Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

PubMed Central

Newman, M C; McCloskey, J T; Tatara, C P

1998-01-01

Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful. PMID:9860900

Development of a model for predicting NASA/MSFC program success

NASA Technical Reports Server (NTRS)

Riggs, Jeffrey; Miller, Tracy; Finley, Rosemary

1990-01-01

Research conducted during the execution of a previous contract (NAS8-36955/0039) firmly established the feasibility of developing a tool to aid decision makers in predicting the potential success of proposed projects. The final report from that investigation contains an outline of the method to be applied in developing this Project Success Predictor Model. As a follow-on to the previous study, this report describes in detail the development of this model and includes full explanation of the data-gathering techniques used to poll expert opinion. The report includes the presentation of the model code itself.

Using landscape disturbance and succession models to support forest management

Treesearch

Eric J. Gustafson; Brian R. Sturtevant; Anatoly S. Shvidenko; Robert M. Scheller

2010-01-01

Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales. These interactions can be of such complexity that predictions of future forest ecosystem states are beyond the analytical capability of the human mind. Landscape disturbance and succession models (LDSM) are predictive and...

Can We Predict Foraging Success in a Marine Predator from Dive Patterns Only? Validation with Prey Capture Attempt Data

PubMed Central

Viviant, Morgane; Monestiez, Pascal; Guinet, Christophe

2014-01-01

Predicting how climatic variations will affect marine predator populations relies on our ability to assess foraging success, but evaluating foraging success in a marine predator at sea is particularly difficult. Dive metrics are commonly available for marine mammals, diving birds and some species of fish. Bottom duration or dive duration are usually used as proxies for foraging success. However, few studies have tried to validate these assumptions and identify the set of behavioral variables that best predict foraging success at a given time scale. The objective of this study was to assess if foraging success in Antarctic fur seals could be accurately predicted from dive parameters only, at different temporal scales. For this study, 11 individuals were equipped with either Hall sensors or accelerometers to record dive profiles and detect mouth-opening events, which were considered prey capture attempts. The number of prey capture attempts was best predicted by descent and ascent rates at the dive scale; bottom duration and descent rates at 30-min, 1-h, and 2-h scales; and ascent rates and maximum dive depths at the all-night scale. Model performances increased with temporal scales, but rank and sign of the factors varied according to the time scale considered, suggesting that behavioral adjustment in response to prey distribution could occur at certain scales only. The models predicted the foraging intensity of new individuals with good accuracy despite high inter-individual differences. Dive metrics that predict foraging success depend on the species and the scale considered, as verified by the literature and this study. The methodology used in our study is easy to implement, enables an assessment of model performance, and could be applied to any other marine predator. PMID:24603534

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

Predictability of the Ningaloo Niño/Niña

PubMed Central

Doi, Takeshi; Behera, Swadhin K.; Yamagata, Toshio

2013-01-01

The seasonal prediction of the coastal oceanic warm event off West Australia, recently named the Ningaloo Niño, is explored by use of a state-of-the-art ocean-atmosphere coupled general circulation model. The Ningaloo Niño/Niña, which generally matures in austral summer, is found to be predictable two seasons ahead. In particular, the unprecedented extreme warm event in February 2011 was successfully predicted 9 months in advance. The successful prediction of the Ningaloo Niño is mainly due to the high prediction skill of La Niña in the Pacific. However, the model deficiency to underestimate its early evolution and peak amplitude needs to be improved. Since the Ningaloo Niño/Niña has potential impacts on regional societies and industries through extreme events, the present success of its prediction may encourage development of its early warning system. PMID:24100593

Nomogram to predict successful placement in surgical subspecialty fellowships using applicant characteristics.

PubMed

Muffly, Tyler M; Barber, Matthew D; Karafa, Matthew T; Kattan, Michael W; Shniter, Abigail; Jelovsek, J Eric

2012-01-01

The purpose of the study was to develop a model that predicts an individual applicant's probability of successful placement into a surgical subspecialty fellowship program. Candidates who applied to surgical fellowships during a 3-year period were identified in a set of databases that included the electronic application materials. Of the 1281 applicants who were available for analysis, 951 applicants (74%) successfully placed into a colon and rectal surgery, thoracic surgery, vascular surgery, or pediatric surgery fellowship. The optimal final prediction model, which was based on a logistic regression, included 14 variables. This model, with a c statistic of 0.74, allowed for the determination of a useful estimate of the probability of placement for an individual candidate. Of the factors that are available at the time of fellowship application, 14 were used to predict accurately the proportion of applicants who will successfully gain a fellowship position. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Temperature-dependence of biomass accumulation rates during secondary succession.

PubMed

Anderson, Kristina J; Allen, Andrew P; Gillooly, James F; Brown, James H

2006-06-01

Rates of ecosystem recovery following disturbance affect many ecological processes, including carbon cycling in the biosphere. Here, we present a model that predicts the temperature dependence of the biomass accumulation rate following disturbances in forests. Model predictions are derived based on allometric and biochemical principles that govern plant energetics and are tested using a global database of 91 studies of secondary succession compiled from the literature. The rate of biomass accumulation during secondary succession increases with average growing season temperature as predicted based on the biochemical kinetics of photosynthesis in chloroplasts. In addition, the rate of biomass accumulation is greater in angiosperm-dominated communities than in gymnosperm-dominated ones and greater in plantations than in naturally regenerating stands. By linking the temperature-dependence of photosynthesis to the rate of whole-ecosystem biomass accumulation during secondary succession, our model and results provide one example of how emergent, ecosystem-level rate processes can be predicted based on the kinetics of individual metabolic rate.

Psychosocial Factors Predicting First-Year College Student Success

ERIC Educational Resources Information Center

Krumrei-Mancuso, Elizabeth J.; Newton, Fred B.; Kim, Eunhee; Wilcox, Dan

2013-01-01

This study made use of a model of college success that involves students achieving academic goals and life satisfaction. Hierarchical regressions examined the role of six psychosocial factors for college success among 579 first-year college students. Academic self-efficacy and organization and attention to study were predictive of first semester…

Disentangling the Predictive Validity of High School Grades for Academic Success in University

ERIC Educational Resources Information Center

Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

2018-01-01

To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

Using Neural Networks to Predict MBA Student Success

ERIC Educational Resources Information Center

Naik, Bijayananda; Ragothaman, Srinivasan

2004-01-01

Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student…

Predicting cutthroat trout (Oncorhynchus clarkii) abundance in high-elevation streams: revisiting a model of translocation success

Treesearch

Michael K. Young; Paula M. Guenther-Gloss; Ashley D. Ficke

2005-01-01

Assessing viability of stream populations of cutthroat trout (Oncorhynchus clarkii) and identifying streams suitable for establishing populations are priorities in the western United States, and a model was recently developed to predict translocation success (as defined by an index of population size) of two subspecies based on mean July water...

Predicting Perceptual Success with Segments: A Test of Japanese Speakers of Russian

ERIC Educational Resources Information Center

Larson-Hall, J.

2004-01-01

A perception experiment involving a novel language pairing, that of Japanese as a first language (L1) and Russian as a second language (L2), was conducted with 33 Japanese learners of Russian to determine whether two phonological models could successfully predict patterns of perceptual difficulty with eight Russian segments. The Featural Model of…

Predicting Academic Success of First-Time College-Bound African American Students at a Predominantly White Four-Year Public Institution: A Preadmission Model

ERIC Educational Resources Information Center

Redmond, M. William, Jr.

2011-01-01

The purpose of this study is to develop a preadmission predictive model of student success for prospective first-time African American college applicants at a predominately White four-year public institution within the Pennsylvania State System of Higher Education. This model will use two types of variables. They are (a) cognitive variables (i.e.,…

Model-data assimilation of multiple phenological observations to constrain and predict leaf area index.

PubMed

Viskari, Toni; Hardiman, Brady; Desai, Ankur R; Dietze, Michael C

2015-03-01

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting. estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

Social support, stress, health, and academic success in Ghanaian adolescents: a path analysis.

PubMed

Glozah, Franklin N; Pevalin, David J

2014-06-01

The aim of this study is to gain a better understanding of the role psychosocial factors play in promoting the health and academic success of adolescents. A total of 770 adolescent boys and girls in Senior High Schools were randomly selected to complete a self-report questionnaire. School reported latest terminal examination grades were used as the measure of academic success. Structural equation modelling indicated a relatively good fit to the posteriori model with four of the hypothesised paths fully supported and two partially supported. Perceived social support was negatively related to stress and predictive of health and wellbeing but not academic success. Stress was predictive of health but not academic success. Finally, health and wellbeing was able to predict academic success. These findings have policy implications regarding efforts aimed at promoting the health and wellbeing as well as the academic success of adolescents in Ghana. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Soil-Bacterium Compatibility Model as a Decision-Making Tool for Soil Bioremediation.

PubMed

Horemans, Benjamin; Breugelmans, Philip; Saeys, Wouter; Springael, Dirk

2017-02-07

Bioremediation of organic pollutant contaminated soil involving bioaugmentation with dedicated bacteria specialized in degrading the pollutant is suggested as a green and economically sound alternative to physico-chemical treatment. However, intrinsic soil characteristics impact the success of bioaugmentation. The feasibility of using partial least-squares regression (PLSR) to predict the success of bioaugmentation in contaminated soil based on the intrinsic physico-chemical soil characteristics and, hence, to improve the success of bioaugmentation, was examined. As a proof of principle, PLSR was used to build soil-bacterium compatibility models to predict the bioaugmentation success of the phenanthrene-degrading Novosphingobium sp. LH128. The survival and biodegradation activity of strain LH128 were measured in 20 soils and correlated with the soil characteristics. PLSR was able to predict the strain's survival using 12 variables or less while the PAH-degrading activity of strain LH128 in soils that show survival was predicted using 9 variables. A three-step approach using the developed soil-bacterium compatibility models is proposed as a decision making tool and first estimation to select compatible soils and organisms and increase the chance of success of bioaugmentation.

Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

ERIC Educational Resources Information Center

Calvert, Carol Elaine

2014-01-01

This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

Does information available at admission for delivery improve prediction of vaginal birth after cesarean?

PubMed Central

Grobman, William A.; Lai, Yinglei; Landon, Mark B.; Spong, Catherine Y.; Leveno, Kenneth J.; Rouse, Dwight J.; Varner, Michael W.; Moawad, Atef H.; Simhan, Hyagriv N.; Harper, Margaret; Wapner, Ronald J.; Sorokin, Yoram; Miodovnik, Menachem; Carpenter, Marshall; O'sullivan, Mary J.; Sibai, Baha M.; Langer, Oded; Thorp, John M.; Ramin, Susan M.; Mercer, Brian M.

2010-01-01

Objective To construct a predictive model for vaginal birth after cesarean (VBAC) that combines factors that can be ascertained only as the pregnancy progresses with those known at initiation of prenatal care. Study design Using multivariable modeling, we constructed a predictive model for VBAC that included patient factors known at the initial prenatal visit as well as those that only became evident as the pregancy progressed to the admission for delivery. Results 9616 women were analyzed. The regression equation for VBAC success included multiple factors that could not be known at the first prenatal visit. The area under the curve for this model was significantly greater (P < .001) than that of a model that included only factors available at the first prenatal visit. Conclusion A prediction model for VBAC success that incorporates factors that can be ascertained only as the pregnancy progresses adds to the predictive accuracy of a model that uses only factors available at a first prenatal visit. PMID:19813165

Prediction of successful weight reduction after bariatric surgery by data mining technologies.

PubMed

Lee, Yi-Chih; Lee, Wei-Jei; Lee, Tian-Shyug; Lin, Yang-Chu; Wang, Weu; Liew, Phui-Ly; Huang, Ming-Te; Chien, Ching-Wen

2007-09-01

Surgery is the only long-lasting effective treatment for morbid obesity. Prediction on successful weight loss after surgery by data mining technologies is lacking. We analyze the available information during the initial evaluation of patients referred to bariatric surgery by data mining methods for predictors of successful weight loss. 249 patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) were enrolled. Logistic Regression and Artificial Neural Network (ANN) technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. We studied 249 patients consisting of 72 men and 177 women over 2 years. Mean age was 33 +/- 9 years. 208 (83.5%) patients had successful weight reduction while 41 (16.5%) did not. Logistic Regression revealed that the type of operation had a significant prediction effect (P = 0.000). Patients receiving LMGB had a better weight loss than those receiving LAGB (78.54% +/- 26.87 vs 43.65% +/- 26.08). ANN provided the same predicted factor on the type of operation but it further proposed that HbAlc and triglyceride were associated with success. HbAlc is lower in the successful than failed group (5.81 +/- 1.06 vs 6.05 +/- 1.49; P = NS), and triglyceride in the successful group is higher than in the failed group (171.29 +/- 112.62 vs 144.07 +/- 89.90; P = NS). Artificial neural network is a better modeling technique and the overall predictive accuracy is higher on the basis of multiple variables related to laboratory tests. LMGB, high preoperative triglyceride level, and low HbAlc level can predict successful weight reduction at 2 years.

Forecasting success via early adoptions analysis: A data-driven study

PubMed Central

Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

2017-01-01

Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement. PMID:29216255

Forecasting success via early adoptions analysis: A data-driven study.

PubMed

Rossetti, Giulio; Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

2017-01-01

Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

Treesearch

David R. Weise; Eunmo Koo; Xiangyang Zhou; Shankar Mahalingam; Frédéric Morandini; Jacques-Henri Balbi

2016-01-01

Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically...

Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

USGS Publications Warehouse

Harris, Ted D.; Graham, Jennifer L.

2017-01-01

Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Only as Happy as the Least Happy Child: Multiple Grown Children's Problems and Successes and Middle-aged Parents’ Well-being

PubMed Central

Cheng, Yen-Pi; Birditt, Kira; Zarit, Steven

2012-01-01

Objectives. Middle-aged parents’ well-being may be tied to successes and failures of grown children. Moreover, most parents have more than one child, but studies have not considered how different children's successes and failures may be associated with parental well-being. Methods. Middle-aged adults (aged 40–60; N = 633) reported on each of their grown children (n = 1,384) and rated their own well-being. Participants indicated problems each child had experienced in the past two years, rated their children's successes, as well as positive and negative relationship qualities. Results. Analyses compared an exposure model (i.e., having one grown child with a problem or deemed successful) and a cumulative model (i.e., total problems or successes in the family). Consistent with the exposure and cumulative models, having one child with problems predicted poorer parental well-being and the more problems in the family, the worse parental well-being. Having one successful child did not predict well-being, but multiple grown children with higher total success in the family predicted enhanced parental well-being. Relationship qualities partially explained associations between children's successes and parental well-being. Discussion. Discussion focuses on benefits and detriments parents derive from how grown progeny turn out and particularly the implications of grown children's problems. PMID:21856677

QSAR study of curcumine derivatives as HIV-1 integrase inhibitors.

PubMed

Gupta, Pawan; Sharma, Anju; Garg, Prabha; Roy, Nilanjan

2013-03-01

A QSAR study was performed on curcumine derivatives as HIV-1 integrase inhibitors using multiple linear regression. The statistically significant model was developed with squared correlation coefficients (r(2)) 0.891 and cross validated r(2) (r(2) cv) 0.825. The developed model revealed that electronic, shape, size, geometry, substitution's information and hydrophilicity were important atomic properties for determining the inhibitory activity of these molecules. The model was also tested successfully for external validation (r(2) pred = 0.849) as well as Tropsha's test for model predictability. Furthermore, the domain analysis was carried out to evaluate the prediction reliability of external set molecules. The model was statistically robust and had good predictive power which can be successfully utilized for screening of new molecules.

BehavePlus fire modeling system: Past, present, and future

Treesearch

Patricia L. Andrews

2007-01-01

Use of mathematical fire models to predict fire behavior and fire effects plays an important supporting role in wildland fire management. When used in conjunction with personal fire experience and a basic understanding of the fire models, predictions can be successfully applied to a range of fire management activities including wildfire behavior prediction, prescribed...

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Extracting falsifiable predictions from sloppy models.

PubMed

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

Plasmonic Light Trapping in Thin-Film Solar Cells: Impact of Modeling on Performance Prediction

PubMed Central

Micco, Alberto; Pisco, Marco; Ricciardi, Armando; Mercaldo, Lucia V.; Usatii, Iurie; La Ferrara, Vera; Delli Veneri, Paola; Cutolo, Antonello; Cusano, Andrea

2015-01-01

We present a comparative study on numerical models used to predict the absorption enhancement in thin-film solar cells due to the presence of structured back-reflectors exciting, at specific wavelengths, hybrid plasmonic-photonic resonances. To evaluate the effectiveness of the analyzed models, they have been applied in a case study: starting from a U-shaped textured glass thin-film, µc-Si:H solar cells have been successfully fabricated. The fabricated cells, with different intrinsic layer thicknesses, have been morphologically, optically and electrically characterized. The experimental results have been successively compared with the numerical predictions. We have found that, in contrast to basic models based on the underlying schematics of the cell, numerical models taking into account the real morphology of the fabricated device, are able to effectively predict the cells performances in terms of both optical absorption and short-circuit current values.

Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain.

PubMed

van Hooff, Miranda L; Spruit, Maarten; O'Dowd, John K; van Lankveld, Wim; Fairbank, Jeremy C T; van Limbeek, Jacques

2014-01-01

The aim of this longitudinal study is to determine the factors which predict a successful 1-year outcome from an intensive combined physical and psychological (CPP) programme in chronic low back pain (CLBP) patients. A prospective cohort of 524 selected consecutive CLBP patients was followed. Potential predictive factors included demographic characteristics, disability, pain and cognitive behavioural factors as measured at pre-treatment assessment. The primary outcome measure was the oswestry disability index (ODI). A successful 1-year follow-up outcome was defined as a functional status equivalent to 'normal' and healthy populations (ODI ≤22). The 2-week residential programme fulfills the recommendations in international guidelines. For statistical analysis we divided the database into two equal samples. A random sample was used to develop a prediction model with multivariate logistic regression. The remaining cases were used to validate this model. The final predictive model suggested being 'in employment' at pre-treatment [OR 3.61 (95 % CI 1.80-7.26)] and an initial 'disability score' [OR 0.94 (95 % CI 0.92-0.97)] as significant predictive factors for a successful 1-year outcome (R (2) = 22 %; 67 % correctly classified). There was no predictive value from measures of psychological distress. CLBP patients who are in work and mild to moderately disabled at the start of a CPP programme are most likely to benefit from it and to have a successful treatment outcome. In these patients, the disability score falls to values seen in healthy populations. This small set of factors is easily identified, allowing selection for programme entry and triage to alternative treatment regimes.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Animal models for microbicide safety and efficacy testing.

PubMed

Veazey, Ronald S

2013-07-01

Early studies have cast doubt on the utility of animal models for predicting success or failure of HIV-prevention strategies, but results of multiple human phase 3 microbicide trials, and interrogations into the discrepancies between human and animal model trials, indicate that animal models were, and are, predictive of safety and efficacy of microbicide candidates. Recent studies have shown that topically applied vaginal gels, and oral prophylaxis using single or combination antiretrovirals are indeed effective in preventing sexual HIV transmission in humans, and all of these successes were predicted in animal models. Further, prior discrepancies between animal and human results are finally being deciphered as inadequacies in study design in the model, or quite often, noncompliance in human trials, the latter being increasingly recognized as a major problem in human microbicide trials. Successful microbicide studies in humans have validated results in animal models, and several ongoing studies are further investigating questions of tissue distribution, duration of efficacy, and continued safety with repeated application of these, and other promising microbicide candidates in both murine and nonhuman primate models. Now that we finally have positive correlations with prevention strategies and protection from HIV transmission, we can retrospectively validate animal models for their ability to predict these results, and more importantly, prospectively use these models to select and advance even safer, more effective, and importantly, more durable microbicide candidates into human trials.

MODEL OF PHYTOPLANKTON COMPETITION FOR LIMITING AND NONLIMITING NUTRIENTS: IMPLICATIONS FOR DEVELOPMENT OF ESTUARINE AND NEARSHORE MANAGEMENT SCHEMES

EPA Science Inventory

The global increase of noxious bloom occurrences has increased the need for phytoplankton management schemes. Such schemes require the ability to predict phytoplankton succession. Equilibrium Resources Competition theory, which is popular for predicting succession in lake systems...

Testing a Model of Environmental Risk and Protective Factors to Predict Middle and High School Students' Academic Success

ERIC Educational Resources Information Center

Peters, S. Colby; Woolley, Michael E.

2015-01-01

Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…

Predicting field weed emergence with empirical models and soft computing techniques

USDA-ARS?s Scientific Manuscript database

Seedling emergence is the most important phenological process that influences the success of weed species; therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling e...

Use the predictive models to explore the key factors affecting phytoplankton succession in Lake Erhai, China.

PubMed

Zhu, Rong; Wang, Huan; Chen, Jun; Shen, Hong; Deng, Xuwei

2018-01-01

Increasing algae in Lake Erhai has resulted in frequent blooms that have not only led to water ecosystem degeneration but also seriously influenced the quality of the water supply and caused extensive damage to the local people, as the lake is a water resource for Dali City. Exploring the key factors affecting phytoplankton succession and developing predictive models with easily detectable parameters for phytoplankton have been proven to be practical ways to improve water quality. To this end, a systematic survey focused on phytoplankton succession was conducted over 2 years in Lake Erhai. The data from the first study year were used to develop predictive models, and the data from the second year were used for model verification. The seasonal succession of phytoplankton in Lake Erhai was obvious. The dominant groups were Cyanobacteria in the summer, Chlorophyta in the autumn and Bacillariophyta in the winter. The developments and verification of predictive models indicated that compared to phytoplankton biomass, phytoplankton density is more effective for estimating phytoplankton variation in Lake Erhai. CCA (canonical correlation analysis) indicated that TN (total nitrogen), TP (total phosphorus), DO (dissolved oxygen), SD (Secchi depth), Cond (conductivity), T (water temperature), and ORP (oxidation reduction potential) had significant influences (p < 0.05) on the phytoplankton community. The CCA of the dominant species found that Microcystis was significantly influenced by T. The dominant Chlorophyta, Psephonema aenigmaticum and Mougeotia, were significantly influenced by TN. All results indicated that TN and T were the two key factors driving phytoplankton succession in Lake Erhai.

A Model for Predicting Student Performance on High-Stakes Assessment

ERIC Educational Resources Information Center

Dammann, Matthew Walter

2010-01-01

This research study examined the use of student achievement on reading and math state assessments to predict success on the science state assessment. Multiple regression analysis was utilized to test the prediction for all students in grades 5 and 8 in a mid-Atlantic state. The prediction model developed from the analysis explored the combined…

Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus.

PubMed

Kulkarni, Abhaya V; Drake, James M; Mallucci, Conor L; Sgouros, Spyros; Roth, Jonathan; Constantini, Shlomi

2009-08-01

To develop a model to predict the probability of endoscopic third ventriculostomy (ETV) success in the treatment for hydrocephalus on the basis of a child's individual characteristics. We analyzed 618 ETVs performed consecutively on children at 12 international institutions to identify predictors of ETV success at 6 months. A multivariable logistic regression model was developed on 70% of the dataset (training set) and validated on 30% of the dataset (validation set). In the training set, 305/455 ETVs (67.0%) were successful. The regression model (containing patient age, cause of hydrocephalus, and previous cerebrospinal fluid shunt) demonstrated good fit (Hosmer-Lemeshow, P = .78) and discrimination (C statistic = 0.70). In the validation set, 105/163 ETVs (64.4%) were successful and the model maintained good fit (Hosmer-Lemeshow, P = .45), discrimination (C statistic = 0.68), and calibration (calibration slope = 0.88). A simplified ETV Success Score was devised that closely approximates the predicted probability of ETV success. Children most likely to succeed with ETV can now be accurately identified and spared the long-term complications of CSF shunting.

Next-Term Student Performance Prediction: A Recommender Systems Approach

ERIC Educational Resources Information Center

Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya

2016-01-01

An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…

Predicting Successful Mathematics Remediation among Latina/o Students

ERIC Educational Resources Information Center

Crisp, Gloria; Reyes, Nicole Alia Salis; Doran, Erin

2017-01-01

This study examines Latina/o students' remedial math needs and outcomes. Data were drawn from a national sample of Latina/o students. Hierarchical generalized linear modeling techniques were used to predict three successful remediation outcomes. Results highlight the importance of providing financial aid and academic support to Latina/o students,…

Profile and determinants of successful aging in the Ibadan Study of Ageing.

PubMed

Gureje, Oye; Oladeji, Bibilola D; Abiona, Taiwo; Chatterji, Somnath

2014-05-01

To determine the profile and determinants of successful aging in a developing country characterized by low life expectancy and where successful agers may represent a unique group. Community-based cohort study. Eight contiguous states in the Yoruba-speaking region of Nigeria. A multistage clustered sampling of households was used to select a representative sample of individuals (N = 2,149) aged 65 and older at baseline. Nine hundred thirty were successfully followed for an average of 64 months between August 2003 and December 2009. Lifestyle and behavioral factors were assessed at baseline. Successful aging, defined using each of three models (absence of chronic health conditions, functional independence, and satisfaction with life), was assessed at follow-up. Between 16% and 75% of respondents could be classified as successful agers using one of the three models while 7.5% could be so classified using a combination of all the models. Correlations between the three models were small, ranging from 0.08 to 0.15. Different features predicted their outcomes, suggesting that they represent relatively independent trajectories of aging. Whichever model was used, more men than women tended to be classified as aging successfully. Men who aged successfully, using a combination of all the three models, were more likely never to have smoked (adjusted odds ratio (aOR) = 4.7, 95% confidence interval (CI) = 1.55-14.46) and to report, at baseline, having contacts with friends (aOR = 4.2, 95% CI = 1.0-18.76) or participating in community activities (aOR = 16.0, 95% CI = 1.23-204.40). In women, there was a nonlinear trend for younger age at baseline to predict this outcome. Modifiable social and lifestyle factors predicted successful aging in this population, suggesting that health promotion targeting behavior change may lead to tangible benefits for health and well-being in old age. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.

Prediction of global and local model quality in CASP8 using the ModFOLD server.

PubMed

McGuffin, Liam J

2009-01-01

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0--an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/. Copyright 2009 Wiley-Liss, Inc.

Monsoons: Processes, predictability, and the prospects for prediction

NASA Astrophysics Data System (ADS)

Webster, P. J.; Magaña, V. O.; Palmer, T. N.; Shukla, J.; Thomas, R. A.; Yanai, M.; Yasunari, T.

1998-06-01

The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.

Initialization and Predictability of a Coupled ENSO Forecast Model

NASA Technical Reports Server (NTRS)

Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

1997-01-01

The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

New and Accurate Predictive Model for the Efficacy of Extracorporeal Shock Wave Therapy in Managing Patients With Chronic Plantar Fasciitis.

PubMed

Yin, Mengchen; Chen, Ni; Huang, Quan; Marla, Anastasia Sulindro; Ma, Junming; Ye, Jie; Mo, Wen

2017-12-01

To identify factors for the outcome of a minimum clinically successful therapy and to establish a predictive model of extracorporeal shock wave therapy (ESWT) in managing patients with chronic plantar fasciitis. Randomized, controlled, prospective study. Outpatient of local medical center settings. Patients treated for symptomatic chronic plantar fasciitis between 2014 and 2016 (N=278). ESWT was performed by the principal authors to treat chronic plantar fasciitis. ESWT was administered in 3 sessions, with an interval of 2 weeks (±4d). In the low-, moderate-, and high-intensity groups, 2400 impulses total of ESWT with an energy flux density of 0.2, 0.4, and 0.6mJ/mm 2 , respectively (a rate of 8 impulses per second), were applied. The independent variables were patient age, sex, body mass index, affected side, duration of symptoms, Roles and Maudsley score, visual analog scale (VAS) score when taking first steps in the morning, edema, bone spurs, and intensity grade of ESWT. A minimal reduction of 50% in the VAS score was considered as minimum clinically successful therapy. The correlations between the achievement of minimum clinically successful therapy and independent variables were analyzed. The statistically significant factors identified were further analyzed by multivariate logistic regression, and the predictive model was established. The success rate of ESWT was 66.9%. Univariate analysis found that VAS score when taking first steps in the morning, edema, and the presence of heel spur in radiograph significantly affected the outcome of the treatment. Logistic regression drew the equation: minimum clinically successful therapy=(1+e [.011+42.807×heel spur+.109×edema+5.395×VAS score] ) -1 .The sensitivity of the predictive factors was 96.77%, 87.63%, and 86.02%, respectively. The specificity of the predictive factors was 45.65%, 42.39%, and 85.87%, respectively. The area under the curve of the predictive factors was .751, .650, and .859, respectively. The Youden index was .4243, .3003, and .7189, respectively. The Hosmer-Lemeshow test showed a good fitting of the predictive model, with an overall accuracy of 89.6%. This study establishes a new and accurate predictive model for the efficacy of ESWT in managing patients with chronic plantar fasciitis. The use of these parameters, in the form of a predictive model for ESWT efficacy, has the potential to improve decision-making in the application of ESWT. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids

EPA Science Inventory

In silico methods are invaluable tools to researchers seeking to understand and predict metabolic processes within PBPK models. Even though these methods have been successfully utilized to predict and quantify metabolic processes, there are many challenges involved. Stereochemica...

Predictive modelling for startup and investor relationship based on crowdfunding platform data

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Buono Asto Nugroho, Tri

2018-03-01

Crowdfunding platform is a place where startup shows off publicly their idea for the purpose to get their project funded. Crowdfunding platform such as Kickstarter are becoming popular today, it provides the efficient way for startup to get funded without liabilities, it also provides variety project category that can be participated. There is an available safety procedure to ensure achievable low-risk environment. The startup promoted project must accomplish their funded goal target. If they fail to reach the target, then there is no investment activity take place. It motivates startup to be more active to promote or disseminate their project idea and it also protect investor from losing money. The study objective is to predict the successfulness of proposed project and mapping investor trend using data mining framework. To achieve the objective, we proposed 3 models. First model is to predict whether a project is going to be successful or failed using K-Nearest Neighbour (KNN). Second model is to predict the number of successful project using Artificial Neural Network (ANN). Third model is to map the trend of investor in investing the project using K-Means clustering algorithm. KNN gives 99.04% model accuracy, while ANN best configuration gives 16-14-1 neuron layers and 0.2 learning rate, and K-Means gives 6 best separation clusters. The results of those models can help startup or investor to make decision regarding startup investment.

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Sense and simplicity in HADDOCK scoring: Lessons from CASP‐CAPRI round 1

PubMed Central

Vangone, A.; Rodrigues, J. P. G. L. M.; Xue, L. C.; van Zundert, G. C. P.; Geng, C.; Kurkcuoglu, Z.; Nellen, M.; Narasimhan, S.; Karaca, E.; van Dijk, M.; Melquiond, A. S. J.; Visscher, K. M.; Trellet, M.; Kastritis, P. L.

2016-01-01

ABSTRACT Our information‐driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community‐wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP‐CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets – a top‐ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico‐chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center‐of‐mass and symmetry restrained protocol, or on a template‐based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417–423. © 2016 Wiley Periodicals, Inc. PMID:27802573

Retention of community college students in online courses

NASA Astrophysics Data System (ADS)

Krajewski, Sarah

The issue of attrition in online courses at higher learning institutions remains a high priority in the United States. A recent rapid growth of online courses at community colleges has been instigated by student demand, as they meet the time constraints many nontraditional community college students have as a result of the need to work and care for dependents. Failure in an online course can cause students to become frustrated with the college experience, financially burdened, or to even give up and leave college. Attrition could be avoided by proper guidance of who is best suited for online courses. This study examined factors related to retention (i.e., course completion) and success (i.e., receiving a C or better) in an online biology course at a community college in the Midwest by operationalizing student characteristics (age, race, gender), student skills (whether or not the student met the criteria to be placed in an AFP course), and external factors (Pell recipient, full/part time status, first term) from the persistence model developed by Rovai. Internal factors from this model were not included in this study. Both univariate analyses and multivariate logistic regression were used to analyze the variables. Results suggest that race and Pell recipient were both predictive of course completion on univariate analyses. However, multivariate analyses showed that age, race, academic load and first term were predictive of completion and Pell recipient was no longer predictive. The univariate results for the C or better showed that age, race, Pell recipient, academic load, and meeting AFP criteria were predictive of success. Multivariate analyses showed that only age, race, and Pell recipient were significant predictors of success. Both regression models explained very little (<15%) of the variability within the outcome variables of retention and success. Therefore, although significant predictors were identified for course completion and retention, there are still many factors that remain unaccounted for in both regression models. Further research into the operationalization of Rovai's model, including internal factors, to predict completion and success is necessary.

Effects of Climate Change and Fisheries Bycatch on Shy Albatross (Thalassarche cauta) in Southern Australia

PubMed Central

2015-01-01

The impacts of climate change on marine species are often compounded by other stressors that make direct attribution and prediction difficult. Shy albatrosses (Thalassarche cauta) breeding on Albatross Island, Tasmania, show an unusually restricted foraging range, allowing easier discrimination between the influence of non-climate stressors (fisheries bycatch) and environmental variation. Local environmental conditions (rainfall, air temperature, and sea-surface height, an indicator of upwelling) during the vulnerable chick-rearing stage, have been correlated with breeding success of shy albatrosses. We use an age-, stage- and sex-structured population model to explore potential relationships between local environmental factors and albatross breeding success while accounting for fisheries bycatch by trawl and longline fisheries. The model uses time-series of observed breeding population counts, breeding success, adult and juvenile survival rates and a bycatch mortality observation for trawl fishing to estimate fisheries catchability, environmental influence, natural mortality rate, density dependence, and productivity. Observed at-sea distributions for adult and juvenile birds were coupled with reported fishing effort to estimate vulnerability to incidental bycatch. The inclusion of rainfall, temperature and sea-surface height as explanatory variables for annual chick mortality rate was statistically significant. Global climate models predict little change in future local average rainfall, however, increases are forecast in both temperatures and upwelling, which are predicted to have detrimental and beneficial effects, respectively, on breeding success. The model shows that mitigation of at least 50% of present bycatch is required to offset losses due to future temperature changes, even if upwelling increases substantially. Our results highlight the benefits of using an integrated modeling approach, which uses available demographic as well as environmental data within a single estimation framework, to provide future predictions. Such predictions inform the development of management options in the face of climate change. PMID:26057739

Effects of Climate Change and Fisheries Bycatch on Shy Albatross (Thalassarche cauta) in Southern Australia.

PubMed

Thomson, Robin B; Alderman, Rachael L; Tuck, Geoffrey N; Hobday, Alistair J

2015-01-01

The impacts of climate change on marine species are often compounded by other stressors that make direct attribution and prediction difficult. Shy albatrosses (Thalassarche cauta) breeding on Albatross Island, Tasmania, show an unusually restricted foraging range, allowing easier discrimination between the influence of non-climate stressors (fisheries bycatch) and environmental variation. Local environmental conditions (rainfall, air temperature, and sea-surface height, an indicator of upwelling) during the vulnerable chick-rearing stage, have been correlated with breeding success of shy albatrosses. We use an age-, stage- and sex-structured population model to explore potential relationships between local environmental factors and albatross breeding success while accounting for fisheries bycatch by trawl and longline fisheries. The model uses time-series of observed breeding population counts, breeding success, adult and juvenile survival rates and a bycatch mortality observation for trawl fishing to estimate fisheries catchability, environmental influence, natural mortality rate, density dependence, and productivity. Observed at-sea distributions for adult and juvenile birds were coupled with reported fishing effort to estimate vulnerability to incidental bycatch. The inclusion of rainfall, temperature and sea-surface height as explanatory variables for annual chick mortality rate was statistically significant. Global climate models predict little change in future local average rainfall, however, increases are forecast in both temperatures and upwelling, which are predicted to have detrimental and beneficial effects, respectively, on breeding success. The model shows that mitigation of at least 50% of present bycatch is required to offset losses due to future temperature changes, even if upwelling increases substantially. Our results highlight the benefits of using an integrated modeling approach, which uses available demographic as well as environmental data within a single estimation framework, to provide future predictions. Such predictions inform the development of management options in the face of climate change.

Modeling Success: Using Preenrollment Data to Identify Academically At-Risk Students

ERIC Educational Resources Information Center

Gansemer-Topf, Ann M.; Compton, Jonathan; Wohlgemuth, Darin; Forbes, Greg; Ralston, Ekaterina

2015-01-01

Improving student success and degree completion is one of the core principles of strategic enrollment management. To address this principle, institutional data were used to develop a statistical model to identify academically at-risk students. The model employs multiple linear regression techniques to predict students at risk of earning below a…

The roles of climate, phylogenetic relatedness, introduction effort, and reproductive traits in the establishment of non-native reptiles and amphibians.

PubMed

van Wilgen, Nicola J; Richardson, David M

2012-04-01

We developed a method to predict the potential of non-native reptiles and amphibians (herpetofauna) to establish populations. This method may inform efforts to prevent the introduction of invasive non-native species. We used boosted regression trees to determine whether nine variables influence establishment success of introduced herpetofauna in California and Florida. We used an independent data set to assess model performance. Propagule pressure was the variable most strongly associated with establishment success. Species with short juvenile periods and species with phylogenetically more distant relatives in regional biotas were more likely to establish than species that start breeding later and those that have close relatives. Average climate match (the similarity of climate between native and non-native range) and life form were also important. Frogs and lizards were the taxonomic groups most likely to establish, whereas a much lower proportion of snakes and turtles established. We used results from our best model to compile a spreadsheet-based model for easy use and interpretation. Probability scores obtained from the spreadsheet model were strongly correlated with establishment success as were probabilities predicted for independent data by the boosted regression tree model. However, the error rate for predictions made with independent data was much higher than with cross validation using training data. This difference in predictive power does not preclude use of the model to assess the probability of establishment of herpetofauna because (1) the independent data had no information for two variables (meaning the full predictive capacity of the model could not be realized) and (2) the model structure is consistent with the recent literature on the primary determinants of establishment success for herpetofauna. It may still be difficult to predict the establishment probability of poorly studied taxa, but it is clear that non-native species (especially lizards and frogs) that mature early and come from environments similar to that of the introduction region have the highest probability of establishment. ©2012 Society for Conservation Biology.

Implications of between-isolate variation for climate change impact modelling of Haemonchus contortus populations.

PubMed

Rose Vineer, H; Steiner, J; Knapp-Lawitzke, F; Bull, K; von Son-de Fernex, E; Bosco, A; Hertzberg, H; Demeler, J; Rinaldi, L; Morrison, A A; Skuce, P; Bartley, D J; Morgan, E R

2016-10-15

The impact of climate change on parasites and parasitic diseases is a growing concern and numerous empirical and mechanistic models have been developed to predict climate-driven spatial and temporal changes in the distribution of parasites and disease risk. Variation in parasite phenotype and life-history traits between isolates could undermine the application of such models at broad spatial scales. Seasonal variation in the transmission of the haematophagous gastrointestinal nematode Haemonchus contortus, one of the most pathogenic helminth species infecting sheep and goats worldwide, is primarily determined by the impact of environmental conditions on the free-living stages. To evaluate variability in the development success and mortality of the free-living stages of H. contortus and the impact of this variability on future climate impact modelling, three isolates of diverse origin were cultured at a range of temperatures between 15°C and 37°C to determine their development success compared with simulations using the GLOWORM-FL H. contortus model. No significant difference was observed in the developmental success of the three isolates of H. contortus tested, nor between isolates and model simulations. However, development success of all isolates at 37°C was lower than predicted by the model, suggesting the potential for overestimation of transmission risk at higher temperatures, such as those predicted under some scenarios of climate change. Recommendations are made for future climate impact modelling of gastrointestinal nematodes. Copyright © 2016 Elsevier B.V. All rights reserved.

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

Using Performance Data Gathered at Several Stages of Achievement in Predicting Subsequent Performance.

ERIC Educational Resources Information Center

Owen, Steven V.; Feldhusen, John F.

This study compares the effectiveness of three models of multivariate prediction for academic success in identifying the criterion variance of achievement in nursing education. The first model involves the use of an optimum set of predictors and one equation derived from a regression analysis on first semester grade average in predicting the…

Predicting red wolf release success in the southeastern United States

USGS Publications Warehouse

van Manen, Frank T.; Crawford, Barron A.; Clark, Joseph D.

2000-01-01

Although the red wolf (Canis rufus) was once found throughout the southeastern United States, indiscriminate killing and habitat destruction reduced its range to a small section of coastal Texas and Louisiana. Wolves trapped from 1973 to 1980 were taken to establish a captive breeding program that was used to repatriate 2 mainland and 3 island red wolf populations. We collected data from 320 red wolf releases in these areas and classified each as a success or failure based on survival and reproductive criteria, and whether recaptures were necessary to resolve conflicts with humans. We evaluated the relations between release success and conditions at the release sites, characteristics of released wolves, and release procedures. Although <44% of the variation in release success was explained, model performance based on jackknife tests indicated a 72-80% correct prediction rate for the 4 operational models we developed. The models indicated that success was associated with human influences on the landscape and the level of wolf habituation to humans prior to release. We applied the models to 31 prospective areas for wolf repatriation and calculated an index of release success for each area. Decision-makers can use these models to objectively rank prospective release areas and compare strengths and weaknesses of each.

