian-Luca

    2015-01-01

    Falls are the most-common causes of unintentional injury and death in older adults. Many clinics, hospitals, and health-care providers are urgently seeking accurate, low-cost, and easy-to-use technology to predict falls before they happen, e.g., by monitoring the human walking pattern (or "gait"). Despite the wide popularity of Microsoft's Kinect and the plethora of solutions for gait monitoring, no strategy has been proposed to date to allow non-expert users to calibrate the cameras, which is essential to accurately fuse the body motion observed by each camera in a single frame of reference. In this paper, we present a novel multi-Kinect calibration algorithm that has advanced features when compared to existing methods: 1) is easy to use, 2) it can be used in any generic Kinect arrangement, and 3) it provides accurate calibration. Extensive real-world experiments have been conducted to validate our algorithm and to compare its performance against other multi-Kinect calibration approaches, especially to show the improved estimate of gait parameters. Finally, a MATLAB Toolbox has been made publicly available for the entire research community.

  18. Modelling Monsoons: Understanding and Predicting Current and Future Behaviour

    SciTech Connect

    Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A

    2008-09-16

    including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Without aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting the current and future behavior of monsoons.

  19. Elevated objectively measured but not self-reported energy intake predicts future weight gain in adolescents

    PubMed Central

    Stice, Eric; Durant, Shelley

    2014-01-01

    Background Although obesity putatively occurs when individuals consume more calories than needed for metabolic needs, numerous risk factor studies have not observed significant positive relations between reported caloric intake and future weight gain, potentially because reported caloric intake is inaccurate. Objective The present study tested the hypothesis that objectively measured habitual energy intake, estimated with doubly labeled water, would show a stronger positive relation to future weight gain than self-reported caloric intake based on a widely used food frequency measure. Design 253 adolescents completed a doubly labeled water (DLW) assessment of energy intake (EI), a food frequency measure, and a resting metabolic rate (RMR) assessment at baseline, and had their body mass index (BMI) measured at baseline and at 1- and 2-year follow-ups. Results Controlling for baseline RMR, elevated objectively measured EI, but not self-reported habitual caloric intake, predicted increases in BMI over a 2-year follow-up. On average, participants under-reported caloric intake by 35%. Conclusions Results provide support for the thesis that self-reported caloric intake has not predicted future weight gain because it is less accurate than objectively measured habitual caloric intake, suggesting that food frequency measures can lead to misleading findings. However, even objectively measured caloric intake showed only a moderate relation to future weight gain, implying that habitual caloric intake fluctuates over time and that it may be necessary to conduct serial assessments of habitual intake to better reflect the time-varying effects of caloric intake on weight gain. PMID:24930597

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

    PubMed Central

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

    2012-01-01

    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. PMID:23162788

  1. 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.

  2. Shrinking the Psoriasis Assessment Gap: Early Gene-Expression Profiling Accurately Predicts Response to Long-Term Treatment.

    PubMed

    Correa da Rosa, Joel; Kim, Jaehwan; Tian, Suyan; Tomalin, Lewis E; Krueger, James G; Suárez-Fariñas, Mayte

    2017-02-01

    There is an "assessment gap" between the moment a patient's response to treatment is biologically determined and when a response can actually be determined clinically. Patients' biochemical profiles are a major determinant of clinical outcome for a given treatment. It is therefore feasible that molecular-level patient information could be used to decrease the assessment gap. Thanks to clinically accessible biopsy samples, high-quality molecular data for psoriasis patients are widely available. Psoriasis is therefore an excellent disease for testing the prospect of predicting treatment outcome from molecular data. Our study shows that gene-expression profiles of psoriasis skin lesions, taken in the first 4 weeks of treatment, can be used to accurately predict (>80% area under the receiver operating characteristic curve) the clinical endpoint at 12 weeks. This could decrease the psoriasis assessment gap by 2 months. We present two distinct prediction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific predictors aimed at forecasting clinical response to treatment with four specific drugs: etanercept, ustekinumab, adalimumab, and methotrexate. We also develop two forms of prediction: one from detailed, platform-specific data and one from platform-independent, pathway-based data. We show that key biomarkers are associated with responses to drugs and doses and thus provide insight into the biology of pathogenesis reversion.

  3. Accurate prediction of unsteady and time-averaged pressure loads using a hybrid Reynolds-Averaged/large-eddy simulation technique

    NASA Astrophysics Data System (ADS)

    Bozinoski, Radoslav

    Significant research has been performed over the last several years on understanding the unsteady aerodynamics of various fluid flows. Much of this work has focused on quantifying the unsteady, three-dimensional flow field effects which have proven vital to the accurate prediction of many fluid and aerodynamic problems. Up until recently, engineers have predominantly relied on steady-state simulations to analyze the inherently three-dimensional ow structures that are prevalent in many of today's "real-world" problems. Increases in computational capacity and the development of efficient numerical methods can change this and allow for the solution of the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations for practical three-dimensional aerodynamic applications. An integral part of this capability has been the performance and accuracy of the turbulence models coupled with advanced parallel computing techniques. This report begins with a brief literature survey of the role fully three-dimensional, unsteady, Navier-Stokes solvers have on the current state of numerical analysis. Next, the process of creating a baseline three-dimensional Multi-Block FLOw procedure called MBFLO3 is presented. Solutions for an inviscid circular arc bump, laminar at plate, laminar cylinder, and turbulent at plate are then presented. Results show good agreement with available experimental, numerical, and theoretical data. Scalability data for the parallel version of MBFLO3 is presented and shows efficiencies of 90% and higher for processes of no less than 100,000 computational grid points. Next, the description and implementation techniques used for several turbulence models are presented. Following the successful implementation of the URANS and DES procedures, the validation data for separated, non-reattaching flows over a NACA 0012 airfoil, wall-mounted hump, and a wing-body junction geometry are presented. Results for the NACA 0012 showed significant improvement in flow predictions

  4. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    PubMed Central

    Græsbøll, Kaare; Kirkeby, Carsten; Nielsen, Søren Saxmose; Halasa, Tariq; Toft, Nils; Christiansen, Lasse Engbo

    2017-01-01

    The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists of several steps to evaluate lifetime milk production and individual cow somatic cell counts and to finally predict the average production for each day that the cow is alive. Herd recording data from 610 Danish Holstein herds were used to train and test a model predicting milk production (including factors associated with milk yield, somatic cell count, and the survival of individual cows). All estimated parameters were either herd- or cow-specific. The model prediction deviated, on average, less than 0.5 kg from the future average milk production of dairy cows in multiple herds after adjusting for the effect of somatic cell count. We conclude that estimates of future average production can be used on a day-to-day basis to rank cows for culling, or can be implemented in simulation models of within-herd disease spread to make operational decisions, such as culling versus treatment. An advantage of the approach presented in this paper is that it requires no specific knowledge of disease status or any other information beyond herd recorded milk yields, somatic cell counts, and reproductive status. PMID:28261585

  5. Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

    PubMed

    Adde, Lars; Helbostad, Jorunn; Jensenius, Alexander R; Langaas, Mette; Støen, Ragnhild

    2013-08-01

    This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

  6. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging

    NASA Astrophysics Data System (ADS)

    Hughes, Timothy J.; Kandathil, Shaun M.; Popelier, Paul L. A.

    2015-02-01

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G**, B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol-1, decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol-1.

  7. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

    PubMed

    Hughes, Timothy J; Kandathil, Shaun M; Popelier, Paul L A

    2015-02-05

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1).

  8. 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

  9. Self-Esteem and Future Orientation Predict Adolescents' Risk Engagement

    ERIC Educational Resources Information Center

    Jackman, Danielle M.; MacPhee, David

    2017-01-01

    This study's purpose was to examine the relations among future orientation, self-esteem, and later adolescent risk behaviors, and to compare two mediational models involving self-esteem versus future orientation as mediators. An ethnically diverse sample of 12- to 14-year-olds (N = 862, 54% female, 53% ethnic minority) was assessed longitudinally.…

  10. The Future of Learning Technology: Some Tentative Predictions

    ERIC Educational Resources Information Center

    Rushby, Nick

    2013-01-01

    This paper is a snapshot of an evolving vision of what the future may hold for learning technology. It offers three personal visions of the future and raises many questions that need to be explored if learning technology is to realise its full potential.

  11. Calculating flux to predict future cave radon concentrations.

    PubMed

    Rowberry, Matt D; Martí, Xavi; Frontera, Carlos; Van De Wiel, Marco J; Briestenský, Miloš

    2016-06-01

    Cave radon concentration measurements reflect the outcome of a perpetual competition which pitches flux against ventilation and radioactive decay. The mass balance equations used to model changes in radon concentration through time routinely treat flux as a constant. This mathematical simplification is acceptable as a first order approximation despite the fact that it sidesteps an intrinsic geological problem: the majority of radon entering a cavity is exhaled as a result of advection along crustal discontinuities whose motions are inhomogeneous in both time and space. In this paper the dynamic nature of flux is investigated and the results are used to predict cave radon concentration for successive iterations. The first part of our numerical modelling procedure focuses on calculating cave air flow velocity while the second part isolates flux in a mass balance equation to simulate real time dependence among the variables. It is then possible to use this information to deliver an expression for computing cave radon concentration for successive iterations. The dynamic variables in the numerical model are represented by the outer temperature, the inner temperature, and the radon concentration while the static variables are represented by the radioactive decay constant and a range of parameters related to geometry of the cavity. Input data were recorded at Driny Cave in the Little Carpathians Mountains of western Slovakia. Here the cave passages have developed along splays of the NE-SW striking Smolenice Fault and a series of transverse faults striking NW-SE. Independent experimental observations of fault slip are provided by three permanently installed mechanical extensometers. Our numerical modelling has revealed four important flux anomalies between January 2010 and August 2011. Each of these flux anomalies was preceded by conspicuous fault slip anomalies. The mathematical procedure outlined in this paper will help to improve our understanding of radon migration

  12. Anticipating Their Future: Adolescent Values for the Future Predict Adult Behaviors

    ERIC Educational Resources Information Center

    Finlay, Andrea K.; Wray-Lake, Laura; Warren, Michael; Maggs, Jennifer

    2015-01-01

    Adolescent future values--beliefs about what will matter to them in the future--may shape their adult behavior. Utilizing a national longitudinal British sample, this study examined whether adolescent future values in six domains (i.e., family responsibility, full-time job, personal responsibility, autonomy, civic responsibility, and hedonistic…

  13. 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

  14. 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.

  15. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  16. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    NASA Astrophysics Data System (ADS)

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S.; Shirley, Eric L.; Prendergast, David

    2017-03-01

    Constrained-occupancy delta-self-consistent-field (Δ SCF ) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1 s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The Δ SCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle Δ SCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  17. How many clinic BP readings are needed to predict cardiovascular events as accurately as ambulatory BP monitoring?

    PubMed

    Eguchi, K; Hoshide, S; Shimada, K; Kario, K

    2014-12-01

    We tested the hypothesis that multiple clinic blood pressure (BP) readings over an extended baseline period would be as predictive as ambulatory BP (ABP) for cardiovascular disease (CVD). Clinic and ABP monitoring were performed in 457 hypertensive patients at baseline. Clinic BP was measured monthly and the means of the first 3, 5 and 10 clinic BP readings were taken as the multiple clinic BP readings. The subjects were followed up, and stroke, HARD CVD, and ALL CVD events were determined as outcomes. In multivariate Cox regression analyses, ambulatory systolic BP (SBP) best predicted three outcomes independently of baseline and multiple clinic SBP readings. The mean of 10 clinic SBP readings predicted stroke (hazards ratio (HR)=1.39, 95% confidence interval (CI)=1.02-1.90, P=0.04) and ALL CVD (HR=1.41, 95% CI=1.13-1.74, P=0.002) independently of baseline clinic SBP. Clinic SBPs by three and five readings were not associated with any CVD events, except that clinic SBP by three readings was associated with ALL CVD (P=0.015). Besides ABP values, the mean of the first 10 clinic SBP values was a significant predictor of stroke and ALL CVD events. It is important to take more than several clinic BP readings early after the baseline period for the risk stratification of future CVD events.

  18. Future Weather Forecasting in the Year 2020-Investing in Technology Today: Improving Weather and Environmental Predictions

    NASA Technical Reports Server (NTRS)

    Anthes, Richard; Schoeberl, Mark

    2000-01-01

    Fast-forward twenty years to the nightly simultaneous TV/webcast. Accurate 8-14 day regional forecasts will be available as will be a whole host of linked products including economic impact, travel, energy usage, etc. On-demand, personalized street-level forecasts will be downloaded into your PDA. Your home system will automatically update the products of interest to you (e.g. severe storm forecasts, hurricane predictions, etc). Short and long range climate forecasts will be used by your "Quicken 2020" to make suggest changes in your "futures" investment portfolio. Through a lively and informative multi-media presentation, leading Space-Earth Science Researchers and Technologists will share their vision for the year 2020, offering a possible futuristic forecast enabled through the application of new technologies under development today. Copies of the 'broadcast' will be available on Beta Tape for your own future use. If sufficient interest exists, the program may also be made available for broadcasters wishing to do stand-ups with roll-ins from the San Francisco meeting for their viewers back home.

  19. Introducing a Novel Applicant Ranking Tool to Predict Future Resident Performance: A Pilot Study.

    PubMed

    Bowe, Sarah N; Weitzel, Erik K; Hannah, William N; Fitzgerald, Brian M; Kraus, Gregory P; Nagy, Christopher J; Harrison, Stephen A

    2017-01-01

    The purposes of this study are to (1) introduce our novel Applicant Ranking Tool that aligns with the Accreditation Council for Graduate Medical Education competencies and (2) share our preliminary results comparing applicant rank to current performance. After a thorough literature review and multiple roundtable discussions, an Applicant Ranking Tool was created. Feasibility, satisfaction, and critiques were discussed via open feedback session. Inter-rater reliability was assessed using weighted kappa statistic (κ) and Kendall coefficient of concordance (W). Fisher's exact tests evaluated the ability of the tool to stratify performance into the top or bottom half of their class. Internal medicine and anesthesiology residents served as the pilot cohorts. The tool was considered user-friendly for both data input and analysis. Inter-rater reliability was strongest with intradisciplinary evaluation (W = 0.8-0.975). Resident performance was successfully stratified into those functioning in the upper vs. lower half of their class within the Clinical Anesthesia-3 grouping (p = 0.008). This novel Applicant Ranking Tool lends support for the use of both cognitive and noncognitive traits in predicting resident performance. While the ability of this instrument to accurately predict future resident performance will take years to answer, this pilot study suggests the instrument is worthy of ongoing investigation.

  20. Relative role of parameter vs. climate uncertainty for predictions of future Southeastern U.S. pine carbon cycling

    NASA Astrophysics Data System (ADS)

    Jersild, A.; Thomas, R. Q.; Brooks, E.; Teskey, R. O.; Wynne, R. H.; Arthur, D.; Gonzalez, C.; Thomas, V. A.; Fox, T. D.; Smallman, L.

    2015-12-01

    Predictions of the how forest productivity and carbon sequestration will respond to climate change are essential for assisting land managers in adapting to future climate. However, current predictions can include considerable uncertainty that is often not well quantified. To address the need for better quantification of uncertainty, we calculated and compared parameter and climate prediction uncertainty for predictions of Southeastern U.S. pine forest productivity. We used a Metropolis-Hastings Markov Chain Monte Carlo-based data assimilation technique to fuse regionally widespread and diverse datasets with the Physiological Principles Predicting Growth model (3PG) model. The datasets incorporated include biomass observations from forest research plots that are part of the Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) project, photosynthesis and evaporation observations from loblolly pine Ameriflux sites, and productivity responses to elevated CO2 from the Duke Free Air C site. These spatially and temporally diverse data sets give our unique analysis a more accurately measured uncertainty by constraining complimentary components of the model. In our analysis, parameter uncertainty was quantified using simulations that integrate across the posterior parameter distributions, while climate model uncertainty was quantified using downscaled RCP 8.5 simulations from twenty different CMIP5 climate models. Overall, we found that the uncertainty in future productivity of Southeastern U.S. managed pine forests that was associated with parameterization is comparable to the uncertainty associated with climate simulations. Our results indicate that reducing parameterization in ecosystem model development can improve future predictions of forest productivity and carbon sequestration, but uncertainties in future climate predictions also need to be properly quantified and communicated to forest owners and managers.

  1. Future Time Perspective: Adolescents' Predictions of Their Interpersonal Lives in the Future.

    ERIC Educational Resources Information Center

    Blinn, Lynn M.; Pike, Gary

    1989-01-01

    Investigated adolescent future time perspective in adolescents (N=125) aged 15 to 20 years. Found adolescents did not perceive divorce in their future although periods of singlehood, widowhood, and nuclear family life were perceived as extremely likely, particularly among female adolescents. (Author/ABL)

  2. Visions: Reflections on the Past, Predictions of the Future

    ERIC Educational Resources Information Center

    Connection: The Journal of the New England Board of Higher Education, 2005

    2005-01-01

    To mark New England Board of Higher Education's (NEBHE) 50th anniversary year, "Connection" invited a small group of visionary commentators to submit short "statements" on the future of New England's economic and civic development, tomorrow's technologies and the changing shape of higher education. This article includes the…

  3. 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

    2015-09-03

    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.

  4. Rolling Bearing Life Prediction-Past, Present, and Future

    NASA Technical Reports Server (NTRS)

    Zaretsky, E V; Poplawski, J. V.; Miller, C. R.

    2000-01-01

    Comparisons were made between the life prediction formulas of Lundberg and Palmgren, Ioannides and Harris, and Zaretsky and full-scale ball and roller bearing life data. The effect of Weibull slope on bearing life prediction was determined. Life factors are proposed to adjust the respective life formulas to the normalized statistical life distribution of each bearing type. The Lundberg-Palmgren method resulted in the most conservative life predictions compared to Ioannides and Harris, and Zaretsky methods which produced statistically similar results. Roller profile can have significant effects on bearing life prediction results. Roller edge loading can reduce life by as much as 98 percent. The resultant predicted life not only depends on the life equation used but on the Weibull slope assumed, the least variation occurring with the Zaretsky equation. The load-life exponent p of 10/3 used in the American National Standards Institute (ANSI)/American Bearing Manufacturers Association (ABMA)/International Organization for Standardization (ISO) standards is inconsistent with the majority roller bearings designed and used today.

  5. 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

  6. 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.

  7. Accurate ab initio predictions of ionization energies and heats of formation for the 2-propyl, phenyl, and benzyl radicals

    NASA Astrophysics Data System (ADS)

    Lau, K.-C.; Ng, C. Y.

    2006-01-01

    The ionization energies (IEs) for the 2-propyl (2-C3H7), phenyl (C6H5), and benzyl (C6H5CH2) radicals have been calculated by the wave-function-based ab initio CCSD(T)/CBS approach, which involves the approximation to the complete basis set (CBS) limit at the coupled cluster level with single and double excitations plus quasiperturbative triple excitation [CCSD(T)]. The zero-point vibrational energy correction, the core-valence electronic correction, and the scalar relativistic effect correction have been also made in these calculations. Although a precise IE value for the 2-C3H7 radical has not been directly determined before due to the poor Franck-Condon factor for the photoionization transition at the ionization threshold, the experimental value deduced indirectly using other known energetic data is found to be in good accord with the present CCSD(T)/CBS prediction. The comparison between the predicted value through the focal-point analysis and the highly precise experimental value for the IE(C6H5CH2) determined in the previous pulsed field ionization photoelectron (PFI-PE) study shows that the CCSD(T)/CBS method is capable of providing an accurate IE prediction for C6H5CH2, achieving an error limit of 35 meV. The benchmarking of the CCSD(T)/CBS IE(C6H5CH2) prediction suggests that the CCSD(T)/CBS IE(C6H5) prediction obtained here has a similar accuracy of 35 meV. Taking into account this error limit for the CCSD(T)/CBS prediction and the experimental uncertainty, the CCSD(T)/CBS IE(C6H5) value is also consistent with the IE(C6H5) reported in the previous HeI photoelectron measurement. Furthermore, the present study provides support for the conclusion that the CCSD(T)/CBS approach with high-level energy corrections can be used to provide reliable IE predictions for C3-C7 hydrocarbon radicals with an uncertainty of +/-35 meV. Employing the atomization scheme, we have also computed the 0 K (298 K) heats of formation in kJ/mol at the CCSD(T)/CBS level for 2-C3H7

  8. 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.

  9. Adolescent Suicide Attempters: What Predicts Future Suicidal Acts?

    ERIC Educational Resources Information Center

    Groholt, Berit; Ekeberg, Oivind; Haldorsen, Tor

    2006-01-01

    Predictors for repetition of suicide attempts were evaluated among 92 adolescent suicide attempters 9 years after an index suicide attempt (90% females). Five were dead, two by suicide. Thirty-one (42%) of 73 had repeated a suicide attempt. In multiple Cox regression analysis, four factors had an independent predictive effect: comorbid disorders,…

  10. Predicting the Future: Studies on the Growth of the Intellect.

    ERIC Educational Resources Information Center

    Educational Testing Service, Princeton, NJ.

    This illustrated booklet describes research procedures in the Infant Laboratory of the Educational Testing Service to investigate measurable factors in infant behavior which can predict intellectual potential. The research is currently focusing on attending, the manner in which infants respond to various stimuli presented to them during their…

  11. Predicting future coexistence in a North American ant community

    PubMed Central

    Bewick, Sharon; Stuble, Katharine L; Lessard, Jean-Phillipe; Dunn, Robert R; Adler, Frederick R; Sanders, Nathan J

    2014-01-01

    Global climate change will remodel ecological communities worldwide. However, as a consequence of biotic interactions, communities may respond to climate change in idiosyncratic ways. This makes predictive models that incorporate biotic interactions necessary. We show how such models can be constructed based on empirical studies in combination with predictions or assumptions regarding the abiotic consequences of climate change. Specifically, we consider a well-studied ant community in North America. First, we use historical data to parameterize a basic model for species coexistence. Using this model, we determine the importance of various factors, including thermal niches, food discovery rates, and food removal rates, to historical species coexistence. We then extend the model to predict how the community will restructure in response to several climate-related changes, such as increased temperature, shifts in species phenology, and altered resource availability. Interestingly, our mechanistic model suggests that increased temperature and shifts in species phenology can have contrasting effects. Nevertheless, for almost all scenarios considered, we find that the most subordinate ant species suffers most as a result of climate change. More generally, our analysis shows that community composition can respond to climate warming in nonintuitive ways. For example, in the context of a community, it is not necessarily the most heat-sensitive species that are most at risk. Our results demonstrate how models that account for niche partitioning and interspecific trade-offs among species can be used to predict the likely idiosyncratic responses of local communities to climate change. PMID:24963378

  12. Prediction of Research Self-Efficacy and Future Research Involvement.

    ERIC Educational Resources Information Center

    Bishop, Rosean M.; And Others

    Although graduate programs hope that their students will be committed to research in their careers, most students express ambivalence towards research. Identifying the variables that predict involvement in research thus seems crucial. In this study 136 doctoral students from a wide range of disciplines completed the Research Self-Efficacy Scale…

  13. Identifying Future Scientists: Predicting Persistence into Research Training

    ERIC Educational Resources Information Center

    McGee, Richard; Keller, Jill L.

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end,…

  14. Dual X-ray absorptiometry accurately predicts carcass composition from live sheep and chemical composition of live and dead sheep.

    PubMed

    Pearce, K L; Ferguson, M; Gardner, G; Smith, N; Greef, J; Pethick, D W

    2009-01-01

    Fifty merino wethers (liveweight range from 44 to 81kg, average of 58.6kg) were lot fed for 42d and scanned through a dual X-ray absorptiometry (DXA) as both a live animal and whole carcass (carcass weight range from 15 to 32kg, average of 22.9kg) producing measures of total tissue, lean, fat and bone content. The carcasses were subsequently boned out into saleable cuts and the weights and yield of boned out muscle, fat and bone recorded. The relationship between chemical lean (protein+water) was highly correlated with DXA carcass lean (r(2)=0.90, RSD=0.674kg) and moderately with DXA live lean (r(2)=0.72, RSD=1.05kg). The relationship between the chemical fat was moderately correlated with DXA carcass fat (r(2)=0.86, RSD=0.42kg) and DXA live fat (r(2)=0.70, RSD=0.71kg). DXA carcass and live animal bone was not well correlated with chemical ash (both r(2)=0.38, RSD=0.3). DXA carcass lean was moderately well predicted from DXA live lean with the inclusion of bodyweight in the regression (r(2)=0.82, RSD=0.87kg). DXA carcass fat was well predicted from DXA live fat (r(2)=0.86, RSD=0.54kg). DXA carcass lean and DXA carcass fat with the inclusion of carcass weight in the regression significantly predicted boned out muscle (r(2)=0.97, RSD=0.32kg) and fat weight, respectively (r(2)=0.92, RSD=0.34kg). The use of DXA live lean and DXA live fat with the inclusion of bodyweight to predict boned out muscle (r(2)=0.83, RSD=0.75kg) and fat (r(2)=0.86, RSD=0.46kg) weight, respectively, was moderate. The use of DXA carcass and live lean and fat to predict boned out muscle and fat yield was not correlated as weight. The future for the DXA will exist in the determination of body composition in live animals and carcasses in research experiments but there is potential for the DXA to be used as an online carcass grading system.