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

The Odds of Success: Predicting Registered Health Information Administrator Exam Success

PubMed Central

Dolezel, Diane; McLeod, Alexander

2017-01-01

The purpose of this study was to craft a predictive model to examine the relationship between grades in specific academic courses, overall grade point average (GPA), on-campus versus online course delivery, and success in passing the Registered Health Information Administrator (RHIA) exam on the first attempt. Because student success in passing the exam on the first attempt is assessed as part of the accreditation process, this study is important to health information management (HIM) programs. Furthermore, passing the exam greatly expands the graduate's job possibilities because the demand for credentialed graduates far exceeds the supply of credentialed graduates. Binary logistic regression was utilized to explore the relationships between the predictor variables and success in passing the RHIA exam on the first attempt. Results indicate that the student's cumulative GPA, specific HIM course grades, and course delivery method were predictive of success. PMID:28566994

Developing a risk prediction model for the functional outcome after hip arthroscopy.

PubMed

Stephan, Patrick; Röling, Maarten A; Mathijssen, Nina M C; Hannink, Gerjon; Bloem, Rolf M

2018-04-19

Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors. Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule. The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17-0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21-1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20-1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99-4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22-0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38-4.18); p = < 0.01) were factors in the final prediction model of successful functional outcome 1 year after hip arthroscopy. The model's discriminating accuracy turned out to be fair, as 71% (95% CI: 64-80%) of the patients were classified correctly. The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.

Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

Prediction model for obtaining spermatozoa with testicular sperm extraction in men with non-obstructive azoospermia.

PubMed

Cissen, M; Meijerink, A M; D'Hauwers, K W; Meissner, A; van der Weide, N; Mochtar, M H; de Melker, A A; Ramos, L; Repping, S; Braat, D D M; Fleischer, K; van Wely, M

2016-09-01

Can an externally validated model, based on biological variables, be developed to predict successful sperm retrieval with testicular sperm extraction (TESE) in men with non-obstructive azoospermia (NOA) using a large nationwide cohort? Our prediction model including six variables was able to make a good distinction between men with a good chance and men with a poor chance of obtaining spermatozoa with TESE. Using ICSI in combination with TESE even men suffering from NOA are able to father their own biological child. Only in approximately half of the patients with NOA can testicular sperm be retrieved successfully. The few models that have been developed to predict the chance of obtaining spermatozoa with TESE were based on small datasets and none of them have been validated externally. We performed a retrospective nationwide cohort study. Data from 1371 TESE procedures were collected between June 2007 and June 2015 in the two fertility centres. All men with NOA undergoing their first TESE procedure as part of a fertility treatment were included. The primary end-point was the presence of one or more spermatozoa (regardless of their motility) in the testicular biopsies.We constructed a model for the prediction of successful sperm retrieval, using univariable and multivariable binary logistic regression analysis and the dataset from one centre. This model was then validated using the dataset from the other centre. The area under the receiver-operating characteristic curve (AUC) was calculated and model calibration was assessed. There were 599 (43.7%) successful sperm retrievals after a first TESE procedure. The prediction model, built after multivariable logistic regression analysis, demonstrated that higher male age, higher levels of serum testosterone and lower levels of FSH and LH were predictive for successful sperm retrieval. Diagnosis of idiopathic NOA and the presence of an azoospermia factor c gene deletion were predictive for unsuccessful sperm retrieval. The AUC was 0.69 (95% confidence interval (CI): 0.66-0.72). The difference between the mean observed chance and the mean predicted chance was <2.0% in all groups, indicating good calibration. In validation, the model had moderate discriminative capacity (AUC 0.65, 95% CI: 0.62-0.72) and moderate calibration: the predicted probability never differed by more than 9.2% of the mean observed probability. The percentage of men with Klinefelter syndrome among men diagnosed with NOA is expected to be higher than in our study population, which is a potential selection bias. The ability of the sperm retrieved to fertilize an oocyte and produce a live birth was not tested. This model can help in clinical decision-making in men with NOA by reliably predicting the chance of obtaining spermatozoa with TESE. This study was partly supported by an unconditional grant from Merck Serono (to D.D.M.B. and K.F.) and by the Department of Obstetrics and Gynaecology of Radboud University Medical Center, Nijmegen, The Netherlands, the Department of Obstetrics and Gynaecology, Jeroen Bosch Hospital, Den Bosch, The Netherlands, and the Department of Obstetrics and Gynaecology, Academic Medical Center, Amsterdam, The Netherlands. Merck Serono had no influence in concept, design nor elaboration of this study. Not applicable. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Simple, validated vaginal birth after cesarean delivery prediction model for use at the time of admission.

PubMed

Metz, Torri D; Stoddard, Gregory J; Henry, Erick; Jackson, Marc; Holmgren, Calla; Esplin, Sean

2013-09-01

To create a simple tool for predicting the likelihood of successful trial of labor after cesarean delivery (TOLAC) during the pregnancy after a primary cesarean delivery using variables available at the time of admission. Data for all deliveries at 14 regional hospitals over an 8-year period were reviewed. Women with one cesarean delivery and one subsequent delivery were included. Variables associated with successful VBAC were identified using multivariable logistic regression. Points were assigned to these characteristics, with weighting based on the coefficients in the regression model to calculate an integer VBAC score. The VBAC score was correlated with TOLAC success rate and was externally validated in an independent cohort using a logistic regression model. A total of 5,445 women met inclusion criteria. Of those women, 1,170 (21.5%) underwent TOLAC. Of the women who underwent trial of labor, 938 (80%) had a successful VBAC. A VBAC score was generated based on the Bishop score (cervical examination) at the time of admission, with points added for history of vaginal birth, age younger than 35 years, absence of recurrent indication, and body mass index less than 30. Women with a VBAC score less than 10 had a likelihood of TOLAC success less than 50%. Women with a VBAC score more than 16 had a TOLAC success rate more than 85%. The model performed well in an independent cohort with an area under the curve of 0.80 (95% confidence interval 0.76-0.84). Prediction of TOLAC success at the time of admission is highly dependent on the initial cervical examination. This simple VBAC score can be utilized when counseling women considering TOLAC. II.

Attempting to Predict Success in the Qualifying Round of the International Chemistry Olympiad

ERIC Educational Resources Information Center

Urhahne, Detlef; Ho, Lok Hang; Parchmann, Ilka; Nick, Sabine

2012-01-01

The aim of this study was trying to predict success in the qualifying round for the International Chemistry Olympiad (IChO) on the basis of the expectancy-value model of achievement motivation by Eccles et al. The investigation with 52 participants, including 14 females, was conducted during the third of four qualifying rounds of the IChO in…

How Preschoolers' Social-Emotional Learning Predicts Their Early School Success: Developing Theory-Promoting, Competency-Based Assessments

ERIC Educational Resources Information Center

Denham, Susanne A.; Bassett, Hideko H.; Zinsser, Katherine; Wyatt, Todd M.

2014-01-01

Starting on positive trajectories at school entry is important for children's later academic success. Using partial least squares, we sought to specify interrelations among all theory-based components of social-emotional learning (SEL), and their ability to predict later classroom adjustment and academic readiness in a modelling context.…

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission

PubMed Central

2011-01-01

Background Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat) for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. Methods 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS) factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Results Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. Conclusions A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of applicants, the proportion of successful completion of the curriculum after two years is expected to rise substantially. PMID:21999767

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission.

PubMed

Hissbach, Johanna C; Klusmann, Dietrich; Hampe, Wolfgang

2011-10-14

Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat) for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS) factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of applicants, the proportion of successful completion of the curriculum after two years is expected to rise substantially.

Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11

PubMed Central

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2015-01-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score=0.736 and RMSD=2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. PMID:26370505

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

PubMed

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2016-09-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

QSAR models for prediction of chromatographic behavior of homologous Fab variants.

PubMed

Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

2017-06-01

While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.

PubMed

Claret, Laurent; Jin, Jin Y; Ferté, Charles; Winter, Helen; Girish, Sandhya; Stroh, Mark; He, Pei; Ballinger, Marcus; Sandler, Alan; Joshi, Amita; Rittmeyer, Achim; Gandara, David; Soria, Jean-Charles; Bruno, René

2018-04-23

Purpose: Standard endpoints often poorly predict overall survival (OS) with immunotherapies. We investigated the predictive performance of model-based tumor growth inhibition (TGI) metrics using data from atezolizumab clinical trials in patients with non-small cell lung cancer. Experimental Design: OS benefit with atezolizumab versus docetaxel was observed in both POPLAR (phase II) and OAK (phase III), although progression-free survival was similar between arms. A multivariate model linking baseline patient characteristics and on-treatment tumor growth rate constant (KG), estimated using time profiles of sum of longest diameters (RECIST 1.1) to OS, was developed using POPLAR data. The model was evaluated to predict OAK outcome based on estimated KG at TGI data cutoffs ranging from 10 to 122 weeks. Results: In POPLAR, TGI profiles in both arms crossed at 25 weeks, with more shrinkage with docetaxel and slower KG with atezolizumab. A log-normal OS model, with albumin and number of metastatic sites as independent prognostic factors and estimated KG, predicted OS HR in subpopulations of patients with varying baseline PD-L1 expression in both POPLAR and OAK: model-predicted OAK HR (95% prediction interval), 0.73 (0.63-0.85), versus 0.73 observed. The POPLAR OS model predicted greater than 97% chance of success of OAK (significant OS HR, P < 0.05) from the 40-week data cutoff onward with 50% of the total number of tumor assessments when a successful study was predicted from 70 weeks onward based on observed OS. Conclusions: KG has potential as a model-based early endpoint to inform decisions in cancer immunotherapy studies. Clin Cancer Res; 1-7. ©2018 AACR. ©2018 American Association for Cancer Research.

WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound

NASA Astrophysics Data System (ADS)

Hunt, Peter A.; Segall, Matthew D.; Tyzack, Jonathan D.

2018-02-01

In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.

Successive smoothing algorithm for constructing the semiempirical model developed at ONERA to predict unsteady aerodynamic forces. [aeroelasticity in helicopters

NASA Technical Reports Server (NTRS)

Petot, D.; Loiseau, H.

1982-01-01

Unsteady aerodynamic methods adopted for the study of aeroelasticity in helicopters are considered with focus on the development of a semiempirical model of unsteady aerodynamic forces acting on an oscillating profile at high incidence. The successive smoothing algorithm described leads to the model's coefficients in a very satisfactory manner.

Modeling aboveground biomass of Tamarix ramosissima in the Arkansas River Basin of Southeastern Colorado, USA

USGS Publications Warehouse

Evangelista, P.; Kumar, S.; Stohlgren, T.J.; Crall, A.W.; Newman, G.J.

2007-01-01

Predictive models of aboveground biomass of nonnative Tamarix ramosissima of various sizes were developed using destructive sampling techniques on 50 individuals and four 100-m2 plots. Each sample was measured for average height (m) of stems and canopy area (m2) prior to cutting, drying, and weighing. Five competing regression models (P < 0.05) were developed to estimate aboveground biomass of T. ramosissima using average height and/or canopy area measurements and were evaluated using Akaike's Information Criterion corrected for small sample size (AICc). Our best model (AICc = -148.69, ??AICc = 0) successfully predicted T. ramosissima aboveground biomass (R2 = 0.97) and used average height and canopy area as predictors. Our 2nd-best model, using the same predictors, was also successful in predicting aboveground biomass (R2 = 0.97, AICc = -131.71, ??AICc = 16.98). A 3rd model demonstrated high correlation between only aboveground biomass and canopy area (R2 = 0.95), while 2 additional models found high correlations between aboveground biomass and average height measurements only (R2 = 0.90 and 0.70, respectively). These models illustrate how simple field measurements, such as height and canopy area, can be used in allometric relationships to accurately predict aboveground biomass of T. ramosissima. Although a correction factor may be necessary for predictions at larger scales, the models presented will prove useful for many research and management initiatives.

Predicting the chance of vaginal delivery after one cesarean section: validation and elaboration of a published prediction model.

PubMed

Fagerberg, Marie C; Maršál, Karel; Källén, Karin

2015-05-01

We aimed to validate a widely used US prediction model for vaginal birth after cesarean (Grobman et al. [8]) and modify it to suit Swedish conditions. Women having experienced one cesarean section and at least one subsequent delivery (n=49,472) in the Swedish Medical Birth Registry 1992-2011 were randomly divided into two data sets. In the development data set, variables associated with successful trial of labor were identified using multiple logistic regression. The predictive ability of the estimates previously published by Grobman et al., and of our modified and new estimates, respectively, was then evaluated using the validation data set. The accuracy of the models for prediction of vaginal birth after cesarean was measured by area under the receiver operating characteristics curve. For maternal age, body mass index, prior vaginal delivery, and prior labor arrest, the odds ratio estimates for vaginal birth after cesarean were similar to those previously published. The prediction accuracy increased when information on indication for the previous cesarean section was added (from area under the receiver operating characteristics curve=0.69-0.71), and increased further when maternal height and delivery unit cesarean section rates were included (area under the receiver operating characteristics curve=0.74). The correlation between the individual predicted vaginal birth after cesarean probability and the observed trial of labor success rate was high in all the respective predicted probability decentiles. Customization of prediction models for vaginal birth after cesarean is of considerable value. Choosing relevant indicators for a Swedish setting made it possible to achieve excellent prediction accuracy for success in trial of labor after cesarean. During the delicate process of counseling about preferred delivery mode after one cesarean section, considering the results of our study may facilitate the choice between a trial of labor or an elective repeat cesarean section. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Dispersal and Germination Patterns of Monterey Spineflower at Fort Ord Natural Reserve.

NASA Astrophysics Data System (ADS)

Chaudhry, Z.

2014-12-01

Some species are rare because they are restricted to certain habitats and/or have small population sizes. Monterey spineflower, a federally listed threatened annual plant, is found in open sandy regions of the California coast, in chaparral vegetation around the Monterey Bay. A model based on previous research suggests that the Monterey spineflower population at Fort Ord Natural Reserve should be rapidly increasing, but it is not. This suggests that the model may be using data that overestimates the percentage of spineflower seeds that successfully germinate. I tested three hypotheses to determine the cause of the difference in population sizes between the predicted model and the field results. First, I predicted that the spineflower seeds are blown by the wind into shrubs such as manzanita, and are unable to germinate due to the lack of a suitable environment. I tested this in two ways. A field experiment showed that seeds are easily blow by wind. Next, I took soil cores and found spineflower seeds within the manzanita shrubs. Secondly, I predicted that the germination rate used by the model (90%) was too high. However, my germination experiments did not support this hypothesis because 91% of new seeds successfully germinated. Lastly, I predicted that the newer seeds are more viable than older seeds and therefore have a higher chance of successfully germinating. After germinating seeds in a controlled environment I observed that the seeds from 2014 had a higher number of successfully germinated seeds compared to the number of successfully germinated seeds from 1995 (91% vs 33%). I conclude that the loss of seeds due to wind decreases germination expectancies and older seeds are less viable than new seeds. Therefore, Monterey spineflower is a rare plant because environmental barriers hinder seeds from dispersing to a suitable habitat and successfully germinating while seeds lose viability as they age.

A Predictive Model for MSSW Student Success

ERIC Educational Resources Information Center

Napier, Angela Michele

2011-01-01

This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

PubMed

Uyar, Asli; Bener, Ayse; Ciray, H Nadir

2015-08-01

Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Retrospective cohort study. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). A machine learning-based decision support system would be useful in improving the success rates of IVF treatment. © The Author(s) 2014.

Development of Novel Repellents Using Structure - Activity Modeling of Compounds in the USDA Archival Database

DTIC Science & Technology

2011-01-01

used in efforts to develop QSAR models. Measurement of Repellent Efficacy Screening for Repellency of Compounds with Unknown Toxicology In screening...CPT) were used to develop Quantitative Structure Activity Relationship ( QSAR ) models to predict repellency. Successful prediction of novel...acylpiperidine QSAR models employed 4 descriptors to describe the relationship between structure and repellent duration. The ANN model of the carboxamides did not

Predicting nest success from habitat features in aspen forests of the central Rocky Mountains

Treesearch

Heather M. Struempf; Deborah M. Finch; Gregory Hayward; Stanley Anderson

2001-01-01

We collected nesting data on bird use of aspen stands in the Routt and Medicine Bow National Forests between 1987 and 1989. We found active nest sites of 28 species of small nongame birds on nine study plots in undisturbed aspen forests. We compared logistic regression models predicting nest success (at least one nestling) from nest-site or stand-level habitat...

Use of a machine learning framework to predict substance use disorder treatment success

PubMed Central

Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed. PMID:28394905

Use of a machine learning framework to predict substance use disorder treatment success.

PubMed

Acion, Laura; Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

A multidimensional model of the effect of gravity on the spatial orientation of the monkey

NASA Technical Reports Server (NTRS)

Merfeld, D. M.; Young, L. R.; Oman, C. M.; Shelhamer, M. J.

1993-01-01

A "sensory conflict" model of spatial orientation was developed. This mathematical model was based on concepts derived from observer theory, optimal observer theory, and the mathematical properties of coordinate rotations. The primary hypothesis is that the central nervous system of the squirrel monkey incorporates information about body dynamics and sensory dynamics to develop an internal model. The output of this central model (expected sensory afference) is compared to the actual sensory afference, with the difference defined as "sensory conflict." The sensory conflict information is, in turn, used to drive central estimates of angular velocity ("velocity storage"), gravity ("gravity storage"), and linear acceleration ("acceleration storage") toward more accurate values. The model successfully predicts "velocity storage" during rotation about an earth-vertical axis. The model also successfully predicts that the time constant of the horizontal vestibulo-ocular reflex is reduced and that the axis of eye rotation shifts toward alignment with gravity following postrotatory tilt. Finally, the model predicts the bias, modulation, and decay components that have been observed during off-vertical axis rotations (OVAR).

Gap model development, validation, and application to succession of secondary subtropical dry forests of Puerto Rico

Treesearch

Jennifer A. Holm; H.H. Shugart; Skip J. Van Bloem; G.R. Larocque

2012-01-01

Because of human pressures, the need to understand and predict the long-term dynamics and development of subtropical dry forests is urgent. Through modifications to the ZELIG simulation model, including the development of species- and site-specific parameters and internal modifications, the capability to model and predict forest change within the 4500-ha Guanica State...

Modeling of short fiber reinforced injection moulded composite

NASA Astrophysics Data System (ADS)

Kulkarni, A.; Aswini, N.; Dandekar, C. R.; Makhe, S.

2012-09-01

A micromechanics based finite element model (FEM) is developed to facilitate the design of a new production quality fiber reinforced plastic injection molded part. The composite part under study is composed of a polyetheretherketone (PEEK) matrix reinforced with 30% by volume fraction of short carbon fibers. The constitutive material models are obtained by using micromechanics based homogenization theories. The analysis is carried out by successfully coupling two commercial codes, Moldflow and ANSYS. Moldflow software is used to predict the fiber orientation by considering the flow kinetics and molding parameters. Material models are inputted into the commercial software ANSYS as per the predicted fiber orientation and the structural analysis is carried out. Thus in the present approach a coupling between two commercial codes namely Moldflow and ANSYS has been established to enable the analysis of the short fiber reinforced injection moulded composite parts. The load-deflection curve is obtained based on three constitutive material model namely an isotropy, transversely isotropy and orthotropy. Average values of the predicted quantities are compared to experimental results, obtaining a good correlation. In this manner, the coupled Moldflow-ANSYS model successfully predicts the load deflection curve of a composite injection molded part.

A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

PubMed Central

Yau, Estelle; Petersson, Carl; Dolgos, Hugues

2017-01-01

Abstract Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter‐mediated secretion (F g) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models – the ADAM, Q gut and Competing Rates – was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under‐predict human F g. Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g. © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd. PMID:28152562

A comparative evaluation of models to predict human intestinal metabolism from nonclinical data.

PubMed

Yau, Estelle; Petersson, Carl; Dolgos, Hugues; Peters, Sheila Annie

2017-04-01

Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter-mediated secretion (F g ) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models - the ADAM, Q gut and Competing Rates - was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under-predict human F g . Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g . © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd. © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

PubMed Central

Maron, Jill L.; Hwang, Jooyeon S.; Pathak, Subash; Ruthazer, Robin; Russell, Ruby L.; Alterovitz, Gil

2014-01-01

Objective To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. Study design Comparative whole transcriptome microarray analysis from 12 preterm newborns pre- and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. Results Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant’s ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Conclusions We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn’s developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. PMID:25620512

Sociodemographic Factors Associated With Changes in Successful Aging in Spain: A Follow-Up Study.

PubMed

Domènech-Abella, Joan; Perales, Jaime; Lara, Elvira; Moneta, Maria Victoria; Izquierdo, Ana; Rico-Uribe, Laura Alejandra; Mundó, Jordi; Haro, Josep Maria

2017-06-01

Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

NASA Astrophysics Data System (ADS)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Emerging approaches in predictive toxicology.

PubMed

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

Emerging Approaches in Predictive Toxicology

PubMed Central

Zhang, Luoping; McHale, Cliona M.; Greene, Nigel; Snyder, Ronald D.; Rich, Ivan N.; Aardema, Marilyn J.; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2016-01-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. PMID:25044351

The impact of children's internalizing and externalizing problems on parenting: Transactional processes and reciprocal change over time.

PubMed

Serbin, Lisa A; Kingdon, Danielle; Ruttle, Paula L; Stack, Dale M

2015-11-01

Most theoretical models of developmental psychopathology involve a transactional, bidirectional relation between parenting and children's behavior problems. The present study utilized a cross-lagged panel, multiple interval design to model change in bidirectional relations between child and parent behavior across successive developmental periods. Two major categories of child behavior problems, internalizing and externalizing, and two aspects of parenting, positive (use of support and structure) and harsh discipline (use of physical punishment), were modeled across three time points spaced 3 years apart. Two successive developmental intervals, from approximately age 7.5 to 10.5 and from 10.5 to 13.5, were included. Mother-child dyads (N = 138; 65 boys) from a lower income longitudinal sample of families participated, with standardized measures of mothers rating their own parenting behavior and teachers reporting on child's behavior. Results revealed different types of reciprocal relations between specific aspects of child and parent behavior, with internalizing problems predicting an increase in positive parenting over time, which subsequently led to a reduction in internalizing problems across the successive 3-year interval. In contrast, externalizing predicted reduced levels of positive parenting in a reciprocal sequence that extended across two successive intervals and predicted increased levels of externalizing over time. Implications for prevention and early intervention are discussed.

Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

PubMed

Zhang, Chengxin; Mortuza, S M; He, Baoji; Wang, Yanting; Zhang, Yang

2018-03-01

We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successfully folds three medium-size FM targets that have more than 150 residues, even though the interplay between the two pipelines still awaits further optimization. Newly developed sequence-based contact prediction by NeBcon plays a critical role to enhance the quality of models, particularly for FM targets, by the new pipelines. The inclusion of NeBcon predicted contacts as restraints in the QUARK simulations results in an average TM-score of 0.41 for the best in top five predicted models, which is 37% higher than that by the QUARK simulations without contacts. In particular, there are seven targets that are converted from non-foldable to foldable (TM-score >0.5) due to the use of contact restraints in the simulations. Another additional feature in the current pipelines is the local structure quality prediction by ResQ, which provides a robust residue-level modeling error estimation. Despite the success, significant challenges still remain in ab initio modeling of multi-domain proteins and folding of β-proteins with complicated topologies bound by long-range strand-strand interactions. Improvements on domain boundary and long-range contact prediction, as well as optimal use of the predicted contacts and multiple threading alignments, are critical to address these issues seen in the CASP12 experiment. © 2017 Wiley Periodicals, Inc.

Comparison of wheat yield simulated using three N cycling options in the SWAT model

USDA-ARS?s Scientific Manuscript database

The Soil and Water Assessment Tool (SWAT) model has been successfully used to predict alterations in streamflow, evapotranspiration and soil water; however, it is not clear how effective or accurate SWAT is at predicting crop growth. Previous research suggests that while the hydrologic balance in e...

Collegiate Student-Athletes' Academic Success: Academic Communication Apprehension's Impact on Prediction Models

ERIC Educational Resources Information Center

James, Kai'Iah A.

2010-01-01

This dissertation study examines the impact of traditional and non-cognitive variables on the academic prediction model for a sample of collegiate student-athletes. Three hundred and fifty-nine NCAA Division IA male and female student-athletes, representing 13 sports, including football and Men's and Women's Basketball provided demographic…

Predicting Failure of Glyburide Therapy in Gestational Diabetes

PubMed Central

Harper, Lorie M.; Glover, Angelica V.; Biggio, Joseph R.; Tita, Alan

2016-01-01

Objective We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Study Design Retrospective cohort of all singletons with GDM treated with glyburide 2007–2013. Glyburide failure was defined as reaching glyburide 20 mg/day and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Results Of 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ≤26 weeks, 1-hour GCT ≥228 mg/dL, 3-hour GTT 1-hour value ≥221 mg/dL, ≥7 post-prandial blood sugars >120 mg/dL in the week glyburide started, and ≥1 blood sugar >200 mg/dL. The model accurately classified 81% of subjects. Conclusions Women with GDM who will require insulin can be identified at initiation of pharmacologic therapy. PMID:26796130

Predicting failure of glyburide therapy in gestational diabetes.

PubMed

Harper, L M; Glover, A V; Biggio, J R; Tita, A

2016-05-01

We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Retrospective cohort of all singletons with GDM treated with glyburide from 2007 to 2013. Glyburide failure was defined as reaching glyburide 20 mg day(-1) and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Of the 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) as glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ⩽26 weeks, 1-h glucose challenge test ⩾228 mg dl(-1), 3-h glucose tolerance test 1-h value ⩾221 mg dl(-1), ⩾7 postprandial blood sugars >120 mg dl(-1) in the week glyburide started and ⩾1 blood sugar >200 mg dl(-1). The model accurately classified 81% of subjects. Women with GDM who will require insulin can be identified at the initiation of pharmacological therapy.

Species traits and network structure predict the success and impacts of pollinator invasions.

PubMed

Valdovinos, Fernanda S; Berlow, Eric L; Moisset de Espanés, Pablo; Ramos-Jiliberto, Rodrigo; Vázquez, Diego P; Martinez, Neo D

2018-05-31

Species invasions constitute a major and poorly understood threat to plant-pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer-resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant-pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien-native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability.

Modeling N Cycling during Succession after Forest Disturbance: an Analysis of N Mining and Retention Hypothesis

NASA Astrophysics Data System (ADS)

Zhou, Z.; Ollinger, S. V.; Ouimette, A.; Lovett, G. M.; Fuss, C. B.; Goodale, C. L.

2017-12-01

Dissolved inorganic nitrogen losses at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, have declined in recent decades, a pattern that counters expectations based on prevailing theory. An unbalanced ecosystem nitrogen (N) budget implies there is a missing component for N sink. Hypotheses to explain this discrepancy include increasing rates of denitrification and accumulation of N in mineral soil pools following N mining by plants. Here, we conducted a modeling analysis fused with field measurements of N cycling, specifically examining the hypothesis relevant to N mining and retention in mineral soils. We included simplified representations of both mechanisms, N mining and retention, in a revised ecosystem process model, PnET-SOM, to evaluate the dynamics of N cycling during succession after forest disturbance at the HBEF. The predicted N mining during the early succession was regulated by a metric representing a potential demand of extra soil N for large wood growth. The accumulation of nitrate in mineral soil pools was a function of the net aboveground biomass accumulation and soil N availability and parameterized based on field 15N tracer incubation data. The predicted patterns of forest N dynamics were consistent with observations. The addition of the new algorithms also improved the predicted DIN export in stream water with an R squared of 0.35 (P<0.01) aganist observations. Predicted mining processes had an average rate of 7.4 kgNha-1yr-1 and Predicted rates of N retention processes were 5.2 kgNha-1yr-1, both of which were in line with estimates only based on field data. The predicted trend of low DIN export could continue for another 70 years to pay back the mined N in mineral soils. Predicted ecosystem N balance showed that N gas loss could account for 14-46% of the total N deposition, the soil mining about 103% during the early succession, and soil retention about 35% at the current forest stage at the HBEF.

Stock market index prediction using neural networks

NASA Astrophysics Data System (ADS)

Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

1994-03-01

A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

Clinical models are inaccurate in predicting bile duct stones in situ for patients with gallbladder.

PubMed

Topal, B; Fieuws, S; Tomczyk, K; Aerts, R; Van Steenbergen, W; Verslype, C; Penninckx, F

2009-01-01

The probability that a patient has common bile duct stones (CBDS) is a key factor in determining diagnostic and treatment strategies. This prospective cohort study evaluated the accuracy of clinical models in predicting CBDS for patients who will undergo cholecystectomy for lithiasis. From October 2005 until September 2006, 335 consecutive patients with symptoms of gallstone disease underwent cholecystectomy. Statistical analysis was performed on prospective patient data obtained at the time of first presentation to the hospital. Demonstrable CBDS at the time of endoscopic retrograde cholangiopancreatography (ERCP) or intraoperative cholangiography (IOC) was considered the gold standard for the presence of CBDS. Common bile duct stones were demonstrated in 53 patients. For 35 patients, ERCP was performed, with successful stone clearance in 24 of 30 patients who had proven CBDS. In 29 patients, IOC showed CBDS, which were managed successfully via laparoscopic common bile duct exploration, with stone extraction at the time of cholecystectomy. Prospective validation of the existing model for CBDS resulted in a predictive accuracy rate of 73%. The new model showed a predictive accuracy rate of 79%. Clinical models are inaccurate in predicting CBDS in patients with cholelithiasis. Management strategies should be based on the local availability of therapeutic expertise.

Sequential and simultaneous choices: testing the diet selection and sequential choice models.

PubMed

Freidin, Esteban; Aw, Justine; Kacelnik, Alex

2009-03-01

We investigate simultaneous and sequential choices in starlings, using Charnov's Diet Choice Model (DCM) and Shapiro, Siller and Kacelnik's Sequential Choice Model (SCM) to integrate function and mechanism. During a training phase, starlings encountered one food-related option per trial (A, B or R) in random sequence and with equal probability. A and B delivered food rewards after programmed delays (shorter for A), while R ('rejection') moved directly to the next trial without reward. In this phase we measured latencies to respond. In a later, choice, phase, birds encountered the pairs A-B, A-R and B-R, the first implementing a simultaneous choice and the second and third sequential choices. The DCM predicts when R should be chosen to maximize intake rate, and SCM uses latencies of the training phase to predict choices between any pair of options in the choice phase. The predictions of both models coincided, and both successfully predicted the birds' preferences. The DCM does not deal with partial preferences, while the SCM does, and experimental results were strongly correlated to this model's predictions. We believe that the SCM may expose a very general mechanism of animal choice, and that its wider domain of success reflects the greater ecological significance of sequential over simultaneous choices.

Distress modeling for DARWin-ME : final report.

DOT National Transportation Integrated Search

2013-12-01

Distress prediction models, or transfer functions, are key components of the Pavement M-E Design and relevant analysis. The accuracy of such models depends on a successful process of calibration and subsequent validation of model coefficients in the ...

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

PubMed

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

2014-01-01

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

Artificial neural network based particle size prediction of polymeric nanoparticles.

PubMed

Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

2017-10-01

Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

Word of Mouth : An Agent-based Approach to Predictability of Stock Prices

NASA Astrophysics Data System (ADS)

Shimokawa, Tetsuya; Misawa, Tadanobu; Watanabe, Kyoko

This paper addresses how communication processes among investors affect stock prices formation, especially emerging predictability of stock prices, in financial markets. An agent based model, called the word of mouth model, is introduced for analyzing the problem. This model provides a simple, but sufficiently versatile, description of informational diffusion process and is successful in making lucidly explanation for the predictability of small sized stocks, which is a stylized fact in financial markets but difficult to resolve by traditional models. Our model also provides a rigorous examination of the under reaction hypothesis to informational shocks.

Meta-Analysis of Land Use / Land Cover Change Factors in the Conterminous US and Prediction of Potential Working Timberlands in the US South from FIA Inventory Plots and NLCD Cover Maps

NASA Astrophysics Data System (ADS)

Jeuck, James A.

This dissertation consists of research projects related to forest land use / land cover (LULC): (1) factors predicting LULC change and (2) methodology to predict particular forest use, or "potential working timberland" (PWT), from current forms of land data. The first project resulted in a published paper, a meta-analysis of 64 econometric models from 47 studies predicting forest land use changes. The response variables, representing some form of forest land change, were organized into four groups: forest conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified, from 21 F2A models, 21 F2D models, 12 F2NF models, and 10 U2D models. These variables were organized into a hierarchy of 119 independent variable groups, 15 categories, and 4 econometric drivers suitable for conducting simple vote count statistics. Vote counts were summarized at the independent variable group level and formed into ratios estimating the predictive success of each variable group. Two ratio estimates were developed based on (1) proportion of times independent variables successfully achieved statistical significance (p ≤0.10), and (2) proportion of times independent variables successfully met the original researchers'expectations. In F2D models, popular independent variables such as population, income, and urban proximity often achieved statistical significance. In F2A models, popular independent variables such as forest and agricultural rents and costs, governmental programs, and site quality often achieved statistical significance. In U2D models, successful independent variables included urban rents and costs, zoning issues concerning forestland loss, site quality, urban proximity, population, and income. F2NF models high success variables were found to be agricultural rents, site quality, population, and income. This meta-analysis provides insight into the general success of econometric independent variables for future forest use or cover change research. The second part of this dissertation developed a method for predicting area estimates and spatial distribution of PWT in the US South. This technique determined land use from USFS Forest Inventory and Analysis (FIA) and land cover from the National Land Cover Database (NLCD). Three dependent variable forms (DV Forms) were derived from the FIA data: DV Form 1, timberland, other; DV Form 2, short timberland, tall timberland, agriculture, other; and DV Form 3, short hardwood (HW) timberland, tall HW timberland, short softwood (SW) timberland, tall SW timberland, agriculture, other. The prediction accuracy of each DV Form was investigated using both random forest model and logistic regression model specifications and data optimization techniques. Model verification employing a "leave-group-out" Monte Carlo simulation determined the selection of a stratified version of the random forest model using one-year NLCD observations with an overall accuracy of 0.53-0.94. The lower accuracy side of the range was when predictions were made from an aggregated NLCD land cover class "grass_shrub". The selected model specification was run using 2011 NLCD and the other predictor variables to produce three levels of timberland prediction and probability maps for the US South. Spatial masks removed areas unlikely to be working forests (protected and urbanized lands) resulting in PWT maps. The area of the resulting maps compared well with USFS area estimates and masked PWT maps and had an 8-11% reduction of the USFS timberland estimate for the US South compared to the DV Form. Change analysis of the 2011 NLCD to PWT showed (1) the majority of the short timberland came from NLCD grass_shrub; (2) the majority of NLCD grass_shrub predicted into tall timberland, and (3) NLCD grass_shrub was more strongly associated with timberland in the Coastal Plain. Resulting map products provide practical analytical tools for those interested in studying the area and distribution of PWT in the US South.

Suitability of the HAM-Nat test and TMS module "basic medical-scientific understanding" for medical school selection

PubMed Central

Hissbach, Johanna; Feddersen, Lena; Sehner, Susanne; Hampe, Wolfgang

2012-01-01

Aims: Tests with natural-scientific content are predictive of the success in the first semesters of medical studies. Some universities in the German speaking countries use the ‘Test for medical studies’ (TMS) for student selection. One of its test modules, namely “medical and scientific comprehension”, measures the ability for deductive reasoning. In contrast, the Hamburg Assessment Test for Medicine, Natural Sciences (HAM-Nat) evaluates knowledge in natural sciences. In this study the predictive power of the HAM-Nat test will be compared to that of the NatDenk test, which is similar to the TMS module “medical and scientific comprehension” in content and structure. Methods: 162 medical school beginners volunteered to complete either the HAM-Nat (N=77) or the NatDenk test (N=85) in 2007. Until spring 2011, 84.2% of these successfully completed the first part of the medical state examination in Hamburg. Via different logistic regression models we tested the predictive power of high school grade point average (GPA or “Abiturnote”) and the test results (HAM-Nat and NatDenk) with regard to the study success criterion “first part of the medical state examination passed successfully up to the end of the 7th semester” (Success7Sem). The Odds Ratios (OR) for study success are reported. Results: For both test groups a significant correlation existed between test results and study success (HAM-Nat: OR=2.07; NatDenk: OR=2.58). If both admission criteria are estimated in one model, the main effects (GPA: OR=2.45; test: OR=2.32) and their interaction effect (OR=1.80) are significant in the HAM-Nat test group, whereas in the NatDenk test group only the test result (OR=2.21) significantly contributes to the variance explained. Conclusions: On their own both HAM-Nat and NatDenk have predictive power for study success, but only the HAM-Nat explains additional variance if combined with GPA. The selection according to HAM-Nat and GPA has under the current circumstances of medical school selection (many good applicants and only a limited number of available spaces) the highest predictive power of all models. PMID:23255967

Predictive models to determine imagery strategies employed by children to judge hand laterality.

PubMed

Spruijt, Steffie; Jongsma, Marijtje L A; van der Kamp, John; Steenbergen, Bert

2015-01-01

A commonly used paradigm to study motor imagery is the hand laterality judgment task. The present study aimed to determine which strategies young children employ to successfully perform this task. Children of 5 to 8 years old (N = 92) judged laterality of back and palm view hand pictures in different rotation angles. Response accuracy and response duration were registered. Response durations of the trials with a correct judgment were fitted to a-priori defined predictive sinusoid models, representing different strategies to successfully perform the hand laterality judgment task. The first model predicted systematic changes in response duration as a function of rotation angle of the displayed hand. The second model predicted that response durations are affected by biomechanical constraints of hand rotation. If observed data could be best described by the first model, this would argue for a mental imagery strategy that does not involve motor processes to solve the task. The second model reflects a motor imagery strategy to solve the task. In line with previous research, we showed an age-related increase in response accuracy and decrease in response duration in children. Observed data for both back and palm view showed that motor imagery strategies were used to perform hand laterality judgments, but that not all the children use these strategies (appropriately) at all times. A direct comparison of response duration patterns across age sheds new light on age-related differences in the strategies employed to solve the task. Importantly, the employment of the motor imagery strategy for successful task performance did not change with age.

Modeling natural regeneration establishment in the northern Rocky Mountains of the U.S.A

Treesearch

D. E. Ferguson

1996-01-01

Retrospective examination of cutover forests enables the development of models that predict regeneration success as a function of plot conditions and time since disturbance. The modeling process uses a two-state system. In the first state, all plots are analyzed to predict the probability of stocking (at least one established seedling on the plot). In the second state...

Differences in evolutionary history translate into differences in invasion success of alien mammals in South Africa

PubMed Central

Yessoufou, Kowiyou; Gere, Jephris; Daru, Barnabas H; van der Bank, Michelle

2014-01-01

Attempts to investigate the drivers of invasion success are generally limited to the biological and evolutionary traits distinguishing native from introduced species. Although alien species introduced to the same recipient environment differ in their invasion intensity – for example, some are “strong invaders”; others are “weak invaders” – the factors underlying the variation in invasion success within alien communities are little explored. In this study, we ask what drives the variation in invasion success of alien mammals in South Africa. First, we tested for taxonomic and phylogenetic signal in invasion intensity. Second, we reconstructed predictive models of the variation in invasion intensity among alien mammals using the generalized linear mixed-effects models. We found that the family Bovidae and the order Artiodactyla contained more “strong invaders” than expected by chance, and that such taxonomic signal did not translate into phylogenetic selectivity. In addition, our study indicates that latitude, gestation length, social group size, and human population density are only marginal determinant of the variation in invasion success. However, we found that evolutionary distinctiveness – a parameter characterising the uniqueness of each alien species – is the most important predictive variable. Our results indicate that the invasive behavior of alien mammals may have been “fingerprinted” in their evolutionary past, and that evolutionary history might capture beyond ecological, biological and life-history traits usually prioritized in predictive modeling of invasion success. These findings have applicability to the management of alien mammals in South Africa. PMID:25360253

Predicting Student Success: A Naïve Bayesian Application to Community College Data

ERIC Educational Resources Information Center

Ornelas, Fermin; Ordonez, Carlos

2017-01-01

This research focuses on developing and implementing a continuous Naïve Bayesian classifier for GEAR courses at Rio Salado Community College. Previous implementation efforts of a discrete version did not predict as well, 70%, and had deployment issues. This predictive model has higher prediction, over 90%, accuracy for both at-risk and successful…

"Plays Nice with Others": Social-Emotional Learning and Academic Success

ERIC Educational Resources Information Center

Denham, Susanne A.; Brown, Chavaughn

2010-01-01

Research Findings: Social-emotional learning (SEL) is increasingly becoming an area of focus for determining children's school readiness and predicting their academic success. Practice or Policy: The current article outlines a model of SEL, identifies specific SEL skills, and discusses how such skills contribute and relate to academic success.…

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Application of artificial neural networks on the prediction of surface ozone concentrations].

PubMed

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.

Data-adaptive Harmonic Decomposition and Real-time Prediction of Arctic Sea Ice Extent

NASA Astrophysics Data System (ADS)

Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

2017-04-01

Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e. from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN, http://www.arcus.org/sipn) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models. Data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017], have been successfully applied to the nonlinear stochastic modeling, as well as retrospective and real-time forecasting of Multisensor Analyzed Sea Ice Extent (MASIE) dataset in key four Arctic regions. In particular, DAH-MSLM predictions outperformed most statistical models and physics-based models in real-time 2016 SIO submissions. The key success factors are associated with DAH ability to disentangle complex regional dynamics of MASIE by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental MSLMs stacked per frequency with fixed and small number of model coefficients to estimate.

Data Mining and Predictive Modeling in Institutional Advancement: How Ten Schools Found Success. Technical Report

ERIC Educational Resources Information Center

Luperchio, Dan

2009-01-01

This technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc., explores the promise of data mining alumni records at educational institutions. Working with individual alumni records from The Johns Hopkins Zanvyl Krieger School of Arts and Sciences, a predictive regression model is…

Predicting length of children's psychiatric hospitalizations: an "ecologic" approach.