  15. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

    PubMed

    Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

    2017-03-31

    One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

  16. Predicting Violence Among Forensic-Correctional Populations: The Past 2 Decades of Advancements and Future Endeavors

    ERIC Educational Resources Information Center

    Loza, Wagdy; Dhaliwal, Gurmeet K.

    2005-01-01

    Research on violence prediction during the past 2 decades has evolved appreciably in terms of depicting determinants of violence and developing psychometrically sound actuarial measures to predict the probability of future violent behavior. This article provides a brief synopsis of information on predicting violence gained in the past 2 decades,…

  17. 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

  18. Predictive, preventive, personalized and participatory medicine: back to the future

    PubMed Central

    2010-01-01

    The pioneering work of Jean Dausset on the HLA system established several principles that were later reflected in the Human Genome Project and contributed to the foundations of predictive, preventive, personalized and participatory (P4) medicine. To effectively develop systems medicine, we should take advantage of the lessons of the HLA saga, emphasizing the importance of exploring a fascinating but mysterious biology, now using systems principles, pioneering new technology developments and creating shared biological and information resources. PMID:20804580

  19. 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.

  20. Foreshocks Are Not Predictive of Future Earthquake Size

    NASA Astrophysics Data System (ADS)

    Page, M. T.; Felzer, K. R.; Michael, A. J.

    2014-12-01

    The standard model for the origin of foreshocks is that they are earthquakes that trigger aftershocks larger than themselves (Reasenberg and Jones, 1989). This can be formally expressed in terms of a cascade model. In this model, aftershock magnitudes follow the Gutenberg-Richter magnitude-frequency distribution, regardless of the size of the triggering earthquake, and aftershock timing and productivity follow Omori-Utsu scaling. An alternative hypothesis is that foreshocks are triggered incidentally by a nucleation process, such as pre-slip, that scales with mainshock size. If this were the case, foreshocks would potentially have predictive power of the mainshock magnitude. A number of predictions can be made from the cascade model, including the fraction of earthquakes that are foreshocks to larger events, the distribution of differences between foreshock and mainshock magnitudes, and the distribution of time lags between foreshocks and mainshocks. The last should follow the inverse Omori law, which will cause the appearance of an accelerating seismicity rate if multiple foreshock sequences are stacked (Helmstetter and Sornette, 2003). All of these predictions are consistent with observations (Helmstetter and Sornette, 2003; Felzer et al. 2004). If foreshocks were to scale with mainshock size, this would be strong evidence against the cascade model. Recently, Bouchon et al. (2013) claimed that the expected acceleration in stacked foreshock sequences before interplate earthquakes is higher prior to M≥6.5 mainshocks than smaller mainshocks. Our re-analysis fails to support the statistical significance of their results. In particular, we find that their catalogs are not complete to the level assumed, and their ETAS model underestimates inverse Omori behavior. To conclude, seismicity data to date is consistent with the hypothesis that the nucleation process is the same for earthquakes of all sizes.

  1. 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.)

  2. Digitization and its discontents: future shock in predictive oncology.

    PubMed

    Epstein, Richard J

    2010-02-01

    Clinical cancer care is being transformed by a high-technology informatics revolution fought out between the forces of personalized (biomarker-guided) and depersonalized (bureaucracy-controlled) medicine. Factors triggering this conflict include the online proliferation of treatment algorithms, rising prices of biological drug therapies, increasing sophistication of genomic-based predictive tools, and the growing entrepreneurialism of offshore treatment facilities. The resulting Napster-like forces unleashed within the oncology marketplace will deliver incremental improvements in cost-efficacy to global healthcare consumers. There will also be a price to pay, however, as the rising wave of digitization encourages third-party payers to make more use of biomarkers for tightening reimbursement criteria. Hence, as in other digitally transformed industries, a new paradigm of professional service delivery-less centered on doctor-patient relationships than in the past, and more dependent on pricing and marketing for standardized biomarker-defined indications-seems set to emerge as the unpredicted deliverable from this brave new world of predictive oncology.

  3. Working Memory-Related Neural Activity Predicts Future Smoking Relapse

    PubMed Central

    Loughead, James; Wileyto, E Paul; Ruparel, Kosha; Falcone, Mary; Hopson, Ryan; Gur, Ruben; Lerman, Caryn

    2015-01-01

    Brief abstinence from smoking impairs cognition, particularly executive function, and this has a role in relapse to smoking. This study examined whether working memory-related brain activity predicts subsequent smoking relapse above and beyond standard clinical and behavioral measures. Eighty treatment-seeking smokers completed two functional magnetic resonance imaging sessions (smoking satiety vs 24 h abstinence challenge) during performance of a visual N-back task. Brief counseling and a short-term quit attempt followed. Relapse during the first 7 days was biochemically confirmed by the presence of the nicotine metabolite cotinine. Mean percent blood oxygen level-dependent (BOLD) signal change was extracted from a priori regions of interest: bilateral dorsolateral prefrontal cortex (DLPFC), medial frontal/cingulate gyrus, posterior cingulate cortex (PCC), and ventromedial prefrontal cortex. Signal from these brain regions and additional clinical measures were used to model outcome status, which was then validated with resampling techniques. Relapse to smoking was predicted by increased withdrawal symptoms, decreased left DLPFC and increased PCC BOLD percent signal change (abstinence vs smoking satiety). Receiver operating characteristic analysis demonstrated 81% area under the curve using these predictors, a significant improvement over the model with clinical variables only. The combination of abstinence-induced decreases in left DLPFC activation and reduced suppression of PCC may be a prognostic marker for poor outcome, specifically early smoking relapse. PMID:25469682

  4. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    PubMed

    Holmlund, A; Lampa, E; Lind, L

    2017-03-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P < 0.001). When adjusting for calendar time, age, sex, educational level, smoking, and baseline values for bleeding on probing, probing pocket depth >4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  5. Can Global Weed Assemblages Be Used to Predict Future Weeds?

    PubMed Central

    Morin, Louise; Paini, Dean R.; Randall, Roderick P.

    2013-01-01

    Predicting which plant taxa are more likely to become weeds in a region presents significant challenges to both researchers and government agencies. Often it is done in a qualitative or semi-quantitative way. In this study, we explored the potential of using the quantitative self-organising map (SOM) approach to analyse global weed assemblages and estimate likelihoods of plant taxa becoming weeds before and after they have been moved to a new region. The SOM approach examines plant taxa associations by analysing where a taxon is recorded as a weed and what other taxa are recorded as weeds in those regions. The dataset analysed was extracted from a pre-existing, extensive worldwide database of plant taxa recorded as weeds or other related status and, following reformatting, included 187 regions and 6690 plant taxa. To assess the value of the SOM approach we selected Australia as a case study. We found that the key and most important limitation in using such analytical approach lies with the dataset used. The classification of a taxon as a weed in the literature is not often based on actual data that document the economic, environmental and/or social impact of the taxon, but mostly based on human perceptions that the taxon is troublesome or simply not wanted in a particular situation. The adoption of consistent and objective criteria that incorporate a standardized approach for impact assessment of plant taxa will be necessary to develop a new global database suitable to make predictions regarding weediness using methods like SOM. It may however, be more realistic to opt for a classification system that focuses on the invasive characteristics of plant taxa without any inference to impacts, which to be defined would require some level of research to avoid bias from human perceptions and value systems. PMID:23393591

  6. Predicting Next Year's Resources--Short-Term Enrollment Forecasting for Accurate Budget Planning. AIR Forum Paper 1978.

    ERIC Educational Resources Information Center

    Salley, Charles D.

    Accurate enrollment forecasts are a prerequisite for reliable budget projections. This is because tuition payments make up a significant portion of a university's revenue, and anticipated revenue is the immediate constraint on current operating expenditures. Accurate forecasts are even more critical to revenue projections when a university's…

  7. Prediction of conversion to psychosis: review and future directions

    PubMed Central

    Gee, Dylan G.; Cannon, Tyrone D.

    2014-01-01

    This article reviews recent findings on predictors of conversion to psychosis among youth deemed at ultra high risk (UHR) based on the presence of subpsychotic-intensity symptoms or genetic risk for psychosis and a recent decline in functioning. Although transition rates differ between studies, the most well powered studies have observed rates of conversion to full psychosis in the 30–40% range over 2–3 years of follow-up. Across studies, severity of subthreshold positive symptoms, poorer social functioning, and genetic risk for schizophrenia appear to be consistent predictors of conversion to psychosis, with algorithms combining these indicators achieving positive predictive power ≥ 80%. Nevertheless, a substantial fraction of UHR cases do not convert to psychosis. Recent work indicates that UHR cases who present with lower levels of negative symptoms and higher levels of social functioning are more likely to recover symptomatically and no longer meet criteria for an at-risk mental state. In general, it appears that about 1/3 of UHR cases convert to psychosis, about 1/3 do not convert but remain symptomatic and functionally impaired, and about 1/3 recover symptomatically and functionally. Continued efforts to detect early risk for psychosis are critical for informing early intervention and provide increasing promise of delaying or even preventing the onset of psychosis. PMID:22286564

  8. Selenium deficiency risk predicted to increase under future climate change.

    PubMed

    Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

    2017-03-14

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

  9. Selenium deficiency risk predicted to increase under future climate change

    PubMed Central

    Jones, Gerrad D.; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P.; Seneviratne, Sonia I.; Smith, Pete; Winkel, Lenny H. E.

    2017-01-01

    Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980–1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate–soil interactions. Using moderate climate-change scenarios for 2080–2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate–soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change. PMID:28223487

  10. Intelligent robot trends and predictions for the .net future

    NASA Astrophysics Data System (ADS)

    Hall, Ernest L.

    2001-10-01

    An intelligent robot is a remarkably useful combination of a manipulator, sensors and controls. The use of these machines in factory automation can improve productivity, increase product quality and improve competitiveness. This paper presents a discussion of recent and future technical and economic trends. During the past twenty years the use of industrial robots that are equipped not only with precise motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. Intelligent robot products have been developed in many cases for factory automation and for some hospital and home applications. To reach an even higher degree of applications, the addition of learning may be required. Recently, learning theories such as the adaptive critic have been proposed. In this type of learning, a critic provides a grade to the controller of an action module such as a robot. The adaptive critic is a good model for human learning. In general, the critic may be considered to be the human with the teach pendant, plant manager, line supervisor, quality inspector or the consumer. If the ultimate critic is the consumer, then the quality inspector must model the consumer's decision-making process and use this model in the design and manufacturing operations. Can the adaptive critic be used to advance intelligent robots? Intelligent robots have historically taken decades to be developed and reduced to practice. Methods for speeding this development include technology such as rapid prototyping and product development and government, industry and university cooperation.

  11. Predictive animal models of mania: hits, misses and future directions

    PubMed Central

    Young, Jared W; Henry, Brook L; Geyer, Mark A

    2011-01-01

    Mania has long been recognized as aberrant behaviour indicative of mental illness. Manic states include a variety of complex and multifaceted symptoms that challenge clear clinical distinctions. Symptoms include over-activity, hypersexuality, irritability and reduced need for sleep, with cognitive deficits recently linked to functional outcome. Current treatments have arisen through serendipity or from other disorders. Hence, treatments are not efficacious for all patients, and there is an urgent need to develop targeted therapeutics. Part of the drug discovery process is the assessment of therapeutics in animal models. Here we review pharmacological, environmental and genetic manipulations developed to test the efficacy of therapeutics in animal models of mania. The merits of these models are discussed in terms of the manipulation used and the facet of mania measured. Moreover, the predictive validity of these models is discussed in the context of differentiating drugs that succeed or fail to meet criteria as approved mania treatments. The multifaceted symptomatology of mania has not been reflected in the majority of animal models, where locomotor activity remains the primary measure. This approach has resulted in numerous false positives for putative treatments. Recent work highlights the need to utilize multivariate strategies to enable comprehensive assessment of affective and cognitive dysfunction. Advances in therapeutic treatment may depend on novel models developed with an integrated approach that includes: (i) a comprehensive battery of tests for different aspects of mania, (ii) utilization of genetic information to establish aetiological validity and (iii) objective quantification of patient behaviour with translational cross-species paradigms. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21410454

  12. Does presentation at the Registrars' Papers Day predict future publication?

    PubMed

    Wong, Shing W; Crowe, Philip J

    2006-06-01

    There are research requirements for trainees to be eligible to present for their final examinations (Fellowship of Royal Australasian College of Surgeons, FRACS). One option is the presentation of a paper or poster at a meeting of which abstracts are subject to review and selection. This includes presentation at the annual Registrars' Papers Day (RPD) in New South Wales. There has been some debate surrounding whether research requirements are fulfilled by presentation at such meetings. Publication in a peer-reviewed journal should be the ultimate aim. A high publication rate will validate the quality of the meeting. All abstracts submitted to the RPD in 1998 and 1999 were analysed. A Medline search was performed in 2005 to identify publication of these presentations in a peer-reviewed journal. Variables of the study that were potentially predictive of subsequent publication were analysed. This included type of presentation, surgical specialty, clinical or laboratory-based study, study design (prospective or retrospective) and sample size. Chi-squared test with Yates' continuity correction was used to compare two independent proportions and significance was set at P < 0.05. The publication rates were: oral presentations 50% (17/34), poster presentations 39% (9/23) and rejected presentations 20% (2/10). The mean and median time to publication was 23.8 and 21.0 months. Prospective design was the only variable identified to have a statistically significant effect on the publication rate (P < 0.002). The most common publishing journal was the Australian and New Zealand Journal of Surgery (12 of 26). Overall consistency (author and study sample consistency) from presentation to publication was 32%. The overall 46% publication rate of this state-based trainees-organized meeting compares favourably with international meetings. The research requirement of the Royal Australasian College of Surgeons (RACS), which includes presentation at the RPD in New South Wales, produces

  13. Callous-Unemotional Traits Robustly Predict Future Criminal Offending in Young Men

    PubMed Central

    Kahn, Rachel E.; Byrd, Amy L.; Pardini, Dustin A.

    2013-01-01

    Callous-unemotional (CU) traits (e.g., lack of empathy, deficient guilt/remorse, and shallow affect) are a circumscribed facet of the adult psychopathic personality. Although several studies have found that adult psychopathy is a robust predictor of future criminal offending, research exploring the predictive utility of CU traits and future offending are lacking. Moreover, empirical studies examining the predictive utility of psychopathic features often neglect to account for other well-documented risk factors (e.g., prior offending, delinquent peers, marital status), and thus the incremental predictive utility of CU traits remains uncertain. To address these limitations, the current study examined the unique contribution of CU traits in the prediction of future criminal offending in a large ethnically diverse community sample of young adult males (Mean Age = 25.76, SD = .95). Official criminal record information was collected approximately 3.5 years later using multiple sources. Results indicated that after controlling for several other well-established predictors of future offending, men with elevated CU traits had a greater number of arrests and criminal charges and were more likely to be charged with a serious offense and obstruction of justice. CU traits also predicted future theft for Caucasian men, but not African American men. Overall, the results support the notion that CU traits significantly add to the prediction of future offending, even after controlling for several other risk factors. PMID:22731505

  14. THE FUTURE OF COMPUTER-BASED TOXICITY PREDICTION: MECHANISM-BASED MODELS VS. INFORMATION MINING APPROACHES

    EPA Science Inventory


    The Future of Computer-Based Toxicity Prediction:
    Mechanism-Based Models vs. Information Mining Approaches

    When we speak of computer-based toxicity prediction, we are generally referring to a broad array of approaches which rely primarily upon chemical structure ...

  15. Population Synthesis in the Blue. IV. Accurate Model Predictions for Lick Indices and UBV Colors in Single Stellar Populations

    NASA Astrophysics Data System (ADS)

    Schiavon, Ricardo P.

    2007-07-01

    We present a new set of model predictions for 16 Lick absorption line indices from Hδ through Fe5335 and UBV colors for single stellar populations with ages ranging between 1 and 15 Gyr, [Fe/H] ranging from -1.3 to +0.3, and variable abundance ratios. The models are based on accurate stellar parameters for the Jones library stars and a new set of fitting functions describing the behavior of line indices as a function of effective temperature, surface gravity, and iron abundance. The abundances of several key elements in the library stars have been obtained from the literature in order to characterize the abundance pattern of the stellar library, thus allowing us to produce model predictions for any set of abundance ratios desired. We develop a method to estimate mean ages and abundances of iron, carbon, nitrogen, magnesium, and calcium that explores the sensitivity of the various indices modeled to those parameters. The models are compared to high-S/N data for Galactic clusters spanning the range of ages, metallicities, and abundance patterns of interest. Essentially all line indices are matched when the known cluster parameters are adopted as input. Comparing the models to high-quality data for galaxies in the nearby universe, we reproduce previous results regarding the enhancement of light elements and the spread in the mean luminosity-weighted ages of early-type galaxies. When the results from the analysis of blue and red indices are contrasted, we find good consistency in the [Fe/H] that is inferred from different Fe indices. Applying our method to estimate mean ages and abundances from stacked SDSS spectra of early-type galaxies brighter than L*, we find mean luminosity-weighed ages of the order of ~8 Gyr and iron abundances slightly below solar. Abundance ratios, [X/Fe], tend to be higher than solar and are positively correlated with galaxy luminosity. Of all elements, nitrogen is the more strongly correlated with galaxy luminosity, which seems to indicate

  16. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    ERIC Educational Resources Information Center

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  17. Predicting violent behavior: The role of violence exposure and future educational aspirations during adolescence.

    PubMed

    Stoddard, Sarah A; Heinze, Justin E; Choe, Daniel Ewon; Zimmerman, Marc A

    2015-10-01

    Few researchers have explored future educational aspirations as a promotive factor against exposure to community violence in relation to adolescents' violent behavior over time. The present study examined the direct and indirect effect of exposure to community violence prior to 9th grade on attitudes about violence and violent behavior in 12th grade, and violent behavior at age 22 via 9th grade future educational aspirations in a sample of urban African American youth (n = 681; 49% male). Multi-group SEM was used to test the moderating effect of gender. Exposure to violence was associated with lower future educational aspirations. For boys, attitudes about violence directly predicted violent behavior at age 22. For boys, future educational aspirations indirectly predicted less violent behavior at age 22. Implications of the findings and suggestions for future research are discussed.

  18. Predicting violent behavior: The role of violence exposure and future educational aspirations during adolescence

    PubMed Central

    Stoddard, Sarah A.; Heinze, Justin E.; Choe, Daniel Ewon; Zimmerman, Marc A.

    2015-01-01

    Few researchers have explored future educational aspirations as a promotive factor against exposure to community violence in relation to adolescents’ violent behavior over time. The present study examined the direct and indirect effect of exposure to community violence prior to 9th grade on attitudes about violence and violent behavior in 12th grade, and violent behavior at age 22 via 9th grade future educational aspirations in a sample of urban African American youth (n = 681; 49% male). Multi-group SEM was used to test the moderating effect of gender. Exposure to violence was associated with lower future educational aspirations. For boys, attitudes about violence directly predicted violent behavior at age 22. For boys, future educational aspirations indirectly predicted less violent behavior at age 22. Implications of the findings and suggestions for future research are discussed. PMID:26282242

  19. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future.

    PubMed

    Schrodi, Steven J; Mukherjee, Shubhabrata; Shan, Ying; Tromp, Gerard; Sninsky, John J; Callear, Amy P; Carter, Tonia C; Ye, Zhan; Haines, Jonathan L; Brilliant, Murray H; Crane, Paul K; Smelser, Diane T; Elston, Robert C; Weeks, Daniel E

    2014-01-01

    Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications.

  20. FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment.

    PubMed

    Chételat, Gaël; Eustache, Francis; Viader, Fausto; De La Sayette, Vincent; Pélerin, Alice; Mézenge, Florence; Hannequin, Didier; Dupuy, Benoît; Baron, Jean-Claude; Desgranges, Béatrice

    2005-02-01

    The accurate prediction, at a pre-dementia stage of Alzheimer's disease (AD), of the subsequent clinical evolution of patients would be a major breakthrough from both therapeutic and research standpoints. Amnestic mild cognitive impairment (MCI) is presently the most common reference to address the pre-dementia stage of AD. However, previous longitudinal studies on patients with MCI assessing neuropsychological and PET markers of future conversion to AD are sparse and yield discrepant findings, while a comprehensive comparison of the relative accuracy of these two categories of measure is still lacking. In the present study, we assessed the global cognitive decline as measured by the Mattis scale in 18 patients with amnestic MCI over an 18-month follow-up period, studying which subtest of this scale showed significant deterioration over time. Using baseline measurements from neuropsychological evaluation of memory and PET, we then assessed significant markers of global cognitive change, that is, percent annual change in the Mattis scale total score, and searched for the best predictor of this global cognitive decline. Altogether, our results revealed significant decline over the 18-month follow-up period in the total score and the verbal initiation and memory-recall subscores of the Mattis scale. The percent annual change in the total Mattis score significantly correlated with age and baseline performances in delayed episodic memory recall as well as semantic autobiographical and category word fluencies. Regarding functional imaging, significant correlations were also found with baseline PET values in the right temporo-parietal and medial frontal areas. Age and right temporo-parietal PET values were the most significant predictors of subsequent global cognitive decline, and the only ones to survive stepwise regression analyses. Our findings are consistent with previous works showing predominant delayed recall and semantic memory impairment at a pre-dementia stage

  1. Cosmological constraints from the CFHTLenS shear measurements using a new, accurate, and flexible way of predicting non-linear mass clustering

    NASA Astrophysics Data System (ADS)

    Angulo, Raul E.; Hilbert, Stefan

    2015-03-01

    We explore the cosmological constraints from cosmic shear using a new way of modelling the non-linear matter correlation functions. The new formalism extends the method of Angulo & White, which manipulates outputs of N-body simulations to represent the 3D non-linear mass distribution in different cosmological scenarios. We show that predictions from our approach for shear two-point correlations at 1-300 arcmin separations are accurate at the ˜10 per cent level, even for extreme changes in cosmology. For moderate changes, with target cosmologies similar to that preferred by analyses of recent Planck data, the accuracy is close to ˜5 per cent. We combine this approach with a Monte Carlo Markov chain sampler to explore constraints on a Λ cold dark matter model from the shear correlation functions measured in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). We obtain constraints on the parameter combination σ8(Ωm/0.27)0.6 = 0.801 ± 0.028. Combined with results from cosmic microwave background data, we obtain marginalized constraints on σ8 = 0.81 ± 0.01 and Ωm = 0.29 ± 0.01. These results are statistically compatible with previous analyses, which supports the validity of our approach. We discuss the advantages of our method and the potential it offers, including a path to model in detail (i) the effects of baryons, (ii) high-order shear correlation functions, and (iii) galaxy-galaxy lensing, among others, in future high-precision cosmological analyses.

  2. Predicting future changes in Muskegon River Watershed game fish distributions under future land cover alteration and climate change scenarios

    USGS Publications Warehouse

    Steen, Paul J.; Wiley, Michael J.; Schaeffer, Jeffrey S.

    2010-01-01

    Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool- and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n-dimensional niches for particular species.

  3. Lessons Learned and Future Goals of the High Lift Prediction Workshops

    NASA Technical Reports Server (NTRS)

    Rumsey, Christopher L.; Lee-Rausch, Elizabeth; Slotnick, Jeffrey P.

    2016-01-01

    The American Institute of Aeronautics and Astronautics (AIAA) High Lift Prediction Workshop series is described. Two workshops have been held to date. Major conclusions are summarized, and plans for future workshops are outlined. A compilation of lessons learned from the first two workshops is provided. This compilation includes a summary of needs for future high-lift experiments that are intended for computational fluid dynamics (CFD) validation.

  4. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    PubMed

    Li, Liqi; Cui, Xiang; 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.

  5. Accurate and computationally efficient prediction of thermochemical properties of biomolecules using the generalized connectivity-based hierarchy.

    PubMed

    Sengupta, Arkajyoti; Ramabhadran, Raghunath O; Raghavachari, Krishnan

    2014-08-14

    In this study we have used the connectivity-based hierarchy (CBH) method to derive accurate heats of formation of a range of biomolecules, 18 amino acids and 10 barbituric acid/uracil derivatives. The hierarchy is based on the connectivity of the different atoms in a large molecule. It results in error-cancellation reaction schemes that are automated, general, and can be readily used for a broad range of organic molecules and biomolecules. Herein, we first locate stable conformational and tautomeric forms of these biomolecules using an accurate level of theory (viz. CCSD(T)/6-311++G(3df,2p)). Subsequently, the heats of formation of the amino acids are evaluated using the CBH-1 and CBH-2 schemes and routinely employed density functionals or wave function-based methods. The calculated heats of formation obtained herein using modest levels of theory and are in very good agreement with those obtained using more expensive W1-F12 and W2-F12 methods on amino acids and G3 results on barbituric acid derivatives. Overall, the present study (a) highlights the small effect of including multiple conformers in determining the heats of formation of biomolecules and (b) in concurrence with previous CBH studies, proves that use of the more effective error-cancelling isoatomic scheme (CBH-2) results in more accurate heats of formation with modestly sized basis sets along with common density functionals or wave function-based methods.

  6. Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

    PubMed Central

    Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

    2015-01-01

    Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

  7. Temporal effects in trend prediction: identifying the most popular nodes in the future.

    PubMed

    Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

    2015-01-01

    Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

  8. Can the conventional sextant prostate biopsy accurately predict unilateral prostate cancer in low-risk, localized, prostate cancer?

    PubMed

    Mayes, Janice M; Mouraviev, Vladimir; Sun, Leon; Tsivian, Matvey; Madden, John F; Polascik, Thomas J

    2011-01-01

    We evaluate the reliability of routine sextant prostate biopsy to detect unilateral lesions. A total of 365 men with complete records including all clinical and pathologic variables who underwent a preoperative sextant biopsy and subsequent radical prostatectomy (RP) for clinically localized prostate cancer at our medical center between January 1996 and December 2006 were identified. When the sextant biopsy detects unilateral disease, according to RP results, the NPV is high (91%) with a low false negative rate (9%). However, the sextant biopsy has a PPV of 28% with a high false positive rate (72%). Therefore, a routine sextant prostate biopsy cannot provide reliable, accurate information about the unilaterality of tumor lesion(s).

  9. Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation.

    PubMed

    Lambert, Emily; Pierce, Graham J; Hall, Karen; Brereton, Tom; Dunn, Timothy E; Wall, Dave; Jepson, Paul D; Deaville, Rob; MacLeod, Colin D

    2014-06-01

    There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio-climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs "hindcasting" of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species-specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white-beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time-scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies.

  10. Validation of Afterbody Aeroheating Predictions for Planetary Probes: Status and Future Work

    NASA Technical Reports Server (NTRS)

    Wright, Michael J.; Brown, James L.; Sinha, Krishnendu; Candler, Graham V.; Milos, Frank S.; Prabhu, DInesh K.

    2005-01-01

    A review of the relevant flight conditions and physical models for planetary probe afterbody aeroheating calculations is given. Readily available sources of afterbody flight data and published attempts to computationally simulate those flights are summarized. A current status of the application of turbulence models to afterbody flows is presented. Finally, recommendations for additional analysis and testing that would reduce our uncertainties in our ability to accurately predict base heating levels are given.

  11. The UT 7/8 February 2013 Sila-Nunam Mutual Event and Future Predictions

    NASA Technical Reports Server (NTRS)

    Benecchi, S. D.; Noll, K. S.; Thirouin, A.; Ryan, E.; Grundy, W. M.; Verbiscer, A.; Doressoundiram, A.; Hestroffer, D.; Beaton, R.; Rabinowitz, D.; Chanover, N.

    2013-01-01

    A superior mutual event of the Kuiper Belt binary system (79360) Sila-Nunam was observed over 15.47 h on UT 7/8 February 2013 by a coordinated effort at four different telescope facilities; it started approximately 1.5 h earlier than anticipated, the duration was approximately 9.5 h (about 10% longer than predicted), and was slightly less deep than predicted. It is the first full event observed for a comparably sized binary Kuiper Belt object. We provide predictions for future events refined by this and other partial mutual event observations obtained since the mutual event season began.

  12. It is hard to predict the future: the evolving nature of threats and vulnerabilities.

    PubMed

    Ackerman, G A

    2006-04-01

    This paper describes the evolving nature of threats and vulnerabilities associated with biological disasters with animal origins, and introduces some of the pitfalls and opportunities associated with anticipating future threats. Evolving threats and vulnerabilities include continued deforestation and encroachment on virgin habitats, the effects of globalisation on trade and transportation, the increased interdependence and social vulnerability of modern society, the commingling of intensive agriculture and traditional farming methods, the periodic appearance of pandemics and epizootics, and indications that numerous human actors are displaying an increasing interest in and capability of using biological agents as weapons. These developments must be viewed in the context of various impediments to accurately gauging future threats, such as the appearance of new elements that depart from current trends and the inherent difficulty in anticipating human, and especially terrorist, behaviour. The paper concludes with some broad recommendations for structuring a policy response to the threat in an environment of uncertainty about the future.

  13. Predicting Future Suicide Attempts Among Adolescent and Emerging Adult Psychiatric Emergency Patients.

    PubMed

    Horwitz, Adam G; Czyz, Ewa K; King, Cheryl A

    2015-01-01

    The purpose of this study was to longitudinally examine specific characteristics of suicidal ideation in combination with histories of suicide attempts and non-suicidal self-injury (NSSI) to best evaluate risk for a future attempt among high-risk adolescents and emerging adults. Participants in this retrospective medical record review study were 473 (53% female; 69% Caucasian) consecutive patients, ages 15 to 24 years (M=19.4 years) who presented for psychiatric emergency services during a 9-month period. These patients' medical records, including a clinician-administered Columbia-Suicide Severity Rating Scale, were coded at the index visit and at future visits occurring within the next 18 months. Logistic regression models were used to predict suicide attempts during this period. Socioeconomic status, suicidal ideation severity (i.e., intent, method), suicidal ideation intensity (i.e., frequency, controllability), a lifetime history of suicide attempt, and a lifetime history of NSSI were significant independent predictors of a future suicide attempt. Suicidal ideation added incremental validity to the prediction of future suicide attempts above and beyond the influence of a past suicide attempt, whereas a lifetime history of NSSI did not. Sex moderated the relationship between the duration of suicidal thoughts and future attempts (predictive for male patients but not female). Results suggest value in incorporating both past behaviors and current thoughts into suicide risk formulation. Furthermore, suicidal ideation duration warrants additional examination as a potential critical factor for screening assessments evaluating suicide risk among high-risk samples, particularly for male patients.

  14. Time Critical Targeting: Predictive Vs Reactionary Methods An Analysis For The Future

    DTIC Science & Technology

    2002-06-01

    Chapter 5 Results & Conclusions Having investigated the different methods and techniques that can be used for time critical targeting in the......Targeting: Predictive Vs Reactionary Methods An Analysis For The Future 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

  15. Twenty Predictions about the Future of Residential Services in Mental Retardation

    ERIC Educational Resources Information Center

    Wolfensberger, Wolf

    2011-01-01

    Twenty predictions about the future of residential services to the mentally retarded are presented. These changes imply: (1) an entirely new model of residential services; (2) increasing continuity between residential and nonresidential services; and (3) increasing acceptance of cost-benefit rationales in the decision to offer residential or other…

  16. Ability of Early Literacy Measures to Predict Future State Assessment Performance

    ERIC Educational Resources Information Center

    Utchell, Lynn A.; Schmitt, Ara J.; McCallum, Elizabeth; McGoey, Kara E.; Piselli, Kate

    2016-01-01

    The purpose of this study was to determine the extent to which early literacy measures administered in kindergarten and Oral Reading Fluency (ORF) measures administered in Grade 1 are related to and predict future state reading assessment performances up to 7 years later. Results indicated that early literacy and ORF performances were…

  17. FORUM - FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

    EPA Science Inventory

    FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo resp...

  18. Prediction of Future Observations in Polynomial Growth Curve Models. Part 1.

    DTIC Science & Technology

    1983-03-01

    UNIT NUMBERS University of Pittsburgh, Ninth Floor, PE6llO2F; 2304/A5 Schenley Hall, Pittsburgh PA 15260 It CONTROLLING OFFICE NAME AND ADDRESS 12...8217. DSIM Enitvd, ’ SR-TR. 8 3 0491 PREDICTION OF FUTURE OBSERVATIONS IN POLYNOMIAL GROWTH CURVE MODELS PART - 1 C. Radhakrishna Rao University of Pittsburgh

  19. Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

    PubMed Central

    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. PMID:25938675

  20. 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.

  1. 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…

  2. Accurate Prediction of Protein Functional Class From Sequence in the Mycobacterium Tuberculosis and Escherichia Coli Genomes Using Data Mining

    PubMed Central

    Karwath, Andreas; Clare, Amanda; Dehaspe, Luc

    2000-01-01

    The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on the M. tuberculosis and E. coli genomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function in M. tuberculosis and 24% of those in E. coli, with an estimated accuracy of 60–80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history of M. tuberculosis and E. coli. PMID:11119305

  3. Herbivore-induced plant volatiles accurately predict history of coexistence, diet breadth, and feeding mode of herbivores.

    PubMed

    Danner, Holger; Desurmont, Gaylord A; Cristescu, Simona M; van Dam, Nicole M

    2017-01-30

    Herbivore-induced plant volatiles (HIPVs) serve as specific cues to higher trophic levels. Novel, exotic herbivores entering native foodwebs may disrupt the infochemical network as a result of changes in HIPV profiles. Here, we analysed HIPV blends of native Brassica rapa plants infested with one of 10 herbivore species with different coexistence histories, diet breadths and feeding modes. Partial least squares (PLS) models were fitted to assess whether HIPV blends emitted by Dutch B. rapa differ between native and exotic herbivores, between specialists and generalists, and between piercing-sucking and chewing herbivores. These models were used to predict the status of two additional herbivores. We found that HIPV blends predicted the evolutionary history, diet breadth and feeding mode of the herbivore with an accuracy of 80% or higher. Based on the HIPVs, the PLS models reliably predicted that Trichoplusia ni and Spodoptera exigua are perceived as exotic, leaf-chewing generalists by Dutch B. rapa plants. These results indicate that there are consistent and predictable differences in HIPV blends depending on global herbivore characteristics, including coexistence history. Consequently, native organisms may be able to rapidly adapt to potentially disruptive effects of exotic herbivores on the infochemical network.

  4. 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

  5. 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.

  6. Structural maturation and brain activity predict future working memory capacity during childhood development.

    PubMed

    Ullman, Henrik; Almeida, Rita; Klingberg, Torkel

    2014-01-29

    Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.

  7. 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.

  8. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

    PubMed Central

    2014-01-01

    Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

  9. Accurate prediction of the electronic properties of low-dimensional graphene derivatives using a screened hybrid density functional.

    PubMed

    Barone, Veronica; Hod, Oded; Peralta, Juan E; Scuseria, Gustavo E

    2011-04-19

    Over the last several years, low-dimensional graphene derivatives, such as carbon nanotubes and graphene nanoribbons, have played a central role in the pursuit of a plausible carbon-based nanotechnology. Their electronic properties can be either metallic or semiconducting depending purely on morphology, but predicting their electronic behavior has proven challenging. The combination of experimental efforts with modeling of these nanometer-scale structures has been instrumental in gaining insight into their physical and chemical properties and the processes involved at these scales. Particularly, approximations based on density functional theory have emerged as a successful computational tool for predicting the electronic structure of these materials. In this Account, we review our efforts in modeling graphitic nanostructures from first principles with hybrid density functionals, namely the Heyd-Scuseria-Ernzerhof (HSE) screened exchange hybrid and the hybrid meta-generalized functional of Tao, Perdew, Staroverov, and Scuseria (TPSSh). These functionals provide a powerful tool for quantitatively studying structure-property relations and the effects of external perturbations such as chemical substitutions, electric and magnetic fields, and mechanical deformations on the electronic and magnetic properties of these low-dimensional carbon materials. We show how HSE and TPSSh successfully predict the electronic properties of these materials, providing a good description of their band structure and density of states, their work function, and their magnetic ordering in the cases in which magnetism arises. Moreover, these approximations are capable of successfully predicting optical transitions (first and higher order) in both metallic and semiconducting single-walled carbon nanotubes of various chiralities and diameters with impressive accuracy. This versatility includes the correct prediction of the trigonal warping splitting in metallic nanotubes. The results predicted

  10. An Electroacoustic Hearing Protector Simulator That Accurately Predicts Pressure Levels in the Ear Based on Standard Performance Metrics

    DTIC Science & Technology

    2013-08-01

    24 Figure 20. ABQ experiment showing five volunteers located 1.0 m from source in upper-left panel wearing...study (Royster et al.,1996) in which users self-fit hearing protectors (ANSI S12.6- 2008 method B: user fit) with no experimenter instruction gives an...values provided by the experimenters and simulator fits for the intact and modified muffs. Figure 22 (upper panel) shows the simulator prediction

  11. Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock

    NASA Astrophysics Data System (ADS)

    Cleves, Ann E.; Jain, Ajay N.

    2015-06-01

    Prediction of the bound configuration of small-molecule ligands that differ substantially from the cognate ligand of a protein co-crystal structure is much more challenging than re-docking the cognate ligand. Success rates for cross-docking in the range of 20-30 % are common. We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands. The benchmark data set, called PINC ("PINC Is Not Cognate"), is publicly available. Protein pocket similarity was used to choose representative structures for ensemble-docking. The docking protocol made use of known ligand poses prior to the cutoff-date, both to help guide the configurational search and to adjust the rank of predicted poses. Overall, the top-scoring pose family was correct over 60 % of the time, with the top-two pose families approaching a 75 % success rate. Correct poses among all those predicted were identified nearly 90 % of the time. The largest improvements came from the use of molecular similarity to improve ligand pose rankings and the strategy for identifying representative protein structures. With the exception of a single outlier target, the knowledge-guided docking protocol produced results matching the quality of cognate-ligand re-docking, but it did so on a very challenging temporally-segregated cross-docking benchmark.

  12. Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock.

    PubMed

    Cleves, Ann E; Jain, Ajay N

    2015-06-01

    Prediction of the bound configuration of small-molecule ligands that differ substantially from the cognate ligand of a protein co-crystal structure is much more challenging than re-docking the cognate ligand. Success rates for cross-docking in the range of 20-30 % are common. We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands. The benchmark data set, called PINC ("PINC Is Not Cognate"), is publicly available. Protein pocket similarity was used to choose representative structures for ensemble-docking. The docking protocol made use of known ligand poses prior to the cutoff-date, both to help guide the configurational search and to adjust the rank of predicted poses. Overall, the top-scoring pose family was correct over 60 % of the time, with the top-two pose families approaching a 75 % success rate. Correct poses among all those predicted were identified nearly 90 % of the time. The largest improvements came from the use of molecular similarity to improve ligand pose rankings and the strategy for identifying representative protein structures. With the exception of a single outlier target, the knowledge-guided docking protocol produced results matching the quality of cognate-ligand re-docking, but it did so on a very challenging temporally-segregated cross-docking benchmark.

  13. 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

  14. Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.

    PubMed

    Baldassi, Carlo; Zamparo, Marco; Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

    2014-01-01

    In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.

  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. Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

    NASA Technical Reports Server (NTRS)

    Viken, Jeff; Dollyhigh, Samuel; Smith, Jeremy; Trani, Antonio; Baik, Hojong; Hinze, Nicholas; Ashiabor, Senanu

    2006-01-01

    The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These

  17. Positive thinking about the future in newspaper reports and presidential addresses predicts economic downturn.

    PubMed

    Sevincer, A Timur; Wagner, Greta; Kalvelage, Johanna; Oettingen, Gabriele

    2014-04-01

    Previous research has shown that positive thinking, in the form of fantasies about an idealized future, predicts low effort and poor performance. In the studies reported here, we used computerized content analysis of historical documents to investigate the relation between positive thinking about the future and economic development. During the financial crisis from 2007 to 2009, the more weekly newspaper articles in the economy page of USA Today contained positive thinking about the future, the more the Dow Jones Industrial Average declined in the subsequent week and 1 month later. In addition, between the New Deal era and the present time, the more presidential inaugural addresses contained positive thinking about the future, the more the gross domestic product and the employment rate declined in the presidents' subsequent tenures. These counterintuitive findings may help reveal the psychological processes that contribute to an economic crisis.

  18. Risk factors that predict future onset of each DSM-5 eating disorder: Predictive specificity in high-risk adolescent females.

    PubMed

    Stice, Eric; Gau, Jeff M; Rohde, Paul; Shaw, Heather

    2017-01-01

    Because no single report has examined risk factors that predict future onset each type of eating disorder and core symptom dimensions that crosscut disorders, we addressed these aims to advance knowledge regarding risk factor specificity. Data from 3 prevention trials that targeted young women with body dissatisfaction (N = 1,272; Mage = 18.5, SD = 4.2) and collected annual diagnostic interview data over 3-year follow-up were combined to identify predictors of subthreshold/threshold anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD). Negative affect and functional impairment predicted onset of all eating disorders. Thin-ideal internalization, body dissatisfaction, dieting, overeating, and mental health care predicted onset of subthreshold/threshold BN, BED, and PD; positive thinness expectations, denial of cost of pursuing the thin ideal, and fasting predicted onset of 2 of these 3 disorders. Similar risk factors predicted core eating disorder symptom onset. Low BMI and dieting specifically predicted onset of subthreshold/threshold AN or low BMI. Only a subset of factors showed unique predictive effects in multivariate models, likely due to moderate correlations between the risk factors (M r = .14). Results provide support for the theory that pursuit of the thin ideal and the resulting body dissatisfaction, dieting, and unhealthy weight control behaviors increase risk for binge/purge spectrum eating disorders, but suggest that youth who are inherently lean, rather than purposely pursuing the thin ideal, are at risk for AN. Impaired interpersonal functioning and negative affect are transdiagnostic risk factors, suggesting these factors should be targeted in prevention programs. (PsycINFO Database Record

  19. Iowa Gambling Task scores predict future drug use in bipolar disorder outpatients with stimulant dependence.

    PubMed

    Nejtek, Vicki A; Kaiser, Kathryn A; Zhang, Bin; Djokovic, Marija

    2013-12-30

    Poor decision-making is associated with poor recovery in persons with bipolar disorder and drug relapse in persons with stimulant dependence. Cognitive predictors of stimulant use in those with comorbid bipolar and stimulant dependence are surprisingly absent. Our goal was to determine if a single baseline assessment of decision-making (Iowa Gambling Task, IGT) would predict future drug use in bipolar disorder outpatients with comorbid stimulant dependence. Ninety-four men and women of multiple race/ethnic origins consented to participate in a 20-week study. Data analyses were performed on 63 comorbid bipolar outpatients completing at least four study weeks and 28 cocaine dependent volunteers without a mood disorder who participated as cocaine controls. There were no significant differences in IGT scores between comorbid patients and cocaine controls. In the comorbid group, IGT scores significantly predicted future drug use during the study. Age, sex, race, years of mental illness, or mood state did not significantly influence IGT scores. This is the first longitudinal study to show that IGT scores obtained at a single baseline assessment predicts future objective drug use in comorbid bipolar disorder outpatients with cocaine or methamphetamine dependence. Evaluating decision-making with the IGT may provide clinicians with valuable insight about the trajectory of their patients' risk for future drug use. These data suggest a need to augment existing treatment with cognitive restructuring to prevent slips and relapses in comorbid bipolar patients. The lack of a bipolar control group and a modest sample size may limit data interpretations.

  20. Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia

    PubMed Central

    Yuan, Xuye; Chen, Jiajia; Lin, Yuxin; Li, Yin; Xu, Lihua; Chen, Luonan; Hua, Haiying; Shen, Bairong

    2017-01-01

    Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia. PMID:28243332

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

    PubMed

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

    2017-01-20

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

  2. Decoding the future from past experience: learning shapes predictions in early visual cortex.

    PubMed

    Luft, Caroline D B; Meeson, Alan; Welchman, Andrew E; Kourtzi, Zoe

    2015-05-01

    Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

  3. Residual forefoot deformity predicts the need for future surgery in clubfeet treated by Ponseti casting.

    PubMed

    Hosseinzadeh, Pooya; Peterson, Erik D; Walker, Janet; Muchow, Ryan D; Iwinski, Henry J; Talwalkar, Vishwas R; Milbrandt, Todd A

    2016-03-01

    Tibialis anterior tendon transfer (TATT) is performed for treatment of recurrent clubfeet. We investigated the predictability of residual adductus on the future need for TATT. A retrospective review of 143 patients with clubfoot was performed. The patients were divided into two groups: group 1 with a history of TATT and group 2 with no TATT. Heel-forefoot angle (HFA) was measured. HFA was compared between the groups. HFA was significantly different between groups 1 and 2. Residual adductus deformity in clubfeet treated by Ponseti casting is a risk factor for future need for surgical treatment.

  4. Genomic inference accurately predicts the timing and severity of a recent bottleneck in a non-model insect population

    PubMed Central

    McCoy, Rajiv C.; Garud, Nandita R.; Kelley, Joanna L.; Boggs, Carol L.; Petrov, Dmitri A.

    2015-01-01

    The analysis of molecular data from natural populations has allowed researchers to answer diverse ecological questions that were previously intractable. In particular, ecologists are often interested in the demographic history of populations, information that is rarely available from historical records. Methods have been developed to infer demographic parameters from genomic data, but it is not well understood how inferred parameters compare to true population history or depend on aspects of experimental design. Here we present and evaluate a method of SNP discovery using RNA-sequencing and demographic inference using the program δaδi, which uses a diffusion approximation to the allele frequency spectrum to fit demographic models. We test these methods in a population of the checkerspot butterfly Euphydryas gillettii. This population was intentionally introduced to Gothic, Colorado in 1977 and has since experienced extreme fluctuations including bottlenecks of fewer than 25 adults, as documented by nearly annual field surveys. Using RNA-sequencing of eight individuals from Colorado and eight individuals from a native population in Wyoming, we generate the first genomic resources for this system. While demographic inference is commonly used to examine ancient demography, our study demonstrates that our inexpensive, all-in-one approach to marker discovery and genotyping provides sufficient data to accurately infer the timing of a recent bottleneck. This demographic scenario is relevant for many species of conservation concern, few of which have sequenced genomes. Our results are remarkably insensitive to sample size or number of genomic markers, which has important implications for applying this method to other non-model systems. PMID:24237665

  5. An accurate method to predict the stress concentration in composite laminates with a circular hole under tensile loading

    NASA Astrophysics Data System (ADS)

    Russo, A.; Zuccarello, B.

    2007-07-01

    The paper presents a theoretical-numerical hybrid method for determining the stresses distribution in composite laminates containing a circular hole and subjected to uniaxial tensile loading. The method is based upon an appropriate corrective function allowing a simple and rapid evaluation of stress distributions in a generic plate of finite width with a hole based on the theoretical stresses distribution in an infinite plate with the same hole geometry and material. In order to verify the accuracy of the method proposed, various numerical and experimental tests have been performed by considering different laminate lay-ups; in particular, the experimental results have shown that a combined use of the method proposed and the well-know point-stress criterion leads to reliable strength predictions for GFRP or CFRP laminates with a circular hole.

  6. 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.

  7. A single bioavailability model can accurately predict Ni toxicity to green microalgae in soft and hard surface waters.

    PubMed

    Deleebeeck, Nele M E; De Laender, Frederik; Chepurnov, Victor A; Vyverman, Wim; Janssen, Colin R; De Schamphelaere, Karel A C

    2009-04-01

    The major research questions addressed in this study were (i) whether green microalgae living in soft water (operationally defined water hardness<10mg CaCO(3)/L) are intrinsically more sensitive to Ni than green microalgae living in hard water (operationally defined water hardness >25mg CaCO(3)/L), and (ii) whether a single bioavailability model can be used to predict the effect of water hardness on the toxicity of Ni to green microalgae in both soft and hard water. Algal growth inhibition tests were conducted with clones of 10 different species collected in soft and hard water lakes in Sweden. Soft water algae were tested in a 'soft' and a 'moderately hard' test medium (nominal water hardness=6.25 and 16.3mg CaCO(3)/L, respectively), whereas hard water algae were tested in a 'moderately hard' and a 'hard' test medium (nominal water hardness=16.3 and 43.4 mg CaCO(3)/L, respectively). The results from the growth inhibition tests in the 'moderately hard' test medium revealed no significant sensitivity differences between the soft and the hard water algae used in this study. Increasing water hardness significantly reduced Ni toxicity to both soft and hard water algae. Because it has previously been demonstrated that Ca does not significantly protect the unicellular green alga Pseudokirchneriella subcapitata against Ni toxicity, it was assumed that the protective effect of water hardness can be ascribed to Mg alone. The logK(MgBL) (=5.5) was calculated to be identical for the soft and the hard water algae used in this study. A single bioavailability model can therefore be used to predict Ni toxicity to green microalgae in soft and hard surface waters as a function of water hardness.