PubMed

Mossman, D; Songer, D A; Baker, D G

1991-08-01

This article describes the development and validation of a simple and modestly successful model for predicting inpatient length of stay (LOS) at a state-funded facility providing acute to long term care for children and adolescents in Ohio. Six variables--diagnostic group, legal status at time of admission, attending physician, age, sex, and county of residence--explained 30% of the variation in log10LOS in the subgroup used to create the model, and 26% of log10LOS variation in the cross-validation subgroup. The model also identified LOS outliers with moderate accuracy (ROC area = .68-0.76). The authors attribute the model's success to inclusion of variables that are correlated to idiosyncratic "ecologic" factors as well as variables related to severity of illness. Future attempts to construct LOS models may adopt similar approaches.

Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008-2013

NASA Astrophysics Data System (ADS)

Stroeve, Julienne; Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward

2014-04-01

Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

PubMed

Kramer, Kirsten E; Small, Gary W

2009-02-01

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

Thermal Modeling of Al-Al and Al-Steel Friction Stir Spot Welding

NASA Astrophysics Data System (ADS)

Jedrasiak, P.; Shercliff, H. R.; Reilly, A.; McShane, G. J.; Chen, Y. C.; Wang, L.; Robson, J.; Prangnell, P.

2016-09-01

This paper presents a finite element thermal model for similar and dissimilar alloy friction stir spot welding (FSSW). The model is calibrated and validated using instrumented lap joints in Al-Al and Al-Fe automotive sheet alloys. The model successfully predicts the thermal histories for a range of process conditions. The resulting temperature histories are used to predict the growth of intermetallic phases at the interface in Al-Fe welds. Temperature predictions were used to study the evolution of hardness of a precipitation-hardened aluminum alloy during post-weld aging after FSSW.

Application of model abstraction techniques to simulate transport in soils

USDA-ARS?s Scientific Manuscript database

Successful understanding and modeling of contaminant transport in soils is the precondition of risk-informed predictions of the subsurface contaminant transport. Exceedingly complex models of subsurface contaminant transport are often inefficient. Model abstraction is the methodology for reducing th...

Dinosaur Fossils Predict Body Temperatures

PubMed Central

Allen, Andrew P; Charnov, Eric L

2006-01-01

Perhaps the greatest mystery surrounding dinosaurs concerns whether they were endotherms, ectotherms, or some unique intermediate form. Here we present a model that yields estimates of dinosaur body temperature based on ontogenetic growth trajectories obtained from fossil bones. The model predicts that dinosaur body temperatures increased with body mass from approximately 25 °C at 12 kg to approximately 41 °C at 13,000 kg. The model also successfully predicts observed increases in body temperature with body mass for extant crocodiles. These results provide direct evidence that dinosaurs were reptiles that exhibited inertial homeothermy. PMID:16817695

Dinosaur fossils predict body temperatures.

PubMed

Gillooly, James F; Allen, Andrew P; Charnov, Eric L

2006-07-01

Perhaps the greatest mystery surrounding dinosaurs concerns whether they were endotherms, ectotherms, or some unique intermediate form. Here we present a model that yields estimates of dinosaur body temperature based on ontogenetic growth trajectories obtained from fossil bones. The model predicts that dinosaur body temperatures increased with body mass from approximately 25 degrees C at 12 kg to approximately 41 degrees C at 13,000 kg. The model also successfully predicts observed increases in body temperature with body mass for extant crocodiles. These results provide direct evidence that dinosaurs were reptiles that exhibited inertial homeothermy.

Arrhenius equation for modeling feedyard ammonia emissions using temperature and diet crude protein.

PubMed

Todd, Richard W; Cole, N Andy; Waldrip, Heidi M; Aiken, Robert M

2013-01-01

Temperature controls many processes of NH volatilization. For example, urea hydrolysis is an enzymatically catalyzed reaction described by the Arrhenius equation. Diet crude protein (CP) controls NH emission by affecting N excretion. Our objectives were to use the Arrhenius equation to model NH emissions from beef cattle () feedyards and test predictions against observed emissions. Per capita NH emission rate (PCER), air temperature (), and CP were measured for 2 yr at two Texas Panhandle feedyards. Data were fitted to analogs of the Arrhenius equation: PCER = () and PCER = (,CP). The models were applied at a third feedyard to predict NH emissions and compare predicted to measured emissions. Predicted mean NH emissions were within -9 and 2% of observed emissions for the () and (T,CP) models, respectively. Annual emission factors calculated from models underestimated annual NH emission by 11% [() model] or overestimated emission by 8% [(,CP) model]. When from a regional weather station and three classes of CP drove the models, the () model overpredicted annual NH emission of the low CP class by 14% and underpredicted emissions of the optimum and high CP classes by 1 and 39%, respectively. The (,CP) model underpredicted NH emissions by 15, 4, and 23% for low, optimum, and high CP classes, respectively. Ammonia emission was successfully modeled using only, but including CP improved predictions. The empirical () and (,CP) models can successfully model NH emissions in the Texas Panhandle. Researchers are encouraged to test the models in other regions where high-quality NH emissions data are available. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Methods for evaluating the predictive accuracy of structural dynamic models

NASA Technical Reports Server (NTRS)

Hasselman, Timothy K.; Chrostowski, Jon D.

1991-01-01

Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

Basic state lower-tropospheric humidity distribution: key to successful simulation and prediction of the Madden-Julian oscillation

NASA Astrophysics Data System (ADS)

Kim, D.; Ahn, M. S.; DeMott, C. A.; Jiang, X.; Klingaman, N. P.; Kim, H. M.; Lee, J. H.; Lim, Y.; Xavier, P. K.

2017-12-01

The Madden-Julian Oscillation (MJO) influences the global weather-climate system, thereby providing the source of predictability on the intraseasonal timescales worldwide. An accurate representation of the MJO, however, is still one of the most challenging tasks for many contemporary global climate models (GCMs). Identifying aspects of the GCMs that are tightly linked to GCMs' MJO simulation capability is a step toward improving the GCM representation of the MJO. This study surveys recent modeling work that collectively evidence that the horizontal distribution of the basic state low-tropospheric humidity is crucial to a successful simulation and prediction of the MJO. Specifically, the simulated horizontal and meridional gradients of the mean low-tropospheric humidity determine the magnitude of the moistening (drying) to the east (west) of the enhance MJO, thereby enabling or disabling the eastward propagation of the MJO. Supporting this argument, many MJO-incompetent GCMs also exhibit biases in the mean humidity that weaken the horizontal moisture gradient. Also, MJO prediction skill of the S2S models is tightly related to the biases in the mean moisture gradient. Implications of the robust relationship between the MJO and the mean state on MJO modeling and prediction will be discussed.

Building Models to Predict Hint-or-Attempt Actions of Students

ERIC Educational Resources Information Center

Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil

2015-01-01

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

Medial Temporal Lobe Contributions to Cued Retrieval of Items and Contexts

PubMed Central

Hannula, Deborah E.; Libby, Laura A.; Yonelinas, Andrew P.; Ranganath, Charan

2013-01-01

Several models have proposed that different regions of the medial temporal lobes contribute to different aspects of episodic memory. For instance, according to one view, the perirhinal cortex represents specific items, parahippocampal cortex represents information regarding the context in which these items were encountered, and the hippocampus represents item-context bindings. Here, we used event-related functional magnetic resonance imaging (fMRI) to test a specific prediction of this model – namely, that successful retrieval of items from context cues will elicit perirhinal recruitment and that successful retrieval of contexts from item cues will elicit parahippocampal cortex recruitment. Retrieval of the bound representation in either case was expected to elicit hippocampal engagement. To test these predictions, we had participants study several item-context pairs (i.e., pictures of objects and scenes, respectively), and then had them attempt to recall items from associated context cues and contexts from associated item cues during a scanned retrieval session. Results based on both univariate and multivariate analyses confirmed a role for hippocampus in content-general relational memory retrieval, and a role for parahippocampal cortex in successful retrieval of contexts from item cues. However, we also found that activity differences in perirhinal cortex were correlated with successful cued recall for both items and contexts. These findings provide partial support for the above predictions and are discussed with respect to several models of medial temporal lobe function. PMID:23466350

A Case Study Using Modeling and Simulation to Predict Logistics Supply Chain Issues

NASA Technical Reports Server (NTRS)

Tucker, David A.

2007-01-01

Optimization of critical supply chains to deliver thousands of parts, materials, sub-assemblies, and vehicle structures as needed is vital to the success of the Constellation Program. Thorough analysis needs to be performed on the integrated supply chain processes to plan, source, make, deliver, and return critical items efficiently. Process modeling provides simulation technology-based, predictive solutions for supply chain problems which enable decision makers to reduce costs, accelerate cycle time and improve business performance. For example, United Space Alliance, LLC utilized this approach in late 2006 to build simulation models that recreated shuttle orbiter thruster failures and predicted the potential impact of thruster removals on logistics spare assets. The main objective was the early identification of possible problems in providing thruster spares for the remainder of the Shuttle Flight Manifest. After extensive analysis the model results were used to quantify potential problems and led to improvement actions in the supply chain. Similarly the proper modeling and analysis of Constellation parts, materials, operations, and information flows will help ensure the efficiency of the critical logistics supply chains and the overall success of the program.

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models.

PubMed

Liang, Shih-Hsiung; Walther, Bruno Andreas; Shieh, Bao-Sen

2017-01-01

Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies.

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

PubMed Central

Liang, Shih-Hsiung; Walther, Bruno Andreas

2017-01-01

Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Discussion Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies. PMID:28316893

Personality traits and achievement motives: theoretical and empirical relations between the NEO Personality Inventory-Revised and the Achievement Motives Scale.

PubMed

Diseth, Age; Martinsen, Øyvind

2009-04-01

Theoretical and empirical relations between personality traits and motive dispositions were investigated by comparing scores of 315 undergraduate psychology students on the NEO Personality Inventory-Revised and the Achievement Motives Scale. Analyses showed all NEO Personality Inventory-Revised factors except agreeableness were significantly correlated with the motive for success and the motive to avoid failure. A structural equation model showed that motive for success was predicted by Extraversion, Openness, Conscientiousness, and Neuroticism (negative relation), and motive to avoid failure was predicted by Neuroticism and Openness (negative relation). Although both achievement motives were predicted by several personality factors, motive for success was most strongly predicted by Openness, and motive to avoid failure was most strongly predicted by neuroticism. These findings extended previous research on the relations of personality traits and achievement motives and provided a basis for the discussion of motive dispositions in personality. The results also added to the construct validity of the Achievement Motives Scale.

Using Empirical Models for Communication Prediction of Spacecraft

NASA Technical Reports Server (NTRS)

Quasny, Todd

2015-01-01

A viable communication path to a spacecraft is vital for its successful operation. For human spaceflight, a reliable and predictable communication link between the spacecraft and the ground is essential not only for the safety of the vehicle and the success of the mission, but for the safety of the humans on board as well. However, analytical models of these communication links are challenged by unique characteristics of space and the vehicle itself. For example, effects of radio frequency during high energy solar events while traveling through a solar array of a spacecraft can be difficult to model, and thus to predict. This presentation covers the use of empirical methods of communication link predictions, using the International Space Station (ISS) and its associated historical data as the verification platform and test bed. These empirical methods can then be incorporated into communication prediction and automation tools for the ISS in order to better understand the quality of the communication path given a myriad of variables, including solar array positions, line of site to satellites, position of the sun, and other dynamic structures on the outside of the ISS. The image on the left below show the current analytical model of one of the communication systems on the ISS. The image on the right shows a rudimentary empirical model of the same system based on historical archived data from the ISS.

Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study)123

PubMed Central

Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

2015-01-01

Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve’s capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. PMID:25733628

Improving Localization Accuracy: Successive Measurements Error Modeling

PubMed Central

Abu Ali, Najah; Abu-Elkheir, Mervat

2015-01-01

Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. We use the Yule Walker equations to determine the degree of correlation between a vehicle’s future position and its past positions, and then propose a p-order Gauss–Markov model to predict the future position of a vehicle from its past p positions. We investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to four minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss–Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle’s future location over time using only its current position. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter. PMID:26140345

Predicting human olfactory perception from chemical features of odor molecules.

PubMed

Keller, Andreas; Gerkin, Richard C; Guan, Yuanfang; Dhurandhar, Amit; Turu, Gabor; Szalai, Bence; Mainland, Joel D; Ihara, Yusuke; Yu, Chung Wen; Wolfinger, Russ; Vens, Celine; Schietgat, Leander; De Grave, Kurt; Norel, Raquel; Stolovitzky, Gustavo; Cecchi, Guillermo A; Vosshall, Leslie B; Meyer, Pablo

2017-02-24

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule. Copyright © 2017, American Association for the Advancement of Science.

Seasonal prediction of winter haze days in the north central North China Plain

NASA Astrophysics Data System (ADS)

Yin, Zhicong; Wang, Huijun

2016-11-01

Recently, the winter (December-February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (-0.07) and -3.33 (-1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

PubMed

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

Openness as a buffer against cognitive decline: The Openness-Fluid-Crystallized-Intelligence (OFCI) model applied to late adulthood.

PubMed

Ziegler, Matthias; Cengia, Anja; Mussel, Patrick; Gerstorf, Denis

2015-09-01

Explaining cognitive decline in late adulthood is a major research area. Models using personality traits as possible influential variables are rare. This study tested assumptions based on an adapted version of the Openness-Fluid-Crystallized-Intelligence (OFCI) model. The OFCI model adapted to late adulthood predicts that openness is related to the decline in fluid reasoning (Gf) through environmental enrichment. Gf should be related to the development of comprehension knowledge (Gc; investment theory). It was also assumed that Gf predicts changes in openness as suggested by the environmental success hypothesis. Finally, the OFCI model proposes that openness has an indirect influence on the decline in Gc through its effect on Gf (mediation hypothesis). Using data from the Berlin Aging Study (N = 516, 70-103 years at T1), these predictions were tested using latent change score and latent growth curve models with indicators of each trait. The current findings and prior research support environmental enrichment and success, investment theory, and partially the mediation hypotheses. Based on a summary of all findings, the OFCI model for late adulthood is suggested. (c) 2015 APA, all rights reserved).

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

Retrospective evaluation of a method to predict fresh-frozen plasma dosage in anticoagulated patients.

PubMed

Frazee, Lawrence A; Bourguet, Claire C; Gutierrez, Wilson; Elder-Arrington, Jacinta; Elackattu, Alphi E P; Haller, Nairmeen Awad

2008-01-01

In the United States, fresh-frozen plasma (FFP) is commonly used for urgent reversal of warfarin; however, dosage recommendations are difficult to find. If validated, a proposed method that uses a nonlinear relationship between international normalized ratio (INR) and clotting factor activity (CFa) would be useful. This study retrospectively evaluated a proposed equation with adult medical inpatients who received FFP for warfarin reversal. For each patient the equation was used to predict the dose of FFP required to achieve the observed change in INR, which was then compared to the actual dose. The equation was considered successful if the predicted dose was within +/-20% of the actual dose. Subgroup analyses included subjects who received concomitant vitamin K; subjects with supratherapeutic INRs (>3); and subjects with significantly elevated INRs (>5). Of the 209 patients screened, 91 met criteria for inclusion in the study. Use of the equation to calculate the predicted dose of FFP was successful in 11 patients (12.1%) with use of actual body weight for prediction and in 23 patients (25.3%) with use of ideal body weight (P = 0.02). The equation performed similarly in all subgroups analyzed. The mean predicted FFP dose was significantly greater than the actual dose in all patients when actual body weight was used (925.2 mL vs. 620.6 mL; P < 0.001). Least-squares regression modeling of repeat INR (converted to CFa) produced a model that accounted for 57% of the variance in repeat INR. The value predicted from the model was closer to the actual CFa than was the value predicted from the published equation in every comparison, but it was statistically different only when actual body weight was used. This study revealed that a published equation for calculation of FFP dose to reverse oral anticoagulation resulted in doses that were significantly higher than the actual dose. Use of ideal body weight improved accuracy but was still not successful for the majority of patients. Until trials are able to prospectively demonstrate the accuracy of a dose-prediction model for FFP, dosing will remain largely empiric.

["Who profits?" - patient characteristics as outcome predictors in psychosomatic rehabilitation].

PubMed

Oster, J; Müller, G; Wietersheim, J von

2009-04-01

The study was to examine how far treatment success in psychosomatic rehabilitation can be predicted from patients' characteristics. The aim of this study included the development of outcome criteria, the analysis of bivariate correlations, as well as development and examination of multivariate models. The motivation for dealing with job-related problems was evaluated separately. Data were available from admission, discharge and three-months follow-up. The data of 463 patients were included. Generated were success criteria concerning sociomedical development, health as well as the ability to work. All success criteria were dichotomized. In the criteria defined, successful outcomes were found in 40 to 60% of the patients. In the bivariate analyses, it was shown that many sick days before rehabilitation, applications for pension, severe disability, high impairment, and suggestion for rehabilitation by the insurance agency, have basically negative effects on success. Correlations with the variables concerning motivation for dealing with job-related problems were rather weak. In multivariate model development, models of different quality were found. For prediction of working ability at discharge, there was an explained variance of nearly 60%. In the other success criteria as well, explained variance amounted to over 20%. The models consist of different constellations of variables, the number of sick days before rehabilitation, variables of application for pension and severity of the impairment frequently included. In case of a current sick leave, rehabilitation should be started early, sociomedical problems have to be dealt with explicitly, and rehabilitation should be accompanied by preparatory and aftercare measures.

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

NASA Astrophysics Data System (ADS)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

CONFOLD2: improved contact-driven ab initio protein structure modeling.

PubMed

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Prediction of High-Lift Flows using Turbulent Closure Models

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; Gatski, Thomas B.; Ying, Susan X.; Bertelrud, Arild

1997-01-01

The flow over two different multi-element airfoil configurations is computed using linear eddy viscosity turbulence models and a nonlinear explicit algebraic stress model. A subset of recently-measured transition locations using hot film on a McDonnell Douglas configuration is presented, and the effect of transition location on the computed solutions is explored. Deficiencies in wake profile computations are found to be attributable in large part to poor boundary layer prediction on the generating element, and not necessarily inadequate turbulence modeling in the wake. Using measured transition locations for the main element improves the prediction of its boundary layer thickness, skin friction, and wake profile shape. However, using measured transition locations on the slat still yields poor slat wake predictions. The computation of the slat flow field represents a key roadblock to successful predictions of multi-element flows. In general, the nonlinear explicit algebraic stress turbulence model gives very similar results to the linear eddy viscosity models.

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

PubMed

Jain, Dharm Skandh; Gupte, Sanket Rajan; Aduri, Raviprasad

2018-06-22

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

PubMed

Ajmani, Subhash; Rogers, Stephen C; Barley, Mark H; Burgess, Andrew N; Livingstone, David J

2010-09-17

In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Can contaminant transport models predict breakthrough?

USGS Publications Warehouse

Peng, Wei-Shyuan; Hampton, Duane R.; Konikow, Leonard F.; Kambham, Kiran; Benegar, Jeffery J.

2000-01-01

A solute breakthrough curve measured during a two-well tracer test was successfully predicted in 1986 using specialized contaminant transport models. Water was injected into a confined, unconsolidated sand aquifer and pumped out 125 feet (38.3 m) away at the same steady rate. The injected water was spiked with bromide for over three days; the outflow concentration was monitored for a month. Based on previous tests, the horizontal hydraulic conductivity of the thick aquifer varied by a factor of seven among 12 layers. Assuming stratified flow with small dispersivities, two research groups accurately predicted breakthrough with three-dimensional (12-layer) models using curvilinear elements following the arc-shaped flowlines in this test. Can contaminant transport models commonly used in industry, that use rectangular blocks, also reproduce this breakthrough curve? The two-well test was simulated with four MODFLOW-based models, MT3D (FD and HMOC options), MODFLOWT, MOC3D, and MODFLOW-SURFACT. Using the same 12 layers and small dispersivity used in the successful 1986 simulations, these models fit almost as accurately as the models using curvilinear blocks. Subtle variations in the curves illustrate differences among the codes. Sensitivities of the results to number and size of grid blocks, number of layers, boundary conditions, and values of dispersivity and porosity are briefly presented. The fit between calculated and measured breakthrough curves degenerated as the number of layers and/or grid blocks decreased, reflecting a loss of model predictive power as the level of characterization lessened. Therefore, the breakthrough curve for most field sites can be predicted only qualitatively due to limited characterization of the hydrogeology and contaminant source strength.

Mental health measures in predicting outcomes for the selection and training of navy divers.

PubMed

van Wijk, Charles H

2011-03-01

Two models have previously been enlisted to predict success in training using psychological markers. Both the Mental Health Model and Trait Anxiety Model have shown some success in predicting behaviours associated with arousal among student divers. This study investigated the potential of these two models to predict outcome in naval diving selection and training. Navy diving candidates (n = 137) completed the Brunel Mood Scale and the State-Trait Personality Inventory (trait-anxiety scale) prior to selection. The mean scores of the candidates accepted for training were compared to those who were not accepted. The mean scores of the candidates who passed training were then compared to those who failed. A number of trainees withdrew from training due to injury, and their scores were also compared to those who completed the training. Candidates who were not accepted were more depressed, fatigued and confused than those who were accepted for training, and reported higher trait anxiety. There were no significant differences between the candidates who passed training and those who did not. However, injured trainees were tenser, more fatigued and reported higher trait anxiety than the rest. Age, gender, home language, geographical region of origin and race had no significant interaction with outcome results. While the models could partially discriminate between the mean scores of different outcome groups, none of them contributed meaningfully to predicting individual outcome in diving training. Both models may have potential in identifying proneness to injury, and this requires further study.

Supercontinent Succession and the Calculation of Absolute Paleolongitude

NASA Astrophysics Data System (ADS)

Mitchell, R. N.; Kilian, T.; Evans, D. A.

2010-12-01

Where will the next supercontinent form? Traditional ‘introversion’ and ‘extraversion’ models of supercontinent succession predict that Super Asia will respectively form whence Pangea was or on the opposite side of the world. We develop the ‘orthoversion’ model whereby a succeeding supercontinent forms 90° away: somewhere along the great circle of subduction encircling its relict predecessor—a mantle topology that arises when supercontinents develop return flow beneath their mature centroids. This centroid defines the minimum moment of inertia (I_min) about which rapid and oscillatory true polar wander occurs owing to the prolate shape of nonhydrostatic Earth. Fitting great circles to each supercontinent’s true polar wander legacy, we determine that the distances between successive supercontinent centers (I_min axes) are 88° and 87° for Nuna→Rodinia and Rodinia→Pangea, respectively—both as predicted by the orthoversion model. Not only can supercontinent centers be pinned back into Precambrian time, they provide fixed points for the calculation of absolute paleolongitude.

Toward a Predictive Model of Community College Student Success in Blended Classes

ERIC Educational Resources Information Center

Volchok, Edward

2018-01-01

This retrospective study evaluates early semester predictors of whether or not community college students will successfully complete blended or hybrid courses. These predictors are available to faculty by the fourth week of the semester. Success is defined as receiving a grade of C- or higher. Failure is defined as a grade below a C- or a…

Predictors of Success and Failure for ADN Students on the NCLEX-RN

ERIC Educational Resources Information Center

Benefiel, Diane

2011-01-01

The purpose of this study was to: 1) analyze the relationship of preprogram and nursing program variables on National Council Licensure Examination for Registered Nurses (NCLEX-RN) success and failure, and 2) develop a model to predict success and failure on the NCLEX-RN. The convenience sample was comprised of 245 spring, summer, and fall midterm…

Impulsivity, perceived self-regulatory success in dieting, and body mass in children and adolescents: A moderated mediation model.

PubMed

Meule, Adrian; Hofmann, Johannes; Weghuber, Daniel; Blechert, Jens

2016-12-01

Impulsivity has been suggested to contribute to overeating and obesity. However, findings are inconsistent and it appears that only specific facets of impulsivity are related to eating-related variables and to body mass. In the current study, relationships between self-reported impulsivity, perceived self-regulatory success in dieting, and objectively measured body mass were examined in N = 122 children and adolescents. Scores on attentional and motor impulsivity interactively predicted perceived self-regulatory success in dieting, but not body mass: Higher attentional impulsivity was associated with lower perceived self-regulatory success at high levels of motor impulsivity, but not at low levels of motor impulsivity. A moderated mediation model revealed an indirect effect of attentional and motor impulsivity on body mass, which was mediated by perceived self-regulatory success in dieting. Thus, results show that only specific facets of impulsivity are relevant in eating- and weight-regulation and interact with each other in the prediction of these variables. These facets of impulsivity, however, are not directly related to higher body mass, but indirectly via lower success in eating-related self-regulation in children and adolescents. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling Seizure Self-Prediction: An E-Diary Study

PubMed Central

Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

2013-01-01

Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

Connecting English Language Learning and Academic Performance: A Prediction Study

ERIC Educational Resources Information Center

Kong, Jadie; Powers, Sonya; Starr, Laura; Williams, Natasha

2012-01-01

The purpose of this study was to investigate the use of English language proficiency and academic reading assessment scores to predict the future academic success of English learner (EL) students. Data from two cohorts of middle-school ELs were used to evaluate three prediction models. One cohort of students was used to develop the prediction…

Vertical structure of mean cross-shore currents across a barred surf zone

USGS Publications Warehouse

Haines, John W.; Sallenger, Asbury H.

1994-01-01

Mean cross-shore currents observed across a barred surf zone are compared to model predictions. The model is based on a simplified momentum balance with a turbulent boundary layer at the bed. Turbulent exchange is parameterized by an eddy viscosity formulation, with the eddy viscosity Aυ independent of time and the vertical coordinate. Mean currents result from gradients due to wave breaking and shoaling, and the presence of a mean setup of the free surface. Descriptions of the wave field are provided by the wave transformation model of Thornton and Guza [1983]. The wave transformation model adequately reproduces the observed wave heights across the surf zone. The mean current model successfully reproduces the observed cross-shore flows. Both observations and predictions show predominantly offshore flow with onshore flow restricted to a relatively thin surface layer. Successful application of the mean flow model requires an eddy viscosity which varies horizontally across the surf zone. Attempts are made to parameterize this variation with some success. The data does not discriminate between alternative parameterizations proposed. The overall variability in eddy viscosity suggested by the model fitting should be resolvable by field measurements of the turbulent stresses. Consistent shortcomings of the parameterizations, and the overall modeling effort, suggest avenues for further development and data collection.

The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Using analogues to quantify geological uncertainty in stochastic reserve modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wells, B.; Brown, I.

1995-08-01

The petroleum industry seeks to minimize exploration risk by employing the best possible expertise, methods and tools. Is it possible to quantify the success of this process of risk reduction? Due to inherent uncertainty in predicting geological reality and due to changing environments for hydrocarbon exploration, it is not enough simply to record the proportion of successful wells drilled; in various parts of the world it has been noted that pseudo-random drilling would apparently have been as successful as the actual drilling programme. How, then, should we judge the success of risk reduction? For many years the E&P industry hasmore » routinely used Monte Carlo modelling to generate a probability distribution for prospect reserves. One aspect of Monte Carlo modelling which has received insufficient attention, but which is essential for quantifying risk reduction, is the consistency and repeatability with which predictions can be made. Reducing the subjective element inherent in the specification of geological uncertainty allows better quantification of uncertainty in the prediction of reserves, in both exploration and appraisal. Building on work reported at the AAPG annual conventions in 1994 and 1995, the present paper incorporates analogue information with uncertainty modelling. Analogues provide a major step forward in the quantification of risk, but their significance is potentially greater still. The two principal contributors to uncertainty in field and prospect analysis are the hydrocarbon life-cycle and the geometry of the trap. These are usually treated separately. Combining them into a single model is a major contribution to the reduction risk. This work is based in part on a joint project with Oryx Energy UK Ltd., and thanks are due in particular to Richard Benmore and Mike Cooper.« less

Predictive validity of behavioural animal models for chronic pain

PubMed Central

Berge, Odd-Geir

2011-01-01

Rodent models of chronic pain may elucidate pathophysiological mechanisms and identify potential drug targets, but whether they predict clinical efficacy of novel compounds is controversial. Several potential analgesics have failed in clinical trials, in spite of strong animal modelling support for efficacy, but there are also examples of successful modelling. Significant differences in how methods are implemented and results are reported means that a literature-based comparison between preclinical data and clinical trials will not reveal whether a particular model is generally predictive. Limited reports on negative outcomes prevents reliable estimate of specificity of any model. Animal models tend to be validated with standard analgesics and may be biased towards tractable pain mechanisms. But preclinical publications rarely contain drug exposure data, and drugs are usually given in high doses and as a single administration, which may lead to drug distribution and exposure deviating significantly from clinical conditions. The greatest challenge for predictive modelling is, however, the heterogeneity of the target patient populations, in terms of both symptoms and pharmacology, probably reflecting differences in pathophysiology. In well-controlled clinical trials, a majority of patients shows less than 50% reduction in pain. A model that responds well to current analgesics should therefore predict efficacy only in a subset of patients within a diagnostic group. It follows that successful translation requires several models for each indication, reflecting critical pathophysiological processes, combined with data linking exposure levels with effect on target. 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:21371010

A Computer Simulation of Organizational Decision-Making.

DTIC Science & Technology

1979-12-01

future research into one class of manpower models. In choosing the voting scen- ario I was more interested in the long-term process of political ... socialization , rather than the prediction of the outcome of a particular election. Successive elections are like successive learning trials. The analysis did

Predicting Student Grade Point Average at a Community College from Scholastic Aptitude Tests and from Measures Representing Three Constructs in Vroom's Expectancy Theory Model of Motivation.

ERIC Educational Resources Information Center

Malloch, Douglas C.; Michael, William B.

1981-01-01

This study was designed to determine whether an unweighted linear combination of community college students' scores on standardized achievement tests and a measure of motivational constructs derived from Vroom's expectance theory model of motivation was predictive of academic success (grade point average earned during one quarter of an academic…

Application of neural network in the study of combustion rate of natural gas/diesel dual fuel engine.

PubMed

Yan, Zhao-Da; Zhou, Chong-Guang; Su, Shi-Chuan; Liu, Zhen-Tao; Wang, Xi-Zhen

2003-01-01

In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect of the operating parameters on combustion rate was also studied by means of this model. The study showed that the predicted results were good agreement with the experimental data. It was proved that the developed combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.

Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

NASA Astrophysics Data System (ADS)

Goodwin, Graham. C.; Medioli, Adrian. M.

2013-08-01

Model predictive control has been a major success story in process control. More recently, the methodology has been used in other contexts, including automotive engine control, power electronics and telecommunications. Most applications focus on set-point tracking and use single-sequence optimisation. Here we consider an alternative class of problems motivated by the scheduling of emergency vehicles. Here disturbances are the dominant feature. We develop a novel closed-loop model predictive control strategy aimed at this class of problems. We motivate, and illustrate, the ideas via the problem of fluid deployment of ambulance resources.

Fluoroscopic removal of retrievable self-expandable metal stents in patients with malignant oesophageal strictures: Experience with a non-endoscopic removal system.

PubMed

Kim, Pyeong Hwa; Song, Ho-Young; Park, Jung-Hoon; Zhou, Wei-Zhong; Na, Han Kyu; Cho, Young Chul; Jun, Eun Jung; Kim, Jun Ki; Kim, Guk Bae

2017-03-01

To evaluate clinical outcomes of fluoroscopic removal of retrievable self-expandable metal stents (SEMSs) for malignant oesophageal strictures, to compare clinical outcomes of three different removal techniques, and to identify predictive factors of successful removal by the standard technique (primary technical success). A total of 137 stents were removed from 128 patients with malignant oesophageal strictures. Primary overall technical success and removal-related complications were evaluated. Logistic regression models were constructed to identify predictive factors of primary technical success. Primary technical success rate was 78.8 % (108/137). Complications occurred in six (4.4 %) cases. Stent location in the upper oesophagus (P=0.004), stricture length over 8 cm (P=0.030), and proximal granulation tissue (P<0.001) were negative predictive factors of primary technical success. If granulation tissue was present at the proximal end, eversion technique was more frequently required (P=0.002). Fluoroscopic removal of retrievable SEMSs for malignant oesophageal strictures using three different removal techniques appeared to be safe and easy. The standard technique is safe and effective in the majority of patients. The presence of proximal granulation tissue, stent location in the upper oesophagus, and stricture length over 8 cm were negative predictive factors for primary technical success by standard extraction and may require a modified removal technique. • Fluoroscopic retrievable SEMS removal is safe and effective. • Standard removal technique by traction is effective in the majority of patients. • Three negative predictive factors of primary technical success were identified. • Caution should be exercised during the removal in those situations. • Eversion technique is effective in cases of proximal granulation tissue.

An Interoceptive Predictive Coding Model of Conscious Presence

PubMed Central

Seth, Anil K.; Suzuki, Keisuke; Critchley, Hugo D.

2011-01-01

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness. PMID:22291673

A Data mining Technique for Analyzing and Predicting the success of Movie

NASA Astrophysics Data System (ADS)

Meenakshi, K.; Maragatham, G.; Agarwal, Neha; Ghosh, Ishitha

2018-04-01

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Beutter, Brent R.; Stone, Leland S.

2000-01-01

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

Modelling of the 10-micrometer natural laser emission from the mesospheres of Mars and Venus

NASA Technical Reports Server (NTRS)

Deming, D.; Mumma, M. J.

1983-01-01

The NLTE radiative transfer problem is solved to obtain the 00 deg 1 vibrational state population. This model successfully reproduces the existing center-to-limb observations, although higher spatial resolution observations are needed for a definitive test. The model also predicts total fluxes which are close to the observed values. The strength of the emission is predicted to be closely related to the instantaneous near-IR solar heating rate.

Modeling of the 10-micron natural laser emission from the mesospheres of Mars and Venus

NASA Technical Reports Server (NTRS)

Deming, D.; Mumma, M. J.

1983-01-01

The NLTE radiative transfer problem is solved to obtain the 00 deg 1 vibrational state population. This model successfully reproduces the existing center-to-limb observations, although higher spatial resolution observations are needed for a definitive test. The model also predicts total fluxes which are close to the observed values. The strength of the emission is predicted to be closely related to the instantaneous near-IR solar heating rate.

Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

EPA Science Inventory

Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

Numerical investigation of the flow in axial water turbines and marine propellers with scale-resolving simulations

NASA Astrophysics Data System (ADS)

Morgut, Mitja; Jošt, Dragica; Nobile, Enrico; Škerlavaj, Aljaž

2015-11-01

The accurate prediction of the performances of axial water turbines and naval propellers is a challenging task, of great practical relevance. In this paper a numerical prediction strategy, based on the combination of a trusted CFD solver and a calibrated mass transfer model, is applied to the turbulent flow in axial turbines and around a model scale naval propeller, under non-cavitating and cavitating conditions. Some selected results for axial water turbines and a marine propeller, and in particular the advantages, in terms of accuracy and fidelity, of ScaleResolving Simulations (SRS), like SAS (Scale Adaptive Simulation) and Zonal-LES (ZLES) compared to standard RANS approaches, are presented. Efficiency prediction for a Kaplan and a bulb turbine was significantly improved by use of the SAS SST model in combination with the ZLES in the draft tube. Size of cavitation cavity and sigma break curve for Kaplan turbine were successfully predicted with SAS model in combination with robust high resolution scheme, while for mass transfer the Zwart model with calibrated constants were used. The results obtained for a marine propeller in non-uniform inflow, under cavitating conditions, compare well with available experimental measurements, and proved that a mass transfer model, previously calibrated for RANS (Reynolds Averaged Navier Stokes), can be successfully applied also within the SRS approaches.

Machine-learning-assisted materials discovery using failed experiments

NASA Astrophysics Data System (ADS)

Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.; Falk, Casey; Wenny, Malia B.; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A.; Schrier, Joshua; Norquist, Alexander J.

2016-05-01

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

Variable terrestrial GPS telemetry detection rates: Addressing the probability of successful acquisitions

USGS Publications Warehouse

Ironside, Kirsten E.; Mattson, David J.; Choate, David; Stoner, David; Arundel, Terry; Hansen, Jered R.; Theimer, Tad; Holton, Brandon; Jansen, Brian; Sexton, Joseph O.; Longshore, Kathleen M.; Edwards, Thomas C.; Peters, Michael

2017-01-01

Studies using global positioning system (GPS) telemetry rarely result in 100% fix success rates (FSR), which may bias datasets because data loss is systematic rather than a random process. Previous spatially explicit models developed to correct for sampling bias have been limited to small study areas, a small range of data loss, or were study-area specific. We modeled environmental effects on FSR from desert to alpine biomes, investigated the full range of potential data loss (0–100% FSR), and evaluated whether animal body position can contribute to lower FSR because of changes in antenna orientation based on GPS detection rates for 4 focal species: cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus nelsoni), and mule deer (Odocoileus hemionus). Terrain exposure and height of over story vegetation were the most influential factors affecting FSR. Model evaluation showed a strong correlation (0.88) between observed and predicted FSR and no significant differences between predicted and observed FSRs using 2 independent validation datasets. We found that cougars and canyon-dwelling bighorn sheep may select for environmental features that influence their detectability by GPS technology, mule deer may select against these features, and elk appear to be nonselective. We observed temporal patterns in missed fixes only for cougars. We provide a model for cougars, predicting fix success by time of day that is likely due to circadian changes in collar orientation and selection of daybed sites. We also provide a model predicting the probability of GPS fix acquisitions given environmental conditions, which had a strong relationship (r 2 = 0.82) with deployed collar FSRs across species.

Model comparison for Escherichia coli growth in pouched food.

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi

2006-06-01

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon's entropy models

NASA Astrophysics Data System (ADS)

Al-Abadi, Alaa M.

2017-05-01

In recent years, delineation of groundwater productivity zones plays an increasingly important role in sustainable management of groundwater resource throughout the world. In this study, groundwater productivity index of northeastern Wasit Governorate was delineated using probabilistic frequency ratio (FR) and Shannon's entropy models in framework of GIS. Eight factors believed to influence the groundwater occurrence in the study area were selected and used as the input data. These factors were elevation (m), slope angle (degree), geology, soil, aquifer transmissivity (m2/d), storativity (dimensionless), distance to river (m), and distance to faults (m). In the first step, borehole location inventory map consisting of 68 boreholes with relatively high yield (>8 l/sec) was prepared. 47 boreholes (70 %) were used as training data and the remaining 21 (30 %) were used for validation. The predictive capability of each model was determined using relative operating characteristic technique. The results of the analysis indicate that the FR model with a success rate of 87.4 % and prediction rate 86.9 % performed slightly better than Shannon's entropy model with success rate of 84.4 % and prediction rate of 82.4 %. The resultant groundwater productivity index was classified into five classes using natural break classification scheme: very low, low, moderate, high, and very high. The high-very high classes for FR and Shannon's entropy models occurred within 30 % (217 km2) and 31 % (220 km2), respectively indicating low productivity conditions of the aquifer system. From final results, both of the models were capable to prospect GWPI with very good results, but FR was better in terms of success and prediction rates. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans.

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.

PubMed

Wu, Mike; Ghassemi, Marzyeh; Feng, Mengling; Celi, Leo A; Szolovits, Peter; Doshi-Velez, Finale

2017-05-01

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Predictors of treatment failure in young patients undergoing in vitro fertilization.

PubMed

Jacobs, Marni B; Klonoff-Cohen, Hillary; Agarwal, Sanjay; Kritz-Silverstein, Donna; Lindsay, Suzanne; Garzo, V Gabriel

2016-08-01

The purpose of the study was to evaluate whether routinely collected clinical factors can predict in vitro fertilization (IVF) failure among young, "good prognosis" patients predominantly with secondary infertility who are less than 35 years of age. Using de-identified clinic records, 414 women <35 years undergoing their first autologous IVF cycle were identified. Logistic regression was used to identify patient-driven clinical factors routinely collected during fertility treatment that could be used to model predicted probability of cycle failure. One hundred ninety-seven patients with both primary and secondary infertility had a failed IVF cycle, and 217 with secondary infertility had a successful live birth. None of the women with primary infertility had a successful live birth. The significant predictors for IVF cycle failure among young patients were fewer previous live births, history of biochemical pregnancies or spontaneous abortions, lower baseline antral follicle count, higher total gonadotropin dose, unknown infertility diagnosis, and lack of at least one fair to good quality embryo. The full model showed good predictive value (c = 0.885) for estimating risk of cycle failure; at ≥80 % predicted probability of failure, sensitivity = 55.4 %, specificity = 97.5 %, positive predictive value = 95.4 %, and negative predictive value = 69.8 %. If this predictive model is validated in future studies, it could be beneficial for predicting IVF failure in good prognosis women under the age of 35 years.

Simple, empirical approach to predict neutron capture cross sections from nuclear masses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Couture, Aaron Joseph; Casten, Richard F.; Cakirli, R. B.

Here, neutron capture cross sections are essential to understanding the astrophysical s and r processes, the modeling of nuclear reactor design and performance, and for a wide variety of nuclear forensics applications. Often, cross sections are needed for nuclei where experimental measurements are difficult. Enormous effort, over many decades, has gone into attempting to develop sophisticated statistical reaction models to predict these cross sections. Such work has met with some success but is often unable to reproduce measured cross sections to better than 40%, and has limited predictive power, with predictions from different models rapidly differing by an order ofmore » magnitude a few nucleons from the last measurement.« less

Simple, empirical approach to predict neutron capture cross sections from nuclear masses

DOE PAGES

Couture, Aaron Joseph; Casten, Richard F.; Cakirli, R. B.

2017-12-20

Here, neutron capture cross sections are essential to understanding the astrophysical s and r processes, the modeling of nuclear reactor design and performance, and for a wide variety of nuclear forensics applications. Often, cross sections are needed for nuclei where experimental measurements are difficult. Enormous effort, over many decades, has gone into attempting to develop sophisticated statistical reaction models to predict these cross sections. Such work has met with some success but is often unable to reproduce measured cross sections to better than 40%, and has limited predictive power, with predictions from different models rapidly differing by an order ofmore » magnitude a few nucleons from the last measurement.« less

Development of a predictive program for Vibrio parahaemolyticus growth under various environmental conditions.

PubMed

Fujikawa, Hiroshi; Kimura, Bon; Fujii, Tateo

2009-09-01

In this study, we developed a predictive program for Vibrio parahaemolyticus growth under various environmental conditions. Raw growth data was obtained with a V. parahaemolyticus O3:K6 strain cultured at a variety of broth temperatures, pH, and salt concentrations. Data were analyzed with our logistic model and the parameter values of the model were analyzed with polynomial equations. A prediction program consisting of the growth model and the polynomial equations was then developed. After the range of the growth environments was modified, the program successfully predicted the growth for all environments tested. The program could be a useful tool to ensure the bacteriological safety of seafood.

The influence of speed abilities and technical skills in early adolescence on adult success in soccer: A long-term prospective analysis using ANOVA and SEM approaches

PubMed Central

2017-01-01

Several talent development programs in youth soccer have implemented motor diagnostics measuring performance factors. However, the predictive value of such tests for adult success is a controversial topic in talent research. This prospective cohort study evaluated the long-term predictive value of 1) motor tests and 2) players’ speed abilities (SA) and technical skills (TS) in early adolescence. The sample consisted of 14,178 U12 players from the German talent development program. Five tests (sprint, agility, dribbling, ball control, shooting) were conducted and players’ height, weight as well as relative age were assessed at nationwide diagnostics between 2004 and 2006. In the 2014/15 season, the players were then categorized as professional (n = 89), semi-professional (n = 913), or non-professional players (n = 13,176), indicating their adult performance level (APL). The motor tests’ prognostic relevance was determined using ANOVAs. Players’ future success was predicted by a logistic regression threshold model. This structural equation model comprised a measurement model with the motor tests and two correlated latent factors, SA and TS, with simultaneous consideration for the manifest covariates height, weight and relative age. Each motor predictor and anthropometric characteristic discriminated significantly between the APL (p < .001; η2 ≤ .02). The threshold model significantly predicted the APL (R2 = 24.8%), and in early adolescence the factor TS (p < .001) seems to have a stronger effect on adult performance than SA (p < .05). Both approaches (ANOVA, SEM) verified the diagnostics’ predictive validity over a long-term period (≈ 9 years). However, because of the limited effect sizes, the motor tests’ prognostic relevance remains ambiguous. A challenge for future research lies in the integration of different (e.g., person-oriented or multilevel) multivariate approaches that expand beyond the “traditional” topic of single tests’ predictive validity and toward more theoretically founded issues. PMID:28806410

Dynamic contraction behaviour of pneumatic artificial muscle

NASA Astrophysics Data System (ADS)

Doumit, Marc D.; Pardoel, Scott

2017-07-01

The development of a dynamic model for the Pneumatic Artificial Muscle (PAM) is an imperative undertaking for understanding and analyzing the behaviour of the PAM as a function of time. This paper proposes a Newtonian based dynamic PAM model that includes the modeling of the muscle geometry, force, inertia, fluid dynamic, static and dynamic friction, heat transfer and valve flow while ignoring the effect of bladder elasticity. This modeling contribution allows the designer to predict, analyze and optimize PAM performance prior to its development. Thus advancing successful implementations of PAM based powered exoskeletons and medical systems. To date, most muscle dynamic properties are determined experimentally, furthermore, no analytical models that can accurately predict the muscle's dynamic behaviour are found in the literature. Most developed analytical models adequately predict the muscle force in static cases but neglect the behaviour of the system in the transient response. This could be attributed to the highly challenging task of deriving such a dynamic model given the number of system elements that need to be identified and the system's highly non-linear properties. The proposed dynamic model in this paper is successfully simulated through MATLAB programing and validated the pressure, contraction distance and muscle temperature with experimental testing that is conducted with in-house built prototype PAM's.

A deformation energy-based model for predicting nucleosome dyads and occupancy

PubMed Central

Liu, Guoqing; Xing, Yongqiang; Zhao, Hongyu; Wang, Jianying; Shang, Yu; Cai, Lu

2016-01-01

Nucleosome plays an essential role in various cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to decode the mechanism of nucleosome positioning and identify nucleosome positions in the genome. In this paper, we present a model for predicting nucleosome positioning based on DNA deformation, in which both bending and shearing of the nucleosomal DNA are considered. The model successfully predicted the dyad positions of nucleosomes assembled in vitro and the in vitro map of nucleosomes in Saccharomyces cerevisiae. Applying the model to Caenorhabditis elegans and Drosophila melanogaster, we achieved satisfactory results. Our data also show that shearing energy of nucleosomal DNA outperforms bending energy in nucleosome occupancy prediction and the ability to predict nucleosome dyad positions is attributed to bending energy that is associated with rotational positioning of nucleosomes. PMID:27053067

Prognostic modelling options for remaining useful life estimation by industry

NASA Astrophysics Data System (ADS)

Sikorska, J. Z.; Hodkiewicz, M.; Ma, L.

2011-07-01

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

Maximum spreading of liquid drop on various substrates with different wettabilities

NASA Astrophysics Data System (ADS)

Choudhury, Raihan; Choi, Junho; Yang, Sangsun; Kim, Yong-Jin; Lee, Donggeun

2017-09-01

This paper describes a novel model developed for a priori prediction of the maximal spread of a liquid drop on a surface. As a first step, a series of experiments were conducted under precise control of the initial drop diameter, its falling height, roughness, and wettability of dry surfaces. The transient liquid spreading was recorded by a high-speed camera to obtain its maximum spreading under various conditions. Eight preexisting models were tested for accurate prediction of the maximum spread; however, most of the model predictions were not satisfactory except one, in comparison with our experimental data. A comparative scaling analysis of the literature models was conducted to elucidate the condition-dependent prediction characteristics of the models. The conditioned bias in the predictions was mainly attributed to the inappropriate formulations of viscous dissipation or interfacial energy of liquid on the surface. Hence, a novel model based on energy balance during liquid impact was developed to overcome the limitations of the previous models. As a result, the present model was quite successful in predicting the liquid spread in all the conditions.

Medial temporal lobe contributions to cued retrieval of items and contexts.

PubMed

Hannula, Deborah E; Libby, Laura A; Yonelinas, Andrew P; Ranganath, Charan

2013-10-01

Several models have proposed that different regions of the medial temporal lobes contribute to different aspects of episodic memory. For instance, according to one view, the perirhinal cortex represents specific items, parahippocampal cortex represents information regarding the context in which these items were encountered, and the hippocampus represents item-context bindings. Here, we used event-related functional magnetic resonance imaging (fMRI) to test a specific prediction of this model-namely, that successful retrieval of items from context cues will elicit perirhinal recruitment and that successful retrieval of contexts from item cues will elicit parahippocampal cortex recruitment. Retrieval of the bound representation in either case was expected to elicit hippocampal engagement. To test these predictions, we had participants study several item-context pairs (i.e., pictures of objects and scenes, respectively), and then had them attempt to recall items from associated context cues and contexts from associated item cues during a scanned retrieval session. Results based on both univariate and multivariate analyses confirmed a role for hippocampus in content-general relational memory retrieval, and a role for parahippocampal cortex in successful retrieval of contexts from item cues. However, we also found that activity differences in perirhinal cortex were correlated with successful cued recall for both items and contexts. These findings provide partial support for the above predictions and are discussed with respect to several models of medial temporal lobe function. Copyright © 2013 Elsevier Ltd. All rights reserved.

Artificial Neural Network and application in calibration transfer of AOTF-based NIR spectrometer

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Jiang, Chengzhi; Xu, Kexin; Wang, Bin

2002-09-01

Chemometrics is widely applied to develop models for quantitative prediction of unknown samples in Near-infrared (NIR) spectroscopy. However, calibrated models generally fail when new instruments are introduced or replacement of the instrument parts occurs. Therefore, calibration transfer becomes necessary to avoid the costly, time-consuming recalibration of models. Piecewise Direct Standardization (PDS) has been proven to be a reference method for standardization. In this paper, Artificial Neural Network (ANN) is employed as an alternative to transfer spectra between instruments. Two Acousto-optic Tunable Filter NIR spectrometers are employed in the experiment. Spectra of glucose solution are collected on the spectrometers through transflectance mode. A Back propagation Network with two layers is employed to simulate the function between instruments piecewisely. Standardization subset is selected by Kennard and Stone (K-S) algorithm in the first two score space of Principal Component Analysis (PCA) of spectra matrix. In current experiment, it is noted that obvious nonlinearity exists between instruments and attempts are made to correct such nonlinear effect. Prediction results before and after successful calibration transfer are compared. Successful transfer can be achieved by adapting window size and training parameters. Final results reveal that ANN is effective in correcting the nonlinear instrumental difference and a only 1.5~2 times larger prediction error is expected after successful transfer.

A Framework for Modeling Emerging Diseases to Inform Management

PubMed Central

Katz, Rachel A.; Richgels, Katherine L.D.; Walsh, Daniel P.; Grant, Evan H.C.

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. PMID:27983501

A Framework for Modeling Emerging Diseases to Inform Management.

PubMed

Russell, Robin E; Katz, Rachel A; Richgels, Katherine L D; Walsh, Daniel P; Grant, Evan H C

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

A framework for modeling emerging diseases to inform management

USGS Publications Warehouse

Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance

ERIC Educational Resources Information Center

Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui

2015-01-01

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

ERIC Educational Resources Information Center

Lee, Young-Jin

2015-01-01

This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

An evaluation of the nursing practice environment and successful change management using the new generation Magnet Model.

PubMed

Grant, Bettyanne; Colello, Sandra; Riehle, Martha; Dende, Denise

2010-04-01

To discuss the new Magnet Model as it relates to the successful implementation of a practice change. There is growing international interest in the Magnet Recognition Programme. The latest generation of the Magnet Model has been designed not only as a road map for organizations seeking to achieve Magnet recognition but also as a framework for nursing practice and research in the future. The Magnet Model was used to identify success factors related to a practice change and to evaluate the nursing practice environment. Even when proposed changes to practice are evidence based and thoughtfully considered, the nurses' work environment must be supportive and empowering in order to yield successful and sustainable implementation of new practice. Success factors for implementation of a practice change can be illuminated by aligning environmental characteristics to the components of the new Magnet Model. The Magnet Model provides an exceptional framework for building an agile and dynamic work force. Thoughtful consideration of the components and inter-relationships represented in the new model can help to both predict and ensure organizational vitality.

Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity.

PubMed

Buxton, Rachel; McKenna, Megan F; Clapp, Mary; Meyer, Erik; Stabenau, Erik; Angeloni, Lisa M; Crooks, Kevin; Wittemyer, George

2018-04-20

Passive acoustic monitoring has the potential to be a powerful approach for assessing biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examine the ability of acoustic indices to predict the diversity and abundance of biological sounds within recordings. First we reviewed the acoustic index literature and found that over 60 indices have been applied to a range of objectives with varying success. We then implemented a subset of the most successful indices on acoustic data collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental U.S., developing a predictive model of the diversity of animal sounds observed in recordings. For terrestrial recordings, random forest models using a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R 2 > = 0.94, mean squared error MSE < = 170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively impacted by insect, weather, and anthropogenic sounds. For marine recordings, random forest models predicted Shannon diversity, richness, and total number of biological sounds with low accuracy (R 2 < = 0.40, MSE > = 195), indicating that alternative methods are necessary in marine habitats. Our results suggest that using a combination of relevant indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats in the face of accelerating human-caused ecological change. This article is protected by copyright. All rights reserved.

The role of population inertia in predicting the outcome of stage-structured biological invasions.

PubMed

Guiver, Chris; Dreiwi, Hanan; Filannino, Donna-Maria; Hodgson, Dave; Lloyd, Stephanie; Townley, Stuart

2015-07-01

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour-phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. Copyright © 2015. Published by Elsevier Inc.

Developing a stochastic traffic volume prediction model for public-private partnership projects

NASA Astrophysics Data System (ADS)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

Early prediction of movie box office success based on Wikipedia activity big data.

PubMed

Mestyán, Márton; Yasseri, Taha; Kertész, János

2013-01-01

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Automated System Checkout to Support Predictive Maintenance for the Reusable Launch Vehicle

NASA Technical Reports Server (NTRS)

Patterson-Hine, Ann; Deb, Somnath; Kulkarni, Deepak; Wang, Yao; Lau, Sonie (Technical Monitor)

1998-01-01

The Propulsion Checkout and Control System (PCCS) is a predictive maintenance software system. The real-time checkout procedures and diagnostics are designed to detect components that need maintenance based on their condition, rather than using more conventional approaches such as scheduled or reliability centered maintenance. Predictive maintenance can reduce turn-around time and cost and increase safety as compared to conventional maintenance approaches. Real-time sensor validation, limit checking, statistical anomaly detection, and failure prediction based on simulation models are employed. Multi-signal models, useful for testability analysis during system design, are used during the operational phase to detect and isolate degraded or failed components. The TEAMS-RT real-time diagnostic engine was developed to utilize the multi-signal models by Qualtech Systems, Inc. Capability of predicting the maintenance condition was successfully demonstrated with a variety of data, from simulation to actual operation on the Integrated Propulsion Technology Demonstrator (IPTD) at Marshall Space Flight Center (MSFC). Playback of IPTD valve actuations for feature recognition updates identified an otherwise undetectable Main Propulsion System 12 inch prevalve degradation. The algorithms were loaded into the Propulsion Checkout and Control System for further development and are the first known application of predictive Integrated Vehicle Health Management to an operational cryogenic testbed. The software performed successfully in real-time, meeting the required performance goal of 1 second cycle time.

Resuspension and redistribution of radionuclides during grassland and forest fires in the Chernobyl exclusion zone: part II. Modeling the transport process.

PubMed

Yoschenko, V I; Kashparov, V A; Levchuk, S E; Glukhovskiy, A S; Khomutinin, Yu V; Protsak, V P; Lundin, S M; Tschiersch, J

2006-01-01

To predict parameters of radionuclide resuspension, transport and deposition during forest and grassland fires, several model modules were developed and adapted. Experimental data of controlled burning of prepared experimental plots in the Chernobyl exclusion zone have been used to evaluate the prognostic power of the models. The predicted trajectories and elevations of the plume match with those visually observed during the fire experiments in the grassland and forest sites. Experimentally determined parameters could be successfully used for the calculation of the initial plume parameters which provide the tools for the description of various fire scenarios and enable prognostic calculations. In summary, the model predicts a release of some per thousand from the radionuclide inventory of the fuel material by the grassland fires. During the forest fire, up to 4% of (137)Cs and (90)Sr and up to 1% of the Pu isotopes can be released from the forest litter according to the model calculations. However, these results depend on the parameters of the fire events. In general, the modeling results are in good accordance with the experimental data. Therefore, the considered models were successfully validated and can be recommended for the assessment of the resuspension and redistribution of radionuclides during grassland and forest fires in contaminated territories.

Predicting length of stay from an electronic patient record system: a primary total knee replacement example.

PubMed

Carter, Evelene M; Potts, Henry W W

2014-04-04

To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay. Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n=2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman's correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques. Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4-6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data). Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

The genetic basis of traits regulating sperm competition and polyandry: can selection favour the evolution of good- and sexy-sperm?

PubMed

Evans, Jonathan P; Simmons, Leigh W

2008-09-01

The good-sperm and sexy-sperm (GS-SS) hypotheses predict that female multiple mating (polyandry) can fuel sexual selection for heritable male traits that promote success in sperm competition. A major prediction generated by these models, therefore, is that polyandry will benefit females indirectly via their sons' enhanced fertilization success. Furthermore, like classic 'good genes' and 'sexy son' models for the evolution of female preferences, GS-SS processes predict a genetic correlation between genes for female mating frequency (analogous to the female preference) and those for traits influencing fertilization success (the sexually selected traits). We examine the premise for these predictions by exploring the genetic basis of traits thought to influence fertilization success and female mating frequency. We also highlight recent debates that stress the possible genetic constraints to evolution of traits influencing fertilization success via GS-SS processes, including sex-linked inheritance, nonadditive effects, interacting parental genotypes, and trade-offs between integrated ejaculate components. Despite these possible constraints, the available data suggest that male traits involved in sperm competition typically exhibit substantial additive genetic variance and rapid evolutionary responses to selection. Nevertheless, the limited data on the genetic variation in female mating frequency implicate strong genetic maternal effects, including X-linkage, which is inconsistent with GS-SS processes. Although the relative paucity of studies on the genetic basis of polyandry does not allow us to draw firm conclusions about the evolutionary origins of this trait, the emerging pattern of sex linkage in genes for polyandry is more consistent with an evolutionary history of antagonistic selection over mating frequency. We advocate further development of GS-SS theory to take account of the complex evolutionary dynamics imposed by sexual conflict over mating frequency.

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

NASA Astrophysics Data System (ADS)

Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

2016-03-01

A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

Modeling the prediction of business intelligence system effectiveness.

PubMed

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

The Use of High Performance Computing (HPC) to Strengthen the Development of Army Systems

DTIC Science & Technology

2011-11-01

accurately predicting the supersonic magus effect about spinning cones, ogive- cylinders , and boat-tailed afterbodies. This work led to the successful...successful computer model of the proposed product or system, one can then build prototypes on the computer and study the effects on the performance of...needed. The NRC report discusses the requirements for effective use of such computing power. One needs “models, algorithms, software, hardware

Biomass and fire dynamics in a temperate forest-grassland mosaic: Integrating multi-species herbivory, climate, and fire with the FireBGCv2/GrazeBGC system

Treesearch

Robert A. Riggs; Robert E. Keane; Norm Cimon; Rachel Cook; Lisa Holsinger; John Cook; Timothy DelCurto; L.Scott Baggett; Donald Justice; David Powell; Martin Vavra; Bridgett Naylor

2015-01-01

Landscape fire succession models (LFSMs) predict spatially-explicit interactions between vegetation succession and disturbance, but these models have yet to fully integrate ungulate herbivory as a driver of their processes. We modified a complex LFSM, FireBGCv2, to include a multi-species herbivory module, GrazeBGC. The system is novel in that it explicitly...

Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

PubMed

Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

2017-04-01

The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Project description and crowdfunding success: an exploratory study.

PubMed

Zhou, Mi Jamie; Lu, Baozhou; Fan, Weiguo Patrick; Wang, G Alan

2018-01-01

Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream's performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.

A fragmentation and reassembly method for ab initio phasing.

PubMed

Shrestha, Rojan; Zhang, Kam Y J

2015-02-01

Ab initio phasing with de novo models has become a viable approach for structural solution from protein crystallographic diffraction data. This approach takes advantage of the known protein sequence information, predicts de novo models and uses them for structure determination by molecular replacement. However, even the current state-of-the-art de novo modelling method has a limit as to the accuracy of the model predicted, which is sometimes insufficient to be used as a template for successful molecular replacement. A fragment-assembly phasing method has been developed that starts from an ensemble of low-accuracy de novo models, disassembles them into fragments, places them independently in the crystallographic unit cell by molecular replacement and then reassembles them into a whole structure that can provide sufficient phase information to enable complete structure determination by automated model building. Tests on ten protein targets showed that the method could solve structures for eight of these targets, although the predicted de novo models cannot be used as templates for successful molecular replacement since the best model for each target is on average more than 4.0 Å away from the native structure. The method has extended the applicability of the ab initio phasing by de novo models approach. The method can be used to solve structures when the best de novo models are still of low accuracy.

Personality and Career Success: Concurrent and Longitudinal Relations.

PubMed

Sutin, Angelina R; Costa, Paul T; Miech, Richard; Eaton, William W

2009-03-01

The present research addresses the dynamic transaction between extrinsic (occupational prestige, income) and intrinsic (job satisfaction) career success and the Five-Factor Model of personality. Participants (N = 731) completed a comprehensive measure of personality and reported their job title, annual income, and job satisfaction; a subset of these participants (n = 302) provided the same information approximately 10 years later. Measured concurrently, emotionally stable and conscientious participants reported higher incomes and job satisfaction. Longitudinal analyses revealed that, among younger participants, higher income at baseline predicted decreases in Neuroticism and baseline Extraversion predicted increases in income across the 10 years. Results suggest that the mutual influence of career success and personality is limited to income and occurs early in the career.

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

PubMed Central

Ding, Fangyu; Ge, Quansheng; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before. PMID:28591138

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.

PubMed

Ding, Fangyu; Ge, Quansheng; Jiang, Dong; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand.

PubMed

Appuhamy, Jayasooriya A D R N; France, James; Kebreab, Ermias

2016-09-01

There are several models in the literature for predicting enteric methane (CH4 ) emissions. These models were often developed on region or country-specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH4 emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH4 measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen et al. (Acta Agriculturae Scand Section A, 63, 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE = 13.1% and CCC = 0.78). The gross energy intake-based model of Yan et al. (Livestock Production Science, 64, 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE = 11.0% and CCC = 0.66) and AUNZ (RMSPE = 15.6% and CCC = 0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat-corrected milk yield data (RMSPE < 12.0% and CCC > 0.60). Using estimated DMI, the Nielsen et al. (2013) (RMSPE = 12.7 and CCC = 0.79) and Yan et al. (2000) (RMSPE = 13.7 and CCC = 0.50) models still predicted emissions in respective regions well. Enteric CH4 emissions from dairy cows can be predicted successfully (i.e., RMSPE < 15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE < 10%). © 2016 John Wiley & Sons Ltd.

Analysis Methods and Models for Small Unit Operations

DTIC Science & Technology

2006-07-01

wordt in andere studies ogebruikt orn a-an te geven welke op welke wijze operationele effectiviteit kan worden gekwalificeerd en gekwanuificeerd...the node ’Prediction’ is called a child of the node ’Success’ and the node ’Success’ is called a parent of the node ’Prediction’. Figure C.2 A simple...event A is a child of event B and event B is a child of event C ( C -- B -- A). The belief network or influence diagram has to be a directed network

Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

NASA Astrophysics Data System (ADS)

Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

2015-10-01

A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.

Untangling Performance from Success

NASA Astrophysics Data System (ADS)

Yucesoy, Burcu; Barabasi, Albert-Laszlo

Fame, popularity and celebrity status, frequently used tokens of success, are often loosely related to, or even divorced from professional performance. This dichotomy is partly rooted in the difficulty to distinguish performance, an individual measure that captures the actions of a performer, from success, a collective measure that captures a community's reactions to these actions. Yet, finding the relationship between the two measures is essential for all areas that aim to objectively reward excellence, from science to business. Here we quantify the relationship between performance and success by focusing on tennis, an individual sport where the two quantities can be independently measured. We show that a predictive model, relying only on a tennis player's performance in tournaments, can accurately predict an athlete's popularity, both during a player's active years and after retirement. Hence the model establishes a direct link between performance and momentary popularity. The agreement between the performance-driven and observed popularity suggests that in most areas of human achievement exceptional visibility may be rooted in detectable performance measures. This research was supported by Air Force Office of Scientific Research (AFOSR) under agreement FA9550-15-1-0077.

Successful Application of Adaptive Emotion Regulation Skills Predicts the Subsequent Reduction of Depressive Symptom Severity but neither the Reduction of Anxiety nor the Reduction of General Distress during the Treatment of Major Depressive Disorder

PubMed Central

Wirtz, Carolin M.; Radkovsky, Anna; Ebert, David D.; Berking, Matthias

2014-01-01

Objective Deficits in general emotion regulation (ER) skills have been linked to symptoms of depression and are thus considered a promising target in the treatment of Major depressive disorder (MDD). However, at this point, the extent to which such skills are relevant for coping with depression and whether they should instead be considered a transdiagnostic factor remain unclear. Therefore, the present study aimed to investigate whether successful ER skills application is associated with changes in depressive symptom severity (DSS), anxiety symptom severity (ASS), and general distress severity (GDS) over the course of treatment for MDD. Methods Successful ER skills application, DSS, ASS, and GDS were assessed four times during the first three weeks of treatment in 175 inpatients who met the criteria for MDD. We computed Pearson correlations to test whether successful ER skills application and the three indicators of psychopathology are cross-sectionally associated. We then performed latent growth curve modelling to test whether changes in successful ER skills application are negatively associated with a reduction of DSS, ASS, or GDS. Finally, we utilized latent change score models to examine whether successful ER skills application predicts subsequent reduction of DSS, ASS, or GDS. Results Successful ER skills application was cross-sectionally associated with lower levels of DSS, ASS, and GDS at all points of assessment. An increase in successful skills application during treatment was associated with a decrease in DSS and GDS but not ASS. Finally, successful ER skills application predicted changes in subsequent DSS but neither changes in ASS nor changes in GDS. Conclusions Although general ER skills might be relevant for a broad range of psychopathological symptoms, they might be particularly important for the maintenance and treatment of depressive symptoms. PMID:25330159

Predictors of Latina/o Community College Student Vocational Choice in STEM

ERIC Educational Resources Information Center

Johnson, Joel D.; Starobin, Soko S.; Santos Laanan, Frankie

2016-01-01

This study confirmed appropriate measurement model fit for a theoretical model, the STEM (science, technology, engineering, and mathematics) vocational choice (STEM-VC) model. This model identified factors that successfully predicted a student's vocational choice decision to pursue a STEM degree for Latina/o and White community college students.…

Development of a program to fit data to a new logistic model for microbial growth.

PubMed

Fujikawa, Hiroshi; Kano, Yoshihiro

2009-06-01

Recently we developed a mathematical model for microbial growth in food. The model successfully predicted microbial growth at various patterns of temperature. In this study, we developed a program to fit data to the model with a spread sheet program, Microsoft Excel. Users can instantly get curves fitted to the model by inputting growth data and choosing the slope portion of a curve. The program also could estimate growth parameters including the rate constant of growth and the lag period. This program would be a useful tool for analyzing growth data and further predicting microbial growth.

Calculating the individual probability of successful ocriplasmin treatment in eyes with VMT syndrome: a multivariable prediction model from the EXPORT study.

PubMed

Paul, Christoph; Heun, Christine; Müller, Hans-Helge; Hoerauf, Hans; Feltgen, Nicolas; Wachtlin, Joachim; Kaymak, Hakan; Mennel, Stefan; Koss, Michael Janusz; Fauser, Sascha; Maier, Mathias M; Schumann, Ricarda G; Mueller, Simone; Chang, Petrus; Schmitz-Valckenberg, Steffen; Kazerounian, Sara; Szurman, Peter; Lommatzsch, Albrecht; Bertelmann, Thomas

2017-10-31

To evaluate predictive factors for the treatment success of ocriplasmin and to use these factors to generate a multivariate model to calculate the individual probability of successful treatment. Data were collected in a retrospective, multicentre cohort study. Patients with vitreomacular traction (VMT) syndrome without a full-thickness macular hole were included if they received an intravitreal injection (IVI) of ocriplasmin. Five factors (age, gender, lens status, presence of epiretinal membrane (ERM) formation and horizontal diameter of VMT) were assessed on their association with VMT resolution. A multivariable logistic regression model was employed to further analyse these factors and calculate the individual probability of successful treatment. 167 eyes of 167 patients were included. Univariate analysis revealed a significant correlation to VMT resolution for all analysed factors: age (years) (OR 0.9208; 95% CI 0.8845 to 0.9586; p<0.0001), gender (male) (OR 0.480; 95% CI 0.241 to 0.957; p=0.0371), lens status (phakic) (OR 2.042; 95% CI 1.054 to 3.958; p=0.0344), ERM formation (present) (OR 0.384; 95% CI 0.179 to 0.821; p=0.0136) and horizontal VMT diameter (µm) (OR 0.99812; 95% CI 0.99684 to 0.99941, p=0.0042). A significant multivariable logistic regression model was established with age and VMT diameter. Known predictive factors for VMT resolution after ocriplasmin IVI were confirmed in our study. We were able to combine them into a formula, ultimately allowing the calculation of an individual probability of treatment success with ocriplasmin in patients with VMT syndrome without FTHM. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Modeling the human invader in the United States

USGS Publications Warehouse

Stohlgren, Thomas J.; Jarnevich, Catherine S.; Giri, Chandra P.

2010-01-01

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km2) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km2> cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km2 > cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km2> cell is urban), while we predict that 24.5% of 1-km2> cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

Modeling Interdependent and Periodic Real-World Action Sequences

PubMed Central

Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions. PMID:29780977

Determination of heat capacity of ionic liquid based nanofluids using group method of data handling technique

NASA Astrophysics Data System (ADS)

Sadi, Maryam

2018-01-01

In this study a group method of data handling model has been successfully developed to predict heat capacity of ionic liquid based nanofluids by considering reduced temperature, acentric factor and molecular weight of ionic liquids, and nanoparticle concentration as input parameters. In order to accomplish modeling, 528 experimental data points extracted from the literature have been divided into training and testing subsets. The training set has been used to predict model coefficients and the testing set has been applied for model validation. The ability and accuracy of developed model, has been evaluated by comparison of model predictions with experimental values using different statistical parameters such as coefficient of determination, mean square error and mean absolute percentage error. The mean absolute percentage error of developed model for training and testing sets are 1.38% and 1.66%, respectively, which indicate excellent agreement between model predictions and experimental data. Also, the results estimated by the developed GMDH model exhibit a higher accuracy when compared to the available theoretical correlations.

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model

NASA Astrophysics Data System (ADS)

Ito, Shin-ichi; Nagao, Hiromichi; Kasuya, Tadashi; Inoue, Junya

2017-12-01

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

From Data-Sharing to Model-Sharing: SCEC and the Development of Earthquake System Science (Invited)

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2009-12-01

Earthquake system science seeks to construct system-level models of earthquake phenomena and use them to predict emergent seismic behavior—an ambitious enterprise that requires high degree of interdisciplinary, multi-institutional collaboration. This presentation will explore model-sharing structures that have been successful in promoting earthquake system science within the Southern California Earthquake Center (SCEC). These include disciplinary working groups to aggregate data into community models; numerical-simulation working groups to investigate system-specific phenomena (process modeling) and further improve the data models (inverse modeling); and interdisciplinary working groups to synthesize predictive system-level models. SCEC has developed a cyberinfrastructure, called the Community Modeling Environment, that can distribute the community models; manage large suites of numerical simulations; vertically integrate the hardware, software, and wetware needed for system-level modeling; and promote the interactions among working groups needed for model validation and refinement. Various socio-scientific structures contribute to successful model-sharing. Two of the most important are “communities of trust” and collaborations between government and academic scientists on mission-oriented objectives. The latter include improvements of earthquake forecasts and seismic hazard models and the use of earthquake scenarios in promoting public awareness and disaster management.

Predicting introductory programming performance: A multi-institutional multivariate study

NASA Astrophysics Data System (ADS)

Bergin, Susan; Reilly, Ronan

2006-12-01

A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

Current status of one- and two-dimensional numerical models: Successes and limitations

NASA Technical Reports Server (NTRS)

Schwartz, R. J.; Gray, J. L.; Lundstrom, M. S.

1985-01-01

The capabilities of one and two-dimensional numerical solar cell modeling programs (SCAP1D and SCAP2D) are described. The occasions when a two-dimensional model is required are discussed. The application of the models to design, analysis, and prediction are presented along with a discussion of problem areas for solar cell modeling.

A creep cavity growth model for creep-fatigue life prediction of a unidirectional W/Cu composite

NASA Astrophysics Data System (ADS)

Kim, Young-Suk; Verrilli, Michael J.; Halford, Gary R.

1992-05-01

A microstructural model was developed to predict creep-fatigue life in a (0)(sub 4), 9 volume percent tungsten fiber-reinforced copper matrix composite at the temperature of 833 K. The mechanism of failure of the composite is assumed to be governed by the growth of quasi-equilibrium cavities in the copper matrix of the composite, based on the microscopically observed failure mechanisms. The methodology uses a cavity growth model developed for prediction of creep fracture. Instantaneous values of strain rate and stress in the copper matrix during fatigue cycles were calculated and incorporated in the model to predict cyclic life. The stress in the copper matrix was determined by use of a simple two-bar model for the fiber and matrix during cyclic loading. The model successfully predicted the composite creep-fatigue life under tension-tension cyclic loading through the use of this instantaneous matrix stress level. Inclusion of additional mechanisms such as cavity nucleation, grain boundary sliding, and the effect of fibers on matrix-stress level would result in more generalized predictions of creep-fatigue life.

A creep cavity growth model for creep-fatigue life prediction of a unidirectional W/Cu composite

NASA Technical Reports Server (NTRS)

Kim, Young-Suk; Verrilli, Michael J.; Halford, Gary R.

1992-01-01

A microstructural model was developed to predict creep-fatigue life in a (0)(sub 4), 9 volume percent tungsten fiber-reinforced copper matrix composite at the temperature of 833 K. The mechanism of failure of the composite is assumed to be governed by the growth of quasi-equilibrium cavities in the copper matrix of the composite, based on the microscopically observed failure mechanisms. The methodology uses a cavity growth model developed for prediction of creep fracture. Instantaneous values of strain rate and stress in the copper matrix during fatigue cycles were calculated and incorporated in the model to predict cyclic life. The stress in the copper matrix was determined by use of a simple two-bar model for the fiber and matrix during cyclic loading. The model successfully predicted the composite creep-fatigue life under tension-tension cyclic loading through the use of this instantaneous matrix stress level. Inclusion of additional mechanisms such as cavity nucleation, grain boundary sliding, and the effect of fibers on matrix-stress level would result in more generalized predictions of creep-fatigue life.

Annual temperature variation as a time machine to understand the effects of long-term climate change on a poleward range shift.

PubMed

Crickenberger, Sam; Wethey, David S

2018-05-10

Range shifts due to annual variation in temperature are more tractable than range shifts linked to decadal to century long temperature changes due to climate change, providing natural experiments to determine the mechanisms responsible for driving long-term distributional shifts. In this study we couple physiologically grounded mechanistic models with biogeographic surveys in 2 years with high levels of annual temperature variation to disentangle the drivers of a historical range shift driven by climate change. The distribution of the barnacle Semibalanus balanoides has shifted 350 km poleward in the past half century along the east coast of the United States. Recruits were present throughout the historical range following the 2015 reproductive season, when temperatures were similar to those in the past century, and absent following the 2016 reproductive season when temperatures were warmer than they have been since 1870, the earliest date for temperature records. Our dispersal dependent mechanistic models of reproductive success were highly accurate and predicted patterns of reproduction success documented in field surveys throughout the historical range in 2015 and 2016. Our mechanistic models of reproductive success not only predicted recruitment dynamics near the range edge but also predicted interior range fragmentation in a number of years between 1870 and 2016. All recruits monitored within the historical range following the 2015 colonization died before 2016 suggesting juvenile survival was likely the primary driver of the historical range retraction. However, if 2016 is indicative of future temperatures mechanisms of range limitation will shift and reproductive failure will lead to further range retraction in the future. Mechanistic models are necessary for accurately predicting the effects of climate change on ranges of species. © 2018 John Wiley & Sons Ltd.

Progress in Earth System Modeling since the ENIAC Calculation

NASA Astrophysics Data System (ADS)

Fung, I.

2009-05-01

The success of the first numerical weather prediction experiment on the ENIAC computer in 1950 was hinged on the expansion of the meteorological observing network, which led to theoretical advances in atmospheric dynamics and subsequently the implementation of the simplified equations on the computer. This paper briefly reviews the progress in Earth System Modeling and climate observations, and suggests a strategy to sustain and expand the observations needed to advance climate science and prediction.

Pre-launch Optical Characteristics of the Oculus-ASR Nanosatellite for Attitude and Shape Recognition Experiments

DTIC Science & Technology

2011-12-02

construction and validation of predictive computer models such as those used in Time-domain Analysis Simulation for Advanced Tracking (TASAT), a...characterization data, successful construction and validation of predictive computer models was accomplished. And an investigation in pose determination from...currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES

PREDICTIVE ORGANOPHOSPHORUS (OP) PESTICIDE QSARS AND PBPK/PD MODELS FOR RISK ASSESSMENT OF SUSCEPTIBLE SUB-POPULATIONS

EPA Science Inventory

Successful use of the Exposure Related Dose Estimating Model (ERDEM) in risk assessment of susceptible human sub-populations, e.g., infants and children, requires input of quality experimental data. In the clear absence of quality data, PBPK models can be developed and possibl...

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features

USGS Publications Warehouse

Gieder, Katherina D.; Karpanty, Sarah M.; Fraser, James D.; Catlin, Daniel H.; Gutierrez, Benjamin T.; Plant, Nathaniel G.; Turecek, Aaron M.; Thieler, E. Robert

2014-01-01

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Ordinary Differential Equation Models for Adoptive Immunotherapy.

PubMed

Talkington, Anne; Dantoin, Claudia; Durrett, Rick

2018-05-01

Modified T cells that have been engineered to recognize the CD19 surface marker have recently been shown to be very successful at treating acute lymphocytic leukemias. Here, we explore four previous approaches that have used ordinary differential equations to model this type of therapy, compare their properties, and modify the models to address their deficiencies. Although the four models treat the workings of the immune system in slightly different ways, they all predict that adoptive immunotherapy can be successful to move a patient from the large tumor fixed point to an equilibrium with little or no tumor.

Twelve years of succession on sandy substrates in a post-mining landscape: a Markov chain analysis.

PubMed

Baasch, Annett; Tischew, Sabine; Bruelheide, Helge

2010-06-01

Knowledge of succession rates and pathways is crucial for devising restoration strategies for highly disturbed ecosystems such as surface-mined land. As these processes have often only been described in qualitative terms, we used Markov models to quantify transitions between successional stages. However, Markov models are often considered not attractive for some reasons, such as model assumptions (e.g., stationarity in space and time, or the high expenditure of time required to estimate successional transitions in the field). Here we present a solution for converting multivariate ecological time series into transition matrices and demonstrate the applicability of this approach for a data set that resulted from monitoring the succession of sandy dry grassland in a post-mining landscape. We analyzed five transition matrices, four one-step matrices referring to specific periods of transition (1995-1998, 1998-2001, 2001-2004, 2004-2007), and one matrix for the whole study period (stationary model, 1995-2007). Finally, the stationary model was enhanced to a partly time-variable model. Applying the stationary and the time-variable models, we started a prediction well outside our calibration period, beginning with 100% bare soil in 1974 as the known start of the succession, and generated the coverage of 12 predefined vegetation types in three-year intervals. Transitions among vegetation types changed significantly in space and over time. While the probability of colonization was almost constant over time, the replacement rate tended to increase, indicating that the speed of succession accelerated with time or fluctuations became stronger. The predictions of both models agreed surprisingly well with the vegetation data observed more than two decades later. This shows that our dry grassland succession in a post-mining landscape can be adequately described by comparably simple types of Markov models, although some model assumptions have not been fulfilled and within-plot transitions have not been observed with point exactness. The major achievement of our proposed way to convert vegetation time series into transition matrices is the estimation of probability of events--a strength not provided by other frequently used statistical methods in vegetation science.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

Bankruptcy prediction based on financial ratios using Jordan Recurrent Neural Networks: a case study in Polish companies

NASA Astrophysics Data System (ADS)

Hardinata, Lingga; Warsito, Budi; Suparti

2018-05-01

Complexity of bankruptcy causes the accurate models of bankruptcy prediction difficult to be achieved. Various prediction models have been developed to improve the accuracy of bankruptcy predictions. Machine learning has been widely used to predict because of its adaptive capabilities. Artificial Neural Networks (ANN) is one of machine learning which proved able to complete inference tasks such as prediction and classification especially in data mining. In this paper, we propose the implementation of Jordan Recurrent Neural Networks (JRNN) to classify and predict corporate bankruptcy based on financial ratios. Feedback interconnection in JRNN enable to make the network keep important information well allowing the network to work more effectively. The result analysis showed that JRNN works very well in bankruptcy prediction with average success rate of 81.3785%.

Integrating in silico models to enhance predictivity for developmental toxicity.

PubMed

Marzo, Marco; Kulkarni, Sunil; Manganaro, Alberto; Roncaglioni, Alessandra; Wu, Shengde; Barton-Maclaren, Tara S; Lester, Cathy; Benfenati, Emilio

2016-08-31

Application of in silico models to predict developmental toxicity has demonstrated limited success particularly when employed as a single source of information. It is acknowledged that modelling the complex outcomes related to this endpoint is a challenge; however, such models have been developed and reported in the literature. The current study explored the possibility of integrating the selected public domain models (CAESAR, SARpy and P&G model) with the selected commercial modelling suites (Multicase, Leadscope and Derek Nexus) to assess if there is an increase in overall predictive performance. The results varied according to the data sets used to assess performance which improved upon model integration relative to individual models. Moreover, because different models are based on different specific developmental toxicity effects, integration of these models increased the applicable chemical and biological spaces. It is suggested that this approach reduces uncertainty associated with in silico predictions by achieving a consensus among a battery of models. The use of tools to assess the applicability domain also improves the interpretation of the predictions. This has been verified in the case of the software VEGA, which makes freely available QSAR models with a measurement of the applicability domain. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Prediction of Significant Wave Heights in the Tropics at Sub-seasonal Time Scales

NASA Astrophysics Data System (ADS)

Kinter, J. L.; Shukla, R. P.; Shin, C. S.