  8. Generalized spin-ratio scaled MP2 method for accurate prediction of intermolecular interactions for neutral and ionic species

    NASA Astrophysics Data System (ADS)

    Tan, Samuel; Barrera Acevedo, Santiago; Izgorodina, Ekaterina I.

    2017-02-01

    The accurate calculation of intermolecular interactions is important to our understanding of properties in large molecular systems. The high computational cost of the current "gold standard" method, coupled cluster with singles and doubles and perturbative triples (CCSD(T), limits its application to small- to medium-sized systems. Second-order Møller-Plesset perturbation (MP2) theory is a cheaper alternative for larger systems, although at the expense of its decreased accuracy, especially when treating van der Waals complexes. In this study, a new modification of the spin-component scaled MP2 method was proposed for a wide range of intermolecular complexes including two well-known datasets, S22 and S66, and a large dataset of ionic liquids consisting of 174 single ion pairs, IL174. It was found that the spin ratio, ɛΔ s=E/INT O SEIN T S S , calculated as the ratio of the opposite-spin component to the same-spin component of the interaction correlation energy fell in the range of 0.1 and 1.6, in contrast to the range of 3-4 usually observed for the ratio of absolute correlation energy, ɛs=E/OSES S , in individual molecules. Scaled coefficients were found to become negative when the spin ratio fell in close proximity to 1.0, and therefore, the studied intermolecular complexes were divided into two groups: (1) complexes with ɛΔ s< 1 and (2) complexes with ɛΔ s≥ 1 . A separate set of coefficients was obtained for both groups. Exclusion of counterpoise correction during scaling was found to produce superior results due to decreased error. Among a series of Dunning's basis sets, cc-pVTZ and cc-pVQZ were found to be the best performing ones, with a mean absolute error of 1.4 kJ mol-1 and maximum errors below 6.2 kJ mol-1. The new modification, spin-ratio scaled second-order Møller-Plesset perturbation, treats both dispersion-driven and hydrogen-bonded complexes equally well, thus validating its robustness with respect to the interaction type ranging from ionic

  9. Brain activity in valuation regions while thinking about the future predicts individual discount rates.

    PubMed

    Cooper, Nicole; Kable, Joseph W; Kim, B Kyu; Zauberman, Gal

    2013-08-07

    People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future--specifically, to judge the subjective length of future time intervals--predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future.

  10. 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

  11. 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

  12. Trajectory Pattern Mining Using Sequential Pattern Mining and K-Means for Predicting Future Location

    NASA Astrophysics Data System (ADS)

    Kautsar, G.; Akbar, S.

    2017-01-01

    Sequential pattern mining is a method used to find patterns while concerning the sequence of an item set. Sequential pattern mining can be used to find trajectory patterns in moving object data. To implement it in the real life, the spatial attribute of the data needs to be generalized/grouped. In this paper, K-Means is used to group the spatial attribute. In order to group the spatial attribute, the temporal attribute is also considered to see how the patterns are related to time. The resulting trajectory patterns are then used to visualize the habit of the moving object. Therefore, trajectory patterns are used as the reference in this paper to predict the future location of the object. Predicting the future location of the object is performed using the movement history of the object. Result of this research is trajectory pattern which repeat at certain time duration according to its data characteristics.

  13. Predictions of space physics are difficult, especially when they are about the future

    NASA Astrophysics Data System (ADS)

    Cassak, P.

    2015-12-01

    This talk is about the future of space physics, the broad field of study addressing how the sun works, its interaction with Earth and other planets via the solar wind and solar eruptions, and the region of interplanetary space out to the edge of the solar system. It is the chief field feeding into the development of tools for space weather prediction. Space physics is at an exciting - yet critical - time in its evolution. Scientifically, the capabilities afforded by new ground- and space-based observations and the rapidly increasing speed of supercomputing resources are leading to unprecedented progress in the field. Recently launched missions such as the Van Allen Probes and the Magnetospheric MultiScale (MMS) mission, and upcoming missions such as Solar Probe Plus and Solar Orbiter, will open doors to science not previously accessible through observations. Predicting the future of space physics is difficult; this talk will offer thoughts on the road forward.

  14. Space Shuttle Launch Probability Analysis: Understanding History so We Can Predict the Future

    NASA Technical Reports Server (NTRS)

    Cates, Grant R.

    2014-01-01

    The Space Shuttle was launched 135 times and nearly half of those launches required 2 or more launch attempts. The Space Shuttle launch countdown historical data of 250 launch attempts provides a wealth of data that is important to analyze for strictly historical purposes as well as for use in predicting future launch vehicle launch countdown performance. This paper provides a statistical analysis of all Space Shuttle launch attempts including the empirical probability of launch on any given attempt and the cumulative probability of launch relative to the planned launch date at the start of the initial launch countdown. This information can be used to facilitate launch probability predictions of future launch vehicles such as NASA's Space Shuttle derived SLS. Understanding the cumulative probability of launch is particularly important for missions to Mars since the launch opportunities are relatively short in duration and one must wait for 2 years before a subsequent attempt can begin.

  15. 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…

  16. 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.

  17. Women's age and embryo developmental speed accurately predict clinical pregnancy after single vitrified-warmed blastocyst transfer.

    PubMed

    Kato, Keiichi; Ueno, Satoshi; Yabuuchi, Akiko; Uchiyama, Kazuo; Okuno, Takashi; Kobayashi, Tamotsu; Segawa, Tomoya; Teramoto, Shokichi

    2014-10-01

    The aim of this study was to establish a simple, objective blastocyst grading system using women's age and embryo developmental speed to predict clinical pregnancy after single vitrified-warmed blastocyst transfer. A 6-year retrospective cohort study was conducted in a private infertility centre. A total of 7341 single vitrified-armed blastocyst transfer cycles were included, divided into those carried out between 2006 and 2011 (6046 cycles) and 2012 (1295 cycles). Clinical pregnancy rate, ongoing pregnancy rate and delivery rates were stratified by women's age (<35, 35-37, 38-39, 40-41, 42-45 years) and time to blastocyst expansion (<120, 120-129, 130-139, 140-149, >149 h) as embryo developmental speed. In all the age groups, clinical pregnancy rate, ongoing pregnancy rate and delivery rates decreased as the embryo developmental speed decreased (P < 0.0001). A simple five-grade score based on women's age and embryo developmental speed was determined by actual clinical pregnancy rates observed in the 2006-2011 cohort. Subsequently, the novel grading score was validated in the 2012 cohort (1295 cycles), finding an excellent association. In conclusion, we established a novel blastocyst grading system using women's age and embryo developmental speed as objective parameters.

  18. Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data

    PubMed Central

    Essaghir, Ahmed; Toffalini, Federica; Knoops, Laurent; Kallin, Anders; van Helden, Jacques; Demoulin, Jean-Baptiste

    2010-01-01

    Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining. PMID:20215436

  19. Role of Climate Change in Predictions of Future Tropospheric Ozone and Aerosols

    NASA Astrophysics Data System (ADS)

    Liao, H.; Chen, W.; Seinfeld, J.

    2006-12-01

    A unified tropospheric chemistry-aerosol model within the Goddard Institute for Space Studies general circulation model II is applied to simulate equilibrium climate change driven by changes in greenhouse gases (GHGs) and/or aerosols over 2000-2100 to examine the effects of climate change on global distributions of tropospheric ozone and sulfate, nitrate, ammonium, black carbon, primary organic carbon, secondary organic carbon, sea salt, and mineral dust aerosols. We consider only direct radiative effect of aerosols on future climate in this study. Since aerosol levels will both affect and be affected by future climate, we identify the role of aerosol-driven climate in predicting future air pollutants by performing a number of sensitivity studies. The year 2100 GHG concentrations as well as the anthropogenic emissions of ozone precursors and aerosols/aerosol precursors are based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) A2. Although greenhouse gases are the most important drivers of global climate change, aerosols are very influential on regional climate through absorption and scattering of solar radiation. As aerosol concentrations increase over 2000-2100, aerosol-induced cooling at the surface, increase in atmospheric stability, and reduction in precipitation are predicted to increase surface-layer concentrations of pollutants over populated areas; Aerosol-induced climate change is therefore predicted to have a positive feedback to tropospheric aerosol concentrations. We also compare the effect of GHG-driven climate on atmospheric composition with that of aerosol-driven climate. Results suggest that it is important to account for climate responses to aerosol forcing in predicting future ozone and aerosols.

  20. Forming attitudes that predict future behavior: a meta-analysis of the attitude-behavior relation.

    PubMed

    Glasman, Laura R; Albarracín, Dolores

    2006-09-01

    A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude-behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object.

  1. Forming Attitudes That Predict Future Behavior: A Meta-Analysis of the Attitude–Behavior Relation

    PubMed Central

    Glasman, Laura R.; Albarracín, Dolores

    2016-01-01

    A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude–behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object. PMID:16910754

  2. 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).

  3. Predicting the Future Impact of Droughts on Ungulate Populations in Arid and Semi-Arid Environments

    PubMed Central

    Duncan, Clare; Chauvenet, Aliénor L. M.; McRae, Louise M.; Pettorelli, Nathalie

    2012-01-01

    Droughts can have a severe impact on the dynamics of animal populations, particularly in semi-arid and arid environments where herbivore populations are strongly limited by resource availability. Increased drought intensity under projected climate change scenarios can be expected to reduce the viability of such populations, yet this impact has seldom been quantified. In this study, we aim to fill this gap and assess how the predicted worsening of droughts over the 21st century is likely to impact the population dynamics of twelve ungulate species occurring in arid and semi-arid habitats. Our results provide support to the hypotheses that more sedentary, grazing and mixed feeding species will be put at high risk from future increases in drought intensity, suggesting that management intervention under these conditions should be targeted towards species possessing these traits. Predictive population models for all sedentary, grazing or mixed feeding species in our study show that their probability of extinction dramatically increases under future emissions scenarios, and that this extinction risk is greater for smaller populations than larger ones. Our study highlights the importance of quantifying the current and future impacts of increasing extreme natural events on populations and species in order to improve our ability to mitigate predicted biodiversity loss under climate change. PMID:23284700

  4. FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

    PubMed Central

    Knudsen, Thomas B.; Keller, Douglas A.; Sander, Miriam; Carney, Edward W.; Doerrer, Nancy G.; Eaton, David L.; Fitzpatrick, Suzanne Compton; Hastings, Kenneth L.; Mendrick, Donna L.; Tice, Raymond R.; Watkins, Paul B.; Whelan, Maurice

    2015-01-01

    FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. PMID:25628403

  5. What should we want to know about our future? A Kantian view on predictive genetic testing.

    PubMed

    Heinrichs, Bert

    2005-01-01

    Recent advances in genomic research have led to the development of new diagnostic tools, including tests which make it possible to predict the future occurrence of monogenetic diseases (e.g. Chorea Huntington) or to determine increased susceptibilities to the future development of more complex diseases (e.g. breast cancer). The use of such tests raises a number of ethical, legal and social issues which are usually discussed in terms of rights. However, in the context of predictive genetic tests a key question arises which lies beyond the concept of rights, namely, What should we want to know about our future? In the following I shall discuss this question against the background of Kant's Doctrine of Virtue. It will be demonstrated that the system of duties of virtue that Kant elaborates in the second part of his Metaphysics of Morals offers a theoretical framework for addressing the question of a proper scope of future knowledge as provided by genetic tests. This approach can serve as a source of moral guidance complementary to a justice perspective. It does, however, not rest on the-rather problematic--claim to be able to define what the "good life" is.

  6. Predictions of future ephemeral springtime waterbird stopover habitat availability under global change

    USGS Publications Warehouse

    Uden, Daniel R.; Allen, Craig R.; Bishop, Andrew A.; Grosse, Roger; Jorgensen, Christopher F.; LaGrange, Theodore G.; Stutheit, Randy G.; Vrtiska, Mark P.

    2015-01-01

    In the present period of rapid, worldwide change in climate and landuse (i.e., global change), successful biodiversity conservation warrants proactive management responses, especially for long-distance migratory species. However, the development and implementation of management strategies can be impeded by high levels of uncertainty and low levels of control over potentially impactful future events and their effects. Scenario planning and modeling are useful tools for expanding perspectives and informing decisions under these conditions. We coupled scenario planning and statistical modeling to explain and predict playa wetland inundation (i.e., presence/absence of water) and ponded area (i.e., extent of water) in the Rainwater Basin, an anthropogenically altered landscape that provides critical stopover habitat for migratory waterbirds. Inundation and ponded area models for total wetlands, those embedded in rowcrop fields, and those not embedded in rowcrop fields were trained and tested with wetland ponding data from 2004 and 2006–2009, and then used to make additional predictions under two alternative climate change scenarios for the year 2050, yielding a total of six predictive models and 18 prediction sets. Model performance ranged from moderate to good, with inundation models outperforming ponded area models, and models for non-rowcrop-embedded wetlands outperforming models for total wetlands and rowcrop-embedded wetlands. Model predictions indicate that if the temperature and precipitation changes assumed under our climate change scenarios occur, wetland stopover habitat availability in the Rainwater Basin could decrease in the future. The results of this and similar studies could be aggregated to increase knowledge about the potential spatial and temporal distributions of future stopover habitat along migration corridors, and to develop and prioritize multi-scale management actions aimed at mitigating the detrimental effects of global change on migratory

  7. 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

  8. The status of and future research into Myalgic Encephalomyelitis and Chronic Fatigue Syndrome: the need of accurate diagnosis, objective assessment, and acknowledging biological and clinical subgroups

    PubMed Central

    Twisk, Frank N. M.

    2014-01-01

    Although Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS) are used interchangeably, the diagnostic criteria define two distinct clinical entities. Cognitive impairment, (muscle) weakness, circulatory disturbances, marked variability of symptoms, and, above all, post-exertional malaise: a long-lasting increase of symptoms after a minor exertion, are distinctive symptoms of ME. This latter phenomenon separates ME, a neuro-immune illness, from chronic fatigue (syndrome), other disorders and deconditioning. The introduction of the label, but more importantly the diagnostic criteria for CFS have generated much confusion, mostly because chronic fatigue is a subjective and ambiguous notion. CFS was redefined in 1994 into unexplained (persistent or relapsing) chronic fatigue, accompanied by at least four out of eight symptoms, e.g., headaches and unrefreshing sleep. Most of the research into ME and/or CFS in the last decades was based upon the multivalent CFS criteria, which define a heterogeneous patient group. Due to the fact that fatigue and other symptoms are non-discriminative, subjective experiences, research has been hampered. Various authors have questioned the physiological nature of the symptoms and qualified ME/CFS as somatization. However, various typical symptoms can be assessed objectively using standardized methods. Despite subjective and unclear criteria and measures, research has observed specific abnormalities in ME/CFS repetitively, e.g., immunological abnormalities, oxidative and nitrosative stress, neurological anomalies, circulatory deficits and mitochondrial dysfunction. However, to improve future research standards and patient care, it is crucial that patients with post-exertional malaise (ME) and patients without this odd phenomenon are acknowledged as separate clinical entities that the diagnosis of ME and CFS in research and clinical practice is based upon accurate criteria and an objective assessment of characteristic symptoms

  9. Testosterone response to courtship predicts future paternal behavior in the California mouse, Peromyscus californicus.

    PubMed

    Gleason, Erin D; Marler, Catherine A

    2010-02-01

    In the monogamous and biparental California mouse (Peromyscus californicus), paternal care is critical for maximal offspring survival. Animals form pair bonds and do not engage in extrapair matings, and thus female evaluation of paternal quality during courtship is likely to be advantageous. We hypothesized that male endocrine or behavioral response to courtship interactions would be predictive of future paternal behavior. To test this hypothesis, we formed 20 pairs of California mice, and evaluated their behavior during the first hour of courtship interactions and again following the birth of young. We also collected blood from males at baseline, 1 hr after pairing, 3 weeks paired, and when young were 4 days old to measure testosterone (T). We found that male T-response to courtship interactions predicted future paternal behavior, specifically the amount of time he huddled over young when challenged by the temporary removal of his mate. Males that mounted T increases at courtship also approached pups more quickly during this challenge than males who had a significant decrease in T at courtship. Proximity of the male and female during courtship predicted paternal huddling during a 1-hr observation, and a multiple regression analysis revealed that courtship behavior was also predictive of birth latency. We speculate that male T-response to a female in P. californicus is an honest indicator of paternal quality, and if detectable by females could provide a basis for evaluation during mate choice.

  10. Pleasure Now, Pain Later: Positive Fantasies About the Future Predict Symptoms of Depression.

    PubMed

    Oettingen, Gabriele; Mayer, Doris; Portnow, Sam

    2016-03-01

    Though common sense suggests that positive thinking shelters people from depression, the four studies reported here showed that this intuition needs to be qualified: Positive thinking in the form of fantasies about the future did indeed relate to decreased symptoms of depression when measured concurrently; however, positive fantasies predicted more depressive symptoms when measured longitudinally. The pattern of results was observed for different indicators of fantasies and depression, in adults and in schoolchildren, and for periods of up to 7 months (Studies 1-4). In college students, low academic success partially mediated the predictive relation between positive fantasies and symptoms of depression (Study 4). Results add to existing research on the problematic effects of positive fantasies on performance by suggesting that indulging in positive fantasies predicts problems in mental health.

  11. Nonperturbative relativistic approach to pion form factors: Predictions for future JLab experiments

    SciTech Connect

    Krutov, A. F.; Troitsky, V. E.; Tsirova, N. A.

    2009-11-15

    Some predictions concerning possible results of the future experiments at the Thomas Jefferson National Accelerator Facility (JLab) on the pion form factor F{sub {pi}}(Q{sup 2}) are made. The calculations exploit the method proposed previously by the authors and based on the instant-form Poincare invariant approach to pions, considered as quark-antiquark systems. This model has predicted with surprising accuracy the values of F{sub {pi}}(Q{sup 2}), which were measured later in JLab experiments. The results are almost independent from the form of wave function. The pion mean square radius and the decay constant f{sub {pi}} also agree with experimental values. The model gives powerlike asymptotic behavior of F{sub {pi}}(Q{sup 2}) at high momentum transfer in agreement with QCD predictions.

  12. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

    PubMed Central

    de Vlaming, Ronald; Groenen, Patrick J. F.

    2015-01-01

    In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially. PMID:26273586

  13. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.

    PubMed

    de Vlaming, Ronald; Groenen, Patrick J F

    2015-01-01

    In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

  14. 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.

  15. 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

  16. 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

  17. Multireference correlation consistent composite approach [MR-ccCA]: toward accurate prediction of the energetics of excited and transition state chemistry.

    PubMed

    Oyedepo, Gbenga A; Wilson, Angela K

    2010-08-26

    The correlation consistent Composite Approach, ccCA [ Deyonker , N. J. ; Cundari , T. R. ; Wilson , A. K. J. Chem. Phys. 2006 , 124 , 114104 ] has been demonstrated to predict accurate thermochemical properties of chemical species that can be described by a single configurational reference state, and at reduced computational cost, as compared with ab initio methods such as CCSD(T) used in combination with large basis sets. We have developed three variants of a multireference equivalent of this successful theoretical model. The method, called the multireference correlation consistent composite approach (MR-ccCA), is designed to predict the thermochemical properties of reactive intermediates, excited state species, and transition states to within chemical accuracy (e.g., 1 kcal/mol for enthalpies of formation) of reliable experimental values. In this study, we have demonstrated the utility of MR-ccCA: (1) in the determination of the adiabatic singlet-triplet energy separations and enthalpies of formation for the ground states for a set of diradicals and unsaturated compounds, and (2) in the prediction of energetic barriers to internal rotation, in ethylene and its heavier congener, disilene. Additionally, we have utilized MR-ccCA to predict the enthalpies of formation of the low-lying excited states of all the species considered. MR-ccCA is shown to give quantitative results without reliance upon empirically derived parameters, making it suitable for application to study novel chemical systems with significant nondynamical correlation effects.

  18. Accurate prediction of death by serial determination of galactose elimination capacity in primary biliary cirrhosis: a comparison with the Mayo model.

    PubMed

    Reichen, J; Widmer, T; Cotting, J

    1991-09-01

    We retrospectively analyzed the predictive accuracy of serial determinations of galactose elimination capacity in 61 patients with primary biliary cirrhosis. Death was predicted from the time that the regression line describing the decline in galactose elimination capacity vs. time intersected a value of 4 mg.min-1.kg-1. Thirty-one patients exhibited decreasing galactose elimination capacity; in 11 patients it remained stable and in 19 patients only one value was available. Among those patients with decreasing galactose elimination capacity, 10 died and three underwent liver transplantation; prediction of death was accurate to 7 +/- 19 mo. This criterion incorrectly predicted death in two patients with portal-vein thrombosis; otherwise, it did better than or as well as the Mayo clinic score. The latter was also tested on our patients and was found to adequately describe risk in yet another independent population of patients with primary biliary cirrhosis. Cox regression analysis selected only bilirubin and galactose elimination capacity, however, as independent predictors of death. We submit that serial determination of galactose elimination capacity in patients with primary biliary cirrhosis may be a useful adjunct to optimize the timing of liver transplantation and to evaluate new pharmacological treatment modalities of this disease.

  19. Automatic mental associations predict future choices of undecided decision-makers.

    PubMed

    Galdi, Silvia; Arcuri, Luciano; Gawronski, Bertram

    2008-08-22

    Common wisdom holds that choice decisions are based on conscious deliberations of the available information about choice options. On the basis of recent insights about unconscious influences on information processing, we tested whether automatic mental associations of undecided individuals bias future choices in a manner such that these choices reflect the evaluations implied by earlier automatic associations. With the use of a computer-based, speeded categorization task to assess automatic mental associations (i.e., associations that are activated unintentionally, difficult to control, and not necessarily endorsed at a conscious level) and self-report measures to assess consciously endorsed beliefs and choice preferences, automatic associations of undecided participants predicted changes in consciously reported beliefs and future choices over a period of 1 week. Conversely, for decided participants, consciously reported beliefs predicted changes in automatic associations and future choices over the same period. These results indicate that decision-makers sometimes have already made up their mind at an unconscious level, even when they consciously indicate that they are still undecided.

  20. Feather and faecal corticosterone concentrations predict future reproductive decisions in harlequin ducks (Histrionicus histrionicus)

    PubMed Central

    Hansen, Warren K.; Bate, Lisa J.; Landry, Devin W.; Chastel, Olivier; Parenteau, Charline; Breuner, Creagh W.

    2016-01-01

    Understanding sources of reproductive variation can inform management and conservation decisions, population ecology and life-history theory. Annual reproductive variation can drive population growth rate and can be influenced by factors from across the annual cycle (known as carry-over effects). The majority of studies, however, focus solely on the role of current environmental events. Past events often influence future reproductive decisions and success but can be logistically difficult to collect and quantify, especially in migratory species. Recent work indicates that glucocorticoids may prove good indicators to evaluate carry-over effects across life-history transitions. Here, we evaluated three different measures of glucocorticoid physiology (feathers, faeces and plasma) to evaluate the predictability of future breeding decision in the harlequin duck (Histrionicus histrionicus). We collected tail and back feathers, plasma and faeces for glucocorticoid analysis, and fitted female harlequin ducks with very high-frequency transmitters to track their breeding decisions. Both back feathers (moulted immediately before the current season) and faecal glucocorticoid metabolites were identified as important predictive factors of reproductive decisions; high concentrations of glucocorticoid metabolites in back feathers and faeces predicted a higher likelihood of reproductive deferral for the year. Although back and tail feather corticosterone concentrations were correlated, tail feathers (moulted at the end of the previous breeding season) did not predict breeding decisions. Plasma corticosterone concentrations were collected over too broad a time range after capture to be useful in this study. This study demonstrates the utility of non-invasive corticosterone metrics in predicting breeding decisions and supports the use of feathers to measure carry-over effects in migratory birds. With this technique, we identified the prenuptial moult as an important life

  1. Walking ability to predict future cognitive decline in old adults: A scoping review.

    PubMed

    Kikkert, Lisette H J; Vuillerme, Nicolas; van Campen, Jos P; Hortobágyi, Tibor; Lamoth, Claudine J

    2016-05-01

    Early identification of individuals at risk for cognitive decline may facilitate the selection of those who benefit most from interventions. Current models predicting cognitive decline include neuropsychological and/or biological markers. Additional markers based on walking ability might improve accuracy and specificity of these models because motor and cognitive functions share neuroanatomical structures and psychological processes. We reviewed the relationship between walking ability at one point of (mid) life and cognitive decline at follow-up. A systematic literature search identified 20 longitudinal studies. The average follow-up time was 4.5 years. Gait speed quantified walking ability in most studies (n=18). Additional gait measures (n=4) were step frequency, variability and step-length. Despite methodological weaknesses, results revealed that gait slowing (0.68-1.1 m/sec) preceded cognitive decline and the presence of dementia syndromes (maximal odds and hazard ratios of 10.4 and 11.1, respectively). The results indicate that measures of walking ability could serve as additional markers to predict cognitive decline. However, gait speed alone might lack specificity. We recommend gait analysis, including dynamic gait parameters, in clinical evaluations of patients with suspected cognitive decline. Future studies should focus on examining the specificity and accuracy of various gait characteristics to predict future cognitive decline.

  2. Futurism.

    ERIC Educational Resources Information Center

    Foy, Jane Loring

    The objectives of this research report are to gain insight into the main problems of the future and to ascertain the attitudes that the general population has toward the treatment of these problems. In the first section of this report the future is explored socially, psychologically, and environmentally. The second section describes the techniques…

  3. Predicting current and future peatmoss drought stress: Impact of hydrological complexity

    NASA Astrophysics Data System (ADS)

    Nijp, Jelmer; Metselaar, Klaas; Limpens, Juul; Teutschbein, Claudia; Peichl, Matthias; Nilsson, Mats; Berendse, Frank; van der Zee, Sjoerd

    2016-04-01

    Northern peatlands sequester enormous amounts of carbon and therefore represent a carbon store of global importance. The vegetation in northern peatlands is dominated by peat-forming bryophytes of the genus Sphagnum. The growth of this carbon fixer, and hence its carbon uptake, strongly depends on the moisture availability in the living moss layer, which is a function of both water table and rewetting by rain. Peatland hydrology models are used to predict how changes in climate may modify the future water balance of peatmoss carpets and influence associated carbon and energy balances. These models, however, differ considerably in the number and type of processes included, which will have yet unknown consequences for peatland drought predictions in a future climate. Here, we assessed the importance of rainwater storage and peat volume change for predicting peatmoss drought projections in northern peatlands using an ensemble of downscaled, bias-corrected climate scenarios for current (1991 - 2020) and future (2061 - 2090) climate. Peatmoss drought projections were compared among four model variants with or without rainwater storage in the peatmoss carpet and peat volume change, which are considered as two important hydrological feedbacks controlling moss moisture availability. The performance of the four model variants was assessed using field data from a site in northern Sweden (Degerö Stormyr, 64°N 19°E). Our results show that adding rainwater storage in the moss layer as well as peat volume change significantly improved model performance; the most complex model had best model performance. Compared to the reference model, including both model extensions reduced the predicted drought frequency experienced by peatmoss with around 50%. Moreover, projected climate change is expected to reduce predicted peatmoss drought stress with about 20% for the studied site. In conclusion, this study shows that including rainwater storage in the peatmoss layer and/or peat volume

  4. The future of satellite remote sensing: A worldwide assessment and prediction

    NASA Technical Reports Server (NTRS)

    Spann, G. W.

    1984-01-01

    A frame-work in which to assess and predict the future prospects for satellite remote sensing markets is provided. The scope of the analysis is the satellite-related market for data, equipment, and services. It encompasses both domestic and international markets and contains an examination of the various market characteristics by market segment (e.g., Federal Government, State and Local Governments, Academic Organizations, Industrial Companies, and Individuals) and primary applications areas (e.g., Geology, Forestry, Land Resource Management, Agriculture and Cartography). The forecasts are derived from an analysis of both U.S. and foreign market data. The evolution and current status of U.S. and Foreign markets to arrive at market growth rates is evaluated. Circumstances and events which are likely to affect the future market development are examined. A market growth scenario is presented that is consistent with past data sales trends and takes into account the dynamic nature of the future satellite remote sensing market. Several areas of current and future business opportunities available in this market are discussed. Specific worldwide forecasts are presented in three market sectors for the period 1980 to 1990.

  5. Predicting and mitigating future biodiversity loss using long-term ecological proxies

    NASA Astrophysics Data System (ADS)

    Fordham, Damien A.; Akçakaya, H. Resit; Alroy, John; Saltré, Frédérik; Wigley, Tom M. L.; Brook, Barry W.

    2016-10-01

    Uses of long-term ecological proxies in strategies for mitigating future biodiversity loss are too limited in scope. Recent advances in geochronological dating, palaeoclimate reconstructions and molecular techniques for inferring population dynamics offer exciting new prospects for using retrospective knowledge to better forecast and manage ecological outcomes in the face of global change. Opportunities include using fossils, genes and computational models to identify ecological traits that caused species to be differentially prone to regional and range-wide extinction, test if threatened-species assessment approaches work and locate habitats that support stable ecosystems in the face of shifting climates. These long-term retrospective analyses will improve efforts to predict the likely effects of future climate and other environmental change on biodiversity, and target conservation management resources most effectively.

  6. 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

  7. Back from a predicted climatic extinction of an island endemic: a future for the Corsican Nuthatch.

    PubMed

    Barbet-Massin, Morgane; Jiguet, Frédéric

    2011-03-25

    The Corsican Nuthatch (Sitta whiteheadi) is red-listed as vulnerable to extinction by the IUCN because of its endemism, reduced population size, and recent decline. A further cause is the fragmentation and loss of its spatially-restricted favourite habitat, the Corsican pine (Pinus nigra laricio) forest. In this study, we aimed at estimating the potential impact of climate change on the distribution of the Corsican Nuthatch using species distribution models. Because this species has a strong trophic association with the Corsican and Maritime pines (P. nigra laricio and P. pinaster), we first modelled the current and future potential distribution of both pine species in order to use them as habitat variables when modelling the nuthatch distribution. However, the Corsican pine has suffered large distribution losses in the past centuries due to the development of anthropogenic activities, and is now restricted to mountainous woodland. As a consequence, its realized niche is likely significantly smaller than its fundamental niche, so that a projection of the current distribution under future climatic conditions would produce misleading results. To obtain a predicted pine distribution at closest to the geographic projection of the fundamental niche, we used available information on the current pine distribution associated to information on the persistence of isolated natural pine coppices. While common thresholds (maximizing the sum of sensitivity and specificity) predicted a potential large loss of the Corsican Nuthatch distribution by 2100, the use of more appropriate thresholds aiming at getting closer to the fundamental distribution of the Corsican pine predicted that 98% of the current presence points should remain potentially suitable for the nuthatch and its range could be 10% larger in the future. The habitat of the endemic Corsican Nuthatch is therefore more likely threatened by an increasing frequency and intensity of wildfires or anthropogenic activities than

  8. Antimicrobial drug resistance: "Prediction is very difficult, especially about the future".

    PubMed

    Courvalin, Patrice

    2005-10-01

    Evolution of bacteria towards resistance to antimicrobial drugs, including multidrug resistance, is unavoidable because it represents a particular aspect of the general evolution of bacteria that is unstoppable. Therefore, the only means of dealing with this situation is to delay the emergence and subsequent dissemination of resistant bacteria or resistance genes. Resistance to antimicrobial drugs in bacteria can result from mutations in housekeeping structural or regulatory genes. Alternatively, resistance can result from the horizontal acquisition of foreign genetic information. The 2 phenomena are not mutually exclusive and can be associated in the emergence and more efficient spread of resistance. This review discusses the predictable future of the relationship between antimicrobial drugs and bacteria.

  9. Predictive modelling of the spatial pattern of past and future forest cover changes in India

    NASA Astrophysics Data System (ADS)

    Reddy, C. Sudhakar; Singh, Sonali; Dadhwal, V. K.; Jha, C. S.; Rao, N. Rama; Diwakar, P. G.

    2017-02-01

    This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km2, which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880-2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

  10. Dose Addition Models Based on Biologically Relevant Reductions in Fetal Testosterone Accurately Predict Postnatal Reproductive Tract Alterations by a Phthalate Mixture in Rats

    PubMed Central

    Howdeshell, Kembra L.; Rider, Cynthia V.; Wilson, Vickie S.; Furr, Johnathan R.; Lambright, Christy R.; Gray, L. Earl

    2015-01-01

    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

  11. 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

  12. Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

    PubMed Central

    Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-01-01

    Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

  13. Lower “Awake and Fed Thermogenesis” Predicts Future Weight Gain in Subjects With Abdominal Adiposity

    PubMed Central

    Piaggi, Paolo; Krakoff, Jonathan; Bogardus, Clifton; Thearle, Marie S.

    2013-01-01

    Awake and fed thermogenesis (AFT) is the energy expenditure (EE) of the nonactive fed condition above the minimum metabolic requirement during sleep and is composed of the thermic effect of food and the cost of being awake. AFT was estimated from whole-room 24-h EE measures in 509 healthy subjects (368 Native Americans and 141 whites) while subjects consumed a eucaloric diet. Follow-up data were available for 290 Native Americans (median follow-up time: 6.6 years). AFT accounted for ∼10% of 24-h EE and explained a significant portion of deviations from expected energy requirements. Energy intake was the major determinant of AFT. AFT, normalized as a percentage of intake, was inversely related to age and fasting glucose concentration and showed a nonlinear relationship with waist circumference and BMI. Spline analysis demonstrated that AFT becomes inversely related to BMI at an inflection point of 29 kg/m2. The residual variance of AFT, after accounting for covariates, predicted future weight change only in subjects with a BMI >29 kg/m2. AFT may influence daily energy balance, is reduced in obese individuals, and predicts future weight gain in these subjects. Once central adiposity develops, a blunting of AFT may occur that then contributes to further weight gain. PMID:23974925

  14. Bigger data, collaborative tools and the future of predictive drug discovery

    NASA Astrophysics Data System (ADS)

    Ekins, Sean; Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-10-01

    Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

  15. Major Uncertainties in the Earth System: Using the Present to Help Predict the Future

    NASA Astrophysics Data System (ADS)

    Denning, S.

    2012-12-01

    Radiative forcing of climate over the next century may change by as much as it did during 100 centuries of deglaciation, with potentially catastrophic results for socioeconomic systems. Predicting these changes is difficult because both human and biogeophysical systems that control the forcing are themselves difficult to predict. Earth system models that simulate a broader range of climate forcing are now becoming available, but some of the most influential processes are much less constrained by observations than meteorological and physical ocean processes that have traditionally been included, so uncertainties in future climate (even for a given level of fossil fuel combustion) remains high. Three particular areas of significant uncertainty that have emerged are (1) the strength of positive feedback among circulation, drought, and forest dieback in the tropics; (2) the degree of saturation of terrestrial carbon sinks in the middle latitudes; and (3) the vulnerability of frozen organic matter in high northern latitudes. Earth system models include these effects, but lack strong observational support. Clever use of field campaigns, manipulative experiments, and diagnostic modeling is required to improve confidence in future radiative forcing, which is now dominated by these poorly-constrained mechanisms.

  16. 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

  17. Predicting the Distribution of Commercially Important Invertebrate Stocks under Future Climate

    PubMed Central

    Russell, Bayden D.; Connell, Sean D.; Mellin, Camille; Brook, Barry W.; Burnell, Owen W.; Fordham, Damien A.

    2012-01-01

    The future management of commercially exploited species is challenging because techniques used to predict the future distribution of stocks under climate change are currently inadequate. We projected the future distribution and abundance of two commercially harvested abalone species (blacklip abalone, Haliotis rubra and greenlip abalone, H. laevigata) inhabiting coastal South Australia, using multiple species distribution models (SDM) and for decadal time slices through to 2100. Projections are based on two contrasting global greenhouse gas emissions scenarios. The SDMs identified August (winter) Sea Surface Temperature (SST) as the best descriptor of abundance and forecast that warming of winter temperatures under both scenarios may be beneficial to both species by allowing increased abundance and expansion into previously uninhabited coasts. This range expansion is unlikely to be realised, however, as projected warming of March SST is projected to exceed temperatures which cause up to 10-fold increases in juvenile mortality. By linking fine-resolution forecasts of sea surface temperature under different climate change scenarios to SDMs and physiological experiments, we provide a practical first approximation of the potential impact of climate-induced change on two species of marine invertebrates in the same fishery. PMID:23251326

  18. Adolescents' expectations for the future predict health behaviors in early adulthood.

    PubMed

    McDade, Thomas W; Chyu, Laura; Duncan, Greg J; Hoyt, Lindsay T; Doane, Leah D; Adam, Emma K

    2011-08-01

    Health-related behaviors in adolescence establish trajectories of risk for obesity and chronic degenerative diseases, and they represent an important pathway through which socio-economic environments shape patterns of morbidity and mortality. Most behaviors that promote health involve making choices that may not pay off until the future, but the factors that predict an individual's investment in future health are not known. In this paper we consider whether expectations for the future in two domains relevant to adolescents in the U.S.-perceived chances of living to middle age and perceived chances of attending college-are associated with an individual's engagement in behaviors that protect health in the long run. We focus on adolescence as an important life stage during which habits formed may shape trajectories of disease risk later in life. We use data from a large, nationally representative sample of American youth (the US National Longitudinal Study of Adolescent Health) to predict levels of physical activity, fast food consumption, and cigarette smoking in young adulthood in relation to perceived life chances in adolescence, controlling for baseline health behaviors and a wide range of potentially confounding factors. We found that adolescents who rated their chances of attending college more highly exercised more frequently and smoked fewer cigarettes in young adulthood. Adolescents with higher expectations of living to age 35 smoked fewer cigarettes as young adults. Parental education was a significant predictor of perceived life chances, as well as health behaviors, but for each outcome the effects of perceived life chances were independent of, and often stronger than, parental education. Perceived life chances in adolescence may therefore play an important role in establishing individual trajectories of health, and in contributing to social gradients in population health.

  19. Predicting the future relapse of alcohol-dependent patients from structural and functional brain images.

    PubMed

    Seo, Sambu; Mohr, Johannes; Beck, Anne; Wüstenberg, Torsten; Heinz, Andreas; Obermayer, Klaus

    2015-11-01

    In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.

  20. The Need for Accurate Risk Prediction Models for Road Mapping, Shared Decision Making and Care Planning for the Elderly with Advanced Chronic Kidney Disease.

    PubMed

    Stryckers, Marijke; Nagler, Evi V; Van Biesen, Wim

    2016-11-01

    As people age, chronic kidney disease becomes more common, but it rarely leads to end-stage kidney disease. When it does, the choice between dialysis and conservative care can be daunting, as much depends on life expectancy and personal expectations of medical care. Shared decision making implies adequately informing patients about their options, and facilitating deliberation of the available information, such that decisions are tailored to the individual's values and preferences. Accurate estimations of one's risk of progression to end-stage kidney disease and death with or without dialysis are essential for shared decision making to be effective. Formal risk prediction models can help, provided they are externally validated, well-calibrated and discriminative; include unambiguous and measureable variables; and come with readily applicable equations or scores. Reliable, externally validated risk prediction models for progression of chronic kidney disease to end-stage kidney disease or mortality in frail elderly with or without chronic kidney disease are scant. Within this paper, we discuss a number of promising models, highlighting both the strengths and limitations physicians should understand for using them judiciously, and emphasize the need for external validation over new development for further advancing the field.

  1. The influence of coarse-scale environmental features on current and predicted future distributions of narrow-range endemic crayfish populations

    USGS Publications Warehouse

    Dyer, Joseph J.; Brewer, Shannon K.; Worthington, Thomas A.; Bergey, Elizabeth A.

    2013-01-01

    1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario

  2. The Value of Hippocampal and Temporal Horn Volumes and Rates of Change in Predicting Future Conversion to AD

    PubMed Central

    Bartlett, Jonathan W.; Leung, Kelvin K.; Ourselin, Sebastien; Barnes, Josephine

    2013-01-01

    Hippocampal pathology occurs early in Alzheimer disease (AD), and atrophy, measured by volumes and volume changes, may predict which subjects will develop AD. Measures of the temporal horn (TH), which is situated adjacent to the hippocampus, may also indicate early changes in AD. Previous studies suggest that these metrics can predict conversion from amnestic mild cognitive impairment (MCI) to AD with conversion and volume change measured concurrently. However, the ability of these metrics to predict future conversion has not been investigated. We compared the abilities of hippocampal, TH, and global measures to predict future conversion from MCI to AD. TH, hippocampi, whole brain, and ventricles were measured using baseline and 12-month scans. Boundary shift integral was used to measure the rate of change. We investigated the prediction of conversion between 12 and 24 months in subjects classified as MCI from baseline to 12 months. All measures were predictive of future conversion. Local and global rates of change were similarly predictive of conversion. There was evidence that the TH expansion rate is more predictive than the hippocampal atrophy rate (P=0.023) and that the TH expansion rate is more predictive than the TH volume (P=0.036). Prodromal atrophy rates may be useful predictors of future conversion to sporadic AD from amnestic MCI. PMID:22760170

  3. How Accurately Can Extended X-ray Absorption Spectra Be Predicted from First Principles? Implications for Modeling the Oxygen-Evolving Complex in Photosystem II.

    PubMed

    Beckwith, Martha A; Ames, William; Vila, Fernando D; Krewald, Vera; Pantazis, Dimitrios A; Mantel, Claire; Pécaut, Jacques; Gennari, Marcello; Duboc, Carole; Collomb, Marie-Noëlle; Yano, Junko; Rehr, John J; Neese, Frank; DeBeer, Serena

    2015-10-14

    First principle calculations of extended X-ray absorption fine structure (EXAFS) data have seen widespread use in bioinorganic chemistry, perhaps most notably for modeling the Mn4Ca site in the oxygen evolving complex (OEC) of photosystem II (PSII). The logic implied by the calculations rests on the assumption that it is possible to a priori predict an accurate EXAFS spectrum provided that the underlying geometric structure is correct. The present study investigates the extent to which this is possible using state of the art EXAFS theory. The FEFF program is used to evaluate the ability of a multiple scattering-based approach to directly calculate the EXAFS spectrum of crystallographically defined model complexes. The results of these parameter free predictions are compared with the more traditional approach of fitting FEFF calculated spectra to experimental data. A series of seven crystallographically characterized Mn monomers and dimers is used as a test set. The largest deviations between the FEFF calculated EXAFS spectra and the experimental EXAFS spectra arise from the amplitudes. The amplitude errors result from a combination of errors in calculated S0(2) and Debye-Waller values as well as uncertainties in background subtraction. Additional errors may be attributed to structural parameters, particularly in cases where reliable high-resolution crystal structures are not available. Based on these investigations, the strengths and weaknesses of using first-principle EXAFS calculations as a predictive tool are discussed. We demonstrate that a range of DFT optimized structures of the OEC may all be considered consistent with experimental EXAFS data and that caution must be exercised when using EXAFS data to obtain topological arrangements of complex clusters.

  4. 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

  5. Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study

    SciTech Connect

    Edwards, Richard E; New, Joshua Ryan; Parker, Lynne Edwards

    2012-01-01

    Whole building input models for energy simulation programs are frequently created in order to evaluate specific energy savings potentials. They are also often utilized to maximize cost-effective retrofits for existing buildings as well as to estimate the impact of policy changes toward meeting energy savings goals. Traditional energy modeling suffers from several factors, including the large number of inputs required to characterize the building, the specificity required to accurately model building materials and components, simplifying assumptions made by underlying simulation algorithms, and the gap between the as-designed and as-built building. Prior works have attempted to mitigate these concerns by using sensor-based machine learning approaches to model energy consumption. However, a majority of these prior works focus only on commercial buildings. The works that focus on modeling residential buildings primarily predict monthly electrical consumption, while commercial models predict hourly consumption. This means there is not a clear indicator of which techniques best model residential consumption, since these methods are only evaluated using low-resolution data. We address this issue by testing seven different machine learning algorithms on a unique residential data set, which contains 140 different sensors measurements, collected every 15 minutes. In addition, we validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, using the original competition metrics. Our validation results confirm existing conclusions that Neural Network-based methods perform best on commercial buildings. However, the results from testing our residential data set show that Feed Forward Neural Networks, Support Vector Regression (SVR), and Linear Regression methods perform poorly, and that Hierarchical Mixture of Experts (HME) with Least Squares Support Vector Machines (LS-SVM) performs best - a technique not previously applied to this domain.

  6. Modeling Spatial Recharge in the Arid Southern Okanagan Basin and Impacts of Future Predicted Climate Change

    NASA Astrophysics Data System (ADS)

    Allen, D. M.; Toews, M. W.

    2007-12-01

    Groundwater systems in arid regions will be particularly sensitive to climate change owing to the strong dependence of evapotranspiration rates on temperature, and potential shifts in the precipitation amounts and timing. In this study, future predicted climate change from three GCMs (CGCM1 GHG+A, CGCM3.1 A2, and HadCM3 A2) are used to evaluate the sensitivity of recharge in the Oliver region of the Okanagan Valley, south- central British Columbia, where annual precipitation is approximately 300~mm. Temperature data were downscaled using Statistical Downscaling Model (SDSM), while precipitation and solar radiation changes were estimated directly from the GCM data. Results for the region suggest that temperature will increase up to 4°C by the end of the century. Precipitation is expected to decrease in the spring, and increase in the fall. Solar radiation may decrease in the late summer. Shifts in climate, from present to future-predicted, were applied to the LARS-WG stochastic weather generator to generate daily stochastic weather series. Recharge was modeled spatially using output from the HELP hydrologic model applied to one-dimensional soil columns. An extensive valley-bottom soil database was used to determine both the spatial variation and vertical assemblage of soil horizons in the Oliver region. Soil hydraulic parameters were estimated from soil descriptions using pedotransfer functions through the ROSETTA program. Leaf area index (LAI) was estimated from ground-truthed Landsat 5 TM imagery, and surface slope was estimated from a digital elevation model. Irrigation application rates were modified for each climate scenario based on estimates of seasonal crop water demand. Daily irrigation was added to precipitation in irrigation districts using proportions of crop types along with daily climate and evapotranspiration data from LARS-WG. The two dominant crop classes are orchard (including peaches, cherries and apples) and vineyards (grapes). Recharge in

  7. Predicting the Future as Bayesian Inference: People Combine Prior Knowledge with Observations when Estimating Duration and Extent

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2011-01-01

    Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…

  8. Predicting future conflict between team-members with parameter-free models of social networks.

    PubMed

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-01-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  9. Predicting future conflict between team-members with parameter-free models of social networks

    NASA Astrophysics Data System (ADS)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-06-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  10. How can Historical Responses of Amazonian Forests to Drought and Fire Inform Future Prediction?

    NASA Astrophysics Data System (ADS)

    Brando, P. M.; dos Santos, C.; Alencar, A.; Asner, G. P.; Coe, M. T.; Silverio, D. V.

    2014-12-01

    The responses of Amazonian forests to droughts have important implications for sustainability, biodiversity, and ecosystem processes. These implications are all potentially large, diverse, and persistent. During recent years, for example, more than half of the Amazon experienced droughts that were severe enough to cause increased tree mortality, reduced tree growth, and widespread forest fires, committing to the atmosphere between 1-2% of the carbon stocks of Amazon forests. As climate and land use change, Amazon droughts may become even more frequent and severe. However, most of the existing ecosystem models used to predict potential forest trajectories in Amazonia only accounts for the effects of climate forcing, although the interaction between fires and droughts is perhaps a more direct mechanism of abrupt forest degradation, especially for the southeastern Amazon. Thus, projections of future vegetation responses to climate change in Amazonia require more than simulation of global climate forcing alone and should also consider interactions of droughts, forest fires, and land-use change.

  11. The UT 8 February 2013 Sila-Nunam Mutual Event & Future Predictions

    NASA Astrophysics Data System (ADS)

    Benecchi, Susan D.; Noll, K.; Thirouin, A.; Ryan, E.; Grundy, W.; Verbiscer, A.; Doressoundiram, A.; Hestroffer, D.; Beaton, R.; Rabinowitz, D.; Chanover, N.

    2013-10-01

    A mutual event of the Kuiper Belt binary system (79360) Sila-Nunam was observed over 15.47 hours on UT 8 February 2013 by a coordinated effort at four telescopes: Telescopio Nationale Galileo in the Canary Islands, the du Pont telescope at Las Campanas Observatory, ARC at Apache Point Observatory and the IRTF on Mauna Kea. It is the first full event observed from start to finish for this binary system. The lightcurve is consistent with two objects of similar, but perhaps not identical, size and albedo. We will present the results from this event and predictions for future events which have been refined by this and other mutual event observations obtained since the events began.

  12. ANTHELMINTICS: THE BEST WAY TO PREDICT THE FUTURE IS TO CREATE IT

    PubMed Central

    Martin, Richard J.; Verma, Saurabh.; Choudhary, Shivani; Kashyap, Sudhanva; Zheng, Melanie Abongwa Fudan; Robertson, Alan P.