2017-12-01

Skillfully predicting the 14-day mean significant wave height (SWH) forecasts at 3 weeks lead-time over the Western Pacific and Indian Oceans has been demonstrated using the WAVEWATCH-3 (WW3) model coupled to a modified version of the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). In this paper, we present results on the effect of the Madden Julian Oscillation (MJO) events and El Niño and the Southern Oscillation (ENSO) on such predictions. Forecasts initialized with multiple ocean analyses in both January and May for 1979-2008 are evaluated. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at 3 weeks lead-time is found over portions of the domain in both January and May cases. The model successfully predicts almost all the important features of the observed SWHA during El Niño events in January, including negative SWHA in the central Indian Ocean and northern western tropical Pacific, and positive SWHA over the southern Ocean and western Pacific. The model also reproduces the spatial pattern of the inverse relationship between SWHA and sea level pressure anomalies during both composite El Niño and La Niña events at 3 weeks lead-time. The model successfully predicts the sign and magnitude of SWHA in May over the Bay of Bengal and South China Sea in composites of phases 2 and 6 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of MJO and ENSO. Analysis of the mechanisms for these relationships is described.

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

PubMed Central

Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

2010-01-01

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record.

PubMed

Harvey, H Benjamin; Liu, Catherine; Ai, Jing; Jaworsky, Cristina; Guerrier, Claude Emmanuel; Flores, Efren; Pianykh, Oleg

2017-10-01

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination. Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve. Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality. Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Predicting cell viability within tissue scaffolds under equiaxial strain: multi-scale finite element model of collagen-cardiomyocytes constructs.

PubMed

Elsaadany, Mostafa; Yan, Karen Chang; Yildirim-Ayan, Eda

2017-06-01

Successful tissue engineering and regenerative therapy necessitate having extensive knowledge about mechanical milieu in engineered tissues and the resident cells. In this study, we have merged two powerful analysis tools, namely finite element analysis and stochastic analysis, to understand the mechanical strain within the tissue scaffold and residing cells and to predict the cell viability upon applying mechanical strains. A continuum-based multi-length scale finite element model (FEM) was created to simulate the physiologically relevant equiaxial strain exposure on cell-embedded tissue scaffold and to calculate strain transferred to the tissue scaffold (macro-scale) and residing cells (micro-scale) upon various equiaxial strains. The data from FEM were used to predict cell viability under various equiaxial strain magnitudes using stochastic damage criterion analysis. The model validation was conducted through mechanically straining the cardiomyocyte-encapsulated collagen constructs using a custom-built mechanical loading platform (EQUicycler). FEM quantified the strain gradients over the radial and longitudinal direction of the scaffolds and the cells residing in different areas of interest. With the use of the experimental viability data, stochastic damage criterion, and the average cellular strains obtained from multi-length scale models, cellular viability was predicted and successfully validated. This methodology can provide a great tool to characterize the mechanical stimulation of bioreactors used in tissue engineering applications in providing quantification of mechanical strain and predicting cellular viability variations due to applied mechanical strain.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Development and application of diurnal thermal modeling for camouflage, concealment, and deception

NASA Astrophysics Data System (ADS)

Rodgers, Mark L. B.

2000-07-01

The art of camouflage is to make a military asset appear to be part of the natural environment: its background. In order to predict the likely performance of countermeasures in attaining this goal it is necessary to model the signatures of targets, backgrounds and the effect of countermeasures. A library of diurnal thermal models has been constructed covering a range of backgrounds from vegetated and non- vegetated surfaces to snow cover. These models, originally developed for Western Europe, have been validated successfully for theatres of operation from the arctic to the desert. This paper will show the basis for and development of physically based models for the diurnal thermal behavior both of these backgrounds and for major passive countermeasures: camouflage nets and continuous textile materials. The countermeasures set up significant challenges for the thermal modeler with their low but non-zero thermal inertial and the extent to which they influence local aerodynamic behavior. These challenges have been met and the necessary extensive validation has shown the ability of the models to predict successfully the behavior of in-service countermeasures.

Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

PubMed

Mokhtari, Fatemeh; Rejeski, W Jack; Zhu, Yingying; Wu, Guorong; Simpson, Sean L; Burdette, Jonathan H; Laurienti, Paul J

2018-06-01

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment. Copyright © 2018 Elsevier Inc. All rights reserved.

Nowcasting Ground Magnetic Perturbations with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Toth, G.; Singer, H. J.; Millward, G. H.; Gombosi, T. I.

2015-12-01

Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized B/t predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation describes the operational SWMF setup and summarizes the changes made to the code to enable R2O progress. The framework's algorithm for calculating ground-based magnetometer observations will be reviewed. Metrics from data-model comparisons will be reviewed to illustrate predictive capabilities. Early data products, such as regional-K index and grids of virtual magnetometer stations, will be presented. Finally, early successes will be shared, including the code's ability to reproduce the recent March 2015 St. Patrick's Day Storm.

Falling Off Track: How Teacher-Student Relationships Predict Early High School Failure Rates.

ERIC Educational Resources Information Center

Miller, Shazia Rafiullah

This paper examines the relationship between the climate of teacher-student relations within a school and individual student's likelihood of freshman year success. Using administrative data from the Chicago Public Schools and survey data, researchers used hierarchical linear modeling to determine whether teacher-student climate predicts students'…

Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle.

PubMed

Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hongbin

2016-04-15

The potential use of feature wavelengths for predicting drip loss in grass carp fish, as affected by being frozen at -20°C for 24 h and thawed at 4°C for 1, 2, 4, and 6 days, was investigated. Hyperspectral images of frozen-thawed fish were obtained and their corresponding spectra were extracted. Least-squares support vector machine and multiple linear regression (MLR) models were established using five key wavelengths, selected by combining a genetic algorithm and successive projections algorithm, and this showed satisfactory performance in drip loss prediction. The MLR model with a determination coefficient of prediction (R(2)P) of 0.9258, and lower root mean square error estimated by a prediction (RMSEP) of 1.12%, was applied to transfer each pixel of the image and generate the distribution maps of exudation changes. The results confirmed that it is feasible to identify the feature wavelengths using variable selection methods and chemometric analysis for developing on-line multispectral imaging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Propagation studies using a theoretical ionosphere model

NASA Technical Reports Server (NTRS)

Lee, M.

1973-01-01

The mid-latitude ionospheric and neutral atmospheric models are coupled with an advanced three dimensional ray tracing program to see what success would be obtained in predicting the wave propagation conditions and to study to what extent the use of theoretical ionospheric models is practical. The Penn State MK 1 ionospheric model, the Mitra-Rowe D region model, and the Groves' neutral atmospheric model are used throughout this work to represent the real electron densities and collision frequencies. The Faraday rotation and differential Doppler velocities from satellites, the propagation modes for long distance high frequency propagation, the group delays for each mode, the ionospheric absorption, and the spatial loss are all predicted.

Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

USGS Publications Warehouse

Gonzalez, Raul; Conn, Kathleen E.; Crosswell, Joey; Noble, Rachel

2012-01-01

Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18–24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.

Successional dynamics in Neotropical forests are as uncertain as they are predictable

PubMed Central

Norden, Natalia; Angarita, Héctor A.; Bongers, Frans; Martínez-Ramos, Miguel; Granzow-de la Cerda, Iñigo; van Breugel, Michiel; Lebrija-Trejos, Edwin; Meave, Jorge A.; Vandermeer, John; Williamson, G. Bruce; Finegan, Bryan; Mesquita, Rita; Chazdon, Robin L.

2015-01-01

Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes—stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration. PMID:26080411

Successional dynamics in Neotropical forests are as uncertain as they are predictable.

PubMed

Norden, Natalia; Angarita, Héctor A; Bongers, Frans; Martínez-Ramos, Miguel; Granzow-de la Cerda, Iñigo; van Breugel, Michiel; Lebrija-Trejos, Edwin; Meave, Jorge A; Vandermeer, John; Williamson, G Bruce; Finegan, Bryan; Mesquita, Rita; Chazdon, Robin L

2015-06-30

Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes--stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration.

Tiltrotor Aeroacoustic Code (TRAC) Prediction Assessment and Initial Comparisons with Tram Test Data

NASA Technical Reports Server (NTRS)

Burley, Casey L.; Brooks, Thomas F.; Charles, Bruce D.; McCluer, Megan

1999-01-01

A prediction sensitivity assessment to inputs and blade modeling is presented for the TiltRotor Aeroacoustic Code (TRAC). For this study, the non-CFD prediction system option in TRAC is used. Here, the comprehensive rotorcraft code, CAMRAD.Mod1, coupled with the high-resolution sectional loads code HIRES, predicts unsteady blade loads to be used in the noise prediction code WOPWOP. The sensitivity of the predicted blade motions, blade airloads, wake geometry, and acoustics is examined with respect to rotor rpm, blade twist and chord, and to blade dynamic modeling. To accomplish this assessment, an interim input-deck for the TRAM test model and an input-deck for a reference test model are utilized in both rigid and elastic modes. Both of these test models are regarded as near scale models of the V-22 proprotor (tiltrotor). With basic TRAC sensitivities established, initial TRAC predictions are compared to results of an extensive test of an isolated model proprotor. The test was that of the TiltRotor Aeroacoustic Model (TRAM) conducted in the Duits-Nederlandse Windtunnel (DNW). Predictions are compared to measured noise for the proprotor operating over an extensive range of conditions. The variation of predictions demonstrates the great care that must be taken in defining the blade motion. However, even with this variability, the predictions using the different blade modeling successfully capture (bracket) the levels and trends of the noise for conditions ranging from descent to ascent.

Tiltrotor Aeroacoustic Code (TRAC) Prediction Assessment and Initial Comparisons With TRAM Test Data

NASA Technical Reports Server (NTRS)

Burley, Casey L.; Brooks, Thomas F.; Charles, Bruce D.; McCluer, Megan

1999-01-01

A prediction sensitivity assessment to inputs and blade modeling is presented for the TiltRotor Aeroacoustic Code (TRAC). For this study, the non-CFD prediction system option in TRAC is used. Here, the comprehensive rotorcraft code, CAMRAD.Mod 1, coupled with the high-resolution sectional loads code HIRES, predicts unsteady blade loads to be used in the noise prediction code WOPWOP. The sensitivity of the predicted blade motions, blade airloads, wake geometry, and acoustics is examined with respect to rotor rpm, blade twist and chord, and to blade dynamic modeling. To accomplish this assessment. an interim input-deck for the TRAM test model and an input-deck for a reference test model are utilized in both rigid and elastic modes. Both of these test models are regarded as near scale models of the V-22 proprotor (tiltrotor). With basic TRAC sensitivities established, initial TRAC predictions are compared to results of an extensive test of an isolated model proprotor. The test was that of the TiltRotor Aeroacoustic Model (TRAM) conducted in the Duits-Nederlandse Windtunnel (DNW). Predictions are compared to measured noise for the proprotor operating over an extensive range of conditions. The variation of predictions demonstrates the great care that must be taken in defining the blade motion. However, even with this variability, the predictions using the different blade modeling successfully capture (bracket) the levels and trends of the noise for conditions ranging from descent to ascent.

Intentional strategies that make co-actors more predictable: the case of signaling.

PubMed

Pezzulo, Giovanni; Dindo, Haris

2013-08-01

Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.

Personality and Career Success: Concurrent and Longitudinal Relations

PubMed Central

Sutin, Angelina R.; Costa, Paul T.; Miech, Richard; Eaton, William W.

2009-01-01

The present research addresses the dynamic transaction between extrinsic (occupational prestige, income) and intrinsic (job satisfaction) career success and the Five-Factor Model of personality. Participants (N = 731) completed a comprehensive measure of personality and reported their job title, annual income, and job satisfaction; a subset of these participants (n = 302) provided the same information approximately 10 years later. Measured concurrently, emotionally stable and conscientious participants reported higher incomes and job satisfaction. Longitudinal analyses revealed that, among younger participants, higher income at baseline predicted decreases in Neuroticism and baseline Extraversion predicted increases in income across the 10 years. Results suggest that the mutual influence of career success and personality is limited to income and occurs early in the career. PMID:19774106

Modeling the effect of succimer (DMSA; dimercaptosuccinic acid) chelation therapy in patients poisoned by lead.

PubMed

van Eijkeren, Jan C H; Olie, J Daniël N; Bradberry, Sally M; Vale, J Allister; de Vries, Irma; Clewell, Harvey J; Meulenbelt, Jan; Hunault, Claudine C

2017-02-01

Kinetic models could assist clinicians potentially in managing cases of lead poisoning. Several models exist that can simulate lead kinetics but none of them can predict the effect of chelation in lead poisoning. Our aim was to devise a model to predict the effect of succimer (dimercaptosuccinic acid; DMSA) chelation therapy on blood lead concentrations. We integrated a two-compartment kinetic succimer model into an existing PBPK lead model and produced a Chelation Lead Therapy (CLT) model. The accuracy of the model's predictions was assessed by simulating clinical observations in patients poisoned by lead and treated with succimer. The CLT model calculates blood lead concentrations as the sum of the background exposure and the acute or chronic lead poisoning. The latter was due either to ingestion of traditional remedies or occupational exposure to lead-polluted ambient air. The exposure duration was known. The blood lead concentrations predicted by the CLT model were compared to the measured blood lead concentrations. Pre-chelation blood lead concentrations ranged between 99 and 150 μg/dL. The model was able to simulate accurately the blood lead concentrations during and after succimer treatment. The pattern of urine lead excretion was successfully predicted in some patients, while poorly predicted in others. Our model is able to predict blood lead concentrations after succimer therapy, at least, in situations where the duration of lead exposure is known.

What Matters from Admissions? Identifying Success and Risk Among Canadian Dental Students.

PubMed

Plouffe, Rachel A; Hammond, Robert; Goldberg, Harvey A; Chahine, Saad

2018-05-01

The aims of this study were to determine whether different student profiles would emerge in terms of high and low GPA performance in each year of dental school and to investigate the utility of preadmissions variables in predicting performance and performance stability throughout each year of dental school. Data from 11 graduating cohorts (2004-14) at the Schulich School of Medicine & Dentistry, University of Western Ontario, Canada, were collected and analyzed using bivariate correlations, latent profile analysis, and hierarchical generalized linear models (HGLMs). The data analyzed were for 616 students in total (332 males and 284 females). Four models were developed to predict adequate and poor performance throughout each of four dental school years. An additional model was developed to predict student performance stability across time. Two separate student profiles reflecting high and low GPA performance across each year of dental school were identified, and scores on cognitive preadmissions variables differentially predicted the probability of grouping into high and low performance profiles. Students with higher pre-dental GPAs and DAT chemistry were most likely to remain stable in a high-performance group across each year of dental school. Overall, the findings suggest that selection committees should consider pre-dental GPA and DAT chemistry scores as important tools for predicting dental school performance and stability across time. This research is important in determining how to better predict success and failure in various areas of preclinical dentistry courses and to provide low-performing students with adequate academic assistance.

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

The Relationship between Spatial Visualization Ability and Students' Ability to Model 3D Objects from Engineering Assembly Drawings

ERIC Educational Resources Information Center

Branoff, T. J.; Dobelis, M.

2012-01-01

Spatial abilities have been used as a predictor of success in several engineering and technology disciplines (Strong & Smith, 2001). In engineering graphics courses, scores on spatial tests have also been used to predict success (Adanez & Velasco, 2002; Leopold, Gorska, & Sorby, 2001). Other studies have shown that some type of…

Base Flow Model Validation

NASA Technical Reports Server (NTRS)

Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

2011-01-01

A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model.

PubMed

Wen, Kuang-Yi; Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

2010-01-01

To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Two of the seven factors, 'organizational motivation' and 'meeting user needs,' were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term.

A class-based link prediction using Distance Dependent Chinese Restaurant Process

NASA Astrophysics Data System (ADS)

Andalib, Azam; Babamir, Seyed Morteza

2016-08-01

One of the important tasks in relational data analysis is link prediction which has been successfully applied on many applications such as bioinformatics, information retrieval, etc. The link prediction is defined as predicting the existence or absence of edges between nodes of a network. In this paper, we propose a novel method for link prediction based on Distance Dependent Chinese Restaurant Process (DDCRP) model which enables us to utilize the information of the topological structure of the network such as shortest path and connectivity of the nodes. We also propose a new Gibbs sampling algorithm for computing the posterior distribution of the hidden variables based on the training data. Experimental results on three real-world datasets show the superiority of the proposed method over other probabilistic models for link prediction problem.

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

NASA Technical Reports Server (NTRS)

Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

1993-01-01

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Fast life history traits promote invasion success in amphibians and reptiles.

PubMed

Allen, William L; Street, Sally E; Capellini, Isabella

2017-02-01

Competing theoretical models make different predictions on which life history strategies facilitate growth of small populations. While 'fast' strategies allow for rapid increase in population size and limit vulnerability to stochastic events, 'slow' strategies and bet-hedging may reduce variance in vital rates in response to stochasticity. We test these predictions using biological invasions since founder alien populations start small, compiling the largest dataset yet of global herpetological introductions and life history traits. Using state-of-the-art phylogenetic comparative methods, we show that successful invaders have fast traits, such as large and frequent clutches, at both establishment and spread stages. These results, together with recent findings in mammals and plants, support 'fast advantage' models and the importance of high potential population growth rate. Conversely, successful alien birds are bet-hedgers. We propose that transient population dynamics and differences in longevity and behavioural flexibility can help reconcile apparently contrasting results across terrestrial vertebrate classes. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Nonlinear filtering techniques for noisy geophysical data: Using big data to predict the future

NASA Astrophysics Data System (ADS)

Moore, J. M.

2014-12-01

Chaos is ubiquitous in physical systems. Within the Earth sciences it is readily evident in seismology, groundwater flows and drilling data. Models and workflows have been applied successfully to understand and even to predict chaotic systems in other scientific fields, including electrical engineering, neurology and oceanography. Unfortunately, the high levels of noise characteristic of our planet's chaotic processes often render these frameworks ineffective. This contribution presents techniques for the reduction of noise associated with measurements of nonlinear systems. Our ultimate aim is to develop data assimilation techniques for forward models that describe chaotic observations, such as episodic tremor and slip (ETS) events in fault zones. A series of nonlinear filters are presented and evaluated using classical chaotic systems. To investigate whether the filters can successfully mitigate the effect of noise typical of Earth science, they are applied to sunspot data. The filtered data can be used successfully to forecast sunspot evolution for up to eight years (see figure).

3P: Personalized Pregnancy Prediction in IVF Treatment Process

NASA Astrophysics Data System (ADS)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

Comparison of Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model in a successive temporal bisection task.

PubMed

Arantes, Joana

2008-06-01

The present research tested the generality of the "context effect" previously reported in experiments using temporal double bisection tasks [e.g., Arantes, J., Machado, A. Context effects in a temporal discrimination task: Further tests of the Scalar Expectancy Theory and Learning-to-Time models. J. Exp. Anal. Behav., in press]. Pigeons learned two temporal discriminations in which all the stimuli appear successively: 1s (red) vs. 4s (green) and 4s (blue) vs. 16s (yellow). Then, two tests were conducted to compare predictions of two timing models, Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model. In one test, two psychometric functions were obtained by presenting pigeons with intermediate signal durations (1-4s and 4-16s). Results were mixed. In the critical test, pigeons were exposed to signals ranging from 1 to 16s and followed by the green or the blue key. Whereas SET predicted that the relative response rate to each of these keys should be independent of the signal duration, LeT predicted that the relative response rate to the green key (compared with the blue key) should increase with the signal duration. Results were consistent with LeT's predictions, showing that the context effect is obtained even when subjects do not need to make a choice between two keys presented simultaneously.

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

PubMed

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictors of Latina/o Community College Student Vocational Choice of STEM Fields: Testing of the STEM-Vocational Choice Model

ERIC Educational Resources Information Center

Johnson, Joel D.

2013-01-01

This study confirmed appropriate measurement model fit for a theoretical model, the STEM vocational choice (STEM-VC) model. This model identifies exogenous factors that successfully predicted, at a statistically significant level, a student's vocational choice decision to pursue a STEM degree at transfer. The student population examined for this…

Galaxy Formation At Extreme Redshifts: Semi-Analytic Model Predictions And Challenges For Observations

NASA Astrophysics Data System (ADS)

Yung, L. Y. Aaron; Somerville, Rachel S.

2017-06-01

The well-established Santa Cruz semi-analytic galaxy formation framework has been shown to be quite successful at explaining observations in the local Universe, as well as making predictions for low-redshift observations. Recently, metallicity-based gas partitioning and H2-based star formation recipes have been implemented in our model, replacing the legacy cold-gas based recipe. We then use our revised model to explore the high-redshift Universe and make predictions up to z = 15. Although our model is only calibrated to observations from the local universe, our predictions seem to match incredibly well with mid- to high-redshift observational constraints available-to-date, including rest-frame UV luminosity functions and the reionization history as constrained by CMB and IGM observations. We provide predictions for individual and statistical galaxy properties at a wide range of redshifts (z = 4 - 15), including objects that are too far or too faint to be detected with current facilities. And using our model predictions, we also provide forecasted luminosity functions and other observables for upcoming studies with JWST.

Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10

PubMed Central

Zhang, Yang

2014-01-01

We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems. PMID:23760925

Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10.

PubMed

Zhang, Yang

2014-02-01

We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems. Copyright © 2013 Wiley Periodicals, Inc.

A Probabilistic Model of Meter Perception: Simulating Enculturation.

PubMed

van der Weij, Bastiaan; Pearce, Marcus T; Honing, Henkjan

2017-01-01

Enculturation is known to shape the perception of meter in music but this is not explicitly accounted for by current cognitive models of meter perception. We hypothesize that the induction of meter is a result of predictive coding: interpreting onsets in a rhythm relative to a periodic meter facilitates prediction of future onsets. Such prediction, we hypothesize, is based on previous exposure to rhythms. As such, predictive coding provides a possible explanation for the way meter perception is shaped by the cultural environment. Based on this hypothesis, we present a probabilistic model of meter perception that uses statistical properties of the relation between rhythm and meter to infer meter from quantized rhythms. We show that our model can successfully predict annotated time signatures from quantized rhythmic patterns derived from folk melodies. Furthermore, we show that by inferring meter, our model improves prediction of the onsets of future events compared to a similar probabilistic model that does not infer meter. Finally, as a proof of concept, we demonstrate how our model can be used in a simulation of enculturation. From the results of this simulation, we derive a class of rhythms that are likely to be interpreted differently by enculturated listeners with different histories of exposure to rhythms.

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

PubMed Central

Shandilya, Sharad; Kurz, Michael C.; Ward, Kevin R.; Najarian, Kayvan

2016-01-01

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent > 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model. PMID:26741805

Culture and Social Relationship as Factors of Affecting Communicative Non-verbal Behaviors

NASA Astrophysics Data System (ADS)

Akhter Lipi, Afia; Nakano, Yukiko; Rehm, Mathias

The goal of this paper is to link a bridge between social relationship and cultural variation to predict conversants' non-verbal behaviors. This idea serves as a basis of establishing a parameter based socio-cultural model, which determines non-verbal expressive parameters that specify the shapes of agent's nonverbal behaviors in HAI. As the first step, a comparative corpus analysis is done for two cultures in two specific social relationships. Next, by integrating the cultural and social parameters factors with the empirical data from corpus analysis, we establish a model that predicts posture. The predictions from our model successfully demonstrate that both cultural background and social relationship moderate communicative non-verbal behaviors.

Effort-reward imbalance is associated with salivary immunoglobulin a and cortisol secretion in disability workers.

PubMed

Wright, Bradley James

2011-03-01

This study attempted to determine the relationship of physiological indices of stress (ie, cortisol and salivary immunoglobulin A) to the effort-reward imbalance model (ERI). A sample of 98 direct-care disability workers completed the Work-Related Questions II-III and provided morning saliva samples on the same day of completion, which were subsequently analyzed for cortisol and salivary immunoglobulin A concentration levels. Using structural equation modeling, the ERI successfully predicted potentially adverse physiological outcomes. The salivary immunoglobulin A scores were predicted more successfully by the ERI than the cortisol data. The present investigation suggests that the ERI may be useful in determining which aspects of work life are associated with ill health and as such may be useful in identifying meaningful intervention.

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.

PubMed

Gruenenfelder, Thomas M; Recchia, Gabriel; Rubin, Tim; Jones, Michael N

2016-08-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts. Copyright © 2015 Cognitive Science Society, Inc.

Animal models of addiction

PubMed Central

Spanagel, Rainer

2017-01-01

In recent years, animal models in psychiatric research have been criticized for their limited translational value to the clinical situation. Failures in clinical trials have thus often been attributed to the lack of predictive power of preclinical animal models. Here, I argue that animal models of voluntary drug intake—under nonoperant and operant conditions—and addiction models based on the Diagnostic and Statistical Manual of Mental Disorders are crucial and informative tools for the identification of pathological mechanisms, target identification, and drug development. These models provide excellent face validity, and it is assumed that the neurochemical and neuroanatomical substrates involved in drug-intake behavior are similar in laboratory rodents and humans. Consequently, animal models of drug consumption and addiction provide predictive validity. This predictive power is best illustrated in alcohol research, in which three approved medications—acamprosate, naltrexone, and nalmefene—were developed by means of animal models and then successfully translated into the clinical situation. PMID:29302222

REVIEWS OF TOPICAL PROBLEMS: Physics of pulsar magnetospheres

NASA Astrophysics Data System (ADS)

Beskin, Vasilii S.; Gurevich, Aleksandr V.; Istomin, Yakov N.

1986-10-01

A self-consistent model of the magnetosphere of a pulsar is constructed. This model is based on a successive solution of the equations describing global properties of the magnetosphere and on a comparison of the basic predictions of the developed theory and observational data.

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

Why abundant tropical tree species are phylogenetically old.

PubMed

Wang, Shaopeng; Chen, Anping; Fang, Jingyun; Pacala, Stephen W

2013-10-01

Neutral models of species diversity predict patterns of abundance for communities in which all individuals are ecologically equivalent. These models were originally developed for Panamanian trees and successfully reproduce observed distributions of abundance. Neutral models also make macroevolutionary predictions that have rarely been evaluated or tested. Here we show that neutral models predict a humped or flat relationship between species age and population size. In contrast, ages and abundances of tree species in the Panamanian Canal watershed are found to be positively correlated, which falsifies the models. Speciation rates vary among phylogenetic lineages and are partially heritable from mother to daughter species. Variable speciation rates in an otherwise neutral model lead to a demographic advantage for species with low speciation rate. This demographic advantage results in a positive correlation between species age and abundance, as found in the Panamanian tropical forest community.

Analysis of Highly-Resolved Simulations of 2-D Humps Toward Improvement of Second-Moment Closures

NASA Technical Reports Server (NTRS)

Jeyapaul, Elbert; Rumsey Christopher

2013-01-01

Fully resolved simulation data of flow separation over 2-D humps has been used to analyze the modeling terms in second-moment closures of the Reynolds-averaged Navier- Stokes equations. Existing models for the pressure-strain and dissipation terms have been analyzed using a priori calculations. All pressure-strain models are incorrect in the high-strain region near separation, although a better match is observed downstream, well into the separated-flow region. Near-wall inhomogeneity causes pressure-strain models to predict incorrect signs for the normal components close to the wall. In a posteriori computations, full Reynolds stress and explicit algebraic Reynolds stress models predict the separation point with varying degrees of success. However, as with one- and two-equation models, the separation bubble size is invariably over-predicted.

Renesting by dusky Canada geese on the Copper River Delta, Alaska

USGS Publications Warehouse

Fondell, Thomas F.; Grand, James B.; Miller, David A.W.; Anthony, R. Michael

2006-01-01

The population of dusky Canada geese (Branta canadensis occidentalis; hereafter duskies) breeding on the Copper River Delta (CRD), Alaska, USA, has been in long-term decline, largely as a result of reduced productivity. Estimates of renesting rates by duskies may be useful for adjusting estimates of the size of the breeding population derived from aerial surveys and for understanding population dynamics. We used a marked population of dusky females to obtain estimates of renesting propensity and renesting interval on the CRD, 1999–2000. Continuation nests, replacement nests initiated without a break in the laying sequence, resulted only after first nests were destroyed in the laying stage with ≤4 eggs laid. Renesting propensity declined with nest age from 72% in mid-laying to 30% in early incubation. Between first nests and renests, mean interval was 11.9 ± 0.6 days, mean distance was 74.5 m (range 0–214 m), and clutch size declined 0.9 ± 0.4 eggs. We incorporated our renesting estimates and available estimates of other nesting parameters into an individual-based model to predict the proportion of first nests, continuation nests, and renests, and to examine female success on the CRD, 1997–2000. Our model predicted that 19–36% of nests each year were continuation nests and renests. Also, through 15 May (the approx. date of breeding ground surveys), 1.1–1.3 nests were initiated per female. Thus, the number of nests per female would have a significant, though relatively consistent, effect on adjusting the relation between numbers of nests found on ground surveys versus numbers of birds seen during aerial surveys. We also suggest a method that managers could use to predict nests per female using nest success of early nests. Our model predicted that relative to observed estimates of nest success, female success was 32–100% greater, due to replacement nests. Thus, although nest success remains low, production for duskies was higher than previously thought. For dusky Canada geese, managers need to consider both continuation nests and renests in designing surveys and in calculating adjustment factors for the expansion of aerial survey data using nest densities.

Effects of distance from models on the fitness of floral mimics.

PubMed

Duffy, K J; Johnson, S D

2017-05-01

Rewardless plants can attract pollinators by mimicking floral traits of rewarding heterospecific plants. This should result in the pollination success of floral mimics being dependent on the relative abundance of their models, as pollinator abundance and conditioning on model signals should be higher in the vicinity of the models. However, the attraction of pollinators to signals of the models may be partially innate, such that spatial isolation of mimics from model species may not strongly affect pollination success of mimics. We tested whether pollination rates and fruit set of the rewardless orchid Disa pulchra were influenced by proximity and abundance of its rewarding model species, Watsonia lepida. Pollination success of the orchid increased with proximity to the model species, while fruit set of the orchid increased with local abundance of the model species. Orchids that were experimentally translocated outside the model population experienced reduced pollinaria removal and increased pollinator-mediated self-pollination. These results confirm predictions that the pollination success of floral mimics should be dependent on the proximity and abundance of model taxa, and thus highlight the importance of ecological facilitation among species involved in mimicry systems. © 2017 German Botanical Society and The Royal Botanical Society of the Netherlands.

Modeling the Insertion Mechanics of Flexible Neural Probes Coated with Sacrificial Polymers for Optimizing Probe Design

PubMed Central

Singh, Sagar; Lo, Meng-Chen; Damodaran, Vinod B.; Kaplan, Hilton M.; Kohn, Joachim; Zahn, Jeffrey D.; Shreiber, David I.

2016-01-01

Single-unit recording neural probes have significant advantages towards improving signal-to-noise ratio and specificity for signal acquisition in brain-to-computer interface devices. Long-term effectiveness is unfortunately limited by the chronic injury response, which has been linked to the mechanical mismatch between rigid probes and compliant brain tissue. Small, flexible microelectrodes may overcome this limitation, but insertion of these probes without buckling requires supporting elements such as a stiff coating with a biodegradable polymer. For these coated probes, there is a design trade-off between the potential for successful insertion into brain tissue and the degree of trauma generated by the insertion. The objective of this study was to develop and validate a finite element model (FEM) to simulate insertion of coated neural probes of varying dimensions and material properties into brain tissue. Simulations were performed to predict the buckling and insertion forces during insertion of coated probes into a tissue phantom with material properties of brain. The simulations were validated with parallel experimental studies where probes were inserted into agarose tissue phantom, ex vivo chick embryonic brain tissue, and ex vivo rat brain tissue. Experiments were performed with uncoated copper wire and both uncoated and coated SU-8 photoresist and Parylene C probes. Model predictions were found to strongly agree with experimental results (<10% error). The ratio of the predicted buckling force-to-predicted insertion force, where a value greater than one would ideally be expected to result in successful insertion, was plotted against the actual success rate from experiments. A sigmoidal relationship was observed, with a ratio of 1.35 corresponding to equal probability of insertion and failure, and a ratio of 3.5 corresponding to a 100% success rate. This ratio was dubbed the “safety factor”, as it indicated the degree to which the coating should be over-designed to ensure successful insertion. Probability color maps were generated to visually compare the influence of design parameters. Statistical metrics derived from the color maps and multi-variable regression analysis confirmed that coating thickness and probe length were the most important features in influencing insertion potential. The model also revealed the effects of manufacturing flaws on insertion potential. PMID:26959021

Interpreting forest biome productivity and cover utilizing nested scales of image resolution and biogeographical analysis

NASA Technical Reports Server (NTRS)

Iverson, Louis R.; Cook, Elizabeth A.; Graham, Robin L.; Olson, Jerry S.; Frank, Thomas D.; Ying, KE

1988-01-01

The objective was to relate spectral imagery of varying resolution with ground-based data on forest productivity and cover, and to create models to predict regional estimates of forest productivity and cover with a quantifiable degree of accuracy. A three stage approach was outlined. In the first stage, a model was developed relating forest cover or productivity to TM surface reflectance values (TM/FOREST models). The TM/FOREST models were more accurate when biogeographic information regarding the landscape was either used to stratigy the landscape into more homogeneous units or incorporated directly into the TM/FOREST model. In the second stage, AVHRR/FOREST models that predicted forest cover and productivity on the basis of AVHRR band values were developed. The AVHRR/FOREST models had statistical properties similar to or better than those of the TM/FOREST models. In the third stage, the regional predictions were compared with the independent U.S. Forest Service (USFS) data. To do this regional forest cover and forest productivity maps were created using AVHRR scenes and the AVHRR/FOREST models. From the maps the county values of forest productivity and cover were calculated. It is apparent that the landscape has a strong influence on the success of the approach. An approach of using nested scales of imagery in conjunction with ground-based data can be successful in generating regional estimates of variables that are functionally related to some variable a sensor can detect.

Factors Associated With Success in an Occupational Rehabilitation Program for Work-Related Musculoskeletal Disorders

PubMed Central

Hardison, Mark E.

2017-01-01

Work-related musculoskeletal disorders are a significant burden; however, no consensus has been reached on how to maximize occupational rehabilitation programs for people with these disorders, and the impact of simulating work tasks as a mode of intervention has not been well examined. In this retrospective cohort study, the authors used logistic regression to identify client and program factors predicting success for 95 clients in a general occupational rehabilitation program and 71 clients in a comprehensive occupational rehabilitation program. The final predictive model for general rehabilitation included gender, number of sessions completed, and performance of work simulation activities. Maximum hours per session was the only significant predictor of success in the comprehensive rehabilitation program. This study identifies new factors associated with success in occupational rehabilitation, specifically highlighting the importance of intensity (i.e., session length and number of sessions) of therapy and occupation-based activities for this population. PMID:28027046

A Case-Series Test of the Interactive Two-step Model of Lexical Access: Predicting Word Repetition from Picture Naming

PubMed Central

Dell, Gary S.; Martin, Nadine; Schwartz, Myrna F.

2010-01-01

Lexical access in language production, and particularly pathologies of lexical access, are often investigated by examining errors in picture naming and word repetition. In this article, we test a computational approach to lexical access, the two-step interactive model, by examining whether the model can quantitatively predict the repetition-error patterns of 65 aphasic subjects from their naming errors. The model’s characterizations of the subjects’ naming errors were taken from the companion paper to this one (Schwartz, Dell, N. Martin, Gahl & Sobel, 2006), and their repetition was predicted from the model on the assumption that naming involves two error prone steps, word and phonological retrieval, whereas repetition only creates errors in the second of these steps. A version of the model in which lexical-semantic and lexical-phonological connections could be independently lesioned was generally successful in predicting repetition for the aphasics. An analysis of the few cases in which model predictions were inaccurate revealed the role of input phonology in the repetition task. PMID:21085621

ZY3-02 Laser Altimeter Footprint Geolocation Prediction

PubMed Central

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-01-01

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test. PMID:28934160

ZY3-02 Laser Altimeter Footprint Geolocation Prediction.

PubMed

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-09-21

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.

Hydroregime prediction models for ephemeral groundwater-driven sinkhole wetlands: a planning tool for climate change and amphibian conservation

Treesearch

C. H. Greenberg; S. Goodrick; J. D. Austin; B. R. Parresol

2015-01-01

Hydroregimes of ephemeral wetlands affect reproductive success of many amphibian species and are sensitive to altered weather patterns associated with climate change.We used 17 years of weekly temperature, precipitation, and waterdepth measurements for eight small, ephemeral, groundwaterdriven sinkhole wetlands in Florida sandhills to develop a hydroregime predictive...

Meeting Report: FutureTox II: Contemporary Concepts in Toxicology “Pathways to Prediction: In Vitro and In Silico Models for Predictive Toxicology”

EPA Science Inventory

The Society of Toxicology (SOT) held avery successful FutureTox II Contemporary Concepts in Toxicology (CCT) Conference in Chapel Hill, North Carolina, on January 16th and 17th, 2014. There were over 291 attendees representing industry, government and academia; the sessions were ...

Predicting Defects Using Information Intelligence Process Models in the Software Technology Project

PubMed Central

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%–80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects. PMID:26495427

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

Reheating predictions in gravity theories with derivative coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dalianis, Ioannis; Koutsoumbas, George; Ntrekis, Konstantinos

2017-02-01

We investigate the inflationary predictions of a simple Horndeski theory where the inflaton scalar field has a non-minimal derivative coupling (NMDC) to the Einstein tensor. The NMDC is very motivated for the construction of successful models for inflation, nevertheless its inflationary predictions are not observationally distinct. We show that it is possible to probe the effects of the NMDC on the CMB observables by taking into account both the dynamics of the inflationary slow-roll phase and the subsequent reheating. We perform a comparative study between representative inflationary models with canonical fields minimally coupled to gravity and models with NMDC. Wemore » find that the inflation models with dominant NMDC generically predict a higher reheating temperature and a different range for the tilt of the scalar perturbation spectrum n {sub s} and scalar-to-tensor ratio r , potentially testable by current and future CMB experiments.« less

Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

PubMed

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.

Prediction of successful weight reduction after laparoscopic adjustable gastric banding.

PubMed

Lee, Yi-Chih; Liew, Phui-Ly; Lee, Wei-Jei; Lin, Yang-Chu; Lee, Chia Ko; Huangs, Ming-Te; Wang, Weu; Lin, Steven C H

2009-01-01

Compared with conventional pharmacological therapies, bariatric surgery has been shown to cause greater and- sustained weight loss. It was aimed to evaluate weight loss in obese patients after laparoscopic adjustable gastric banding surgery using information typically available during the initial evaluation studied before bariatric surgery and genes. 74 patients undergoing laparoscopic adjustable gastric banding (LAGB) were enrolled. Artificial Neural Network technology was used to predict weight loss. We studied 74 patients consisting of 22 men and 52 women 2 years after operation. Mean age was 31.7 +/- 9.1 years. 27 (36.5%) patients had successful weight reduction (excess weight loss >50%) while 47 (63.5%) did not. ANN provided predicted factors on gender, insulin, albumin and two genes: re4684846_r, rs660339_r which were associated with success. Artificial neural network is a better modeling technique and the predictive accuracy is higher on the basis of multiple variables related to laboratory tests. Our finding gave demonstrated result that obese patients of successful weight reduction after laparoscopic adjustable gastric banding surgery were women, having little lower insulin and albumin, and carrying GG genotype on rs4684846 and with at least one T allele on rs660339. In these cases, weight loss will give better results.

Dark matter and MOND dynamical models of the massive spiral galaxy NGC 2841

NASA Astrophysics Data System (ADS)

Samurović, S.; Vudragović, A.; Jovanović, M.

2015-08-01

We study dynamical models of the massive spiral galaxy NGC 2841 using both the Newtonian models with Navarro-Frenk-White (NFW) and isothermal dark haloes, as well as various MOND (MOdified Newtonian Dynamics) models. We use the observations coming from several publicly available data bases: we use radio data, near-infrared photometry as well as spectroscopic observations. In our models, we find that both tested Newtonian dark matter approaches can successfully fit the observed rotational curve of NGC 2841. The three tested MOND models (standard, simple and, for the first time applied to another spiral galaxy than the Milky Way, Bekenstein's toy model) provide fits of the observed rotational curve with various degrees of success: the best result was obtained with the standard MOND model. For both approaches, Newtonian and MOND, the values of the mass-to-light ratios of the bulge are consistent with the predictions from the stellar population synthesis (SPS) based on the Salpeter initial mass function (IMF). Also, for Newtonian and simple and standard MOND models, the estimated stellar mass-to-light ratios of the disc agree with the predictions from the SPS models based on the Kroupa IMF, whereas the toy MOND model provides too low a value of the stellar mass-to-light ratio, incompatible with the predictions of the tested SPS models. In all our MOND models, we vary the distance to NGC 2841, and our best-fitting standard and toy models use the values higher than the Cepheid-based distance to the galaxy NGC 2841, and the best-fitting simple MOND model is based on the lower value of the distance. The best-fitting NFW model is inconsistent with the predictions of the Λ cold dark matter cosmology, because the inferred concentration index is too high for the established virial mass.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Soil Systems for Upscaling Saturated Hydraulic Conductivity (Ksat) for Hydrological Modeling in the Critical Zone

USDA-ARS?s Scientific Manuscript database

Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity (Ksat) is one of the most important properties influencing water movement through soil un...

Helper effects on breeder allocations to direct care.