    2015-01-01

    ‘The best way to predict the future is to create it.’ When we look at drugs that are used to control parasites, we see that new knowledge has been created (discovered) about their modes of action. This knowledge will allow us to predict combinations of drugs which can be used together rationally to increase the spectrum of action and to slow the development of anthelmintic resistance. In this paper we comment on some recent observations of ours on the modes of action of emodepside, diethylcarbamazine and tribendimidine. Emodepside increases the activation of a SLO-1 K+ current inhibiting movement, and diethylcarbamazine has a synergistic effect on the effect of emodepside on the SLO-1 K+ current, increasing the size of the response. The combination may be considered for further testing for therapeutic use. Tribendimidine is a selective cholinergic nematode B-subtype nAChR agonist, producing muscle depolarization and contraction. It has different subtype selectivity to levamisole and may be effective in the presence of some types of levamisole resistance. The new information about the modes of action may aid the design of rational drug combinations designed to slow the development of resistance or increase the spectrum of action. PMID:26138153

  13. Future Cognitive Ability: US IQ Prediction until 2060 Based on NAEP

    PubMed Central

    2015-01-01

    The US National Assessment of Educational Progress (NAEP) measures cognitive competences in reading and mathematics of US students (last 2012 survey N = 50,000). The long-term development based on results from 1971 to 2012 allows a prediction of future cognitive trends. For predicting US averages also demographic trends have to be considered. The largest groups’ (White) average of 1978/80 was set at M = 100 and SD = 15 and was used as a benchmark. Based on two past NAEP development periods for 17-year-old students, 1978/80 to 2012 (more optimistic) and 1992 to 2012 (more pessimistic), and demographic projections from the US Census Bureau, cognitive trends until 2060 for the entire age cohort and ethnic groups were estimated. Estimated population averages for 2060 are 103 (optimistic) or 102 (pessimistic). The average rise per decade is dec = 0.76 or 0.45 IQ points. White-Black and White-Hispanic gaps are declining by half, Asian-White gaps treble. The catch-up of minorities (their faster ability growth) contributes around 2 IQ to the general rise of 3 IQ; however, their larger demographic increase reduces the general rise at about the similar amount (-1.4 IQ). Because minorities with faster ability growth also rise in their population proportion the interactive term is positive (around 1 IQ). Consequences for economic and societal development are discussed. PMID:26460731

  14. Polar predictability: exploring the influence of GCM and regional model uncertainty on future ice sheet climates

    NASA Astrophysics Data System (ADS)

    Reusch, D. B.

    2015-12-01

    Evaluating uncertainty in GCMs and regional-scale forecast models is an essential step in the development of climate change predictions. Polar-region skill is particularly important due to the potential for changes affecting both local (ice sheet) and global (sea level) environments through more frequent/intense surface melting and changes in precipitation type/amount. High-resolution, regional-scale models also use GCMs as a source of boundary/initial conditions in future scenarios, thus inheriting a measure of GCM-derived externally-driven uncertainty. We examine inter- and intramodel uncertainty through statistics from decadal climatologies and analyses of variability based on self-organizing maps (SOMs), a nonlinear data analysis tool. We evaluate a 19-member CMIP5 subset and the 30-member CESM1.0-CAM5-BGC Large Ensemble (CESMLE) during polar melt seasons (boreal/austral summer) for recent (1981-2000) and future (2081-2100, RCP 8.5) decades. Regional-model uncertainty is examined with a subset of these GCMs driving Polar WRF simulations. Decadal climatologies relative to a reference (recent: the ERA-Interim reanalysis; future: a skillful modern GCM) identify model uncertainty in bulk, e.g., BNU-ESM is too warm, CMCC-CM too cold. While quite useful for model screening, diagnostic benefit is often indirect. SOMs extend our diagnostics by providing a concise, objective summary of model variability as a set of generalized patterns. Joint analysis of reference and test models summarizes the variability of multiple realizations of climate (all the models), benchmarks each model versus the reference (frequency analysis helps identify the patterns behind GCM bias), and places each GCM in a common context. Joint SOM analysis of CESMLE members shows how initial conditions contribute to differences in modeled climates, providing useful information about internal variability, such as contributions from each member to overall uncertainty using pattern frequencies. In the

  15. Psychological approach to successful ageing predicts future quality of life in older adults

    PubMed Central

    2011-01-01

    Background Public policies aim to promote well-being, and ultimately the quality of later life. Positive perspectives of ageing are underpinned by a range of appraoches to successful ageing. This study aimed to investigate whether baseline biological, psychological and social aproaches to successful ageing predicted future QoL. Methods Postal follow-up in 2007/8 of a national random sample of 999 people aged 65 and over in 1999/2000. Of 496 valid addresses of survivors at follow-up, the follow-up response rate was 58% (287). Measures of the different concepts of successful ageing were constructed using baseline indicators. They were assessed for their ability to independently predict quality of life at follow-up. Results Few respondents achieved all good scores within each of the approaches to successful ageing. Each approach was associated with follow-up QoL when their scores were analysed continuously. The biomedical (health) approach failed to achieve significance when the traditional dichotomous cut-off point for successfully aged (full health), or not (less than full health), was used. In multiple regression analyses of the relative predictive ability of each approach, only the psychological approach (perceived self-efficacy and optimism) retained significance. Conclusion Only the psychological approach to successful ageing independently predicted QoL at follow-up. Successful ageing is not only about the maintenance of health, but about maximising one's psychological resources, namely self-efficacy and resilience. Increasing use of preventive care, better medical management of morbidity, and changing lifestyles in older people may have beneficial effects on health and longevity, but may not improve their QoL. Adding years to life and life to years may require two distinct and different approaches, one physical and the other psychological. Follow-up health status, number of supporters and social activities, and self-rated active ageing also significantly

  16. Integrating trans-abdominal ultrasonography with fecal steroid metabolite monitoring to accurately diagnose pregnancy and predict the timing of parturition in the red panda (Ailurus fulgens styani).

    PubMed

    Curry, Erin; Browning, Lissa J; Reinhart, Paul; Roth, Terri L

    2017-02-23

    Red pandas (Ailurus fulgens styani) exhibit a variable gestation length and may experience a pseudopregnancy indistinguishable from true pregnancy; therefore, it is not possible to deduce an individual's true pregnancy status and parturition date based on breeding dates or fecal progesterone excretion patterns alone. The goal of this study was to evaluate the use of transabdominal ultrasonography for pregnancy diagnosis in red pandas. Two to three females were monitored over 4 consecutive years, generating a total of seven profiles (four pregnancies, two pseudopregnancies, and one lost pregnancy). Fecal samples were collected and assayed for progesterone (P4) and estrogen conjugate (EC) to characterize patterns associated with breeding activity and parturition events. Animals were trained for voluntary transabdominal ultrasound and examinations were performed weekly. Breeding behaviors and fecal EC data suggest that the estrus cycle of this species is 11-12 days in length. Fecal steroid metabolite analyses also revealed that neither P4 nor EC concentrations were suitable indicators of pregnancy in this species; however, a secondary increase in P4 occurred 69-71 days prior to parturition in all pregnant females, presumably coinciding with embryo implantation. Using ultrasonography, embryos were detected as early as 62 days post-breeding/50 days pre-partum and serial measurements of uterine lumen diameter were documented throughout four pregnancies. Advances in reproductive diagnostics, such as the implementation of ultrasonography, may facilitate improved husbandry of pregnant females and allow for the accurate prediction of parturition.

  17. Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images

    PubMed Central

    Bagci, Ulas; Yao, Jianhua; Miller-Jaster, Kirsten; Chen, Xinjian; Mollura, Daniel J.

    2013-01-01

    We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on 18F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 18F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUVmax (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUVmax. We also found that integrating textural features with SUV measurements

  18. Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects.

    PubMed

    Mason, Tom H E; Stephens, Philip A; Apollonio, Marco; Willis, Stephen G

    2014-12-01

    The altitudinal shifts of many montane populations are lagging behind climate change. Understanding habitual, daily behavioural rhythms, and their climatic and environmental influences, could shed light on the constraints on long-term upslope range-shifts. In addition, behavioural rhythms can be affected by interspecific interactions, which can ameliorate or exacerbate climate-driven effects on ecology. Here, we investigate the relative influences of ambient temperature and an interaction with domestic sheep (Ovis aries) on the altitude use and activity budgets of a mountain ungulate, the Alpine chamois (Rupicapra rupicapra). Chamois moved upslope when it was hotter but this effect was modest compared to that of the presence of sheep, to which they reacted by moving 89-103 m upslope, into an entirely novel altitudinal range. Across the European Alps, a range-shift of this magnitude corresponds to a 46% decrease in the availability of suitable foraging habitat. This highlights the importance of understanding how factors such as competition and disturbance shape a given species' realised niche when predicting potential future responses to change. Furthermore, it exposes the potential for manipulations of species interactions to ameliorate the impacts of climate change, in this case by the careful management of livestock. Such manipulations could be particularly appropriate for species where competition or disturbance already strongly restricts their available niche. Our results also reveal the potential role of behavioural flexibility in responses to climate change. Chamois reduced their activity when it was warmer, which could explain their modest altitudinal migrations. Considering this behavioural flexibility, our model predicts a small 15-30 m upslope shift by 2100 in response to climate change, less than 4% of the altitudinal shift that would be predicted using a traditional species distribution model-type approach (SDM), which assumes that species' behaviour

  19. The predictive state: Science, territory and the future of the Indian climate.

    PubMed

    Mahony, Martin

    2014-02-01

    Acts of scientific calculation have long been considered central to the formation of the modern nation state, yet the transnational spaces of knowledge generation and political action associated with climate change seem to challenge territorial modes of political order. This article explores the changing geographies of climate prediction through a study of the ways in which climate change is rendered knowable at the national scale in India. The recent controversy surrounding an erroneous prediction of melting Himalayan glaciers by the Intergovernmental Panel on Climate Change provides a window onto the complex and, at times, antagonistic relationship between the Panel and Indian political and scientific communities. The Indian reaction to the error, made public in 2009, drew upon a national history of contestation around climate change science and corresponded with the establishment of a scientific assessment network, the Indian Network for Climate Change Assessment, which has given the state a new platform on which to bring together knowledge about the future climate. I argue that the Indian Network for Climate Change Assessment is indicative of the growing use of regional climate models within longer traditions of national territorial knowledge-making, allowing a rescaling of climate change according to local norms and practices of linking scientific knowledge to political action. I illustrate the complex co-production of the epistemic and the normative in climate politics, but also seek to show how co-productionist understandings of science and politics can function as strategic resources in the ongoing negotiation of social order. In this case, scientific rationalities and modes of environmental governance contribute to the contested epistemic construction of territory and the evolving spatiality of the modern nation state under a changing climate.

  20. Climate-Driven Range Extension of Amphistegina (Protista, Foraminiferida): Models of Current and Predicted Future Ranges

    PubMed Central

    Langer, Martin R.; Weinmann, Anna E.; Lötters, Stefan; Bernhard, Joan M.; Rödder, Dennis

    2013-01-01

    Species-range expansions are a predicted and realized consequence of global climate change. Climate warming and the poleward widening of the tropical belt have induced range shifts in a variety of marine and terrestrial species. Range expansions may have broad implications on native biota and ecosystem functioning as shifting species may perturb recipient communities. Larger symbiont-bearing foraminifera constitute ubiquitous and prominent components of shallow water ecosystems, and range shifts of these important protists are likely to trigger changes in ecosystem functioning. We have used historical and newly acquired occurrence records to compute current range shifts of Amphistegina spp., a larger symbiont-bearing foraminifera, along the eastern coastline of Africa and compare them to analogous range shifts currently observed in the Mediterranean Sea. The study provides new evidence that amphisteginid foraminifera are rapidly progressing southwestward, closely approaching Port Edward (South Africa) at 31°S. To project future species distributions, we applied a species distribution model (SDM) based on ecological niche constraints of current distribution ranges. Our model indicates that further warming is likely to cause a continued range extension, and predicts dispersal along nearly the entire southeastern coast of Africa. The average rates of amphisteginid range shift were computed between 8 and 2.7 km year−1, and are projected to lead to a total southward range expansion of 267 km, or 2.4° latitude, in the year 2100. Our results corroborate findings from the fossil record that some larger symbiont-bearing foraminifera cope well with rising water temperatures and are beneficiaries of global climate change. PMID:23405081

  1. IMPre: An Accurate and Efficient Software for Prediction of T- and B-Cell Receptor Germline Genes and Alleles from Rearranged Repertoire Data

    PubMed Central

    Zhang, Wei; Wang, I-Ming; Wang, Changxi; Lin, Liya; Chai, Xianghua; Wu, Jinghua; Bett, Andrew J.; Dhanasekaran, Govindarajan; Casimiro, Danilo R.; Liu, Xiao

    2016-01-01

    Large-scale study of the properties of T-cell receptor (TCR) and B-cell receptor (BCR) repertoires through next-generation sequencing is providing excellent insights into the understanding of adaptive immune responses. Variable(Diversity)Joining [V(D)J] germline genes and alleles must be characterized in detail to facilitate repertoire analyses. However, most species do not have well-characterized TCR/BCR germline genes because of their high homology. Also, more germline alleles are required for humans and other species, which limits the capacity for studying immune repertoires. Herein, we developed “Immune Germline Prediction” (IMPre), a tool for predicting germline V/J genes and alleles using deep-sequencing data derived from TCR/BCR repertoires. We developed a new algorithm, “Seed_Clust,” for clustering, produced a multiway tree for assembly and optimized the sequence according to the characteristics of rearrangement. We trained IMPre on human samples of T-cell receptor beta (TRB) and immunoglobulin heavy chain and then tested it on additional human samples. Accuracy of 97.7, 100, 92.9, and 100% was obtained for TRBV, TRBJ, IGHV, and IGHJ, respectively. Analyses of subsampling performance for these samples showed IMPre to be robust using different data quantities. Subsequently, IMPre was tested on samples from rhesus monkeys and human long sequences: the highly accurate results demonstrated IMPre to be stable with animal and multiple data types. With rapid accumulation of high-throughput sequence data for TCR and BCR repertoires, IMPre can be applied broadly for obtaining novel genes and a large number of novel alleles. IMPre is available at https://github.com/zhangwei2015/IMPre. PMID:27867380

  2. Accurate Establishment of Error Models for the Satellite Gravity Gradiometry Recovery and Requirements Analysis for the Future GOCE Follow-On Mission

    NASA Astrophysics Data System (ADS)

    Zheng, Wei; Wang, Zhaokui; Ding, Yanwei; Li, Zhaowei

    2016-06-01

    Firstly, the new single and combined error models applied to estimate the cumulative geoid height error are efficiently produced by the dominating error sources consisting of the gravity gradient of the satellite-equipped gradiometer and the orbital position of the space-borne GPS/GLONASS receiver using the power spectral principle. At degree 250, the cumulative geoid height error is 1.769 × 10-1 m based on the new combined error model, which preferably accords with a recovery accuracy of 1.760 ×10-1 m from the GOCE-only Earth gravity field model GO_CONS_GCF_2_TIM_R2 released in Germany. Therefore, the new combined error model of the cumulative geoid height is correct and reliable in this study. Secondly, the requirements analysis for the future GOCE Follow-On satellite system is carried out in respect of the preferred design of the matching measurement accuracy of key payloads comprising the gravity gradient and orbital position and the optimal selection of the orbital altitude of the satellite. We recommend the gravity gradient with an accuracy of 10-13-10-15 /s2, the orbital position with a precision of 1-0.1 cm and the orbital altitude of 200-250 km in the future GOCE Follow-On mission.

  3. A comparison of adolescent smoking initiation measures on predicting future smoking behavior

    PubMed Central

    Azagba, Sunday; Baskerville, Neill Bruce; Minaker, Leia

    2015-01-01

    Objectives Evidence suggests that age at smoking initiation has implications for tobacco use, nicotine dependence, and resulting long-term health and chronic disease outcomes. The objective of the current study was to examine two different measures of smoking onset and to compare their validity in predicting future adolescent smoking survey. Methods Data from grades 9–12 students who participated in the 2012/2013 Youth Smoking Survey, a nationally-generalizable Canadian survey, and who had ever tried a cigarette, even a few puffs (n = 8126) were used in a multivariable logistic regression analysis to examine the association between age at smoking onset and current smoking behavior. Results Both “age at first puff” and “age at first whole cigarette” were significantly associated with current smoking status. Specifically, a delay of one year in the age at first puff was associated with lower odds of being a current smoker by 24% (AOR = 0.76, 95% CI = 0.73–0.79). Similarly, high school students who smoked their first whole cigarette at old age were less likely to report being a current smoker (AOR = 0.66, 95% CI = 0.62–0.71). Conclusion Efforts to prevent smoking uptake among youth, especially younger youth, are especially important in tobacco control efforts. PMID:26844068

  4. Ability of matrix models to explain the past and predict the future of plant populations.

    PubMed

    Crone, Elizabeth E; Ellis, Martha M; Morris, William F; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlén, Johan; Kaye, Thomas N; Knight, Tiffany M; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer L; Doak, Daniel F; Ganesan, Rengaian; McEachern, Kathyrn; Thorpe, Andrea S; Menges, Eric S

    2013-10-01

    Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

  5. Nitrogen oxides emissions from thermal power plants in china: current status and future predictions.

    PubMed

    Tian, Hezhong; Liu, Kaiyun; Hao, Jiming; Wang, Yan; Gao, Jiajia; Qiu, Peipei; Zhu, Chuanyong

    2013-10-01

    Increasing emissions of nitrogen oxides (NOx) over the Chinese mainland have been of great concern due to their adverse impacts on regional air quality and public health. To explore and obtain the temporal and spatial characteristics of NOx emissions from thermal power plants in China, a unit-based method is developed. The method assesses NOx emissions based on detailed information on unit capacity, boiler and burner patterns, feed fuel types, emission control technologies, and geographical locations. The national total NOx emissions in 2010 are estimated at 7801.6 kt, of which 5495.8 kt is released from coal-fired power plant units of considerable size between 300 and 1000 MW. The top provincial emitter is Shandong where plants are densely concentrated. The average NOx-intensity is estimated at 2.28 g/kWh, markedly higher than that of developed countries, mainly owing to the inadequate application of high-efficiency denitrification devices such as selective catalytic reduction (SCR). Future NOx emissions are predicted by applying scenario analysis, indicating that a reduction of about 40% by the year 2020 can be achieved compared with emissions in 2010. These results suggest that NOx emissions from Chinese thermal power plants could be substantially mitigated within 10 years if reasonable control measures were implemented effectively.

  6. Ability of matrix models to explain the past and predict the future of plant populations.

    USGS Publications Warehouse

    McEachern, Kathryn; Crone, Elizabeth E.; Ellis, Martha M.; Morris, William F.; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlen, Johan; Kaye, Thomas N.; Knight, Tiffany M.; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F.; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer I.; Doak, Daniel F.; Ganesan, Rengaian; Thorpe, Andrea S.; Menges, Eric S.

    2013-01-01

    Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

  7. Predictability and Market Efficiency in Agricultural Futures Markets: a Perspective from Price-Volume Correlation Based on Wavelet Coherency Analysis

    NASA Astrophysics Data System (ADS)

    He, Ling-Yun; Wen, Xing-Chun

    2015-12-01

    In this paper, we use a time-frequency domain technique, namely, wavelet squared coherency, to examine the associations between the trading volumes of three agricultural futures and three different forms of these futures' daily closing prices, i.e. prices, returns and volatilities, over the past several years. These agricultural futures markets are selected from China as a typical case of the emerging countries, and from the US as a representative of the developed economies. We investigate correlations and lead-lag relationships between the trading volumes and the prices to detect the predictability and efficiency of these futures markets. The results suggest that the information contained in the trading volumes of the three agricultural futures markets in China can be applied to predict the prices or returns, while that in US has extremely weak predictive power for prices or returns. We also conduct the wavelet analysis on the relationships between the volumes and returns or volatilities to examine the existence of the two "stylized facts" proposed by Karpoff [J. M. Karpoff, The relation between price changes and trading volume: A survey, J. Financ. Quant. Anal.22(1) (1987) 109-126]. Different markets in the two countries perform differently in reproducing the two stylized facts. As the wavelet tools can decode nonlinear regularities and hidden patterns behind price-volume relationship in time-frequency space, different from the conventional econometric framework, this paper offers a new perspective into the market predictability and efficiency.

  8. Towards an Accurate Alignment of the VLBI Frame and the Future Gaia Optical Frame: Global VLBI Imaging Observations of a Sample of Candidate Sources for this Alignment

    NASA Astrophysics Data System (ADS)

    Bourda, G.; Collioud, A.; Charlot, P.; Porcas, R.; Garrington, S.

    2012-12-01

    The space astrometry mission Gaia will construct a dense optical QSO-based celestial reference frame. For consistency between optical and radio positions, it will be important to align the Gaia and VLBI frames with the highest accuracy. However, the number of quasars that are bright at optical wavelengths (for the best position accuracy with Gaia), that have a compact core (to be detectable on VLBI scales), and that do not exhibit complex structures (to ensure a good astrometric quality) was found to be limited. It was then realized that the densification of the list of such objects was necessary. Therefore, we initiated a multi-step VLBI observational project, dedicated to finding additional suitable radio sources for aligning the two frames. The sample consists of ~450 optically- bright weak extragalactic radio sources, which have been selected by cross-correlating optical and radio catalogs. The initial observations, aimed at checking whether these sources are detectable with VLBI, and conducted with the European VLBI Network (EVN) in 2007, showed an excellent ~90% detection rate. The second step, dedicated to identifying the most point-like sources of the sample, by imaging their VLBI structures, was initiated in 2008. Approximately 25% of the detected targets were observed with the Global VLBI array (EVN+VLBA; Very Long Baseline Array) during a pilot imaging experiment, revealing that approximately 50% of them are point-like sources on VLBI scales. The rest of the sources were observed during three additional imaging experiments in March 2010, November 2010, and March 2011. In this paper, we present the results of these imaging campaigns and report plans for the final stage of the project, which will be dedicated to accurately measuring the VLBI position of the most point-like sources.

  9. On predicting future economic losses from tropical cyclones: Comparing damage functions for the Eastern USA

    NASA Astrophysics Data System (ADS)

    Geiger, Tobias; Levermann, Anders; Frieler, Katja

    2015-04-01

    Recent years have seen an intense scientific debate of what to expect from future tropical cyclone activity under climate change [1,2]. Besides the projection of cyclones' genesis points and trajectories it is the cyclone's impact on future societies that needs to be quantified. In our present work, where we focus on the Eastern USA, we start out with a comprehensive comparison of a variety of presently available and novel functional relationships that are used to link cyclones' physical properties with their damage caused on the ground. These so-called damage functions make use of high quality data sets consisting of gridded population data, exposed capital at risk, and information on the cyclone's extension and its translational and locally resolved maximum wind speed. Based on a cross-validation ansatz we train a multitude of damage functions on a large variety of data sets in order to evaluate their performance on an equally sized test sample. Although different damage analyses have been conducted in the literature [3,4,5,6], the efforts have so far primarily been focused on determining fit parameters for individual data sets. As our analysis consists of a wide range of damage functions implemented on identical data sets, we can rigorously evaluate which (type of) damage function (for which set of parameters) does best in reproducing damages and should therefore be used for future loss analysis with highest certainty. We find that the benefits of using locally resolved data input tend to be outweighed by the large uncertainties that accompany the data. More coarse and generalized data input therefore captures the diversity of cyclonic features better. Furthermore, our analysis shows that a non-linear relation between wind speed and damage outperforms the linear as well as the exponential relationship discussed in the literature. In a second step, the damage function with the highest predictive quality is implemented to predict potential future cyclone losses

  10. Analysis of Regional Climate Changes adjusted Future Urban Growth Scenarios and possibility of the future air quality prediction in Seoul Metropolitan Area (SMA), Korea

    NASA Astrophysics Data System (ADS)

    Kim, H.; Kim, Y.; Jeong, J.

    2012-12-01

    Land-use changes give effects to physical properties such as albedo, moisture availability and roughness length in the atmosphere, but future urban growth has not been considered widely to predict the future regional climate change because it is hard to predict the future land-use changes. In this study, we used the urban growth model called SLEUTH (Slope, Land-use, Excluded, Urban, Transportation, Hill-shade) based on Cellular Automata (CA) technique to predict the future land-use (especially, urban growth) changes. Seoul Metropolitan Area (SMA), the research area in this study, is the most explosively developed region in the Korean peninsula due to the continuous industrialization since 1970s. SLEUTH was calibrated to know the pattern and process of the urban growth and expansion in SMA with historical data for 35 years (1975-2000) provided from WAter Management Information System (WAMIS) in Korea and then future urban growth was projected out to 2050 assuming three different scenarios: (1) historical trends of urban growth (SC1), (2) future urban policy and plan (SC2), (3) ecological protection and growth (SC3). We used the FNL data of NCEP/NCAR for one month, Oct. in 2005 to evaluate the performance of the WRF on the long-term climate simulation and compared results of WRF with the ASOS/AWS (Automated Surface Observing Systems and Automated Weather System) observation data of the Korea Meteorology Administration. Based on the accuracy of the model, we performed various numerical experiments by the urban growth scenarios using the 6 hourly data of ECHAM5/OM-1 A1B scenarios generated by Max-Plank Institute for Meteorology in Hamburg, Germany on Oct. for 5 years (2046-2050), respectively. The difference of urban ratio under various urban growth scenarios in SMA consequently caused the spatial distributions of temperature to change, the average temperature to increase in the urban area. PBL height with a maximum of about 200m also appeared locally in newly

  11. Interfacing models of wildlife habitat and human development to predict the future distribution of puma habitat

    USGS Publications Warehouse

    Burdett, Christopher L.; Crooks, Kevin R.; Theobald, David M.; Wilson, Kenneth R.; Boydston, Erin E.; Lyren, Lisa A.; Fisher, Robert N.; Vickers, T. Winston; Morrison, Scott A.; Boyce, Walter M.

    2010-01-01

    The impact of human land uses on ecological systems typically differ relative to how extensively natural conditions are modified. Exurban development is intermediate-intensity residential development that often occurs in natural landscapes. Most species-habitat models do not evaluate the effects of such intermediate levels of human development and even fewer predict how future development patterns might affect the amount and configuration of habitat. We addressed these deficiencies by interfacing a habitat model with a spatially-explicit housing-density model to study the effect of human land uses on the habitat of pumas (Puma concolor) in southern California. We studied the response of pumas to natural and anthropogenic features within their home ranges and how mortality risk varied across a gradient of human development. We also used our housing-density model to estimate past and future housing densities and model the distribution of puma habitat in 1970, 2000, and 2030. The natural landscape for pumas in our study area consisted of riparian areas, oak woodlands, and open, conifer forests embedded in a chaparral matrix. Pumas rarely incorporated suburban or urban development into their home ranges, which is consistent with the hypothesis that the behavioral decisions of individuals can be collectively manifested as population-limiting factors at broader spatial scales. Pumas incorporated rural and exurban development into their home ranges, apparently perceiving these areas as modified, rather than non-habitat. Overall, pumas used exurban areas less than expected and showed a neutral response to rural areas. However, individual pumas that selected for or showed a neutral response to exurban areas had a higher risk of mortality than pumas that selected against exurban habitat. Exurban areas are likely hotspots for puma-human conflict in southern California. Approximately 10% of our study area will transform from exurban, rural, or undeveloped areas to suburban or

  12. An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes

    PubMed Central

    Shapira, Stav; Novack, Lena; Bar-Dayan, Yaron; Aharonson-Daniel, Limor

    2016-01-01

    Background A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities’ preparedness and response capabilities and to mitigate future consequences. Methods An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model’s algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel. Results the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard. Conclusion The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties. PMID:26959647

  13. Does a history of hypertensive disorders of pregnancy help predict future essential hypertension? Findings from a prospective pregnancy cohort study.

    PubMed

    Callaway, L K; Mamun, A; McIntyre, H D; Williams, G M; Najman, J M; Nitert, M D; Lawlor, D A

    2013-05-01

    Hypertensive disorder of pregnancy (HDP) is considered an important determinant in the prediction of future hypertension. The aim of this study is to examine whether HDP improves prediction of future hypertension, over prediction based on established risk factors measured during pregnancy. We used a community based cohort study of 2117 women who received antenatal care at a major hospital in Brisbane between 1981 and 1983 and had blood pressure assessed 21 years after the index pregnancy. Of these 2117 women, 193 (9.0%) experienced HDP and 345 (16.3%) had hypertension at 21 years postpartum. For women with HDP, the odds of being hypertensive at 21 years postpartum were 2.46 (95% CI 1.70, 3.56), adjusted for established risk factors including age, education, race, alcohol, cigarettes, exercise and body mass index. Addition of HDP did not improve the prediction model that included these established risk factors, with the area under the curve of receiver operator (AUROC) increasing from 0.710 to 0.716 (P-value for difference in AUROC=0.185). Our findings suggest that HDP is strongly and independently associated with future hypertension, and women who experience this condition should be counselled regarding lifestyle modification and careful ongoing blood pressure monitoring. However, the development of HDP during pregnancy does not improve our capacity to predict future hypertension, over risk factors identifiable at the time of pregnancy. This suggests that counseling regarding lifestyle modification and ongoing blood pressure monitoring might reasonably be provided to all pregnant and postpartum women with identifiable risk factors for future hypertension.

  14. Temperament and Parenting during the First Year of Life Predict Future Child Conduct Problems

    ERIC Educational Resources Information Center

    Lahey, Benjamin B.; Van Hulle, Carol A.; Keenan, Kate; Rathouz, Paul J.; D'Onofrio, Brian M.; Rodgers, Joseph Lee; Waldman, Irwin D.

    2008-01-01

    Predictive associations between parenting and temperament during the first year of life and child conduct problems were assessed longitudinally in 1,863 offspring of a representative sample of women. Maternal ratings of infant fussiness, activity level, predictability, and positive affect each independently predicted maternal ratings of conduct…

  15. What Do Children Know about Their Futures: Do Children's Expectations Predict Outcomes in Middle Age?

    ERIC Educational Resources Information Center

    Hallerod, Bjorn

    2011-01-01

    Are children's statements about their futures related to outcomes in middle age? In 1966 almost 13,500 children ages 12-13 were asked whether they thought their futures would be worse, similar or better as compared to others of their own age. It was shown that children with low, and surprisingly high, expectations did suffer from increased…

  16. The Future Is Bright and Predictable: The Development of Prospective Life Stories across Childhood and Adolescence

    ERIC Educational Resources Information Center

    Bohn, Annette; Berntsen, Dorthe

    2013-01-01

    When do children develop the ability to imagine their future lives in terms of a coherent prospective life story? We investigated whether this ability develops in parallel with the ability to construct a life story for the past and narratives about single autobiographical events in the past and future. Four groups of school children aged 9 to 15…

  17. Forming Attitudes that Predict Future Behavior: A Meta-Analysis of the Attitude-Behavior Relation

    ERIC Educational Resources Information Center

    Glasman, Laura R.; Albarracin, Dolores

    2006-01-01

    A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of…

  18. Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand's planted forests.

    PubMed

    Moore, John R; Watt, Michael S

    2015-08-01

    Wind is the major abiotic disturbance in New Zealand's planted forests, but little is known about how the risk of wind damage may be affected by future climate change. We linked a mechanistic wind damage model (ForestGALES) to an empirical growth model for radiata pine (Pinus radiata D. Don) and a process-based growth model (cenw) to predict the risk of wind damage under different future emissions scenarios and assumptions about the future wind climate. The cenw model was used to estimate site productivity for constant CO2 concentration at 1990 values and for assumed increases in CO2 concentration from current values to those expected during 2040 and 2090 under the B1 (low), A1B (mid-range) and A2 (high) emission scenarios. Stand development was modelled for different levels of site productivity, contrasting silvicultural regimes and sites across New Zealand. The risk of wind damage was predicted for each regime and emission scenario combination using the ForestGALES model. The sensitivity to changes in the intensity of the future wind climate was also examined. Results showed that increased tree growth rates under the different emissions scenarios had the greatest impact on the risk of wind damage. The increase in risk was greatest for stands growing at high stand density under the A2 emissions scenario with increased CO2 concentration. The increased productivity under this scenario resulted in increased tree height, without a corresponding increase in diameter, leading to more slender trees that were predicted to be at greater risk from wind damage. The risk of wind damage was further increased by the modest increases in the extreme wind climate that are predicted to occur. These results have implications for the development of silvicultural regimes that are resilient to climate change and also indicate that future productivity gains may be offset by greater losses from disturbances.

  19. Future rainfall variability in Indonesia under different ENSO and IOD composites based on decadal predictions of CMIP5 datasets

    NASA Astrophysics Data System (ADS)

    Bilhaqqi Qalbi, Harisa; Faqih, Akhmad; Hidayat, Rahmat

    2017-01-01

    El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are amongst important climate drivers that play a significant role in driving rainfall variability in Indonesia, especially on inter-annual timescales. The phenomena are suggested to have an association with interdecadal climate variability through the modulation of their oscillations. This study aims to analyse the characteristics of future rainfall variability in Indonesia during different condition of ENSO and IOD events based on decadal predictions of near-term climate change CMIP5 GCM data outputs up to year 2035. Monthly data of global rainfall data with 5x5 km grid resolutions of CHIRPS dataset is used in this study to represent historical rainfall variability as well to serve as a reference for future rainfall predictions. The current and future rainfall and sea surface temperature data have been bias corrected before performing the analysis. Given the comparison between rainfall composites during El-Nino and positive IOD events, the study showed that the future rainfall conditions in Indonesia will become drier than the historical condition resulted from the same composite approach. In general, this study showed the Indonesian rainfall variability in the future is expected to respond differently to a different combination of ENSO and IOD conditions.

  20. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Ul-Saufie, Ahmad Zia; Yahaya, Ahmad Shukri; Ramli, Nor Azam; Rosaida, Norrimi; Hamid, Hazrul Abdul

    2013-10-01

    Future PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia. Annual hourly observations for PM10 in Negeri Sembilan, Malaysia from January 2003 to December 2010 were selected for predicting PM10 concentration level. Eighty percent of the monitoring records were used for training and twenty percent were used for validation of the models. Three accuracy measures - Prediction Accuracy (PA), Coefficient of Determination (R2) and Index of Agreement (IA), as well as two error measures - Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) were used to evaluate the performance of the models. Results show that PCA models combined with MLR and PCA with FFBP improved MLR and FFBP models for all three days in advance of predicting PM10 concentration, with reduced errors by as much as 18.1% (PCA-MLR) and 17.68% (PCA-FFBP) for next day, 19.2% (PCA-MLR) and 22.1% (PCA-FFBP) for next two-day and 18.7% (PCA-MLR) and 22.79% (PCA-FFBP) for next three-day predictions. Including PCA improved the accuracy of the models by as much as by 12.9% (PCA-MLR) and 13.3% (PCA-FFBP) for next day, 32.3% (PCA-MLR) and 14.7% (PCA-FFBP) for next two-day and 46.1% (PCA-MLR) and 19.3% (PCA-FFBP) for next three-day predictions.

  1. Predicted Megafire Locations under Future Climate Scenarios in the Contiguous United States

    NASA Astrophysics Data System (ADS)

    Lorentz, K. A.; Drury, S.; Raffuse, S. M.; Larkin, N. K.

    2014-12-01

    Over the past several years, large high-intensity wildfires, or "megafires," have set records for the greatest burn area and most costly fires in several U.S. states. Megafires can release many tons of fine particles and other pollutants that are hazardous to human health over a short period of time. Under future climate scenarios, megafires may increase in some regions. The danger of smoke exposure from megafires in the future depends on several spatial factors, including the likelihood of megafire occurrence, emission rates, air transport patterns, and population density. We combined climatological transport modeling, smoke emission rates, and population density to determine the areas within the U.S. where a megafire would result in the greatest human exposure to smoke. Coupled with a synthesis of recent studies on the likelihood of megafire occurrence under future climate scenarios, these results provide a view of future smoke management and emergency response needs.

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

    EPA Science Inventory

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

  3. Predictive Value of National Football League Scouting Combine on Future Performance of Running Backs and Wide Receivers.

    PubMed

    Teramoto, Masaru; Cross, Chad L; Willick, Stuart E

    2016-05-01

    The National Football League (NFL) Scouting Combine is held each year before the NFL Draft to measure athletic abilities and football skills of college football players. Although the NFL Scouting Combine can provide the NFL teams with an opportunity to evaluate college players for the upcoming NFL Draft, its value for predicting future success of players has been questioned. This study examined whether the NFL Combine measures can predict future performance of running backs (RBs) and wide receivers (WRs) in the NFL. We analyzed the 2000-09 Combine data of RBs (N = 276) and WRs (N = 447) and their on-field performance for the first 3 years after the draft and over their entire careers in the NFL, using correlation and regression analyses, along with a principal component analysis (PCA). The results of the analyses showed that, after accounting for the number of games played, draft position, height (HT), and weight (WT), the time on 10-yard dash was the most important predictor of rushing yards per attempt of the first 3 years (p = 0.002) and of the careers (p < 0.001) in RBs. For WRs, vertical jump was found to be significantly associated with receiving yards per reception of the first 3 years (p = 0.001) and of the careers (p = 0.004) in the NFL, after adjusting for the covariates above. Furthermore, HT was most important in predicting future performance of WRs. The analyses also revealed that the 8 athletic drills in the Combine seemed to have construct validity. It seems that the NFL Scouting Combine has some value for predicting future performance of RBs and WRs in the NFL.

  4. Analysis of the decadal predictability of the North Atlantic volume and heat transport in a future climate projection

    NASA Astrophysics Data System (ADS)

    Fischer, Matthias; Müller, Wolfgang A.; Domeisen, Daniela I. V.; Baehr, Johanna

    2015-04-01

    The North Atlantic ocean is predicted to change considerably with climate change. An analysis of the North Atlantic meridional overturning circulation (AMOC) and the meridional heat transport (OHT) in CMIP5 climate projections in the global coupled Max Planck Institute Earth System Model (MPI-ESM-LR) has shown potential changes in the AMOC's and OHT's seasonal cycle in a future climate. From the CMIP5 historical simulation to RCP4.5, both the AMOC and the OHT indicate latitude dependent temporal shifts of about 1 month until 2050. Based on these results, we here examine potential changes in the decadal predictability of the AMOC and OHT under climate change. In MPI-ESM-LR, we generate two hindcast ensembles with 20 start dates and 10 ensemble members per start date for (i) the current climate state in the CMIP5 historical simulation starting in 1995 and (ii) a future climate state in RCP4.5 starting in 2045. These two hindcast ensembles are compared against the historical simulation and RCP4.5 as control simulation, respectively, using anomaly correlation, root-mean-square error (RMSE) and the Brier skill score decomposition. We investigate whether the decadal predictability of the AMOC and OHT might change under future climate conditions both for the annual mean and individual seasons or climate indices (e.g. for the NAO).

  5. Validating health impact assessment: Prediction is difficult (especially about the future)

    SciTech Connect

    Petticrew, Mark . E-mail: mark@msoc.mrc.gla.ac.uk; Cummins, Steven; Sparks, Leigh; Findlay, Anne

    2007-01-15

    Health impact assessment (HIA) has been recommended as a means of estimating how policies, programmes and projects may impact on public health and on health inequalities. This paper considers the difference between predicting health impacts and measuring those impacts. It draws upon a case study of the building of a new hypermarket in a deprived area of Glasgow, which offered an opportunity to reflect on the issue of the predictive validity of HIA, and to consider the difference between potential and actual impacts. We found that the actual impacts of the new hypermarket on diet differed from that which would have been predicted based on previous studies. Furthermore, they challenge current received wisdom about the impact of food retail outlets in poorer areas. These results are relevant to the validity of HIA as a process and emphasise the importance of further research on the predictive validity of HIA, which should help improve its value to decision-makers.

  6. Hierarchy of gene expression data is predictive of future breast cancer outcome

    NASA Astrophysics Data System (ADS)

    Chen, Man; Deem, Michael W.

    2013-10-01

    We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to the correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with the values of network hierarchy in the expression network of all genes, due to the dedifferentiation of gene pathways and circuits. These results suggest that the hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis.

  7. How well can we predict soil respiration with climate indicators, now and in the future?

    NASA Astrophysics Data System (ADS)

    Berridge, C. T.; Hadju, L. H.; Dolman, A. J.

    2014-02-01

    Soils contain the largest terrestrial store of carbon; three times greater than present atmospheric concentrations, whilst the annual soil-atmosphere exchange of carbon is an order of magnitude larger than all anthropogenic effluxes. Quantifying future pool sizes and fluxes is therefore sensitive to small methodological errors, yet unfortunately remains the second largest area of uncertainty in Intergovernmental Panel on Climate Change projections. The flux of carbon from heterotrophic decomposition of soil organic matter is parameterized as a rate constant. This parameter is calculated from observed total soil carbon efflux and contemporaneously observed temperature and soil moisture. This metric is then used to simulate future rates of heterotrophic respiration, as driven by the projections of future climate- temperature and precipitation. We examine two underlying assumptions: how well current climate (mean temperature and precipitation) can account for contemporary soil respiration, and whether an observational parameter derived from this data will be valid in the future. We find mean climate values to be of some use in capturing total soil respiration to the 95% confidence interval, but note an inability to distinguish between subtropical and Mediterranean fluxes, or wetland-grassland and wetland-forest fluxes. Regarding the future, we present a collection of CO2 enrichment studies demonstrating a strong agreement in soil respiration response (a 25% increase) independent of changes in temperature and moisture, however these data are spatially limited to the northern mid-latitudes. In order to "future-proof" simple statistical parameters used to calculate the output from heterotrophic soil respiration, we propose a correction factor derived from empirical observations, but note the spatial and temporal limitations. In conclusion, there seems to be no sound basis to assume that models with the best fit to contemporary data will produce the best estimates of

  8. A new predictive indicator by arthrography for future acetabular growth following conservative treatment of developmental dysplasia of the hip.

    PubMed

    Satsuma, Shinichi; Kobayashi, Daisuke; Kinugasa, Maki; Takeoka, Yoshiki; Kuroda, Ryosuke; Kurosaka, Masahiro

    2016-05-01

    The aim of this study was to find a new predictive indicator for acetabular growth of developmental dysplasia of the hip. Seventy-three hips that were diagnosed with developmental dysplasia of the hip and treated by conservative reduction were included in our study. In 30 hips with center-edge angle ≤ 10° at age 4, the center-edge of the acetabular limbus angle (CEALA) in the arthrogram was measured. On the basis of the results, CEALA was significantly smaller in the secondary acetabular dysplasia group than in the normal group at maturity. In conclusion, CEALA is a more reliable and accurate predictive indicator for acetabular development than center-edge angle or acetabular index.

  9. Interspecies scaling of urinary excretory amounts of nine drugs belonging to different therapeutic areas with diverse chemical structures - accurate prediction of the human urinary excretory amounts.

    PubMed

    Bhamidipati, Ravi Kanth; Mullangi, Ramesh; Srinivas, Nuggehally R

    2017-02-01

    1. The human urinary excretory amounts of total drug (parent + metabolites) were predicted for nine drugs with diverse chemical structures using simple allometry. The drugs used for scaling were cephapirin, olanzapine, labetolol, carisbamate, voriconazole, tofacitinib, nevirapine, ropinirole, and cyclindole. 2. The traditional allometric scaling was attempted using Y = aW(b) relationship. The corresponding predicted urinary amounts were converted into % recovery by using appropriate human dose. Appropriate statistical tests comprising of fold-difference (predicted/observed values) and error calculations (MAE and RMSE) were performed. 3. The interspecies scaling of all nine drugs tested showed excellent correlation (r > 0.9672). The predictions for eight out of nine drugs (exception was cephaphirin) were contained within 0.80-1.25 fold-differences. The MAE and RMSE were within ± 18% and 14.64%, respectively. 4. The present work supported the potential application of prospective allometry scaling to predict the urinary excretory amounts of the total drug and gauge any issues for the renal handling of the total drug.

  10. Enduring Risk? Old Criminal Records and Predictions of Future Criminal Involvement

    ERIC Educational Resources Information Center

    Kurlychek, Megan C.; Brame, Robert; Bushway, Shawn D.

    2007-01-01

    It is well accepted that criminal records impose collateral consequences on offenders. Such records affect access to public housing, student financial aid, welfare benefits, and voting rights. An axiom of these policies is that individuals with criminal records--even old criminal records--exhibit significantly higher risk of future criminal…

  11. Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress toward Graduation

    ERIC Educational Resources Information Center

    Davis, Alan; Solberg, V. Scott; de Baca, Christine; Gore, Taryn Hargrove

    2014-01-01

    This study evaluated the degree to which a range of social emotional learning skills--academic self-efficacy, academic motivation, social connections, importance of school, and managing psychological and emotional distress and academic stress--could be used as an indicator of future academic outcomes. Using a sample of 4,797 from a large urban…

  12. Future global and regional climate change: From near-term prediction to long-term projections (Invited)

    NASA Astrophysics Data System (ADS)

    Knutti, R.; Collins, M.; Power, S.; Kirtman, B. P.; Christensen, J. H.; Krishna Kumar, K.

    2013-12-01

    The IPCC AR5 assessed results from a hierarchy of different climate models on how climate might change in the future from decades to millennia. The projections are based on a series of new climate models and for new scenarios. They are very consistent with projections in AR4 and confirm widespread changes in the atmosphere, ocean, sea ice and land under emission scenarios without mitigation. In the late 21st century and beyond, the warming is dominated by the total emissions of CO2, and many changes will persist for centuries even if emissions were stopped. Stabilization of global temperature at 2°C above the preindustrial value for example, requires strong emission reductions over the 21st century. In the near term and locally however, interannual and decadal climate variability remains a large and mostly irreducible component of the uncertainty in projections. Improving the quality of information on regional climate change and improving the ability of the scientific community to perform near-term climate predictions are key challenges for the future. The development of a consensus in the climate science community on (i) the major directions for future model development and (ii) the scope of future coordinated model experiments will help serve the needs of both future IPCC assessments and the wider research community.

  13. The importance of considering shifts in seasonal changes in discharges when predicting future phosphorus loads in streams

    USGS Publications Warehouse

    LaBeau, Meredith B.; Mayer, Alex S.; Griffis, Veronica; Watkins, David Jr.; Robertson, Dale; Gyawali, Rabi

    2015-01-01

    In this work, we hypothesize that phosphorus (P) concentrations in streams vary seasonally and with streamflow and that it is important to incorporate this variation when predicting changes in P loading associated with climate change. Our study area includes 14 watersheds with a range of land uses throughout the U.S. Great Lakes Basin. We develop annual seasonal load-discharge regression models for each watershed and apply these models with simulated discharges generated for future climate scenarios to simulate future P loading patterns for two periods: 2046–2065 and 2081–2100. We utilize output from the Coupled Model Intercomparison Project phase 3 downscaled climate change projections that are input into the Large Basin Runoff Model to generate future discharge scenarios, which are in turn used as inputs to the seasonal P load regression models. In almost all cases, the seasonal load-discharge models match observed loads better than the annual models. Results using the seasonal models show that the concurrence of nonlinearity in the load-discharge model and changes in high discharges in the spring months leads to the most significant changes in P loading for selected tributaries under future climate projections. These results emphasize the importance of using seasonal models to understand the effects of future climate change on nutrient loads.

  14. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

    PubMed Central

    Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

    2016-01-01

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

  15. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

    PubMed

    Miotto, Riccardo; Li, Li; Kidd, Brian A; Dudley, Joel T

    2016-05-17

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

  16. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

    NASA Astrophysics Data System (ADS)

    Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

    2016-05-01

    Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

  17. Past and predicted future changes in the land cover of the Upper Mississippi River floodplain, USA

    USGS Publications Warehouse

    De Jager, N. R.; Rohweder, J.J.; Nelson, J.C.

    2013-01-01

    This study provides one historical and two alternative future contexts for evaluating land cover modifications within the Upper Mississippi River (UMR) floodplain. Given previously documented changes in land use, river engineering, restoration efforts and hydro-climatic changes within the UMR basin and floodplain, we wanted to know which of these changes are the most important determinants of current and projected future floodplain land cover. We used Geographic Information System data covering approximately 37% of the UMR floodplain (3232 km2) for ca 1890 (pre-lock and dam) and three contemporary periods (1975, 1989 and 2000) across which river restoration actions have increased and hydro-climatic changes have occurred. We further developed two 50-year future scenarios from the spatially dependent land cover transitions that occurred from 1975 to 1989 (scenario A) and from 1989 to 2000 (scenario B) using Markov models.Land cover composition of the UMR did not change significantly from 1975 to 2000, indicating that current land cover continues to reflect historical modifications that support agricultural production and commercial navigation despite some floodplain restoration efforts and variation in river discharge. Projected future land cover composition based on scenario A was not significantly different from the land cover for 1975, 1989 or 2000 but was different from the land cover of scenario B, which was also different from all other periods. Scenario B forecasts transition of some forest and marsh habitat to open water by the year 2050 for some portions of the northern river and projects that some agricultural lands will transition to open water in the southern portion of the river. Future floodplain management and restoration planning efforts in the UMR should consider the potential consequences of continued shifts in hydro-climatic conditions that may occur as a result of climate change and the potential effects on floodplain land cover.

  18. Future time perspective and awareness of age-related change: Examining their role in predicting psychological well-being.