PubMed

Kushnick, Geoff

2012-01-01

Mothers receive childcare and productive assistance from allomaternal helpers in many societies. Although much effort has been aimed toward showing helper effects on maternal reproductive success, less has been directed toward highlighting the full range of potential effects on breeder behavior. I present a model of optimal maternal care with helpers, and tests of derived hypotheses with data collected among the Karo Batak-a group of Indonesian agriculturalists. To test the model's predictions I compared the effect of women receiving help from patrilateral versus matrilateral kin because those kin may provide help with different maternal responsibilities. The model predicts a decrease in maternal allocation to care that is substitutable with the helper contribution and the helper assists with that type of care; it predicts an increase in care that is nonsubstitutable with the helper contribution or substitutable care when the helper assists with other responsibilities. With the exception of one other, most models have failed to account for an increase. Analyses of time spent carrying children supported the model. With matrilateral helpers, women increased carrying; with patrilateral helpers, they decreased it. Time spent farmworking showed the opposite pattern, suggesting that matrilateral helpers effectively decrease costs, nudging optimal maternal care upward. Patterns of breastfeeding provided little support for the model. The results do, however, suggest potential proximate mechanisms by which helpers influence maternal reproductive success in cooperative breeding societies. Copyright © 2012 Wiley Periodicals, Inc.

Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

PubMed

Cui, Zaixu; Su, Mengmeng; Li, Liangjie; Shu, Hua; Gong, Gaolang

2018-05-01

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

Creating Digital Games as Interactive Learning Environments: Factors That Affect Palestinian Teachers' Success in Modifying Video Games for Instruction

ERIC Educational Resources Information Center

Younis, Bilal Khaleel

2012-01-01

The purpose of this study was to investigate the factors that might predict Palestinian teachers' success in modding games for instruction. An instructional game design model named Game Modding for Non-Professionals (GMNP) was created specifically for the training of Palestinian teachers during this study. This study addressed the question: To…

Short-Term Free Recall and Sequential Memory for Pictures and Words: A Simultaneous-Successive Processing Interpretation.

ERIC Educational Resources Information Center

Randhawa, Bikkar S.; And Others

1982-01-01

Replications of two basic experiments in support of the dual-coding processing model with grade 10 and college subjects used pictures, concrete words, and abstract words as stimuli presented at fast and slow rates for immediate and sequential recall. Results seem to be consistent with predictions of simultaneous-successive cognitive theory. (MBR)

Climate suitability and human influences combined explain the range expansion of an invasive horticultural plant

Treesearch

Carolyn M. Beans; Francis F. Kilkenny; Laura F. Galloway

2012-01-01

Ecological niche models are commonly used to identify regions at risk of species invasions. Relying on climate alone may limit a model's success when additional variables contribute to invasion. While a climate-based model may predict the future spread of an invasive plant, we hypothesized that a model that combined climate with human influences would most...

Theoretical models of helicopter rotor noise

NASA Technical Reports Server (NTRS)

Hawkings, D. L.

1978-01-01

For low speed rotors, it is shown that unsteady load models are only partially successful in predicting experimental levels. A theoretical model is presented which leads to the concept of unsteady thickness noise. This gives better agreement with test results. For high speed rotors, it is argued that present models are incomplete and that other mechanisms are at work. Some possibilities are briefly discussed.

Template-based modeling and ab initio refinement of protein oligomer structures using GALAXY in CAPRI round 30.

PubMed

Lee, Hasup; Baek, Minkyung; Lee, Gyu Rie; Park, Sangwoo; Seok, Chaok

2017-03-01

Many proteins function as homo- or hetero-oligomers; therefore, attempts to understand and regulate protein functions require knowledge of protein oligomer structures. The number of available experimental protein structures is increasing, and oligomer structures can be predicted using the experimental structures of related proteins as templates. However, template-based models may have errors due to sequence differences between the target and template proteins, which can lead to functional differences. Such structural differences may be predicted by loop modeling of local regions or refinement of the overall structure. In CAPRI (Critical Assessment of PRotein Interactions) round 30, we used recently developed features of the GALAXY protein modeling package, including template-based structure prediction, loop modeling, model refinement, and protein-protein docking to predict protein complex structures from amino acid sequences. Out of the 25 CAPRI targets, medium and acceptable quality models were obtained for 14 and 1 target(s), respectively, for which proper oligomer or monomer templates could be detected. Symmetric interface loop modeling on oligomer model structures successfully improved model quality, while loop modeling on monomer model structures failed. Overall refinement of the predicted oligomer structures consistently improved the model quality, in particular in interface contacts. Proteins 2017; 85:399-407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

PubMed

Pothos, Emmanuel M; Bailey, Todd M

2009-07-01

Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM-the authors refer to this way of applying the GCM as "unsupervised GCM." The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness.

Predicting effects of environmental change on a migratory herbivore

USGS Publications Warehouse

Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

2015-01-01

Changes in climate, food abundance and disturbance from humans threaten the ability of species to successfully use stopover sites and migrate between non-breeding and breeding areas. To devise successful conservation strategies for migratory species we need to be able to predict how such changes will affect both individuals and populations. Such predictions should ideally be process-based, focusing on the mechanisms through which changes alter individual physiological state and behavior. In this study we use a process-based model to evaluate how Black Brant (Branta bernicla nigricans) foraging on common eelgrass (Zostera marina) at a stopover site (Humboldt Bay, USA), may be affected by changes in sea level, food abundance and disturbance. The model is individual-based, with empirically based parameters, and incorporates the immigration of birds into the site, tidal changes in availability of eelgrass, seasonal and depth-related changes in eelgrass biomass, foraging behavior and energetics of the birds, and their mass-dependent decisions to emigrate. The model is validated by comparing predictions to observations across a range of system properties including the time birds spent foraging, probability of birds emigrating, mean stopover duration, peak bird numbers, rates of mass gain and distribution of birds within the site: all 11 predictions were within 35% of the observed value, and 8 within 20%. The model predicted that the eelgrass within the site could potentially support up to five times as many birds as currently use the site. Future predictions indicated that the rate of mass gain and mean stopover duration were relatively insensitive to sea level rise over the next 100 years, primarily because eelgrass habitat could redistribute shoreward into intertidal mudflats within the site to compensate for higher sea levels. In contrast, the rate of mass gain and mean stopover duration were sensitive to changes in total eelgrass biomass and the percentage of time for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.

PubMed

Simkovic, Felix; Thomas, Jens M H; Keegan, Ronan M; Winn, Martyn D; Mayans, Olga; Rigden, Daniel J

2016-07-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds

PubMed Central

Simkovic, Felix; Thomas, Jens M. H.; Keegan, Ronan M.; Winn, Martyn D.; Mayans, Olga; Rigden, Daniel J.

2016-01-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions (‘decoys’), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing. PMID:27437113

Pharmacokinetic/Pharmacodynamic Predictors of Clinical Potency for Hepatitis C Virus Nonnucleoside Polymerase and Protease Inhibitors

PubMed Central

Morcos, Peter N.; Le Pogam, Sophie; Ou, Ying; Frank, Karl; Lave, Thierry; Smith, Patrick

2012-01-01

This analysis was conducted to determine whether the hepatitis C virus (HCV) viral kinetics (VK) model can predict viral load (VL) decreases for nonnucleoside polymerase inhibitors (NNPolIs) and protease inhibitors (PIs) after 3-day monotherapy studies of patients infected with genotype 1 chronic HCV. This analysis includes data for 8 NNPolIs and 14 PIs, including VL decreases from 3-day monotherapy, total plasma trough concentrations on day 3 (Cmin), replicon data (50% effective concentration [EC50] and protein-shifted EC50 [EC50,PS]), and for PIs, liver-to-plasma ratios (LPRs) measured in vivo in preclinical species. VK model simulations suggested that achieving additional log10 VL decreases greater than one required 10-fold increases in the Cmin. NNPolI and PI data further supported this result. The VK model was successfully used to predict VL decreases in 3-day monotherapy for NNPolIs based on the EC50,PS and the day 3 Cmin. For PIs, however, predicting VL decreases using the same model and the EC50,PS and day 3 Cmin was not successful; a model including LPR values and the EC50 instead of the EC50,PS provided a better prediction of VL decrease. These results are useful for designing phase 1 monotherapy studies for NNPolIs and PIs by clarifying factors driving VL decreases, such as the day 3 Cmin and the EC50,PS for NNPolIs or the EC50 and LPR for PIs. This work provides a framework for understanding the pharmacokinetic/pharmacodynamic relationship for other HCV drug classes. The availability of mechanistic data on processes driving the target concentration, such as liver uptake transporters, should help to improve the predictive power of the approach. PMID:22470110

Prediction of subjective ratings of emotional pictures by EEG features

NASA Astrophysics Data System (ADS)

McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

2017-02-01

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

Applications of a simulation model to decisions in mallard management

USGS Publications Warehouse

Cowardin, L.M.; Johnson, D.H.; Shaffer, T.L.; Sparling, D.W.

1988-01-01

A system comprising simulation models and data bases for habitat availability and nest success rates was used to predict results from a mallard (Anas platyrhynchos) management plan and to compare six management methods with a control. Individual treatments in the applications included land purchase for waterfowl production, wetland easement purchase, lease of uplands for waterfowl management, cropland retirement, use of no-till winter wheat, delayed cutting of alfalfa, installation of nest baskets, nesting island construction, and use of predator-resistant fencing.The simulations predicted that implementation of the management plan would increase recruits by 24%. Nest baskets were the most effective treatment, accounting for 20.4% of the recruits. No-till winter wheat was the second most effective, accounting for 5.9% of the recruits. Wetland loss due to drainage would cause an 11% loss of breeding population in 10 years.The models were modified to account for migrational homing. The modification indicated that migrational homing would enhance the effects of management. Nest success rates were critical contributions to individual management methods. The most effective treatments, such as nest baskets, had high success rates and affected a large portion of the breeding population.Economic analyses indicated that nest baskets would be the most economical of the three techniques tested. The applications indicated that the system is a useful tool to aid management decisions, but data are scarce for several important variables. Basic research will be required to adequately model the effect of migrational homing and density dependence on production. The comprehensive nature of predictions desired by managers will also require that production models like the one described here be extended to encompass the entire annual cycle of waterfowl.

Scoring annual earthquake predictions in China

NASA Astrophysics Data System (ADS)

Zhuang, Jiancang; Jiang, Changsheng

2012-02-01

The Annual Consultation Meeting on Earthquake Tendency in China is held by the China Earthquake Administration (CEA) in order to provide one-year earthquake predictions over most China. In these predictions, regions of concern are denoted together with the corresponding magnitude range of the largest earthquake expected during the next year. Evaluating the performance of these earthquake predictions is rather difficult, especially for regions that are of no concern, because they are made on arbitrary regions with flexible magnitude ranges. In the present study, the gambling score is used to evaluate the performance of these earthquake predictions. Based on a reference model, this scoring method rewards successful predictions and penalizes failures according to the risk (probability of being failure) that the predictors have taken. Using the Poisson model, which is spatially inhomogeneous and temporally stationary, with the Gutenberg-Richter law for earthquake magnitudes as the reference model, we evaluate the CEA predictions based on 1) a partial score for evaluating whether issuing the alarmed regions is based on information that differs from the reference model (knowledge of average seismicity level) and 2) a complete score that evaluates whether the overall performance of the prediction is better than the reference model. The predictions made by the Annual Consultation Meetings on Earthquake Tendency from 1990 to 2003 are found to include significant precursory information, but the overall performance is close to that of the reference model.

Does taking endurance into account improve the prediction of weaning outcome in mechanically ventilated children?

PubMed Central

Noizet, Odile; Leclerc, Francis; Sadik, Ahmed; Grandbastien, Bruno; Riou, Yvon; Dorkenoo, Aimée; Fourier, Catherine; Cremer, Robin; Leteurtre, Stephane

2005-01-01

Introduction We conducted the present study to determine whether a combination of the mechanical ventilation weaning predictors proposed by the collective Task Force of the American College of Chest Physicians (TF) and weaning endurance indices enhance prediction of weaning success. Method Conducted in a tertiary paediatric intensive care unit at a university hospital, this prospective study included 54 children receiving mechanical ventilation (≥6 hours) who underwent 57 episodes of weaning. We calculated the indices proposed by the TF (spontaneous respiratory rate, paediatric rapid shallow breathing, rapid shallow breathing occlusion pressure [ROP] and maximal inspiratory pressure during an occlusion test [Pimax]) and weaning endurance indices (pressure-time index, tension-time index obtained from P0.1 [TTI1] and from airway pressure [TTI2]) during spontaneous breathing. Performances of each TF index and combinations of them were calculated, and the best single index and combination were identified. Weaning endurance parameters (TTI1 and TTI2) were calculated and the best index was determined using a logistic regression model. Regression coefficients were estimated using the maximum likelihood ratio (LR) method. Hosmer–Lemeshow test was used to estimate goodness-of-fit of the model. An equation was constructed to predict weaning success. Finally, we calculated the performances of combinations of best TF indices and best endurance index. Results The best single TF index was ROP, the best TF combination was represented by the expression (0.66 × ROP) + (0.34 × Pimax), and the best endurance index was the TTI2, although their performance was poor. The best model resulting from the combination of these indices was defined by the following expression: (0.6 × ROP) – (0.1 × Pimax) + (0.5 × TTI2). This integrated index was a good weaning predictor (P < 0.01), with a LR+ of 6.4 and LR+/LR- ratio of 12.5. However, at a threshold value <1.3 it was only predictive of weaning success (LR- = 0.5). Conclusion The proposed combined index, incorporating endurance, was of modest value in predicting weaning outcome. This is the first report of the value of endurance parameters in predicting weaning success in children. Currently, clinical judgement associated with spontaneous breathing trials apparently remain superior. PMID:16356229

Interactions of timing and prediction error learning.

PubMed

Kirkpatrick, Kimberly

2014-01-01

Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. Copyright © 2013 Elsevier B.V. All rights reserved.

Precipitation Modeling in Nitriding in Fe-M Binary System

NASA Astrophysics Data System (ADS)

Tomio, Yusaku; Miyamoto, Goro; Furuhara, Tadashi

2016-10-01

Precipitation of fine alloy nitrides near the specimen surface results in significant surface hardening in nitriding of alloyed steels. In this study, a simulation model of alloy nitride precipitation during nitriding is developed for Fe-M binary system based upon the Kampmann-Wagner numerical model in order to predict variations in the distribution of precipitates with depth. The model can predict the number density, average radius, and volume fraction of alloy nitrides as a function of depth from the surface and nitriding time. By a comparison with the experimental observation in a nitrided Fe-Cr alloy, it was found that the model can predict successfully the observed particle distribution from the surface into depth when appropriate solubility of CrN, interfacial energy between CrN and α, and nitrogen flux at the surface are selected.

Model for Predicting Passage of Invasive Fish Species Through Culverts

NASA Astrophysics Data System (ADS)

Neary, V.

2010-12-01

Conservation efforts to promote or inhibit fish passage include the application of simple fish passage models to determine whether an open channel flow allows passage of a given fish species. Derivations of simple fish passage models for uniform and nonuniform flow conditions are presented. For uniform flow conditions, a model equation is developed that predicts the mean-current velocity threshold in a fishway, or velocity barrier, which causes exhaustion at a given maximum distance of ascent. The derivation of a simple expression for this exhaustion-threshold (ET) passage model is presented using kinematic principles coupled with fatigue curves for threatened and endangered fish species. Mean current velocities at or above the threshold predict failure to pass. Mean current velocities below the threshold predict successful passage. The model is therefore intuitive and easily applied to predict passage or exclusion. The ET model’s simplicity comes with limitations, however, including its application only to uniform flow, which is rarely found in the field. This limitation is addressed by deriving a model that accounts for nonuniform conditions, including backwater profiles and drawdown curves. Comparison of these models with experimental data from volitional swimming studies of fish indicates reasonable performance, but limitations are still present due to the difficulty in predicting fish behavior and passage strategies that can vary among individuals and different fish species.

Trend analysis of vegetation in Louisiana's Atchafalaya river basin

USGS Publications Warehouse

O'Neil, Calvin P.; deSteiguer, J. Edward; North, Gary W.

1978-01-01

The purpose of the study was to determine vegetation succession trends; produce a current vegetation map of the basin; and to develop a mathematical model capable of predicting vegetation changes based on hydrologic factors. A statistical relationship of forests and hydrological variables with forest succession constraints predicted forest acreage totals for 16 forest categories within 70% or better of actual values in two-thirds of the cases. Using time-lapsed photography covering 42 years, 23 categories were described. The succession trend of vegetation since 1930, by sedimentation, had been toward mixed hardwoods, except for isolated areas. Satellite MSS Band 7 imagery was used to map the current vegetation into three main categories and for assessment of acreage. Additionally, a geological anomaly was recognized on satellite imagery indication an effect on drainage and sedimentation.

Blending geological observations and convection models to reconstruct mantle dynamics

NASA Astrophysics Data System (ADS)

Coltice, Nicolas; Bocher, Marie; Fournier, Alexandre; Tackley, Paul

2015-04-01

Knowledge of the state of the Earth mantle and its temporal evolution is fundamental to a variety of disciplines in Earth Sciences, from the internal dynamics to its many expressions in the geological record (postglacial rebound, sea level change, ore deposit, tectonics or geomagnetic reversals). Mantle convection theory is the centerpiece to unravel the present and past state of the mantle. For the past 40 years considerable efforts have been made to improve the quality of numerical models of mantle convection. However, they are still sparsely used to estimate the convective history of the solid Earth, in comparison to ocean or atmospheric models for weather and climate prediction. The main shortcoming is their inability to successfully produce Earth-like seafloor spreading and continental drift self-consistently. Recent convection models have begun to successfully predict these processes. Such breakthrough opens the opportunity to retrieve the recent dynamics of the Earth's mantle by blending convection models together with advanced geological datasets. A proof of concept will be presented, consisting in a synthetic test based on a sequential data assimilation methodology.

Linking the development and functioning of a carnivorous pitcher plant's microbial digestive community.

PubMed

Armitage, David W

2017-11-01

Ecosystem development theory predicts that successional turnover in community composition can influence ecosystem functioning. However, tests of this theory in natural systems are made difficult by a lack of replicable and tractable model systems. Using the microbial digestive associates of a carnivorous pitcher plant, I tested hypotheses linking host age-driven microbial community development to host functioning. Monitoring the yearlong development of independent microbial digestive communities in two pitcher plant populations revealed a number of trends in community succession matching theoretical predictions. These included mid-successional peaks in bacterial diversity and metabolic substrate use, predictable and parallel successional trajectories among microbial communities, and convergence giving way to divergence in community composition and carbon substrate use. Bacterial composition, biomass, and diversity positively influenced the rate of prey decomposition, which was in turn positively associated with a host leaf's nitrogen uptake efficiency. Overall digestive performance was greatest during late summer. These results highlight links between community succession and ecosystem functioning and extend succession theory to host-associated microbial communities.

INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.

PubMed

Weber, Ingmar; Achananuparp, Palakorn

2016-01-01

To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token "mcdonalds" or the category "dessert" being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the "quick added calories" functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm.

PubMed

Zhou, Pei; Jin, Bowen; Li, Hao; Huang, Sheng-You

2018-05-09

Protein-peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein-peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein-peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

PubMed

Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

Predictive codes of familiarity and context during the perceptual learning of facial identities

NASA Astrophysics Data System (ADS)

Apps, Matthew A. J.; Tsakiris, Manos

2013-11-01

Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Development and initial evaluation of the Clinical Information Systems Success Model (CISSM).

PubMed

Garcia-Smith, Dianna; Effken, Judith A

2013-06-01

Most clinical information systems (CIS) today are technically sound, but the number of successful implementations of these systems is low. The purpose of this study was to develop and test a theoretically based integrated CIS Success Model (CISSM) from the nurse perspective. Model predictors of CIS success were taken from existing research on information systems acceptance, user satisfaction, use intention, user behavior and perceptions, as well as clinical research. Data collected online from 234 registered nurses in four hospitals were used to test the model. Each nurse had used the Cerner Power Chart Admission Health Profile for at least 3 months. Psychometric testing and factor analysis of the 23-item CISSM instrument established its construct validity and reliability. Initial analysis showed nurses' satisfaction with and dependency on CIS use predicted their perceived CIS use Net Benefit. Further analysis identified Social Influence and Facilitating Conditions as other predictors of CIS user Net Benefit. The level of hospital CIS integration may account for the role of CIS Use Dependency in the success of CIS. Based on our experience, CISSM provides a formative as well as summative tool for evaluating CIS success from the nurse's perspective. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

PubMed Central

Mestyán, Márton; Yasseri, Taha; Kertész, János

2013-01-01

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia. PMID:23990938

A comparison between theoretical prediction and experimental measurement of the dynamic behavior of spur gears

NASA Technical Reports Server (NTRS)

Rebbechi, Brian; Forrester, B. David; Oswald, Fred B.; Townsend, Dennis P.

1992-01-01

A comparison was made between computer model predictions of gear dynamics behavior and experimental results. The experimental data were derived from the NASA gear noise rig, which was used to record dynamic tooth loads and vibration. The experimental results were compared with predictions from the DSTO Aeronautical Research Laboratory's gear dynamics code for a matrix of 28 load speed points. At high torque the peak dynamic load predictions agree with the experimental results with an average error of 5 percent in the speed range 800 to 6000 rpm. Tooth separation (or bounce), which was observed in the experimental data for light torque, high speed conditions, was simulated by the computer model. The model was also successful in simulating the degree of load sharing between gear teeth in the multiple tooth contact region.

Shuttle TPS thermal performance and analysis methodology

NASA Technical Reports Server (NTRS)

Neuenschwander, W. E.; Mcbride, D. U.; Armour, G. A.

1983-01-01

Thermal performance of the thermal protection system was approximately as predicted. The only extensive anomalies were filler bar scorching and over-predictions in the high Delta p gap heating regions of the orbiter. A technique to predict filler bar scorching has been developed that can aid in defining a solution. Improvement in high Delta p gap heating methodology is still under study. Minor anomalies were also examined for improvements in modeling techniques and prediction capabilities. These include improved definition of low Delta p gap heating, an analytical model for inner mode line convection heat transfer, better modeling of structure, and inclusion of sneak heating. The limited number of problems related to penetration items that presented themselves during orbital flight tests were resolved expeditiously, and designs were changed and proved successful within the time frame of that program.

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

USGS Publications Warehouse

Friedel, Michael J.

2008-01-01

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.

PubMed

Lai, Jinxing; Qiu, Junling; Feng, Zhihua; Chen, Jianxun; Fan, Haobo

2016-01-01

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

PubMed Central

Lai, Jinxing

2016-01-01

In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

A Contigency Model for Predicting Institutionalization of Innovation Across Divergent Organizations.

ERIC Educational Resources Information Center

Howes, Nancy J.

This study was undertaken to compare the variables related to the successful institutionalization of changes across divergent organizations, and to design, through cross-validation, an interorganization model of change. Descriptive survey questionnaires and structured interviews were the instruments used. The respondent sample consisted of 1,500…

Exploring Students' Reflective Thinking Practice, Deep Processing Strategies, Effort, and Achievement Goal Orientations

ERIC Educational Resources Information Center

Phan, Huy Phuong

2009-01-01

Recent research indicates that study processing strategies, effort, reflective thinking practice, and achievement goals are important factors contributing to the prediction of students' academic success. Very few studies have combined these theoretical orientations within one conceptual model. This study tested a conceptual model that included, in…

Modeling the toxicokinetics of 24-hour toluene exposure in rats, impact of activity patterns and enzyme induction

EPA Science Inventory

Toluene, a solvent used in numerous consumer and industrial applications, exerts its critical effects on the brain and nervous system following inhalation exposure. Our previously published PBPK model successfully predicted toluene concentrations in blood and brain over a range o...

Using the domain identification model to study major and career decision-making processes

NASA Astrophysics Data System (ADS)

Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

2018-03-01

The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We used a structural equation model to test the hypothesised relationship between variables in the partial domain identification model. The findings suggested that engineering identification significantly predicted engineering major intentions and career intentions and had the highest effect on those two variables compared to other motivational constructs. Furthermore, results suggested that success, interest, and caring are plausible contributors to students' engineering identification. Overall, there is strong evidence that the domain identification model can be used as a lens to study career decision-making processes in engineering, and potentially, in other fields as well.

Modelling biological invasions: species traits, species interactions, and habitat heterogeneity.

PubMed

Cannas, Sergio A; Marco, Diana E; Páez, Sergio A

2003-05-01

In this paper we explore the integration of different factors to understand, predict and control ecological invasions, through a general cellular automaton model especially developed. The model includes life history traits of several species in a modular structure interacting multiple cellular automata. We performed simulations using field values corresponding to the exotic Gleditsia triacanthos and native co-dominant trees in a montane area. Presence of G. triacanthos juvenile bank was a determinant condition for invasion success. Main parameters influencing invasion velocity were mean seed dispersal distance and minimum reproductive age. Seed production had a small influence on the invasion velocity. Velocities predicted by the model agreed well with estimations from field data. Values of population density predicted matched field values closely. The modular structure of the model, the explicit interaction between the invader and the native species, and the simplicity of parameters and transition rules are novel features of the model.

A test of reproductive power in snakes.

PubMed

Boback, Scott M; Guyer, Craig

2008-05-01

Reproductive power is a contentious concept among ecologists, and the model has been criticized on theoretical and empirical grounds. Despite these criticisms, the model has successfully predicted the modal (optimal) size in three large taxonomic groups and the shape of the body size distribution in two of these groups. We tested the reproductive power model on snakes, a group that differs markedly in physiology, foraging ecology, and body shape from the endothermic groups upon which the model was derived. Using detailed field data from the published literature, snake-specific constants associated with reproductive power were determined using allometric relationships of energy invested annually in egg production and population productivity. The resultant model accurately predicted the mode and left side of the size distribution for snakes but failed to predict the right side of that distribution. If the model correctly describes what is possible in snakes, observed size diversity is limited, especially in the largest size classes.

Tractable flux-driven temperature, density, and rotation profile evolution with the quasilinear gyrokinetic transport model QuaLiKiz

NASA Astrophysics Data System (ADS)

Citrin, J.; Bourdelle, C.; Casson, F. J.; Angioni, C.; Bonanomi, N.; Camenen, Y.; Garbet, X.; Garzotti, L.; Görler, T.; Gürcan, O.; Koechl, F.; Imbeaux, F.; Linder, O.; van de Plassche, K.; Strand, P.; Szepesi, G.; Contributors, JET

2017-12-01

Quasilinear turbulent transport models are a successful tool for prediction of core tokamak plasma profiles in many regimes. Their success hinges on the reproduction of local nonlinear gyrokinetic fluxes. We focus on significant progress in the quasilinear gyrokinetic transport model QuaLiKiz (Bourdelle et al 2016 Plasma Phys. Control. Fusion 58 014036), which employs an approximated solution of the mode structures to significantly speed up computation time compared to full linear gyrokinetic solvers. Optimisation of the dispersion relation solution algorithm within integrated modelling applications leads to flux calculations × {10}6-7 faster than local nonlinear simulations. This allows tractable simulation of flux-driven dynamic profile evolution including all transport channels: ion and electron heat, main particles, impurities, and momentum. Furthermore, QuaLiKiz now includes the impact of rotation and temperature anisotropy induced poloidal asymmetry on heavy impurity transport, important for W-transport applications. Application within the JETTO integrated modelling code results in 1 s of JET plasma simulation within 10 h using 10 CPUs. Simultaneous predictions of core density, temperature, and toroidal rotation profiles for both JET hybrid and baseline experiments are presented, covering both ion and electron turbulence scales. The simulations are successfully compared to measured profiles, with agreement mostly in the 5%-25% range according to standard figures of merit. QuaLiKiz is now open source and available at www.qualikiz.com.

A neural network - based algorithm for predicting stone -free status after ESWL therapy

PubMed Central

Seckiner, Ilker; Seckiner, Serap; Sen, Haluk; Bayrak, Omer; Dogan, Kazım; Erturhan, Sakip

2017-01-01

ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones. PMID:28727384

Exposure–response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development

PubMed Central

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure–response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks’ treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure–response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects’ sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure–response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs. PMID:26392753

Exposure-response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development.

PubMed

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure-response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks' treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure-response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects' sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure-response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs.

Predicting Galaxy Star Formation Rates via the Co-evolution of Galaxies and Halos

DOE PAGES

Watson, Douglas F.; Hearin, Andrew P.; Berlind, Andreas A.; ...

2014-03-06

In this paper, we test the age matching hypothesis that the star formation rate (SFR) of a galaxy is determined by its dark matter halo formation history, and as such, that more quiescent galaxies reside in older halos. This simple model has been remarkably successful at predicting color-based galaxy statistics at low redshift as measured in the Sloan Digital Sky Survey (SDSS). To further test this method with observations, we present new SDSS measurements of the galaxy two-point correlation function and galaxy-galaxy lensing as a function of stellar mass and SFR, separated into quenched and star forming galaxy samples. Wemore » find that our age matching model is in excellent agreement with these new measurements. We also employ a galaxy group finder and show that our model is able to predict: (1) the relative SFRs of central and satellite galaxies, (2) the SFR-dependence of the radial distribution of satellite galaxy populations within galaxy groups, rich groups, and clusters and their surrounding larger scale environments, and (3) the interesting feature that the satellite quenched fraction as a function of projected radial distance from the central galaxy exhibits an approx r -.15 slope, independent of environment. The accurate prediction for the spatial distribution of satellites is intriguing given the fact that we do not explicitly model satellite-specific processes after infall, and that in our model the virial radius does not mark a special transition region in the evolution of a satellite, contrary to most galaxy evolution models. The success of the model suggests that present-day galaxy SFR is strongly correlated with halo mass assembly history.« less

An approach to adjustment of relativistic mean field model parameters

NASA Astrophysics Data System (ADS)

Bayram, Tuncay; Akkoyun, Serkan

2017-09-01

The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN) method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs) of 58Ni and 208Pb have been found in agreement with the literature values.

An optical model for the microwave properties of sea ice

NASA Technical Reports Server (NTRS)

Gloersen, P.; Larabee, J. K.

1981-01-01

The complex refractive index of sea ice is modeled and used to predict the microwave signatures of various sea ice types. Results are shown to correspond well with the observed values of the complex index inferred from dielectic constant and dielectric loss measurements performed in the field, and with observed microwave signatures of sea ice. The success of this modeling procedure vis a vis modeling of the dielectric properties of sea ice constituents used earlier by several others is explained. Multiple layer radiative transfer calculations are used to predict the microwave properties of first-year sea ice with and without snow, and multiyear sea ice.

Percutaneous Coronary Revascularization for Chronic Total Occlusions: A Novel Predictive Score of Technical Failure Using Advanced Technologies.

PubMed

Galassi, Alfredo R; Boukhris, Marouane; Azzarelli, Salvatore; Castaing, Marine; Marzà, Francesco; Tomasello, Salvatore D

2016-05-09

The aims of this study were to describe the 10-year experience of a single operator dedicated to chronic total occlusion (CTO) and to establish a model for predicting technical failure. During the last decade, the interest in percutaneous coronary interventions (PCIs) of chronic total occlusions (CTOs) has increased, allowing the improvement of success rate. One thousand nineteen patients with CTO underwent 1,073 CTO procedures performed by a single CTO-dedicated operator. The study population was subdivided into 2 groups by time period: period 1 (January 2005 to December 2009, n = 378) and period 2 (January 2010 to December 2014, n = 641). Observations were randomly assigned to a derivation set and a validation set (in a 2:1 ratio). A prediction score was established by assigning points for each independent predictor of technical failure in the derivation set according to the beta coefficient and summing all points accrued. Lesions attempted in period 2 were more complex in comparison with those in period 1. Compared with period 1, both technical and clinical success rates significantly improved (from 87.8% to 94.4% [p = 0.001] and from 77.6% to 89.9% [p < 0.001], respectively). A prediction score for technical failure including age ≥75 years (1 point), ostial location (1 point), and collateral filling Rentrop grade <2 (2 points) was established, stratifying procedures into 4 difficulty groups: easy (0), intermediate (1), difficult (2), and very difficult (3 or 4), with decreasing technical success rates. In derivation and validation sets, areas under the curve were comparable (0.728 and 0.772, respectively). With growing expertise, the success rate has increased despite increasing complexity of attempted lesions. The established model predicted the probability of technical failure and thus might be applied to grading the difficulty of CTO procedures. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

DEFINING THE CHEMICAL SPACE OF PUBLIC GENOMIC DATA.

EPA Science Inventory

The pharmaceutical industry has demonstrated success in integrating of chemogenomic knowledge into predictive toxicological models, due in part to industry's access to large amounts of proprietary and commercial reference genomic data sets.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

PubMed

Heddam, Salim

2014-11-01

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

Brain size predicts problem-solving ability in mammalian carnivores

PubMed Central

Benson-Amram, Sarah; Dantzer, Ben; Stricker, Gregory; Swanson, Eli M.; Holekamp, Kay E.

2016-01-01

Despite considerable interest in the forces shaping the relationship between brain size and cognitive abilities, it remains controversial whether larger-brained animals are, indeed, better problem-solvers. Recently, several comparative studies have revealed correlations between brain size and traits thought to require advanced cognitive abilities, such as innovation, behavioral flexibility, invasion success, and self-control. However, the general assumption that animals with larger brains have superior cognitive abilities has been heavily criticized, primarily because of the lack of experimental support for it. Here, we designed an experiment to inquire whether specific neuroanatomical or socioecological measures predict success at solving a novel technical problem among species in the mammalian order Carnivora. We presented puzzle boxes, baited with food and scaled to accommodate body size, to members of 39 carnivore species from nine families housed in multiple North American zoos. We found that species with larger brains relative to their body mass were more successful at opening the boxes. In a subset of species, we also used virtual brain endocasts to measure volumes of four gross brain regions and show that some of these regions improve model prediction of success at opening the boxes when included with total brain size and body mass. Socioecological variables, including measures of social complexity and manual dexterity, failed to predict success at opening the boxes. Our results, thus, fail to support the social brain hypothesis but provide important empirical support for the relationship between relative brain size and the ability to solve this novel technical problem. PMID:26811470

Brain size predicts problem-solving ability in mammalian carnivores.

PubMed

Benson-Amram, Sarah; Dantzer, Ben; Stricker, Gregory; Swanson, Eli M; Holekamp, Kay E

2016-03-01

Despite considerable interest in the forces shaping the relationship between brain size and cognitive abilities, it remains controversial whether larger-brained animals are, indeed, better problem-solvers. Recently, several comparative studies have revealed correlations between brain size and traits thought to require advanced cognitive abilities, such as innovation, behavioral flexibility, invasion success, and self-control. However, the general assumption that animals with larger brains have superior cognitive abilities has been heavily criticized, primarily because of the lack of experimental support for it. Here, we designed an experiment to inquire whether specific neuroanatomical or socioecological measures predict success at solving a novel technical problem among species in the mammalian order Carnivora. We presented puzzle boxes, baited with food and scaled to accommodate body size, to members of 39 carnivore species from nine families housed in multiple North American zoos. We found that species with larger brains relative to their body mass were more successful at opening the boxes. In a subset of species, we also used virtual brain endocasts to measure volumes of four gross brain regions and show that some of these regions improve model prediction of success at opening the boxes when included with total brain size and body mass. Socioecological variables, including measures of social complexity and manual dexterity, failed to predict success at opening the boxes. Our results, thus, fail to support the social brain hypothesis but provide important empirical support for the relationship between relative brain size and the ability to solve this novel technical problem.

An inverse modeling strategy and a computer program to model garnet growth and resorption

NASA Astrophysics Data System (ADS)

Lanari, Pierre; Giuntoli, Francesco

2017-04-01

GrtMod is a computer program that allows numerical simulation of the pressure-temperature (P-T) evolution of garnet porphyroblasts based on the composition of successive growth zones preserved in natural samples. For each garnet growth stage, a new reactive bulk composition is optimized, allowing for resorption and/or fractionation of the previously crystalized garnet. The successive minimizations are performed using a heuristic search method and an objective function that quantify the amount by which the predicted garnet composition deviates from the measured values. The automated strategy of GrtMod includes a two stages optimization and one refinement stage. In this contribution, we will present several application examples. The new strategy provides quantitative estimates of the optimal P-T conditions whereas it was generally derived in a qualitatively way by using garnet isopleth intersections in equilibrium phase diagrams. GrtMod can also be used to model the evolution of the reactive bulk composition along any P-T trajectories. The results for typical MORB and metapelite compositions demonstrate that fractional crystallization models are required to derive accurate P-T information from garnet compositional zoning. GrtMod can also be used to retrieve complex garnet histories involving several stages of resorption. For instance, it has been used to model the P-T condition of garnet growth in grains from the Sesia Zone (Western Alps). The compositional variability of successive growth zones is characterized using standardized X-ray maps and the program XMapTools. Permian garnet cores crystalized under granulite facies conditions (T > 800°C and P = 6 kbar), whereas Alpine garnet rims grew at eclogite facies conditions (650°C and 16 kbar) involving several successive episodes of resorption. The model predicts that up to 50 vol% of garnet was dissolved before a new episode of garnet growth.

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Short-term Forecasting Ground Magnetic Perturbations with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, Daniel; Toth, Gabor; Gombosi, Tamas; Singer, Howard; Millward, George

2016-04-01

Predicting ground-based magnetic perturbations is a critical step towards specifying and predicting geomagnetically induced currents (GICs) in high voltage transmission lines. Currently, the Space Weather Modeling Framework (SWMF), a flexible modeling framework for simulating the multi-scale space environment, is being transitioned from research to operational use (R2O) by NOAA's Space Weather Prediction Center. Upon completion of this transition, the SWMF will provide localized dB/dt predictions using real-time solar wind observations from L1 and the F10.7 proxy for EUV as model input. This presentation describes the operational SWMF setup and summarizes the changes made to the code to enable R2O progress. The framework's algorithm for calculating ground-based magnetometer observations will be reviewed. Metrics from data-model comparisons will be reviewed to illustrate predictive capabilities. Early data products, such as regional-K index and grids of virtual magnetometer stations, will be presented. Finally, early successes will be shared, including the code's ability to reproduce the recent March 2015 St. Patrick's Day Storm.

A Computational Fluid Dynamics Study of Transitional Flows in Low-Pressure Turbines under a Wide Range of Operating Conditions

NASA Technical Reports Server (NTRS)

Suzen, Y. B.; Huang, P. G.; Ashpis, D. E.; Volino, R. J.; Corke, T. C.; Thomas, F. O.; Huang, J.; Lake, J. P.; King, P. I.

2007-01-01

A transport equation for the intermittency factor is employed to predict the transitional flows in low-pressure turbines. The intermittent behavior of the transitional flows is taken into account and incorporated into computations by modifying the eddy viscosity, mu(sub p) with the intermittency factor, gamma. Turbulent quantities are predicted using Menter's two-equation turbulence model (SST). The intermittency factor is obtained from a transport equation model which can produce both the experimentally observed streamwise variation of intermittency and a realistic profile in the cross stream direction. The model had been previously validated against low-pressure turbine experiments with success. In this paper, the model is applied to predictions of three sets of recent low-pressure turbine experiments on the Pack B blade to further validate its predicting capabilities under various flow conditions. Comparisons of computational results with experimental data are provided. Overall, good agreement between the experimental data and computational results is obtained. The new model has been shown to have the capability of accurately predicting transitional flows under a wide range of low-pressure turbine conditions.

Computational optimization and biological evolution.

PubMed

Goryanin, Igor

2010-10-01

Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

Adapting Price Predictions in TAC SCM

NASA Astrophysics Data System (ADS)

Pardoe, David; Stone, Peter

In agent-based markets, adapting to the behavior of other agents is often necessary for success. When it is not possible to directly model individual competitors, an agent may instead model and adapt to the market conditions that result from competitor behavior. Such an agent could still benefit from reasoning about specific competitor strategies by considering how various combinations of these strategies would impact the conditions being modeled. We present an application of such an approach to a specific prediction problem faced by the agent TacTex-06 in the Trading Agent Competition's Supply Chain Management scenario (TAC SCM).

Will male advertisement be a reliable indicator of paternal care, if offspring survival depends on male care?

PubMed Central

Kelly, Natasha B.; Alonzo, Suzanne H.

2009-01-01

Existing theory predicts that male signalling can be an unreliable indicator of paternal care, but assumes that males with high levels of mating success can have high current reproductive success, without providing any parental care. As a result, this theory does not hold for the many species where offspring survival depends on male parental care. We modelled male allocation of resources between advertisement and care for species with male care where males vary in quality, and the effect of care and advertisement on male fitness is multiplicative rather than additive. Our model predicts that males will allocate proportionally more of their resources to whichever trait (advertisement or paternal care) is more fitness limiting. In contrast to previous theory, we find that male advertisement is always a reliable indicator of paternal care and male phenotypic quality (e.g. males with higher levels of advertisement never allocate less to care than males with lower levels of advertisement). Our model shows that the predicted pattern of male allocation and the reliability of male signalling depend very strongly on whether paternal care is assumed to be necessary for offspring survival and how male care affects offspring survival and male fitness. PMID:19520802

A model of ecological and evolutionary consequences of color polymorphism.

PubMed

Forsman, Anders; Ahnesjö, Jonas; Caesar, Sofia; Karlsson, Magnus

2008-01-01

We summarize direct and indirect effects on fitness components of animal color pattern and present a synthesis of theories concerning the ecological and evolutionary dynamics of chromatic multiple niche polymorphisms. Previous endeavors have aimed primarily at identifying conditions that promote the evolution and maintenance of polymorphisms. We consider in a conceptual model also the reciprocal influence of color polymorphism on population processes and propose that polymorphism entails selective advantages that may promote the ecological success of polymorphic species. The model begins with an evolutionary branching event from mono- to polymorphic condition that, under the influence of correlational selection, is predicted to promote the evolution of physical integration of coloration and other traits (cf. multi-trait coevolution and complex phenotypes). We propose that the coexistence within a population of alternative ecomorphs with coadapted gene complexes promotes utilization of diverse environmental resources, population stability and persistence, colonization success, and range expansions, and enhances the evolutionary potential and speciation. Conversely, we predict polymorphic populations to be less vulnerable to environmental change and at lower risk of range contractions and extinctions compared with monomorphic populations. We offer brief suggestions as to how these falsifiable predictions may be tested.