    PubMed

    Brothers, Allyson; Gabrian, Martina; Wahl, Hans-Werner; Diehl, Manfred

    2016-09-01

    This study examined how 2 distinct facets of perceived personal lifetime-future time perspective (FTP) and awareness of age-related change (AARC)-are associated with another, and how they may interact to predict psychological well-being. To better understand associations among subjective perceptions of lifetime, aging, and well-being, we tested a series of models to investigate questions of directionality, indirect effects, and conditional processes among FTP, AARC-Gains, AARC-Losses, and psychological well-being. In all models, we tested for differences between middle-aged and older adults, and between adults from the United States and Germany. Analyses were conducted within a structural equation modeling framework on a cross-national, 2.5-year longitudinal sample of 537 community-residing adults (age 40-98 years). Awareness of age-related losses (AARC-Losses) at Time 1 predicted FTP at Time 2, but FTP did not predict AARC-Gains or AARC-Losses. Furthermore, future time perspective mediated the association between AARC-Losses and well-being. Moderation analyses revealed a buffering effect of awareness of age-related gains (AARC-Gains) in which perceptions of more age-related gains diminished the negative effect of a limited future time perspective on well-being. Effects were robust across age groups and countries. Taken together, these findings suggest that perceived age-related loss experiences may sensitize individuals to perceive a more limited future lifetime which may then lead to lower psychological well-being. In contrast, perceived age-related gains may function as a resource to preserve psychological well-being, in particular when time is perceived as running out. (PsycINFO Database Record

  19. Commentary: Children and Predictive Genomic Testing: Disease Prevention, Research Protection, and Our Future

    PubMed Central

    Tercyak, Kenneth P.; Wilfond, Benjamin S.

    2011-01-01

    Genetic testing offered by direct-to-consumer companies—herein referred to as “predictive genomic testing”—has come under federal scrutiny. Critics claim testing yields uninterpretable and potentially harmful information. Supporters assert individuals have a right to this information, which could catalyze preventive health actions. Despite contentions that predictive genomic testing is a tool of primary disease prevention, little discussion has focused on its use with children. This partly stems from concerns expressed in existing professional guidelines about the potential for psychological and behavioral harm to children engendered by predictive genetic tests for Mendelian diseases. Conducting research to understand the actual benefits and harms is important for policy development and practice guidance and can be ethically justified within the pediatric regulatory framework of research that offers a prospect of direct benefit. Child health psychologists are well poised to contribute to this research effort, and promote the translation of genomic discoveries to improve pediatric medicine. PMID:21816897

  20. Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

    USGS Publications Warehouse

    Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T; Yahn, Jeremiah; Porter, Warren P.

    2017-01-01

    How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  1. Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

    PubMed

    Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

    2017-03-01

    How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect

  2. Print-speech convergence predicts future reading outcomes in early readers

    PubMed Central

    Preston, Jonathan L.; Molfese, Peter J.; Frost, Stephen J.; Mencl, W. Einar; Fulbright, Robert K.; Hoeft, Fumiko; Landi, Nicole; Shankweiler, Donald; Pugh, Kenneth R.

    2015-01-01

    Becoming a skilled reader requires building a functional neurocircuitry for printed language processing that converges on spoken language processing networks. In this longitudinal study, functional magnetic resonance imaging (fMRI) was used to examine whether convergence for printed and spoken language in beginning readers predicts reading outcomes two years later. Print-speech co-activation across the left hemisphere reading network predicted later reading achievement beyond the effects of brain activity for either modality alone; moreover, co-activation effects accounted for variance in later reading after controlling for initial reading performance. Within the reading network, effects of co-activation were significant in bilateral inferior frontal gyrus (IFG) and left inferior parietal cortex and fusiform. The contribution of left and right IFG differed, with more co-activation in left IFG predicting better achievement but more co-activation in right IFG predicting poorer achievement. Findings point to the centrality of print-speech convergence in building an efficient reading circuitry in children. PMID:26589242

  3. Reconstruction techniques of erythemal UV-radiation and future UV predictions

    NASA Astrophysics Data System (ADS)

    Wagner, J. E.; Rieder, H. E.; Simic, S.; Weihs, P.

    2009-04-01

    Since the discovery of anthropogenic ozone depletion more than 30 year ago, the scientific community has shown an increasing interest in UV-B radiation and started to monitor UV-radiation. However, difficulties involved in the routine operation and maintenance of the instruments have limited the length of reliable data records to about two decades. Further the number of places where they were measured, resulting in a set of observations too short and too sparse for a good understanding of past UV changes. Moreover state of the art climate models do not calculate future scenarios of UV-doses. Therefore detailed information about past and future UV-trends are lacking. Reconstruction techniques are indispensable to derive long-term time series of UV-radiation and fill this gap. Apart from the astronomical parameters, like solar zenith angle and sun-earth-distance, UV radiation is strongly influenced by clouds, ozone and surface albedo. We developed and evaluated a reconstruction technique for UV-doses that first calculates the UV-doses under clear-sky condition and afterwards applies corrections in order to take cloud effects into account. Since the input parameters cloud cover, total ozone column and surface albedo are available from the Regional Climate Model (REMO), we applied our reconstruction technique also for future scenarios using REMO data as input. Hence we are able to derive a seamless UV long-term time series from the past to the future. Our method was applied for the high alpine station Hoher Sonnblick (3108m) situated in Austrian Alps.

  4. Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database.

    PubMed

    Malshe, M; Pukrittayakamee, A; Raff, L M; Hagan, M; Bukkapatnam, S; Komanduri, R

    2009-09-28

    A novel method is presented that significantly reduces the computational bottleneck of executing high-level, electronic structure calculations of the energies and their gradients for a large database that adequately samples the configuration space of importance for systems containing more than four atoms that are undergoing multiple, simultaneous reactions in several energetically open channels. The basis of the method is the high-degree of correlation that generally exists between the Hartree-Fock (HF) and higher-level electronic structure energies. It is shown that if the input vector to a neural network (NN) includes both the configuration coordinates and the HF energies of a small subset of the database, MP4(SDQ) energies with the same basis set can be predicted for the entire database using only the HF and MP4(SDQ) energies for the small subset and the HF energies for the remainder of the database. The predictive error is shown to be less than or equal to the NN fitting error if a NN is fitted to the entire database of higher-level electronic structure energies. The general method is applied to the computation of MP4(SDQ) energies of 68,308 configurations that comprise the database for the simultaneous, unimolecular decomposition of vinyl bromide into six different reaction channels. The predictive accuracy of the method is investigated by employing successively smaller subsets of the database to train the NN to predict the MP4(SDQ) energies of the remaining configurations of the database. The results indicate that for this system, the subset can be as small as 8% of the total number of configurations in the database without loss of accuracy beyond that expected if a NN is employed to fit the higher-level energies for the entire database. The utilization of this procedure is shown to save about 78% of the total computational time required for the execution of the MP4(SDQ) calculations. The sampling error involved with selection of the subset is shown to be

  5. Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database

    NASA Astrophysics Data System (ADS)

    Malshe, M.; Pukrittayakamee, A.; Raff, L. M.; Hagan, M.; Bukkapatnam, S.; Komanduri, R.

    2009-09-01

    A novel method is presented that significantly reduces the computational bottleneck of executing high-level, electronic structure calculations of the energies and their gradients for a large database that adequately samples the configuration space of importance for systems containing more than four atoms that are undergoing multiple, simultaneous reactions in several energetically open channels. The basis of the method is the high-degree of correlation that generally exists between the Hartree-Fock (HF) and higher-level electronic structure energies. It is shown that if the input vector to a neural network (NN) includes both the configuration coordinates and the HF energies of a small subset of the database, MP4(SDQ) energies with the same basis set can be predicted for the entire database using only the HF and MP4(SDQ) energies for the small subset and the HF energies for the remainder of the database. The predictive error is shown to be less than or equal to the NN fitting error if a NN is fitted to the entire database of higher-level electronic structure energies. The general method is applied to the computation of MP4(SDQ) energies of 68 308 configurations that comprise the database for the simultaneous, unimolecular decomposition of vinyl bromide into six different reaction channels. The predictive accuracy of the method is investigated by employing successively smaller subsets of the database to train the NN to predict the MP4(SDQ) energies of the remaining configurations of the database. The results indicate that for this system, the subset can be as small as 8% of the total number of configurations in the database without loss of accuracy beyond that expected if a NN is employed to fit the higher-level energies for the entire database. The utilization of this procedure is shown to save about 78% of the total computational time required for the execution of the MP4(SDQ) calculations. The sampling error involved with selection of the subset is shown to be

  6. Do PCL-R scores from state or defense experts best predict future misconduct among civilly committed sex offenders?

    PubMed

    Boccaccini, Marcus T; Turner, Darrel B; Murrie, Daniel C; Rufino, Katrina A

    2012-06-01

    In a recent study of sex offender civil commitment proceedings, Murrie et al. (Psychol Public Policy Law 15:19-53, 2009) found that state-retained experts consistently assigned higher PCL-R total scores than defense-retained experts for the same offenders (Cohen's d > .83). This finding raises an important question about the validity of these discrepant scores: Which type of score, state or defense evaluator, provides the most useful information about risk? We examined the ability of PCL-R total scores from state and defense evaluators to predict future misconduct among civilly committed sex offenders (N = 38). For comparison, we also examined predictive validity when two state experts evaluated the same offender (N = 32). Agreement between evaluators was low for cases with opposing experts (ICCA,1 = .43 to .52) and for cases with two state experts (ICCA,1 = .40). Nevertheless, scores from state and defense experts demonstrated similar levels of predictive validity (AUC values in the .70 range), although scores from different types of state evaluators (corrections-contracted vs. prosecution-retained) did not. The finding of mean differences between opposing evaluator scores, but similar levels of predictive validity, suggests that scores from opposing experts in SVP cases may need to be interpreted differently depending on who assigned them. Findings have important implications for understanding how rater disagreement may relate to predictive validity.

  7. Reconstruction of past and prediction of future erythemal UV-radiation at two sites in Austria

    NASA Astrophysics Data System (ADS)

    Weihs, Philipp; Rieder, Harald; Wagner, Jochen; Simic, Stana; Dameris, Martin

    2010-05-01

    Since the discovery of anthropogenic ozone depletion more than 30 year ago, the scientific community has shown an increasing interest in UV-B radiation and started to monitor UV-radiation. However, difficulties involved in the routine operation and maintenance of the instruments have limited the length of reliable data records to about two decades. Further the number of places where they were measured, result in a set of observations too short and too sparse for a good understanding of past UV changes. Moreover state of the art climate models do not calculate future scenarios of UV-doses. Therefore detailed information about past and future UV-trends are lacking. Reconstruction techniques are indispensable to derive long-term time series of UV-radiation and fill this gap. Apart from the astronomical parameters, like solar zenith angle and sun-earth-distance, UV radiation is strongly influenced by clouds, ozone and surface albedo. We developed and evaluated a reconstruction technique for UV-doses (from regional climate model output) that first calculates the UV-doses under clear-sky condition and afterwards applies corrections in order to take cloud effects into account. Since the input parameters cloud cover, total ozone column and surface albedo are available from the Regional Climate Models REMO and E39/C (DLR-model), we applied our reconstruction technique for the past and for future scenarios using REMO and E39/C data as input. Hence we simulated a seamless UV long-term time series from the past to the future. Our method was applied for the high alpine station Hoher Sonnblick (3106m) situated in the Austrian Alps and for Vienna (170m) in the Eastern part of the Austrian territory. We first analyse the accuracy of the obtained backward reconstruction and intercompare the modelled and measured input parameters ozone, cloud modification factor, and ground albedo. Several approaches to improve the accuracy of the reconstruction are presented. Then we present the

  8. GMS-based"Future Time" Rainfall Data Assimilation for Mesoscale Weather Prediction over Korean Peninsula and Future Prospects with GPM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Smith, Eric A.; Ou, Mi-Lim

    2004-01-01

    This study examines the use of satellite-derived nowcasted (short-term forecasted) rainfall over 3-hour time periods to gain an equivalent time increment in initializing a nonhydrostatic mesoscale model used for predicting convective rainfall events over the Korean peninsula. Infrared (IR) window measurements from the Japanese Geostationary Meteorological Satellite (GMS) are used to specify latent heating for a spinup period of the model - but in future time -- thus initializing in advance of actual time in the framework of a prediction scenario. The main scientific objective of the study is to investigate the strengths and weaknesses of this approach insofar as data assimilation, in which the nowcasted assimilation data are derived independently of the prognostic model itself. Although there have been various recent improvements in formulating the dynamics, thermodynamics, and microphysics of mesoscale models, as well as computer advances which allow the use of high resolution cloud-resolving grids and explicit latent heating over regional domains, spinup remains at the forefront of unresolved mesoscale modeling problems. In general, non-realistic spinup limits the skill in predicting the spatial-temporal distribution of convection and precipitation, primarily in the early hours of a. forecast, stemming from standard prognostic variables not representing the initial diabatic heating field produced by the ambient convection and cloud fields. The long-term goal of this research is to improve short-range (12-hour) quantitative precipitation forecasting (QPF) over the Korean peninsula through the use of innovative data assimilation methods based on geosynchronous satellite measurements. As a step in ths direction, a non-standard data assimilation experiment in conjunction with GMS-retrieved nowcasted rainfall information introduced to the mesoscale model is conducted. The 3-hourly precipitation forecast information is assimilated through nudging the associated

  9. Closed-loop spontaneous baroreflex transfer function is inappropriate for system identification of neural arc but partly accurate for peripheral arc: predictability analysis.

    PubMed

    Kamiya, Atsunori; Kawada, Toru; Shimizu, Shuji; Sugimachi, Masaru

    2011-04-01

    Although the dynamic characteristics of the baroreflex system have been described by baroreflex transfer functions obtained from open-loop analysis, the predictability of time-series output dynamics from input signals, which should confirm the accuracy of system identification, remains to be elucidated. Moreover, despite theoretical concerns over closed-loop system identification, the accuracy and the predictability of the closed-loop spontaneous baroreflex transfer function have not been evaluated compared with the open-loop transfer function. Using urethane and α-chloralose anaesthetized, vagotomized and aortic-denervated rabbits (n = 10), we identified open-loop baroreflex transfer functions by recording renal sympathetic nerve activity (SNA) while varying the vascularly isolated intracarotid sinus pressure (CSP) according to a binary random (white-noise) sequence (operating pressure ± 20 mmHg), and using a simplified equation to calculate closed-loop-spontaneous baroreflex transfer function while matching CSP with systemic arterial pressure (AP). Our results showed that the open-loop baroreflex transfer functions for the neural and peripheral arcs predicted the time-series SNA and AP outputs from measured CSP and SNA inputs, with r2 of 0.8 ± 0.1 and 0.8 ± 0.1, respectively. In contrast, the closed-loop-spontaneous baroreflex transfer function for the neural arc was markedly different from the open-loop transfer function (enhanced gain increase and a phase lead), and did not predict the time-series SNA dynamics (r2; 0.1 ± 0.1). However, the closed-loop-spontaneous baroreflex transfer function of the peripheral arc partially matched the open-loop transfer function in gain and phase functions, and had limited but reasonable predictability of the time-series AP dynamics (r2, 0.7 ± 0.1). A numerical simulation suggested that a noise predominantly in the neural arc under resting conditions might be a possible mechanism responsible for our findings. Furthermore

  10. The histological quantification of alpha-smooth muscle actin predicts future graft fibrosis in pediatric liver transplant recipients.

    PubMed

    Varma, Sharat; Stéphenne, Xavier; Komuta, Mina; Bouzin, Caroline; Ambroise, Jerome; Smets, Françoise; Reding, Raymond; Sokal, Etienne M

    2017-02-01

    Activated hepatic stellate cells express cytoplasmic ASMA prior to secreting collagen and consequent liver fibrosis. We hypothesized that quantifying ASMA could predict severity of future fibrosis after LT. For this, 32 pairs of protocol biopsies, that is, "baseline" and "follow-up" biopsies taken at 1- to 2-year intervals from 18 stable pediatric LT recipients, transplanted between 2006 and 2012 were selected. Morphometric quantification of "ASMA-positive area percentage" was performed on the baseline biopsy. Histological and fibrosis assessment using Metavir and LAFSc was performed on all biopsies. The difference of fibrosis severity between the "baseline" and "follow-up" was termed "prospective change in fibrosis." Significant association was seen between extent of ASMA positivity on baseline biopsy and "prospective change in fibrosis" using Metavir (P=.02), cumulative LAFSc (P=.02), and portal LAFSc (P=.01) values. ASMA-positive area percentage >1.05 predicted increased fibrosis on next biopsy with 90.0% specificity. Additionally, an association was observed between extent of ASMA positivity and concomitant ductular reaction (P=.06), but not with histological inflammation in the portal tract or lobular area. Hence, ASMA quantification can predict the future course of fibrosis.

  11. 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.

  12. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE

    PubMed Central

    Zhang, Jie; Stonnington, Cynthia; Li, Qingyang; Shi, Jie; Bauer, Robert J.; Gutman, Boris A.; Chen, Kewei; Reiman, Eric M.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin

    2016-01-01

    Alzheimer’s disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance. PMID:27499829

  13. 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

  14. Predicting River Macroinvertebrate Communities Distributional Shifts under Future Global Change Scenarios in the Spanish Mediterranean Area.

    PubMed

    Alba-Tercedor, Javier; Sáinz-Bariáin, Marta; Poquet, José Manuel; Rodríguez-López, Roberto

    2017-01-01

    Several studies on global change over the next century predict increases in mean air temperatures of between 1°C to 5°C that would affect not only water temperature but also river flow. Climate is the predominant environmental driver of thermal and flow regimes of freshwater ecosystems, determining survival, growth, metabolism, phenology and behaviour as well as biotic interactions of aquatic fauna. Thus, these changes would also have consequences for species phenology, their distribution range, and the composition and dynamics of communities. These effects are expected to be especially severe in the Mediterranean basin due its particular climate conditions, seriously threatening Southern European ecosystems. In addition, species with restricted distributions and narrow ecological requirements, such as those living in the headwaters of rivers, will be severely affected. The study area corresponds to the Spanish Mediterranean and Balearic Islands, delimited by the Köppen climate boundary. With the application of the MEDPACS (MEDiterranean Prediction And Classification System) predictive approach, the macroinvertebrate community was predicted for current conditions and compared with three posible scenarios of watertemperature increase and its associated water flow reductions. The results indicate that the aquatic macroinvertebrate communities will undergo a drastic impact, with reductions in taxa richness for each scenario in relation to simulated current conditions, accompanied by changes in the taxa distribution pattern. Accordingly, the distribution area of most of the taxa (65.96%) inhabiting the mid-high elevations would contract and rise in altitude. Thus, families containing a great number of generalist species will move upstream to colonize new zones with lower water temperatures. By contrast, more vulnerable taxa will undergo reductions in their distribution area.

  15. Predicting River Macroinvertebrate Communities Distributional Shifts under Future Global Change Scenarios in the Spanish Mediterranean Area

    PubMed Central

    Sáinz-Bariáin, Marta; Poquet, José Manuel; Rodríguez-López, Roberto

    2017-01-01

    Several studies on global change over the next century predict increases in mean air temperatures of between 1°C to 5°C that would affect not only water temperature but also river flow. Climate is the predominant environmental driver of thermal and flow regimes of freshwater ecosystems, determining survival, growth, metabolism, phenology and behaviour as well as biotic interactions of aquatic fauna. Thus, these changes would also have consequences for species phenology, their distribution range, and the composition and dynamics of communities. These effects are expected to be especially severe in the Mediterranean basin due its particular climate conditions, seriously threatening Southern European ecosystems. In addition, species with restricted distributions and narrow ecological requirements, such as those living in the headwaters of rivers, will be severely affected. The study area corresponds to the Spanish Mediterranean and Balearic Islands, delimited by the Köppen climate boundary. With the application of the MEDPACS (MEDiterranean Prediction And Classification System) predictive approach, the macroinvertebrate community was predicted for current conditions and compared with three posible scenarios of watertemperature increase and its associated water flow reductions. The results indicate that the aquatic macroinvertebrate communities will undergo a drastic impact, with reductions in taxa richness for each scenario in relation to simulated current conditions, accompanied by changes in the taxa distribution pattern. Accordingly, the distribution area of most of the taxa (65.96%) inhabiting the mid-high elevations would contract and rise in altitude. Thus, families containing a great number of generalist species will move upstream to colonize new zones with lower water temperatures. By contrast, more vulnerable taxa will undergo reductions in their distribution area. PMID:28135280

  16. Lookup Tables for Predicting CHF and Film-Boiling Heat Transfer: Past, Present, and Future

    SciTech Connect

    Groeneveld, D.C.; Leung, L.K. H.; Guo, Y.; Vasic, A.; El Nakla, M.; Peng, S.W.; Yang, J.; Cheng, S.C.

    2005-10-15

    Lookup tables (LUTs) have been used widely for the prediction of critical heat flux (CHF) and film-boiling heat transfer for water-cooled tubes. LUTs are basically normalized data banks. They eliminate the need to choose between the many different CHF and film-boiling heat transfer prediction methods available.The LUTs have many advantages; e.g., (a) they are simple to use, (b) there is no iteration required, (c) they have a wide range of applications, (d) they may be applied to nonaqueous fluids using fluid-to-fluid modeling relationships, and (e) they are based on a very large database. Concerns associated with the use of LUTs include (a) there are fluctuations in the value of the CHF or film-boiling heat transfer coefficient (HTC) with pressure, mass flux, and quality, (b) there are large variations in the CHF or the film-boiling HTC between the adjacent table entries, and (c) there is a lack or scarcity of data at certain flow conditions.Work on the LUTs is continuing. This will resolve the aforementioned concerns and improve the LUT prediction capability. This work concentrates on better smoothing of the LUT entries, increasing the database, and improving models at conditions where data are sparse or absent.

  17. Print-Speech Convergence Predicts Future Reading Outcomes in Early Readers.

    PubMed

    Preston, Jonathan L; Molfese, Peter J; Frost, Stephen J; Mencl, W Einar; Fulbright, Robert K; Hoeft, Fumiko; Landi, Nicole; Shankweiler, Donald; Pugh, Kenneth R

    2016-01-01

    Becoming a skilled reader requires building a functional neurocircuitry for printed-language processing that integrates with spoken-language-processing networks. In this longitudinal study, functional MRI (fMRI) was used to examine convergent activation for printed and spoken language (print-speech coactivation) in selected regions implicated in printed-language processing (the reading network). We found that print-speech coactivation across the left-hemisphere reading network in beginning readers predicted reading achievement 2 years later beyond the effects of brain activity for either modality alone; moreover, coactivation effects accounted for variance in later reading after controlling for initial reading performance. Within the reading network, effects of coactivation were significant in bilateral inferior frontal gyrus (IFG) and left inferior parietal cortex and fusiform gyrus. The contribution of left and right IFG differed, with more coactivation in left IFG predicting better achievement but more coactivation in right IFG predicting poorer achievement. Findings point to the centrality of print-speech convergence in building an efficient reading circuitry in children.

  18. In silico ADMET prediction: recent advances, current challenges and future trends.

    PubMed

    Cheng, Feixiong; Li, Weihua; Liu, Guixia; Tang, Yun

    2013-01-01

    There are numerous small molecular compounds around us to affect our health, such as drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over decades, properties related to absorption, distribution, metabolism, excretion, and toxicity (ADMET) have become one of the most important issues to assess the effects or risks of these compounds on human body. Recent high-rate drug withdrawals increase the pressure on regulators and pharmaceutical industry to improve preclinical safety testing. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques have been widely used to estimate these properties. In this review, we would briefly describe the recent advances of in silico ADMET prediction, with emphasis on substructure pattern recognition method that we developed recently. Challenges and limitations in the area of in silico ADMET prediction were further discussed, such as application domain of models, models validation techniques, and global versus local models. At last, several new promising research directions were provided, such as computational systems toxicology (toxicogenomics), data-integration and meta-decision making systems, which could be used for systemic in silico ADMET prediction in drug discovery and hazard risk assessment.

  19. The importance of vegetation change in the prediction of future tropical cyclone flood statistics

    NASA Astrophysics Data System (ADS)

    Irish, J. L.; Resio, D.; Bilskie, M. V.; Hagen, S. C.; Weiss, R.

    2015-12-01

    Global sea level rise is a near certainty over the next century (e.g., Stocker et al. 2013 [IPCC] and references therein). With sea level rise, coastal topography and land cover (hereafter "landscape") is expected to change and tropical cyclone flood hazard is expected to accelerate (e.g., Irish et al. 2010 [Ocean Eng], Woodruff et al. 2013 [Nature], Bilskie et al. 2014 [Geophys Res Lett], Ferreira et al. 2014 [Coast Eng], Passeri et al. 2015 [Nat Hazards]). Yet, the relative importance of sea-level rise induced landscape change on