Will male advertisement be a reliable indicator of paternal care, if offspring survival depends on male care?

PubMed

Kelly, Natasha B; Alonzo, Suzanne H

2009-09-07

Existing theory predicts that male signalling can be an unreliable indicator of paternal care, but assumes that males with high levels of mating success can have high current reproductive success, without providing any parental care. As a result, this theory does not hold for the many species where offspring survival depends on male parental care. We modelled male allocation of resources between advertisement and care for species with male care where males vary in quality, and the effect of care and advertisement on male fitness is multiplicative rather than additive. Our model predicts that males will allocate proportionally more of their resources to whichever trait (advertisement or paternal care) is more fitness limiting. In contrast to previous theory, we find that male advertisement is always a reliable indicator of paternal care and male phenotypic quality (e.g. males with higher levels of advertisement never allocate less to care than males with lower levels of advertisement). Our model shows that the predicted pattern of male allocation and the reliability of male signalling depend very strongly on whether paternal care is assumed to be necessary for offspring survival and how male care affects offspring survival and male fitness.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Predictions of structural integrity of steam generator tubes under normal operating, accident, an severe accident conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Majumdar, S.

1997-02-01

Available models for predicting failure of flawed and unflawed steam generator tubes under normal operating, accident, and severe accident conditions are reviewed. Tests conducted in the past, though limited, tended to show that the earlier flow-stress model for part-through-wall axial cracks overestimated the damaging influence of deep cracks. This observation was confirmed by further tests at high temperatures, as well as by finite-element analysis. A modified correlation for deep cracks can correct this shortcoming of the model. Recent tests have shown that lateral restraint can significantly increase the failure pressure of tubes with unsymmetrical circumferential cracks. This observation was confirmedmore » by finite-element analysis. The rate-independent flow stress models that are successful at low temperatures cannot predict the rate-sensitive failure behavior of steam generator tubes at high temperatures. Therefore, a creep rupture model for predicting failure was developed and validated by tests under various temperature and pressure loadings that can occur during postulated severe accidents.« less

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance.

PubMed

Muratov, Eugene; Lewis, Margaret; Fourches, Denis; Tropsha, Alexander; Cox, Wendy C

2017-04-01

Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

[Tumor cells transfer between the patient and laboratory animal as a basic methodological approach to the study of cancerogenesis and identification of biomarkers].

PubMed

Klos, D; Stašek, M; Loveček, M; Skalický, P; Vrba, R; Aujeský, R; Havlík, R; Neoral, Č; Varanashi, L; Hajdúch, M; Vrbková, J; Džubák, P

The investigation of prognostic and predictive factors for early diagnosis of tumors, their surveillance and monitoring of the impact of therapeutic modalities using hybrid laboratory models in vitro/in vivo is an experimental approach with a significant potential. It is preconditioned by the preparation of in vivo tumor models, which may face a number of potential technical difficulties. The assessment of technical success of grafting and xenotransplantation based on the type of the tumor or cell line is important for the preparation of these models and their further use for proteomic and genomic analyses. Surgically harvested gastrointestinal tract tumor tissue was processed or stable cancer cell lines were cultivated; the viability was assessed, and subsequently the cells were inoculated subcutaneously to SCID mice with an individual duration of tumor growth, followed by its extraction. We analysed 140 specimens of tumor tissue including 17 specimens of esophageal cancer (viability 13/successful inoculations 0), 13 tumors of the cardia (11/0), 39 gastric tumors (24/4), 47 pancreatic tumors (34/1) and 24 specimens of colorectal cancer (22/9). 3 specimens were excluded due to histological absence of the tumor (complete remission after neoadjuvant therapy in 2 cases of esophageal carcinoma, 1 case of chronic pancreatitis). We observed successful inoculation in 17 of 28 tumor cell lines. The probability of successful grafting to the mice model in tumors of the esophagus, stomach and pancreas is significantly lower in comparison with colorectal carcinoma and cell lines generated tumors. The success rate is enhanced upon preservation of viability of the harvested tumor tissue, which depends on the sequence of clinical and laboratory algorithms with a high level of cooperation.Key words: proteomic analysis - xenotransplantation - prognostic and predictive factors - gastrointestinal tract tumors.

Coulomb stress change sensitivity due to variability in mainshock source models and receiving fault parameters: A case study of the 2010-2011 Christchurch, New Zealand, earthquakes

USGS Publications Warehouse

Zhan, Zhongwen; Jin, Bikai; Wei, Shengji; Graves, Robert W.

2011-01-01

Strong aftershocks following major earthquakes present significant challenges for infrastructure recovery as well as for emergency rescue efforts. A tragic instance of this is the 22 February 2011 Mw 6.3 Christchurch aftershock in New Zealand, which caused more than 100 deaths while the 2010 Mw 7.1 Canterbury mainshock did not cause a single fatality (Figure 1). Therefore, substantial efforts have been directed toward understanding the generation mechanisms of aftershocks as well as mitigating hazards due to aftershocks. Among these efforts are the prediction of strong aftershocks, earthquake early warning, and aftershock probability assessment. Zhang et al. (1999) reported a successful case of strong aftershock prediction with precursory data such as changes in seismicity pattern, variation of b-value, and geomagnetic anomalies. However, official reports of such successful predictions in geophysical journals are extremely rare, implying that deterministic prediction of potentially damaging aftershocks is not necessarily more scientifically feasible than prediction of mainshocks.

Long term impact of emotional, social and cognitive intelligence competencies and GMAT on career and life satisfaction and career success

PubMed Central

Amdurer, Emily; Boyatzis, Richard E.; Saatcioglu, Argun; Smith, Melvin L.; Taylor, Scott N.

2014-01-01

Career scholars have called for a broader definition of career success by inviting greater exploration of its antecedents. While success in various jobs has been predicted by intelligence and in other studies by competencies, especially in management, long term impact of having intelligence and using competencies has not been examined. Even in collegiate outcome studies, few have examined the longer term impact on graduates' careers or lives. This study assesses the impact of demonstrated emotional, social, and cognitive intelligence competencies assessed at graduation and g measured through GMAT at entry from an MBA program on career and life satisfaction, and career success assessed 5 to 19 years after graduation. Using behavioral measures of competencies (i.e., as assessed by others), we found that emotional intelligence competencies predict career satisfaction and success. Adaptability had a positive impact, but influence had the opposite effect on these career measures and life satisfaction. Life satisfaction was negatively affected by achievement orientation and positively affected by teamwork. Current salary, length of marriage, and being younger at time of graduation positively affect all three measures of life and career satisfaction and career success. GMAT (as a measure of g) predicted life satisfaction and career success to a slight but significant degree in the final model analyzed. Meanwhile, being female and number of children positively affected life satisfaction but cognitive intelligence competencies negatively affected it, and in particular demonstrated systems thinking was negative. PMID:25566128

Long term impact of emotional, social and cognitive intelligence competencies and GMAT on career and life satisfaction and career success.

PubMed

Amdurer, Emily; Boyatzis, Richard E; Saatcioglu, Argun; Smith, Melvin L; Taylor, Scott N

2014-01-01

Career scholars have called for a broader definition of career success by inviting greater exploration of its antecedents. While success in various jobs has been predicted by intelligence and in other studies by competencies, especially in management, long term impact of having intelligence and using competencies has not been examined. Even in collegiate outcome studies, few have examined the longer term impact on graduates' careers or lives. This study assesses the impact of demonstrated emotional, social, and cognitive intelligence competencies assessed at graduation and g measured through GMAT at entry from an MBA program on career and life satisfaction, and career success assessed 5 to 19 years after graduation. Using behavioral measures of competencies (i.e., as assessed by others), we found that emotional intelligence competencies predict career satisfaction and success. Adaptability had a positive impact, but influence had the opposite effect on these career measures and life satisfaction. Life satisfaction was negatively affected by achievement orientation and positively affected by teamwork. Current salary, length of marriage, and being younger at time of graduation positively affect all three measures of life and career satisfaction and career success. GMAT (as a measure of g) predicted life satisfaction and career success to a slight but significant degree in the final model analyzed. Meanwhile, being female and number of children positively affected life satisfaction but cognitive intelligence competencies negatively affected it, and in particular demonstrated systems thinking was negative.

The predictive power of SIMION/SDS simulation software for modeling ion mobility spectrometry instruments

NASA Astrophysics Data System (ADS)

Lai, Hanh; McJunkin, Timothy R.; Miller, Carla J.; Scott, Jill R.; Almirall, José R.

2008-09-01

The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOSFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene, 2,7-dinitrofluorene, and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: (1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctly predict the ion drift times; (2) a drift gas composition study evaluates the accuracy in predicting the resolution; (3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.

The Predictive Power of SIMION/SDS Simulation Software for Modeling Ion Mobility Spectrometry Instruments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanh Lai; Timothy R. McJunkin; Carla J. Miller

2008-09-01

The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: 1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctlymore » predict the ion drift times; 2) a drift gas composition study evaluates the accuracy in predicting the resolution; and 3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.« less

Modeling and Predicting the Stress Relaxation of Composites with Short and Randomly Oriented Fibers

PubMed Central

Obaid, Numaira; Sain, Mohini

2017-01-01

The addition of short fibers has been experimentally observed to slow the stress relaxation of viscoelastic polymers, producing a change in the relaxation time constant. Our recent study attributed this effect of fibers on stress relaxation behavior to the interfacial shear stress transfer at the fiber-matrix interface. This model explained the effect of fiber addition on stress relaxation without the need to postulate structural changes at the interface. In our previous study, we developed an analytical model for the effect of fully aligned short fibers, and the model predictions were successfully compared to finite element simulations. However, in most industrial applications of short-fiber composites, fibers are not aligned, and hence it is necessary to examine the time dependence of viscoelastic polymers containing randomly oriented short fibers. In this study, we propose an analytical model to predict the stress relaxation behavior of short-fiber composites where the fibers are randomly oriented. The model predictions were compared to results obtained from Monte Carlo finite element simulations, and good agreement between the two was observed. The analytical model provides an excellent tool to accurately predict the stress relaxation behavior of randomly oriented short-fiber composites. PMID:29053601

Paths to Success in Young Adulthood from Mental Health and Life Transitions in Emerging Adulthood

ERIC Educational Resources Information Center

Howard, Andrea L.; Galambos, Nancy L.; Krahn, Harvey J.

2010-01-01

This study followed a school-based sample (N = 920) to explore how trajectories of depressive symptoms and expressed anger from age 18 to 25, along with important life transitions, predicted life and career satisfaction at age 32. A two-group (women and men) bivariate growth model revealed that higher depressive symptoms at age 18 predicted lower…

New methods for estimating parameters of weibull functions to characterize future diameter distributions in forest stands

Treesearch

Quang V. Cao; Shanna M. McCarty

2006-01-01

Diameter distributions in a forest stand have been successfully characterized by use of the Weibull function. Of special interest are cases where parameters of a Weibull distribution that models a future stand are predicted, either directly or indirectly, from current stand density and dominant height. This study evaluated four methods of predicting the Weibull...

Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts.

NASA Astrophysics Data System (ADS)

Luo, Jing-Jia; Masson, Sebastien; Behera, Swadhin; Shingu, Satoru; Yamagata, Toshio

2005-11-01

Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured.Hindcast results for the period 1982 2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9 12-month lead times.In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7 12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

Toward large eddy simulation of turbulent flow over an airfoil

NASA Technical Reports Server (NTRS)

Choi, Haecheon

1993-01-01

The flow field over an airfoil contains several distinct flow characteristics, e.g. laminar, transitional, turbulent boundary layer flow, flow separation, unstable free shear layers, and a wake. This diversity of flow regimes taxes the presently available Reynolds averaged turbulence models. Such models are generally tuned to predict a particular flow regime, and adjustments are necessary for the prediction of a different flow regime. Similar difficulties are likely to emerge when the large eddy simulation technique is applied with the widely used Smagorinsky model. This model has not been successful in correctly representing different turbulent flow fields with a single universal constant and has an incorrect near-wall behavior. Germano et al. (1991) and Ghosal, Lund & Moin have developed a new subgrid-scale model, the dynamic model, which is very promising in alleviating many of the persistent inadequacies of the Smagorinsky model: the model coefficient is computed dynamically as the calculation progresses rather than input a priori. The model has been remarkably successful in prediction of several turbulent and transitional flows. We plan to simulate turbulent flow over a '2D' airfoil using the large eddy simulation technique. Our primary objective is to assess the performance of the newly developed dynamic subgrid-scale model for computation of complex flows about aircraft components and to compare the results with those obtained using the Reynolds average approach and experiments. The present computation represents the first application of large eddy simulation to a flow of aeronautical interest and a key demonstration of the capabilities of the large eddy simulation technique.

Multi-nutrient, multi-group model of present and future oceanic phytoplankton communities

NASA Astrophysics Data System (ADS)

Litchman, E.; Klausmeier, C. A.; Miller, J. R.; Schofield, O. M.; Falkowski, P. G.

2006-11-01

Phytoplankton community composition profoundly affects patterns of nutrient cycling and the dynamics of marine food webs; therefore predicting present and future phytoplankton community structure is crucial to understand how ocean ecosystems respond to physical forcing and nutrient limitations. We develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups (diatoms, coccolithophores and prasinophytes), nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. We test the model at two contrasting sites in the modern ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). The model successfully predicts general patterns of community composition and succession at both sites: In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. We then use the model to predict community reorganization under different global change scenarios: a later onset and extended duration of stratification, with shallower mixed layer depths due to increased greenhouse gas concentrations; increase in deep water nitrogen; decrease in deep water phosphorus and increase or decrease in iron concentration. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced acceptable modern day outcomes and the robustness of the predictions was determined. Change in the onset and duration of stratification altered the timing and the magnitude of the spring diatom bloom in the North Atlantic and increased total phytoplankton and zooplankton biomass in the North Pacific. Changes in nutrient concentrations in some cases changed dominance patterns of major groups, as well as total chlorophyll and zooplankton biomass. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Recent tests of the equilibrium-point hypothesis (lambda model).

PubMed

Feldman, A G; Ostry, D J; Levin, M F; Gribble, P L; Mitnitski, A B

1998-07-01

The lambda model of the equilibrium-point hypothesis (Feldman & Levin, 1995) is an approach to motor control which, like physics, is based on a logical system coordinating empirical data. The model has gone through an interesting period. On one hand, several nontrivial predictions of the model have been successfully verified in recent studies. In addition, the explanatory and predictive capacity of the model has been enhanced by its extension to multimuscle and multijoint systems. On the other hand, claims have recently appeared suggesting that the model should be abandoned. The present paper focuses on these claims and concludes that they are unfounded. Much of the experimental data that have been used to reject the model are actually consistent with it.

Use of Continuous Monitors and Autosamplers to Predict Unmeasured Water-Quality Constituents in Tributaries of the Tualatin River, Oregon

USGS Publications Warehouse

Anderson, Chauncey W.; Rounds, Stewart A.

2010-01-01

Management of water quality in streams of the United States is becoming increasingly complex as regulators seek to control aquatic pollution and ecological problems through Total Maximum Daily Load programs that target reductions in the concentrations of certain constituents. Sediment, nutrients, and bacteria, for example, are constituents that regulators target for reduction nationally and in the Tualatin River basin, Oregon. These constituents require laboratory analysis of discrete samples for definitive determinations of concentrations in streams. Recent technological advances in the nearly continuous, in situ monitoring of related water-quality parameters has fostered the use of these parameters as surrogates for the labor intensive, laboratory-analyzed constituents. Although these correlative techniques have been successful in large rivers, it was unclear whether they could be applied successfully in tributaries of the Tualatin River, primarily because these streams tend to be small, have rapid hydrologic response to rainfall and high streamflow variability, and may contain unique sources of sediment, nutrients, and bacteria. This report evaluates the feasibility of developing correlative regression models for predicting dependent variables (concentrations of total suspended solids, total phosphorus, and Escherichia coli bacteria) in two Tualatin River basin streams: one draining highly urbanized land (Fanno Creek near Durham, Oregon) and one draining rural agricultural land (Dairy Creek at Highway 8 near Hillsboro, Oregon), during 2002-04. An important difference between these two streams is their response to storm runoff; Fanno Creek has a relatively rapid response due to extensive upstream impervious areas and Dairy Creek has a relatively slow response because of the large amount of undeveloped upstream land. Four other stream sites also were evaluated, but in less detail. Potential explanatory variables included continuously monitored streamflow (discharge), stream stage, specific conductance, turbidity, and time (to account for seasonal processes). Preliminary multiple-regression models were identified using stepwise regression and Mallow's Cp, which maximizes regression correlation coefficients and accounts for the loss of additional degrees of freedom when extra explanatory variables are used. Several data scenarios were created and evaluated for each site to assess the representativeness of existing monitoring data and autosampler-derived data, and to assess the utility of the available data to develop robust predictive models. The goodness-of-fit of candidate predictive models was assessed with diagnostic statistics from validation exercises that compared predictions against a subset of the available data. The regression modeling met with mixed success. Functional model forms that have a high likelihood of success were identified for most (but not all) dependent variables at each site, but there were limitations in the available datasets, notably the lack of samples from high-flows. These limitations increase the uncertainty in the predictions of the models and suggest that the models are not yet ready for use in assessing these streams, particularly under high-flow conditions, without additional data collection and recalibration of model coefficients. Nonetheless, the results reveal opportunities to use existing resources more efficiently. Baseline conditions are well represented in the available data, and, for the most part, the models reproduced these conditions well. Future sampling might therefore focus on high flow conditions, without much loss of ability to characterize the baseline. Seasonal cycles, as represented by trigonometric functions of time, were not significant in the evaluated models, perhaps because the baseline conditions are well characterized in the datasets or because the other explanatory variables indirectly incorporate seasonal aspects. Multicollinearity among independent variabl

Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

NASA Astrophysics Data System (ADS)

Chen, W.-B.; Liu, W.-C.; Hsu, M.-H.

2012-12-01

Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation.

PubMed

Smeers, Inge; Decorte, Ronny; Van de Voorde, Wim; Bekaert, Bram

2018-05-01

DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data. Copyright © 2018 Elsevier B.V. All rights reserved.

Can Creatinine Height Index Predict Weaning and Survival Outcomes in Patients on Prolonged Mechanical Ventilation After Critical Illness?

PubMed

Datta, Debapriya; Foley, Raymond; Wu, Rong; Grady, James; Scalise, Paul

2018-02-01

Malnutrition is common in chronic critically ill patients on prolonged mechanical ventilation (PMV) and may affect weaning. The creatinine height index (CHI), which reflects lean muscle mass, is regarded as the most accurate indicator of malnutrition. The objective of this study was to determine the impact of CHI in comparison with other traditional nutritional indices on successful weaning and survival in patients on PMV after critical illness. Records of 167 patients on PMV following critical illness, admitted for weaning, were reviewed. Parameters studied included age, gender, body mass index (BMI), percentage ideal body weight (%IBW), total protein, albumin, prealbumin, hemoglobin (Hb), and cause of respiratory failure. Number successfully weaned and number discharged alive and time to wean and time to discharge alive were determined from records. The CHI was calculated from 24-hour urine creatinine using a standard formula. Unpaired 2-sample t test was performed to determine the association between the studied nutritional parameters and outcomes. Predictive value of studied parameters for successful weaning and survival was determined by multivariate logistic regression analysis to model dichotomous outcome of successful weaning and survival. Mean age was 68 ± 14 years, 49% were males, 64% were successfully weaned, and 65.8% survived. Total protein, Hb, and CHI had a significant impact on successful weaning. Weight, %IBW, BMI, and CHI had a significant effect on survival. Of all parameters, CHI was most strongly predictive of successful weaning and survival. The CHI is a strong predictor of successful weaning and survival in patients on PMV.

Perceived Academic Control and Academic Emotions Predict Undergraduate University Student Success: Examining Effects on Dropout Intention and Achievement.

PubMed

Respondek, Lisa; Seufert, Tina; Stupnisky, Robert; Nett, Ulrike E

2017-01-01

The present study addressed concerns over the high risk of university students' academic failure. It examined how perceived academic control and academic emotions predict undergraduate students' academic success, conceptualized as both low dropout intention and high achievement (indicated by GPA). A cross-sectional survey was administered to 883 undergraduate students across all disciplines of a German STEM orientated university. The study additionally compared freshman students ( N = 597) vs. second-year students ( N = 286). Using structural equation modeling, for the overall sample of undergraduate students we found that perceived academic control positively predicted enjoyment and achievement, as well as negatively predicted boredom and anxiety. The prediction of dropout intention by perceived academic control was fully mediated via anxiety. When taking perceived academic control into account, we found no specific impact of enjoyment or boredom on the intention to dropout and no specific impact of all three academic emotions on achievement. The multi-group analysis showed, however, that perceived academic control, enjoyment, and boredom among second-year students had a direct relationship with dropout intention. A major contribution of the present study was demonstrating the important roles of perceived academic control and anxiety in undergraduate students' academic success. Concerning corresponding institutional support and future research, the results suggested distinguishing incoming from advanced undergraduate students.

Verification of models for ballistic movement time and endpoint variability.

PubMed

Lin, Ray F; Drury, Colin G

2013-01-01

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.

Can a prediction model for vaginal birth after cesarean also predict the probability of morbidity related to a trial of labor?

PubMed

Grobman, William A; Lai, Yinglei; Landon, Mark B; Spong, Catherine Y; Leveno, Kenneth J; Rouse, Dwight J; Varner, Michael W; Moawad, Atef H; Caritis, Steve N; Harper, Margaret; Wapner, Ronald J; Sorokin, Yoram; Miodovnik, Menachem; Carpenter, Marshall; O'Sullivan, Mary J; Sibai, Baha M; Langer, Oded; Thorp, John M; Ramin, Susan M; Mercer, Brian M

2009-01-01

The objective of the study was to determine whether a model for predicting vaginal birth after cesarean (VBAC) can also predict the probabilty of morbidity associated with a trial of labor (TOL). Using a previously published prediction model, we categorized women with 1 prior cesarean by chance of VBAC. Prevalence of maternal and neonatal morbidity was stratfied by probability of VBAC success and delivery approach. Morbidity became less frequent as the predicted chance of VBAC increased among women who underwent TOL (P < .001) but not elective repeat cesarean section (ERCS) (P > .05). When the predicted chance of VBAC was less than 70%, women undergoing a TOL were more likely to have maternal morbidity (relative risk [RR], 2.2; 95% confidence interval [CI], 1.5-3.1) than those who underwent an ERCS; when the predicted chance of VBAC was at least 70%, total maternal morbidity was not different between the 2 groups (RR, 0.8; 95% CI, 0.5-1.2). The results were similar for neonatal morbidity. A prediction model for VBAC provides information regarding the chance of TOL-related morbidity and suggests that maternal morbidity is not greater for those women who undergo TOL than those who undergo ERCS if the chance of VBAC is at least 70%.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

New generation of elastic network models.

PubMed

López-Blanco, José Ramón; Chacón, Pablo

2016-04-01

The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Student nurse selection and predictability of academic success: The Multiple Mini Interview project.

PubMed

Gale, Julia; Ooms, Ann; Grant, Robert; Paget, Kris; Marks-Maran, Di

2016-05-01

With recent reports of public enquiries into failure to care, universities are under pressure to ensure that candidates selected for undergraduate nursing programmes demonstrate academic potential as well as characteristics and values such as compassion, empathy and integrity. The Multiple Mini Interview (MMI) was used in one university as a way of ensuring that candidates had the appropriate numeracy and literacy skills as well as a range of communication, empathy, decision-making and problem-solving skills as well as ethical insights and integrity, initiative and team-work. To ascertain whether there is evidence of bias in MMIs (gender, age, nationality and location of secondary education) and to determine the extent to which the MMI is predictive of academic success in nursing. A longitudinal retrospective analysis of student demographics, MMI data and the assessment marks for years 1, 2 and 3. One university in southwest London. One cohort of students who commenced their programme in September 2011, including students in all four fields of nursing (adult, child, mental health and learning disability). Inferential statistics and a Bayesian Multilevel Model. MMI in conjunction with MMI numeracy test and MMI literacy test shows little or no bias in terms of ages, gender, nationality or location of secondary school education. Although MMI in conjunction with numeracy and literacy testing is predictive of academic success, it is only weakly predictive. The MMI used in conjunction with literacy and numeracy testing appears to be a successful technique for selecting candidates for nursing. However, other selection methods such as psychological profiling or testing of emotional intelligence may add to the extent to which selection methods are predictive of academic success on nursing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genetic determinants of freckle occurrence in the Spanish population: Towards ephelides prediction from human DNA samples.

PubMed

Hernando, Barbara; Ibañez, Maria Victoria; Deserio-Cuesta, Julio Alberto; Soria-Navarro, Raquel; Vilar-Sastre, Inca; Martinez-Cadenas, Conrado

2018-03-01

Prediction of human pigmentation traits, one of the most differentiable externally visible characteristics among individuals, from biological samples represents a useful tool in the field of forensic DNA phenotyping. In spite of freckling being a relatively common pigmentation characteristic in Europeans, little is known about the genetic basis of this largely genetically determined phenotype in southern European populations. In this work, we explored the predictive capacity of eight freckle and sunlight sensitivity-related genes in 458 individuals (266 non-freckled controls and 192 freckled cases) from Spain. Four loci were associated with freckling (MC1R, IRF4, ASIP and BNC2), and female sex was also found to be a predictive factor for having a freckling phenotype in our population. After identifying the most informative genetic variants responsible for human ephelides occurrence in our sample set, we developed a DNA-based freckle prediction model using a multivariate regression approach. Once developed, the capabilities of the prediction model were tested by a repeated 10-fold cross-validation approach. The proportion of correctly predicted individuals using the DNA-based freckle prediction model was 74.13%. The implementation of sex into the DNA-based freckle prediction model slightly improved the overall prediction accuracy by 2.19% (76.32%). Further evaluation of the newly-generated prediction model was performed by assessing the model's performance in a new cohort of 212 Spanish individuals, reaching a classification success rate of 74.61%. Validation of this prediction model may be carried out in larger populations, including samples from different European populations. Further research to validate and improve this newly-generated freckle prediction model will be needed before its forensic application. Together with DNA tests already validated for eye and hair colour prediction, this freckle prediction model may lead to a substantially more detailed physical description of unknown individuals from DNA found at the crime scene. Copyright © 2017 Elsevier B.V. All rights reserved.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saylor, Kyle, E-mail: saylor@vt.edu; Zhang, Chenmi

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down intomore » different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. - Highlights: • Modelling of nicotine disposition in the presence of anti-nicotine antibodies • Key vaccine efficacy factors are evaluated in silico in rats and in humans. • Model predicts insufficient antibody binding in past human nicotine vaccines. • Improving immunogenicity and antibody specificity may lead to vaccine success.« less

Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

PubMed

Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

2018-02-01

The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

Changing the approach to treatment choice in epilepsy using big data.

PubMed

Devinsky, Orrin; Dilley, Cynthia; Ozery-Flato, Michal; Aharonov, Ranit; Goldschmidt, Ya'ara; Rosen-Zvi, Michal; Clark, Chris; Fritz, Patty

2016-03-01

A UCB-IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients. Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change. Based on the trained model, a model-predicted AED regimen with the lowest likelihood of treatment change was assigned to each patient in the group of test claims, and outcomes were evaluated to test model validity. The model had 72% area under receiver operator characteristic curve, indicating good predictive power. Patients who were given the model-predicted AED regimen had significantly longer survival rates (time until a treatment change event) and lower expected health resource utilization on average than those who received another treatment. The actual prescribed AED regimen at the index date matched the model-predicted AED regimen in only 13% of cases; there were large discrepancies in the frequency of use of certain AEDs/combinations between model-predicted AED regimens and those actually prescribed. Chances of treatment success were improved if patients received the model-predicted treatment. Using the model's prediction system may enable personalized, evidence-based epilepsy care, accelerating the match between patients and their ideal therapy, thereby delivering significantly better health outcomes for patients and providing health-care savings by applying resources more efficiently. Our goal will be to strengthen the predictive power of the model by integrating diverse data sets and potentially moving to prospective data collection. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Mechano-regulation of mesenchymal stem cell differentiation and collagen organisation during skeletal tissue repair.

PubMed

Nagel, Thomas; Kelly, Daniel J

2010-06-01

A number of mechano-regulation theories have been proposed that relate the differentiation pathway of mesenchymal stem cells (MSCs) to their local biomechanical environment. During spontaneous repair processes in skeletal tissues, the organisation of the extracellular matrix is a key determinant of its mechanical fitness. In this paper, we extend the mechano-regulation theory proposed by Prendergast et al. (J Biomech 30(6):539-548, 1997) to include the role of the mechanical environment on the collagen architecture in regenerating soft tissues. A large strain anisotropic poroelastic material model is used in a simulation of tissue differentiation in a fracture subject to cyclic bending (Cullinane et al. in J Orthop Res 20(3):579-586, 2002). The model predicts non-union with cartilage and fibrous tissue formation in the defect. Predicted collagen fibre angles, as determined by the principal decomposition of strain- and stress-type tensors, are similar to the architecture seen in native articular cartilage and neoarthroses induced by bending of mid-femoral defects in rats. Both stress and strain-based remodelling stimuli successfully predicted the general patterns of collagen fibre organisation observed in vivo. This provides further evidence that collagen organisation during tissue differentiation is determined by the mechanical environment. It is envisioned that such predictive models can play a key role in optimising MSC-based skeletal repair therapies where recapitulation of the normal tissue architecture is critical to successful repair.

Facial Shape Analysis Identifies Valid Cues to Aspects of Physiological Health in Caucasian, Asian, and African Populations.

PubMed

Stephen, Ian D; Hiew, Vivian; Coetzee, Vinet; Tiddeman, Bernard P; Perrett, David I

2017-01-01

Facial cues contribute to attractiveness, including shape cues such as symmetry, averageness, and sexual dimorphism. These cues may represent cues to objective aspects of physiological health, thereby conferring an evolutionary advantage to individuals who find them attractive. The link between facial cues and aspects of physiological health is therefore central to evolutionary explanations of attractiveness. Previously, studies linking facial cues to aspects of physiological health have been infrequent, have had mixed results, and have tended to focus on individual facial cues in isolation. Geometric morphometric methodology (GMM) allows a bottom-up approach to identifying shape correlates of aspects of physiological health. Here, we apply GMM to facial shape data, producing models that successfully predict aspects of physiological health in 272 Asian, African, and Caucasian faces - percentage body fat (21.0% of variance explained), body mass index (BMI; 31.9%) and blood pressure (BP; 21.3%). Models successfully predict percentage body fat and blood pressure even when controlling for BMI, suggesting that they are not simply measuring body size. Predicted values of BMI and BP, but not percentage body fat, correlate with health ratings. When asked to manipulate the shape of faces along the physiological health variable axes (as determined by the models), participants reduced predicted BMI, body fat and (marginally) BP, suggesting that facial shape provides a valid cue to aspects of physiological health.

Facial Shape Analysis Identifies Valid Cues to Aspects of Physiological Health in Caucasian, Asian, and African Populations

PubMed Central

Stephen, Ian D.; Hiew, Vivian; Coetzee, Vinet; Tiddeman, Bernard P.; Perrett, David I.

2017-01-01

Facial cues contribute to attractiveness, including shape cues such as symmetry, averageness, and sexual dimorphism. These cues may represent cues to objective aspects of physiological health, thereby conferring an evolutionary advantage to individuals who find them attractive. The link between facial cues and aspects of physiological health is therefore central to evolutionary explanations of attractiveness. Previously, studies linking facial cues to aspects of physiological health have been infrequent, have had mixed results, and have tended to focus on individual facial cues in isolation. Geometric morphometric methodology (GMM) allows a bottom–up approach to identifying shape correlates of aspects of physiological health. Here, we apply GMM to facial shape data, producing models that successfully predict aspects of physiological health in 272 Asian, African, and Caucasian faces – percentage body fat (21.0% of variance explained), body mass index (BMI; 31.9%) and blood pressure (BP; 21.3%). Models successfully predict percentage body fat and blood pressure even when controlling for BMI, suggesting that they are not simply measuring body size. Predicted values of BMI and BP, but not percentage body fat, correlate with health ratings. When asked to manipulate the shape of faces along the physiological health variable axes (as determined by the models), participants reduced predicted BMI, body fat and (marginally) BP, suggesting that facial shape provides a valid cue to aspects of physiological health. PMID:29163270

Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail.

PubMed

Woodin, Sarah A; Hilbish, Thomas J; Helmuth, Brian; Jones, Sierra J; Wethey, David S

2013-09-01

Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CTmax, and the upper lethal limit, defined as LTmax. If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure.

Increased odds of patient-reported success at 2 years after anterior cruciate ligament reconstruction in patients without cartilage lesions: a cohort study from the Swedish National Knee Ligament Register.

PubMed

Hamrin Senorski, Eric; Alentorn-Geli, Eduard; Musahl, Volker; Fu, Freddie; Krupic, Ferid; Desai, Neel; Westin, Olof; Samuelsson, Kristian

2018-04-01

To investigate whether the surgical technique of single-bundle anterior cruciate ligament (ACL) reconstruction, the visualization of anatomic surgical factors and the presence or absence of concomitant injuries at primary ACL reconstruction are able to predict patient-reported success and failure. The hypothesis of this study was that anatomic single-bundle surgical procedures would be predictive of patient-reported success. This cohort study was based on data from the Swedish National Knee Ligament Register during the period of 1 January 2005 through 31 December 2014. Patients who underwent primary single-bundle ACL reconstruction with hamstring tendons were included. Details on surgical technique were collected using an online questionnaire comprising essential anatomic anterior cruciate ligament reconstruction scoring checklist items, defined as the utilization of accessory medial portal drilling, anatomic tunnel placement, the visualization of insertion sites and pertinent landmarks. A univariate logistic regression model adjusted for age and gender was used to determine predictors of patient-reported success and failure, i.e. 20th and 80th percentile, respectively, in the Knee injury and Osteoarthritis Outcome Score (KOOS), 2 years after ACL reconstruction. In the 6889 included patients, the surgical technique used for single-bundle ACL reconstruction did not predict the predefined patient-reported success or patient-reported failure in the KOOS 4 . Patient-reported success was predicted by the absence of concomitant injury to the meniscus (OR = 0.81 [95% CI, 0.72-0.92], p = 0.001) and articular cartilage (OR = 0.70 [95% CI, 0.61-0.81], p < 0.001). Patient-reported failure was predicted by the presence of a concomitant injury to the articular cartilage (OR = 1.27 [95% CI, 1.11-1.44], p < 0.001). Surgical techniques used in primary single-bundle ACL reconstruction did not predict the KOOS 2 years after the reconstruction. However, the absence of concomitant injuries at index surgery predicted patient-reported success in the KOOS. The results provide further evidence that concomitant injuries at ACL reconstruction affect subjective knee function and a detailed knowledge of the treatment of these concomitant injuries is needed. Retrospective cohort study, Level III.

Template based protein structure modeling by global optimization in CASP11.

PubMed

Joo, Keehyoung; Joung, InSuk; Lee, Sun Young; Kim, Jong Yun; Cheng, Qianyi; Manavalan, Balachandran; Joung, Jong Young; Heo, Seungryong; Lee, Juyong; Nam, Mikyung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

2016-09-01

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Are your students ready for anatomy and physiology? Developing tools to identify students at risk for failure.

PubMed

Gultice, Amy; Witham, Ann; Kallmeyer, Robert

2015-06-01

High failure rates in introductory college science courses, including anatomy and physiology, are common at institutions across the country, and determining the specific factors that contribute to this problem is challenging. To identify students at risk for failure in introductory physiology courses at our open-enrollment institution, an online pilot survey was administered to 200 biology students. The survey results revealed several predictive factors related to academic preparation and prompted a comprehensive analysis of college records of >2,000 biology students over a 5-yr period. Using these historical data, a model that was 91% successful in predicting student success in these courses was developed. The results of the present study support the use of surveys and similar models to identify at-risk students and to provide guidance in the development of evidence-based advising programs and pedagogies. This comprehensive approach may be a tangible step in improving student success for students from a wide variety of backgrounds in anatomy and physiology courses. Copyright © 2015 The American Physiological Society.

The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise

PubMed Central

Boorman, Erie D.; O’Doherty, John P.; Adolphs, Ralph; Rangel, Antonio

2013-01-01

Summary Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. PMID:24360551

Modeling hydrology and in-stream transport on drained forested lands in coastal Carolinas, U.S.A.

Treesearch

Devendra Amatya

2005-01-01

This study summarizes the successional development and testing of forest hydrologic models based on DRAINMOD that predicts the hydrology of low-gradient poorly drained watersheds as affected by land management and climatic variation. The field scale (DRAINLOB) and watershed-scale in-stream routing (DRAINWAT) models were successfully tested with water table and outflow...

Models of vegetation change for landscape planning: a comparison of FETM, LANDSUM, SIMPPLLE, and VDDT

Treesearch

T. M. Barrett

2001-01-01

Landscape assessment and planning often depend on the ability to predict change of vegetation. This report compares four modeling systems (FETM, LANDSUM, SIMPPLLE, and VDDT) that can be used to understand changes resulting from succession, natural disturbance, and management activities. The four models may be useful for regional or local assessments in National Forest...

Evidence for complex contagion models of social contagion from observational data

PubMed Central

Sprague, Daniel A.

2017-01-01

Social influence can lead to behavioural ‘fads’ that are briefly popular and quickly die out. Various models have been proposed for these phenomena, but empirical evidence of their accuracy as real-world predictive tools has so far been absent. Here we find that a ‘complex contagion’ model accurately describes the spread of behaviours driven by online sharing. We found that standard, ‘simple’, contagion often fails to capture both the rapid spread and the long tails of popularity seen in real fads, where our complex contagion model succeeds. Complex contagion also has predictive power: it successfully predicted the peak time and duration of the ALS Icebucket Challenge. The fast spread and longer duration of fads driven by complex contagion has important implications for activities such as publicity campaigns and charity drives. PMID:28686719

Seeing what you want to see: priors for one's own actions represent exaggerated expectations of success

PubMed Central

Wolpe, Noham; Wolpert, Daniel M.; Rowe, James B.

2014-01-01

People perceive the consequences of their own actions differently to how they perceive other sensory events. A large body of psychology research has shown that people also consistently overrate their own performance relative to others, yet little is known about how these “illusions of superiority” are normally maintained. Here we examined the visual perception of the sensory consequences of self-generated and observed goal-directed actions. Across a series of visuomotor tasks, we found that the perception of the sensory consequences of one's own actions is more biased toward success relative to the perception of observed actions. Using Bayesian models, we show that this bias could be explained by priors that represent exaggerated predictions of success. The degree of exaggeration of priors was unaffected by learning, but was correlated with individual differences in trait optimism. In contrast, when observing these actions, priors represented more accurate predictions of the actual performance. The results suggest that the brain internally represents optimistic predictions for one's own actions. Such exaggerated predictions bind the sensory consequences of our own actions with our intended goal, explaining how it is that when acting we tend to see what we want to see. PMID:25018710

PARALLEL MEASUREMENT AND MODELING OF TRANSPORT IN THE DARHT II BEAMLINE ON ETA II

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chambers, F W; Raymond, B A; Falabella, S

To successfully tune the DARHT II transport beamline requires the close coupling of a model of the beam transport and the measurement of the beam observables as the beam conditions and magnet settings are varied. For the ETA II experiment using the DARHT II beamline components this was achieved using the SUICIDE (Simple User Interface Connecting to an Integrated Data Environment) data analysis environment and the FITS (Fully Integrated Transport Simulation) model. The SUICIDE environment has direct access to the experimental beam transport data at acquisition and the FITS predictions of the transport for immediate comparison. The FITS model ismore » coupled into the control system where it can read magnet current settings for real time modeling. We find this integrated coupling is essential for model verification and the successful development of a tuning aid for the efficient convergence on a useable tune. We show the real time comparisons of simulation and experiment and explore the successes and limitations of this close coupled approach.« less

The galaxy clustering crisis in abundance matching

NASA Astrophysics Data System (ADS)

Campbell, Duncan; van den Bosch, Frank C.; Padmanabhan, Nikhil; Mao, Yao-Yuan; Zentner, Andrew R.; Lange, Johannes U.; Jiang, Fangzhou; Villarreal, Antonio

2018-06-01

Galaxy clustering on small scales is significantly underpredicted by sub-halo abundance matching (SHAM) models that populate (sub-)haloes with galaxies based on peak halo mass, Mpeak. SHAM models based on the peak maximum circular velocity, Vpeak, have had much better success. The primary reason for Mpeak-based models fail is the relatively low abundance of satellite galaxies produced in these models compared to those based on Vpeak. Despite success in predicting clustering, a simple Vpeak-based SHAM model results in predictions for galaxy growth that are at odds with observations. We evaluate three possible remedies that could `save' mass-based SHAM: (1) SHAM models require a significant population of `orphan' galaxies as a result of artificial disruption/merging of sub-haloes in modern high-resolution dark matter simulations; (2) satellites must grow significantly after their accretion; and (3) stellar mass is significantly affected by halo assembly history. No solution is entirely satisfactory. However, regardless of the particulars, we show that popular SHAM models based on Mpeak cannot be complete physical models as presented. Either Vpeak truly is a better predictor of stellar mass at z ˜ 0 and it remains to be seen how the correlation between stellar mass and Vpeak comes about, or SHAM models are missing vital component(s) that significantly affect galaxy clustering.

Web-based decision support system to predict risk level of long term rice production

NASA Astrophysics Data System (ADS)

Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

2017-09-01

Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care

PubMed Central

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables—training, self-efficacy, system quality and information quality—on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time. PMID:29315323

Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care.

PubMed

Yu, Ping; Qian, Siyu

2018-01-01

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables-training, self-efficacy, system quality and information quality-on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time.

Selection, calibration, and validation of models of tumor growth.

PubMed

Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

2016-11-01

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

Estimating West Nile virus transmission period in Pennsylvania using an optimized degree-day model.

PubMed

Chen, Shi; Blanford, Justine I; Fleischer, Shelby J; Hutchinson, Michael; Saunders, Michael C; Thomas, Matthew B

2013-07-01

Abstract We provide calibrated degree-day models to predict potential West Nile virus (WNV) transmission periods in Pennsylvania. We begin by following the standard approach of treating the degree-days necessary for the virus to complete the extrinsic incubation period (EIP), and mosquito longevity as constants. This approach failed to adequately explain virus transmission periods based on mosquito surveillance data from 4 locations (Harrisburg, Philadelphia, Pittsburgh, and Williamsport) in Pennsylvania from 2002 to 2008. Allowing the EIP and adult longevity to vary across time and space improved model fit substantially. The calibrated models increase the ability to successfully predict the WNV transmission period in Pennsylvania to 70-80% compared to less than 30% in the uncalibrated model. Model validation showed the optimized models to be robust in 3 of the locations, although still showing errors for Philadelphia. These models and methods could provide useful tools to predict WNV transmission period from surveillance datasets, assess potential WNV risk, and make informed mosquito surveillance strategies.

The Use of Dynamic Stochastic Social Behavior Models to Produce Likelihood Functions for Risk Modeling of Proliferation and Terrorist Attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Jonathan; Thompson, Sandra E.; Brothers, Alan J.

The ability to estimate the likelihood of future events based on current and historical data is essential to the decision making process of many government agencies. Successful predictions related to terror events and characterizing the risks will support development of options for countering these events. The predictive tasks involve both technical and social component models. The social components have presented a particularly difficult challenge. This paper outlines some technical considerations of this modeling activity. Both data and predictions associated with the technical and social models will likely be known with differing certainties or accuracies – a critical challenge is linkingmore » across these model domains while respecting this fundamental difference in certainty level. This paper will describe the technical approach being taken to develop the social model and identification of the significant interfaces between the technical and social modeling in the context of analysis of diversion of nuclear material.« less

Modeling the Endogenous Sunlight Inactivation Rates of Laboratory Strain and Wastewater E. coli and Enterococci Using Biological Weighting Functions.

PubMed

Silverman, Andrea I; Nelson, Kara L

2016-11-15

Models that predict sunlight inactivation rates of bacteria are valuable tools for predicting the fate of pathogens in recreational waters and designing natural wastewater treatment systems to meet disinfection goals. We developed biological weighting function (BWF)-based numerical models to estimate the endogenous sunlight inactivation rates of E. coli and enterococci. BWF-based models allow the prediction of inactivation rates under a range of environmental conditions that shift the magnitude or spectral distribution of sunlight irradiance (e.g., different times, latitudes, water absorbances, depth). Separate models were developed for laboratory strain bacteria cultured in the laboratory and indigenous organisms concentrated directly from wastewater. Wastewater bacteria were found to be 5-7 times less susceptible to full-spectrum simulated sunlight than the laboratory bacteria, highlighting the importance of conducting experiments with bacteria sourced directly from wastewater. The inactivation rate models fit experimental data well and were successful in predicting the inactivation rates of wastewater E. coli and enterococci measured in clear marine water by researchers from a different laboratory. Additional research is recommended to develop strategies to account for the effects of elevated water pH on predicted inactivation rates.

QSAR Modeling of Rat Acute Toxicity by Oral Exposure

PubMed Central

Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

2009-01-01

Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

PubMed

Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander

2009-12-01

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

Direct CFD Predictions of Low Frequency Sounds Generated by a Helicopter Main Rotor

NASA Technical Reports Server (NTRS)

Sim, Ben W.; Potsdam, Mark A.; Conner, Dave A.; Conner, Dave A.; Watts, Michael E.

2010-01-01

The use of CFD to directly predict helicopter main rotor noise is shown to be quite promising as an alternative mean for low frequency source noise evaluation. Results using existing state-of-the-art grid structures and finite-difference schemes demonstrated that small perturbation pressures, associated with acoustics radiation, can be extracted with some degree of fidelity. Accuracy of the predictions are demonstrated via comparing to predictions from conventional acoustic analogy-based models, and with measurements obtained from wind tunnel and flight tests for the MD-902 helicopter at several operating conditions. Findings show that the direct CFD approach is quite successfully in yielding low frequency results due to thickness and steady loading noise mechanisms. Mid-to-high frequency contents, due to blade-vortex interactions, are not predicted due to CFD modeling and grid constraints.

Predicting the establishment success of introduced target species in grassland restoration by functional traits.

PubMed

Engst, Karina; Baasch, Annett; Bruelheide, Helge

2017-09-01

Species-rich semi-natural grasslands are highly endangered habitats in Central Europe and numerous restoration efforts have been made to compensate for the losses in the last decades. However, some plant species could become more easily established than others. The establishment success of 37 species was analyzed over 6 years at two study sites of a restoration project in Germany where hay transfer and sowing of threshing material in combination with additional sowing were applied. The effects of the restoration method applied, time since the restoration took place, traits related to germination, dispersal, and reproduction, and combinations of these traits on the establishment were analyzed. While the specific restoration method of how seeds were transferred played a subordinate role, the establishment success depended in particular on traits such as flower season or the lifeform. Species flowering in autumn, such as Pastinaca sativa and Serratula tinctoria , became established better than species flowering in other seasons, probably because they could complete their life cycle, resulting in increasingly stronger seed pressure with time. Geophytes, like Allium angulosum and Galium boreale , became established very poorly, but showed an increase with study duration. For various traits, we found significant trait by method and trait by year interactions, indicating that different traits promoted establishment under different conditions. Using a multi-model approach, we tested whether traits acted in combination. For the first years and the last year, we found that models with three traits explained establishment success better than models with a single trait or two traits. While traits had only an additive effect on the establishment success in the first years, trait interactions became important thereafter. The most important trait was the season of flowering, which occurred in all best models from the third year onwards. Overall, our approach revealed the potential of functional trait analysis to predict success in restoration projects.

Thermomechanical Controls on the Success and Failure of Continental Rift Systems

NASA Astrophysics Data System (ADS)

Brune, S.

2017-12-01

Studies of long-term continental rift evolution are often biased towards rifts that succeed in breaking the continent like the North Atlantic, South China Sea, or South Atlantic rifts. However there are many prominent rift systems on Earth where activity stopped before the formation of a new ocean basin such as the North Sea, the West and Central African Rifts, or the West Antarctic Rift System. The factors controlling the success and failure of rifts can be divided in two groups: (1) Intrinsic processes - for instance frictional weakening, lithospheric thinning, shear heating or the strain-dependent growth of rift strength by replacing weak crust with strong mantle. (2) External processes - such as a change of plate divergence rate, the waning of a far-field driving force, or the arrival of a mantle plume. Here I use numerical and analytical modeling to investigate the role of these processes for the success and failure of rift systems. These models show that a change of plate divergence rate under constant force extension is controlled by the non-linearity of lithospheric materials. For successful rifts, a strong increase in divergence velocity can be expected to take place within few million years, a prediction that agrees with independent plate tectonic reconstructions of major Mesozoic and Cenozoic ocean-forming rift systems. Another model prediction is that oblique rifting is mechanically favored over orthogonal rifting, which means that simultaneous deformation within neighboring rift systems of different obliquity and otherwise identical properties will lead to success and failure of the more and less oblique rift, respectively. This can be exemplified by the Cretaceous activity within the Equatorial Atlantic and the West African Rifts that lead to the formation of a highly oblique oceanic spreading center and the failure of the West African Rift System. While in nature the circumstances of rift success or failure may be manifold, simplified numerical and analytical models allow the isolated analysis of various contributing factors and to define a characteristic time scale for each process.

Characterizing Ship Navigation Patterns Using Automatic Identification System (AIS) Data in the Baltic Sea

DTIC Science & Technology

in the Saint Petersburg area. We use three random forest models, that differ in their use of past information , to predict a vessels next port of visit...network where past information is used to more accurately predict the future state. The transitional probabilities change when predictor variables are...added that reach deeper into the past. Our findings suggest that successful prediction of the movement of a vessel depends on having accurate information on its recent history.

A general-purpose machine learning framework for predicting properties of inorganic materials

DOE PAGES

Ward, Logan; Agrawal, Ankit; Choudhary, Alok; ...

2016-08-26

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less

Linear bubble plume model for hypolimnetic oxygenation: Full-scale validation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Singleton, V. L.; Gantzer, P.; Little, J. C.

2007-02-01

An existing linear bubble plume model was improved, and data collected from a full-scale diffuser installed in Spring Hollow Reservoir, Virginia, were used to validate the model. The depth of maximum plume rise was simulated well for two of the three diffuser tests. Temperature predictions deviated from measured profiles near the maximum plume rise height, but predicted dissolved oxygen profiles compared very well with observations. A sensitivity analysis was performed. The gas flow rate had the greatest effect on predicted plume rise height and induced water flow rate, both of which were directly proportional to gas flow rate. Oxygen transfer within the hypolimnion was independent of all parameters except initial bubble radius and was inversely proportional for radii greater than approximately 1 mm. The results of this work suggest that plume dynamics and oxygen transfer can successfully be predicted for linear bubble plumes using the discrete-bubble approach.

A general-purpose machine learning framework for predicting properties of inorganic materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ward, Logan; Agrawal, Ankit; Choudhary, Alok

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

PubMed

Xu, Xilin; Wu, Aiping; Zhang, Xinlei; Su, Mingming; Jiang, Taijiao; Yuan, Zhe-Ming

2016-01-01

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).

Admission Models for At-Risk Graduate Students in Different Academic Disciplines.

ERIC Educational Resources Information Center

Nelson, C. Van; Nelson, Jacquelyn S.; Malone, Bobby G.

In this study, models were constructed for eight academic areas, including applied sciences, communication sciences, education, physical sciences, life sciences, humanities and arts, psychology, and social sciences, to predict whether or not an at-risk graduate student would be successful in obtaining a master's degree. Records were available for…

Predictive modeling of infrared radiative heating in tomato dry-peeling process: Part I. Model development

USDA-ARS?s Scientific Manuscript database

Infrared (IR) dry-peeling has emerged as an effective non-chemical alternative to conventional lye and steam methods of peeling tomatoes. Successful peel separation induced by IR radiation requires the delivery of a sufficient amount of thermal energy onto tomato surface in a very short duration. Th...

Understanding the Behaviors of Stealth Applicants in the College Search Process

ERIC Educational Resources Information Center

Dupaul, Stephanie

2010-01-01

Successful enrollment management uses predictive modeling to achieve specific goals for admission rates, yield rates, and class size. Many of these models rely on evaluating an applicant's interest in the institution through measures of pre-application engagement. Recent increases in the number of applicants who do not visibly interact with…

Visible Machine Learning for Biomedicine.

PubMed

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

Cue Salience and Infant Perseverative Reaching: Tests of the Dynamic Field Theory

ERIC Educational Resources Information Center

Clearfield, Melissa W.; Dineva, Evelina; Smith, Linda B.; Diedrich, Frederick J.; Thelen, Esther

2009-01-01

Skilled behavior requires a balance between previously successful behaviors and new behaviors appropriate to the present context. We describe a dynamic field model for understanding this balance in infant perseverative reaching. The model predictions are tested with regard to the interaction of two aspects of the typical perseverative reaching…

Cost Perception and the Expectancy-Value Model of Achievement Motivation.

ERIC Educational Resources Information Center

Anderson, Patricia N.

The expectancy-value model of achievement motivation, first described by J. Atkinson (1957) and refined by J. Eccles and her colleagues (1983, 1992, 1994) predicts achievement motivation based on expectancy for success and perceived task value. Cost has been explored very little. To explore the possibility that cost is different from expectancy…

Micro-mechanics modelling of smart materials

NASA Astrophysics Data System (ADS)

Shah, Syed Asim Ali

Metal Matrix ceramic-reinforced composites are rapidly becoming strong candidates as structural materials for many high temperature and engineering applications. Metal matrix composites (MMC) combine the ductile properties of the matrix with a brittle phase of the reinforcement, leading to high stiffness and strength with a reduction in structural weight. The main objective of using a metal matrix composite system is to increase service temperature or improve specific mechanical properties of structural components by replacing existing super alloys.The purpose of the study is to investigate, develop and implement second phase reinforcement alloy strengthening empirical model with SiCp reinforced A359 aluminium alloy composites on the particle-matrix interface and the overall mechanical properties of the material.To predict the interfacial fracture strength of aluminium, in the presence of silicon segregation, an empirical model has been modified. This model considers the interfacial energy caused by segregation of impurities at the interface and uses Griffith crack type arguments to predict the formation energies of impurities at the interface. Based on this, model simulations were conducted at nano scale specifically at the interface and the interfacial strengthening behaviour of reinforced aluminium alloy system was expressed in terms of elastic modulus.The numerical model shows success in making prediction possible of trends in relation to segregation and interfacial fracture strength behaviour in SiC particle-reinforced aluminium matrix composites. The simulation models using various micro scale modelling techniques to the aluminum alloy matrix composite, strengthenedwith varying amounts of silicon carbide particulate were done to predict the material state at critical points with properties of Al-SiC which had been heat treated.In this study an algorithm is developed to model a hard ceramic particle in a soft matrix with a clear distinct interface and a strain based relationship has been proposed for the strengthening behaviour of the MMC at the interface rather than stress based, by successfully completing the numerical modelling of particulate reinforced metal matrix composites.

Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

PubMed

Holland, E Penelope; James, Alex; Ruscoe, Wendy A; Pech, Roger P; Byrom, Andrea E

2015-01-01

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

General overview on structure prediction of twilight-zone proteins.

PubMed

Khor, Bee Yin; Tye, Gee Jun; Lim, Theam Soon; Choong, Yee Siew

2015-09-04

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

Analysis of phase II studies on targeted agents and subsequent phase III trials: what are the predictors for success?

PubMed

Chan, John K; Ueda, Stefanie M; Sugiyama, Valerie E; Stave, Christopher D; Shin, Jacob Y; Monk, Bradley J; Sikic, Branimir I; Osann, Kathryn; Kapp, Daniel S

2008-03-20

To identify the characteristics of phase II studies that predict for subsequent "positive" phase III trials (those that reached the proposed primary end points of study or those wherein the study drug was superior to the standard regimen investigating targeted agents in advanced tumors. We identified all phase III clinical trials of targeted therapies against advanced cancers published from 1985 to 2005. Characteristics of the preceding phase II studies were reviewed to identify predictive factors for success of the subsequent phase III trial. Data were analyzed using the chi(2) test and logistic regression models. Of 351 phase II studies, 167 (47.6%) subsequent phase III trials were positive and 184 (52.4%) negative. Phase II studies from multiple rather than single institutions were more likely to precede a successful trial (60.4% v 39.4%; P < .001). Positive phase II results were more likely to lead to a successful phase III trial (50.8% v 22.5%; P = .003). The percentage of successful trials from pharmaceutical companies was significantly higher compared with academic, cooperative groups, and research institutes (89.5% v 44.2%, 45.2%, and 46.3%, respectively; P = .002). On multivariate analysis, these factors and shorter time interval between publication of phase II results and III study publication were independent predictive factors for a positive phase III trial. In phase II studies of targeted agents, multiple- versus single-institution participation, positive phase II trial, pharmaceutical company-based trials, and shorter time period between publication of phase II to phase III trial were independent predictive factors of success in a phase III trial. Investigators should be cognizant of these factors in phase II studies before designing phase III trials.

A simple nonlinear model for the return to isotropy in turbulence

NASA Technical Reports Server (NTRS)

Sarkar, Sutanu; Speziale, Charles G.

1990-01-01

A quadratic nonlinear generalization of the linear Rotta model for the slow pressure-strain correlation of turbulence is developed. The model is shown to satisfy realizability and to give rise to no stable nontrivial equilibrium solutions for the anisotropy tensor in the case of vanishing mean velocity gradients. The absence of stable nontrivial equilibrium solutions is a necessary condition to ensure that the model predicts a return to isotropy for all relaxational turbulent flows. Both the phase space dynamics and the temporal behavior of the model are examined and compared against experimental data for the return to isotropy problem. It is demonstrated that the quadratic model successfully captures the experimental trends which clearly exhibit nonlinear behavior. Direct comparisons are also made with the predictions of the Rotta model and the Lumley model.

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335

Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone

NASA Astrophysics Data System (ADS)

Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo

2017-12-01

The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.

Modeling protein complexes with BiGGER.

PubMed

Krippahl, Ludwig; Moura, José J; Palma, P Nuno

2003-07-01

This article describes the method and results of our participation in the Critical Assessment of PRediction of Interactions (CAPRI) experiment, using the protein docking program BiGGER (Bimolecular complex Generation with Global Evaluation and Ranking) (Palma et al., Proteins 2000;39:372-384). Of five target complexes (CAPRI targets 2, 4, 5, 6, and 7), only one was successfully predicted (target 6), but BiGGER generated reasonable models for targets 4, 5, and 7, which could have been identified if additional biochemical information had been available. Copyright 2003 Wiley-Liss, Inc.

Interactive effects of prey and weather on golden eagle reproduction

USGS Publications Warehouse

Steenhof, Karen; Kochert, Michael N.; McDonald, T.L.

1997-01-01

1. The reproduction of the golden eagle Aquila chrysaetos was studied in southwestern Idaho for 23 years, and the relationship between eagle reproduction and jackrabbit Lepus californicus abundance, weather factors, and their interactions, was modelled using general linear models. Backward elimination procedures were used to arrive at parsimonious models.2. The number of golden eagle pairs occupying nesting territories each year showed a significant decline through time that was unrelated to either annual rabbit abundance or winter severity. However, eagle hatching dates were significantly related to both winter severity and jackrabbit abundance. Eagles hatched earlier when jackrabbits were abundant, and they hatched later after severe winters.3. Jackrabbit abundance influenced the proportion of pairs that laid eggs, the proportion of pairs that were successful, mean brood size at fledging, and the number of young fledged per pair. Weather interacted with prey to influence eagle reproductive rates.4. Both jackrabbit abundance and winter severity were important in predicting the percentage of eagle pairs that laid eggs. Percentage laying was related positively to jackrabbit abundance and inversely related to winter severity.5. The variables most useful in predicting percentage of laying pairs successful were rabbit abundance and the number of extremely hot days during brood-rearing. The number of hot days and rabbit abundance were also significant in a model predicting eagle brood size at fledging. Both success and brood size were positively related to jackrabbit abundance and inversely related to the frequency of hot days in spring.6. Eagle reproduction was limited by rabbit abundance during approximately twothirds of the years studied. Weather influenced how severely eagle reproduction declined in those years.7. This study demonstrates that prey and weather can interact to limit a large raptor population's productivity. Smaller raptors could be affected more strongly, especially in colder or wetter climates.

Origins of the high flux hohlraum model

NASA Astrophysics Data System (ADS)

Rosen, M. D.; Hinkel, D. E.; Williams, E. A.; Callahan, D. A.; Town, R. P. J.; Scott, H. A.; Kruer, W. L.; Suter, L. J.

2010-11-01

We review how the ``high flux model'' (HFM) helped clarify the performance of the Autumn 09 National Ignition Campaign (NIC) gas filled/capsule imploding hohlraum energetics campaign. This campaign showed good laser-hohlraum coupling, reasonably high drive, and implosion symmetry control via cross beam transfer. Mysteries that remained included the level and spectrum of the Stimulated Raman light, the tendency towards pancaked implosions, and drive that exceeded (standard model) predictions early in the campaign, and lagged those predictions late in the campaign. The HFM uses a detailed configuration accounting (DCA) atomic physics and a generous flux limiter (f=0.2) both of which contribute to predicting a hohlraum plasma that is cooler than the standard, XSN average atom, f=0.05 model. This cooler plasma proved to be key in solving all of those mysteries. Despite past successes of the HFM in correctly modeling Omega Laser Au sphere data and NIC empty hohlraum drive, the model lacked some credibility for this energetics campaign, because it predicted too much hohlraum drive. Its credibility was then boosted by a re-evaluation of the initially reported SRS levels.

Jet Noise Modeling for Supersonic Business Jet Application

NASA Technical Reports Server (NTRS)

Stone, James R.; Krejsa, Eugene A.; Clark, Bruce J.

2004-01-01

This document describes the development of an improved predictive model for coannular jet noise, including noise suppression modifications applicable to small supersonic-cruise aircraft such as the Supersonic Business Jet (SBJ), for NASA Langley Research Center (LaRC). For such aircraft a wide range of propulsion and integration options are under consideration. Thus there is a need for very versatile design tools, including a noise prediction model. The approach used is similar to that used with great success by the Modern Technologies Corporation (MTC) in developing a noise prediction model for two-dimensional mixer ejector (2DME) nozzles under the High Speed Research Program and in developing a more recent model for coannular nozzles over a wide range of conditions. If highly suppressed configurations are ultimately required, the 2DME model is expected to provide reasonable prediction for these smaller scales, although this has not been demonstrated. It is considered likely that more modest suppression approaches, such as dual stream nozzles featuring chevron or chute suppressors, perhaps in conjunction with inverted velocity profiles (IVP), will be sufficient for the SBJ.

Amasia and Supercontinent Formation by Orthoversion

NASA Astrophysics Data System (ADS)

Mitchell, R. N.; Evans, D. A.; Kilian, T. M.

2015-12-01

Traditional models of the supercontinent cycle predict that the next supercontinent—'Amasia'—will form either where Pangaea rifted (the 'introversion' model) or on the opposite side of the world (the 'extroversion' models). In contrast, a new model termed "orthoversion", predicts a new supercontinent will form 90° away from the previous one, somewhere along the subduction girdle encircling its predecessor. As continents are expected to aggregate over mantle convective downwellings, orthoversion predicts that a continent central to the assembling supercontinent would plot 90° away from the center of the dispersing continent. Supercontinent centers can be quantified by identifying the long-lived axis of oscillatory true polar wander associated with each supercontinent cycle; measuring the angle between two successive true polar wander axes allows one to test between the various models (0˚, 90˚, or 180˚) of supercontinent formation. The past three supercontinents (Pangea, Rodinia, and Nuna) appear to follow the 90° "orthoversion" model closely. Orthoversion predicts that Amasia will take form ~100 million years from now over the North Pole by closing the Caribbean and Artic oceans which are located in Pangea's subduction girdle.

Development of a Model to Predict the Primary Infection Date of Bacterial Spot (Xanthomonas campestris pv. vesicatoria) on Hot Pepper.

PubMed

Kim, Ji-Hoon; Kang, Wee-Soo; Yun, Sung-Chul

2014-06-01

A population model of bacterial spot caused by Xanthomonas campestris pv. vesicatoria on hot pepper was developed to predict the primary disease infection date. The model estimated the pathogen population on the surface and within the leaf of the host based on the wetness period and temperature. For successful infection, at least 5,000 cells/ml of the bacterial population were required. Also, wind and rain were necessary according to regression analyses of the monitored data. Bacterial spot on the model is initiated when the pathogen population exceeds 10(15) cells/g within the leaf. The developed model was validated using 94 assessed samples from 2000 to 2007 obtained from monitored fields. Based on the validation study, the predicted initial infection dates varied based on the year rather than the location. Differences in initial infection dates between the model predictions and the monitored data in the field were minimal. For example, predicted infection dates for 7 locations were within the same month as the actual infection dates, 11 locations were within 1 month of the actual infection, and only 3 locations were more than 2 months apart from the actual infection. The predicted infection dates were mapped from 2009 to 2012; 2011 was the most severe year. Although the model was not sensitive enough to predict disease severity of less than 0.1% in the field, our model predicted bacterial spot severity of 1% or more. Therefore, this model can be applied in the field to determine when bacterial spot control is required.

Development of a Model to Predict the Primary Infection Date of Bacterial Spot (Xanthomonas campestris pv. vesicatoria) on Hot Pepper

PubMed Central

Kim, Ji-Hoon; Kang, Wee-Soo; Yun, Sung-Chul

2014-01-01

A population model of bacterial spot caused by Xanthomonas campestris pv. vesicatoria on hot pepper was developed to predict the primary disease infection date. The model estimated the pathogen population on the surface and within the leaf of the host based on the wetness period and temperature. For successful infection, at least 5,000 cells/ml of the bacterial population were required. Also, wind and rain were necessary according to regression analyses of the monitored data. Bacterial spot on the model is initiated when the pathogen population exceeds 1015 cells/g within the leaf. The developed model was validated using 94 assessed samples from 2000 to 2007 obtained from monitored fields. Based on the validation study, the predicted initial infection dates varied based on the year rather than the location. Differences in initial infection dates between the model predictions and the monitored data in the field were minimal. For example, predicted infection dates for 7 locations were within the same month as the actual infection dates, 11 locations were within 1 month of the actual infection, and only 3 locations were more than 2 months apart from the actual infection. The predicted infection dates were mapped from 2009 to 2012; 2011 was the most severe year. Although the model was not sensitive enough to predict disease severity of less than 0.1% in the field, our model predicted bacterial spot severity of 1% or more. Therefore, this model can be applied in the field to determine when bacterial spot control is required. PMID:25288995

Predicting VO[subscript 2max] in College-Aged Participants Using Cycle Ergometry and Perceived Functional Ability

ERIC Educational Resources Information Center

Nielson, David E.; George, James D.; Vehrs, Pat R.; Hager, Ron L.; Webb, Carrie V.

2010-01-01

The purpose of this study was to develop a multiple linear regression model to predict treadmill VO[subscript 2max] scores using both exercise and non-exercise data. One hundred five college-aged participants (53 male, 52 female) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test on a motorized treadmill.…

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

Successful N{sub 2} leptogenesis with flavour coupling effects in realistic unified models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bari, Pasquale Di; King, Stephen F.

2015-10-02

In realistic unified models involving so-called SO(10)-inspired patterns of Dirac and heavy right-handed (RH) neutrino masses, the lightest right-handed neutrino N{sub 1} is too light to yield successful thermal leptogenesis, barring highly fine tuned solutions, while the second heaviest right-handed neutrino N{sub 2} is typically in the correct mass range. We show that flavour coupling effects in the Boltzmann equations may be crucial to the success of such N{sub 2} dominated leptogenesis, by helping to ensure that the flavour asymmetries produced at the N{sub 2} scale survive N{sub 1} washout. To illustrate these effects we focus on N{sub 2} dominatedmore » leptogenesis in an existing model, the A to Z of flavour with Pati-Salam, where the neutrino Dirac mass matrix may be equal to an up-type quark mass matrix and has a particular constrained structure. The numerical results, supported by analytical insight, show that in order to achieve successful N{sub 2} leptogenesis, consistent with neutrino phenomenology, requires a “flavour swap scenario” together with a less hierarchical pattern of RH neutrino masses than naively expected, at the expense of some mild fine-tuning. In the considered A to Z model neutrino masses are predicted to be normal ordered, with an atmospheric neutrino mixing angle well into the second octant and the Dirac phase δ≃20{sup ∘}, a set of predictions that will be tested in the next years in neutrino oscillation experiments. Flavour coupling effects may be relevant for other SO(10)-inspired unified models where N{sub 2} leptogenesis is necessary.« less

Successful N{sub 2} leptogenesis with flavour coupling effects in realistic unified models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bari, Pasquale Di; King, Stephen F., E-mail: P.Di-Bari@soton.ac.uk, E-mail: king@soton.ac.uk

2015-10-01

In realistic unified models involving so-called SO(10)-inspired patterns of Dirac and heavy right-handed (RH) neutrino masses, the lightest right-handed neutrino N{sub 1} is too light to yield successful thermal leptogenesis, barring highly fine tuned solutions, while the second heaviest right-handed neutrino N{sub 2} is typically in the correct mass range. We show that flavour coupling effects in the Boltzmann equations may be crucial to the success of such N{sub 2} dominated leptogenesis, by helping to ensure that the flavour asymmetries produced at the N{sub 2} scale survive N{sub 1} washout. To illustrate these effects we focus on N{sub 2} dominatedmore » leptogenesis in an existing model, the A to Z of flavour with Pati-Salam, where the neutrino Dirac mass matrix may be equal to an up-type quark mass matrix and has a particular constrained structure. The numerical results, supported by analytical insight, show that in order to achieve successful N{sub 2} leptogenesis, consistent with neutrino phenomenology, requires a ''flavour swap scenario'' together with a less hierarchical pattern of RH neutrino masses than naively expected, at the expense of some mild fine-tuning. In the considered A to Z model neutrino masses are predicted to be normal ordered, with an atmospheric neutrino mixing angle well into the second octant and the Dirac phase δ≅ 20{sup o}, a set of predictions that will be tested in the next years in neutrino oscillation experiments. Flavour coupling effects may be relevant for other SO(10)-inspired unified models where N{sub 2} leptogenesis is necessary.« less

Population Physiologically-Based Pharmacokinetic Modeling for the Human Lactational Transfer of PCB 153 with Consideration of Worldwide Human Biomonitoring Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Redding, Laurel E.; Sohn, Michael D.; McKone, Thomas E.

2008-03-01

We developed a physiologically based pharmacokinetic model of PCB 153 in women, and predict its transfer via lactation to infants. The model is the first human, population-scale lactational model for PCB 153. Data in the literature provided estimates for model development and for performance assessment. Physiological parameters were taken from a cohort in Taiwan and from reference values in the literature. We estimated partition coefficients based on chemical structure and the lipid content in various body tissues. Using exposure data in Japan, we predicted acquired body burden of PCB 153 at an average childbearing age of 25 years and comparemore » predictions to measurements from studies in multiple countries. Forward-model predictions agree well with human biomonitoring measurements, as represented by summary statistics and uncertainty estimates. The model successfully describes the range of possible PCB 153 dispositions in maternal milk, suggesting a promising option for back estimating doses for various populations. One example of reverse dosimetry modeling was attempted using our PBPK model for possible exposure scenarios in Canadian Inuits who had the highest level of PCB 153 in their milk in the world.« less

Modelling seagrass growth and development to evaluate transplanting strategies for restoration.

PubMed

Renton, Michael; Airey, Michael; Cambridge, Marion L; Kendrick, Gary A

2011-10-01

Seagrasses are important marine plants that are under threat globally. Restoration by transplanting vegetative fragments or seedlings into areas where seagrasses have been lost is possible, but long-term trial data are limited. The goal of this study is to use available short-term data to predict long-term outcomes of transplanting seagrass. A functional-structural plant model of seagrass growth that integrates data collected from short-term trials and experiments is presented. The model was parameterized for the species Posidonia australis, a limited validation of the model against independent data and a sensitivity analysis were conducted and the model was used to conduct a preliminary evaluation of different transplanting strategies. The limited validation was successful, and reasonable long-term outcomes could be predicted, based only on short-term data. This approach for modelling seagrass growth and development enables long-term predictions of the outcomes to be made from different strategies for transplanting seagrass, even when empirical long-term data are difficult or impossible to collect. More validation is required to improve confidence in the model's predictions, and inclusion of more mechanism will extend the model's usefulness. Marine restoration represents a novel application of functional-structural plant modelling.

Nuclear Graphite - Fracture Behavior and Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burchell, Timothy D; Battiste, Rick; Strizak, Joe P

2011-01-01

Evidence for the graphite fracture mechanism is reviewed and discussed. The roles of certain microstructural features in the graphite fracture process are reported. The Burchell fracture model is described and its derivation reported. The successful application of the fracture model to uniaxial tensile data from several graphites with widely ranging structure and texture is reported. The extension of the model to multiaxial loading scenarios using two criteria is discussed. Initially, multiaxial strength data for H-451 graphite were modeled using the fracture model and the Principle of Independent Action. The predicted 4th stress quadrant failure envelope was satisfactory but the 1stmore » quadrant predictions were not conservative and thus were unsatisfactory. Multiaxial strength data from the 1st and 4th stress quadrant for NBG-18 graphite are reported. To improve the conservatism of the predicted 1st quadrant failure envelope for NBG-18 the Shetty criterion has been applied to obtain the equivalent critical stress intensity factor, KIc (Equi), for each applied biaxial stress ratio. The equivalent KIc value is used in the Burchell fracture model to predict the failure envelope. The predicted 1st stress quadrant failure envelope is conservative and thus more satisfactory than achieved previously using the fracture model combined with the Principle of Independent Action.« less

Robust predictive cruise control for commercial vehicles

NASA Astrophysics Data System (ADS)

Junell, Jaime; Tumer, Kagan

2013-10-01

In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.

Prediction of compressibility parameters of the soils using artificial neural network.

PubMed

Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

2016-01-01

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

Dynamic finite element method modeling of the upper shelf energy of precracked Charpy specimens of neutron irradiated weld metal 72W

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, A.S.; Sidener, S.E.; Hamilton, M.L.

1999-10-01

Dynamic finite element modeling of the fracture behavior of fatigue-precracked Charpy specimens in both unirradiated and irradiated conditions was performed using a computer code, ABAQUS Explicit, to predict the upper shelf energy of precracked specimens of a given size from experimental data obtained for a different size. A tensile fracture-strain based method for modeling crack extension and propagation was used. It was found that the predicted upper shelf energies of full and half size precracked specimens based on third size data were in reasonable agreement with their respective experimental values. Similar success was achieved for predicting the upper shelf energymore » of subsize precracked specimens based on full size data.« less

Development of Detonation Modeling Capabilities for Rocket Test Facilities: Hydrogen-Oxygen-Nitrogen Mixtures

NASA Technical Reports Server (NTRS)

Allgood, Daniel C.

2016-01-01

The objective of the presented work was to develop validated computational fluid dynamics (CFD) based methodologies for predicting propellant detonations and their associated blast environments. Applications of interest were scenarios relevant to rocket propulsion test and launch facilities. All model development was conducted within the framework of the Loci/CHEM CFD tool due to its reliability and robustness in predicting high-speed combusting flow-fields associated with rocket engines and plumes. During the course of the project, verification and validation studies were completed for hydrogen-fueled detonation phenomena such as shock-induced combustion, confined detonation waves, vapor cloud explosions, and deflagration-to-detonation transition (DDT) processes. The DDT validation cases included predicting flame acceleration mechanisms associated with turbulent flame-jets and flow-obstacles. Excellent comparison between test data and model predictions were observed. The proposed CFD methodology was then successfully applied to model a detonation event that occurred during liquid oxygen/gaseous hydrogen rocket diffuser testing at NASA Stennis Space Center.

Eulerian particle flamelet modeling of a bluff-body CH{sub 4}/H{sub 2} flame

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odedra, Anand; Malalasekera, W.

2007-11-15

In this paper an axisymmetric RANS simulation of a bluff-body stabilized flame has been attempted using steady and unsteady flamelet models. The unsteady effects are considered in a postprocessing manner through the Eulerian particle flamelet model (EPFM). In this model the transient history of scalar dissipation rate, conditioned by stoichiometric mixture fraction, is required to generate unsteady flamelets and is obtained by tracing Eulerian particles. In this approach unsteady convective-diffusive transport equations are solved to consider the transport of Eulerian particles in the domain. Comparisons of the results of steady and unsteady calculations show that transient effects do not havemore » much influence on major species, including OH, and the structure of the flame therefore can be successfully predicted by steady or unsteady approaches. However, it appears that slow processes such as NO formation can only be captured accurately if unsteady effects are taken into account, while steady simulations tend to overpredict NO. In this work turbulence has been modeled using the Reynolds stress model. Predictions of velocity, velocity rms, mean mixture fraction, and its rms show very good agreement with experiments. Performance of three detailed chemical mechanisms, the GRI Mech 2.11, the San Diego mechanism, and the GRI Mech 3.0, has also been evaluated in this study. All three mechanisms performed well with both steady and unsteady approaches and produced almost identical results for major species and OH. However, the difference between mechanisms and flamelet models becomes clearly apparent in the NO predictions. The unsteady model incorporating the GRI Mech 2.11 provided better predictions of NO than steady calculations and showed close agreement with experiments. The other two mechanisms showed overpredictions of NO with both unsteady and steady models. The level of overprediction is severe with the steady approach. GRI Mech 3.0 appears to overpredict NO by a factor of 2 compared to GRI Mech 2.11. The NO predictions by the San Diego mechanism fall between those of the two GRI mechanisms. The present study demonstrates the success of the EPFM model and when used with the GRI 2.11 mechanism predicts all flame properties and major and minor species very well, and most importantly the correct NO levels. (author)« less

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

PubMed Central

Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

2015-01-01

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations. PMID:26206368

Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Trave-Massuyes, Louise

2004-01-01

Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.

In-situ biogas upgrading process: Modeling and simulations aspects.

PubMed

Lovato, Giovanna; Alvarado-Morales, Merlin; Kovalovszki, Adam; Peprah, Maria; Kougias, Panagiotis G; Rodrigues, José Alberto Domingues; Angelidaki, Irini

2017-12-01

Biogas upgrading processes by in-situ hydrogen (H 2 ) injection are still challenging and could benefit from a mathematical model to predict system performance. Therefore, a previous model on anaerobic digestion was updated and expanded to include the effect of H 2 injection into the liquid phase of a fermenter with the aim of modeling and simulating these processes. This was done by including hydrogenotrophic methanogen kinetics for H 2 consumption and inhibition effect on the acetogenic steps. Special attention was paid to gas to liquid transfer of H 2 . The final model was successfully validated considering a set of Case Studies. Biogas composition and H 2 utilization were correctly predicted, with overall deviation below 10% compared to experimental measurements. Parameter sensitivity analysis revealed that the model is highly sensitive to the H 2 injection rate and mass transfer coefficient. The model developed is an effective tool for predicting process performance in scenarios with biogas upgrading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Experimental prediction of severe droughts on seasonal to intra-annual time scales with GFDL High-Resolution Atmosphere Model

NASA Astrophysics Data System (ADS)

Yu, Z.; Lin, S.

2011-12-01

Regional heat waves and drought have major economic and societal impacts on regional and even global scales. For example, during and following the 2010-2011 La Nina period, severe droughts have been reported in many places around the world including China, the southern US, and the east Africa, causing severe hardship in China and famine in east Africa. In this study, we investigate the feasibility and predictability of severe spring-summer draught events, 3 to 6 months in advance with the 25-km resolution Geophysical Fluid Dynamics Laboratory High-Resolution Atmosphere Model (HiRAM), which is built as a seamless weather-climate model, capable of long-term climate simulations as well as skillful seasonal predictions (e.g., Chen and Lin 2011, GRL). We adopted a similar methodology and the same (HiRAM) model as in Chen and Lin (2011), which is used successfully for seasonal hurricane predictions. A series of initialized 7-month forecasts starting from Dec 1 are performed each year (5 members each) during the past decade (2000-2010). We will then evaluate the predictability of the severe drought events during this period by comparing model predictions vs. available observations. To evaluate the predictive skill, in this preliminary report, we will focus on the anomalies of precipitation, sea-level-pressure, and 500-mb height. These anomalies will be computed as the individual model prediction minus the mean climatology obtained by an independent AMIP-type "simulation" using observed SSTs (rather than using predictive SSTs in the forecasts) from the same model.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

PubMed

Kilambi, Krishna Praneeth; Pacella, Michael S; Xu, Jianqing; Labonte, Jason W; Porter, Justin R; Muthu, Pravin; Drew, Kevin; Kuroda, Daisuke; Schueler-Furman, Ora; Bonneau, Richard; Gray, Jeffrey J

2013-12-01

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems. Copyright © 2013 Wiley Periodicals, Inc.

WHAT PREDICTS A SUCCESSFUL LIFE? A LIFE-COURSE MODEL OF WELL-BEING*

PubMed Central

Layard, Richard; Clark, Andrew E.; Cornaglia, Francesca; Powdthavee, Nattavudh; Vernoit, James

2014-01-01

Policy-makers who care about well-being need a recursive model of how adult life-satisfaction is predicted by childhood influences, acting both directly and (indirectly) through adult circumstances. We estimate such a model using the British Cohort Study (1970). We show that the most powerful childhood predictor of adult life-satisfaction is the child’s emotional health, followed by the child’s conduct. The least powerful predictor is the child’s intellectual development. This may have implications for educational policy. Among adult circumstances, family income accounts for only 0.5% of the variance of life-satisfaction. Mental and physical health are much more important. PMID:25422527

Environmental factors limiting fertilisation and larval success in corals

NASA Astrophysics Data System (ADS)

Woods, Rachael M.; Baird, Andrew H.; Mizerek, Toni L.; Madin, Joshua S.

2016-12-01

Events in the early life history of reef-building corals, including fertilisation and larval survival, are susceptible to changes in the chemical and physical properties of sea water. Quantifying how changes in water quality affect these events is therefore important for understanding and predicting population establishment in novel and changing environments. A review of the literature identified that levels of salinity, temperature, pH, suspended sediment, nutrients and heavy metals affect coral early life-history stages to various degrees. In this study, we combined published experimental data to determine the relative importance of sea water properties for coral fertilisation success and larval survivorship. Of the water properties manipulated in experiments, fertilisation success was most sensitive to suspended sediment, copper, salinity, phosphate and ammonium. Larval survivorship was sensitive to copper, lead and salinity. A combined model was developed that estimated the joint probability of both fertilisation and larval survivorship in sea water with different chemical and physical properties. We demonstrated the combined model using water samples from Sydney and Lizard Island in Australia to estimate the likelihood of larvae surviving through both stages of development to settlement competency. Our combined model could be used to recommend targets for water quality in coastal waterways as well as to predict the potential for species to expand their geographical ranges in response to climate change.