Prospective evaluation of a Bayesian model to predict organizational change.

PubMed

Molfenter, Todd; Gustafson, Dave; Kilo, Chuck; Bhattacharya, Abhik; Olsson, Jesper

2005-01-01

This research examines a subjective Bayesian model's ability to predict organizational change outcomes and sustainability of those outcomes for project teams participating in a multi-organizational improvement collaborative.

Meteorological data-processing package

NASA Technical Reports Server (NTRS)

Billingsly, J. B.; Braken, P. A.

1979-01-01

METPAK, meteorological data-processing package of satellite data used to develop cloud-tracking maps, is given. Data can develop and enhance numerical prediction models for mesoscale phenomena and improve ability to detect and predict storms.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

PubMed

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

INTEGRATION OF ANIMAL AND HUMAN GENE EXPRESSION DATA TO IMPROVE THE PREDICTIVE VALUE OF EXPOUSRE, EFFECTS AND SUSCEPTIBILITY BIOMARKERS IN ASTHMATIC CHILDREN

EPA Science Inventory

Advances in biomarker development have improved our ability to detect early changes at the molecular, cellular and pre-clinical level that are often predictive of adverse health outcomes. Integration of human and animal studies addresses key concerns about animal-human extrapolat...

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Disease Management: The Need for a Focus on Broader Self-Management Abilities and Quality of Life

PubMed Central

Nieboer, Anna Petra

2015-01-01

Abstract The study objective was to investigate long-term effects of disease management programs (DMPs) on (1) health behaviors (smoking, physical exercise); (2) self-management abilities (self-efficacy, investment behavior, initiative taking); and (3) physical and mental quality of life among chronically ill patients. The study also examined whether (changes in) health behaviors and self-management abilities predicted quality of life. Questionnaires were sent to all 5076 patients participating in 18 Dutch DMPs in 2010 (T0; 2676 [53%] respondents). Two years later (T1), questionnaires were sent to 4350 patients still participating in DMPs (1722 [40%] respondents). Structured interviews were held with the 18 DMP project leaders. DMP implementation improved patients' health behavior and physical quality of life, but mental quality of life and self-management abilities declined over time. Changes in patients' investment behavior predicted physical quality of life at T1 (P<.001); physical activity, investment behavior (both P<.05), and self-efficacy (P<.01) at T0, and changes in self-efficacy and investment behavior (both P<.001) predicted patients' mental quality of life at T1. The long-term benefits of these DMPs include successful improvement of chronically ill patients' health behaviors and physical quality of life. However, these programs were not able to improve or maintain broader self-management abilities or mental quality of life, highlighting the need to focus on these abilities and overall quality of life. As coproducers of care, patients should be stimulated and enabled to manage their health and quality of life. (Population Health Management 2015;18:246–255) PMID:25607246

Disease Management: The Need for a Focus on Broader Self-Management Abilities and Quality of Life.

PubMed

Cramm, Jane Murray; Nieboer, Anna Petra

2015-08-01

The study objective was to investigate long-term effects of disease management programs (DMPs) on (1) health behaviors (smoking, physical exercise); (2) self-management abilities (self-efficacy, investment behavior, initiative taking); and (3) physical and mental quality of life among chronically ill patients. The study also examined whether (changes in) health behaviors and self-management abilities predicted quality of life. Questionnaires were sent to all 5076 patients participating in 18 Dutch DMPs in 2010 (T0; 2676 [53%] respondents). Two years later (T1), questionnaires were sent to 4350 patients still participating in DMPs (1722 [40%] respondents). Structured interviews were held with the 18 DMP project leaders. DMP implementation improved patients' health behavior and physical quality of life, but mental quality of life and self-management abilities declined over time. Changes in patients' investment behavior predicted physical quality of life at T1 (P<.001); physical activity, investment behavior (both P<.05), and self-efficacy (P<.01) at T0, and changes in self-efficacy and investment behavior (both P<.001) predicted patients' mental quality of life at T1. The long-term benefits of these DMPs include successful improvement of chronically ill patients' health behaviors and physical quality of life. However, these programs were not able to improve or maintain broader self-management abilities or mental quality of life, highlighting the need to focus on these abilities and overall quality of life. As coproducers of care, patients should be stimulated and enabled to manage their health and quality of life.

"Ask Ernö": a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra.

PubMed

Castillo, Andrés M; Bernal, Andrés; Dieden, Reiner; Patiny, Luc; Wist, Julien

2016-01-01

We present "Ask Ernö", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. This concept was tested by training such a system with a dataset of 2341 molecules and their (1)H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm. Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.

Characterizing attention with predictive network models

PubMed Central

Rosenberg, M. D.; Finn, E. S.; Scheinost, D.; Constable, R. T.; Chun, M. M.

2017-01-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals’ attentional abilities. Some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that (1) attention is a network property of brain computation, (2) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task, and (3) this architecture supports a general attentional ability common to several lab-based tasks and impaired in attention deficit hyperactivity disorder. Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. PMID:28238605

Spontaneous recovery of language in patients with aphasia between 4 and 34 weeks after stroke.

PubMed

Lendrem, W; Lincoln, N B

1985-08-01

The paper describes the spontaneous recovery of language abilities of 52 stroke patients who were aphasic for more than 4 weeks. These patients had been randomly allocated to receive no speech therapy and had been assessed at 6-weekly intervals after a stroke. There was improvement in language abilities over time. Age, sex and aphasia type were not related to the amount of improvement. An aphasic patient's level of language ability at 6 months could be predicted on the basis of the test score on the Porch Index of Communicative Ability at 4 weeks.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

PubMed

Wu, Howard G; Miyamoto, Yohsuke R; Gonzalez Castro, Luis Nicolas; Ölveczky, Bence P; Smith, Maurice A

2014-02-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

PubMed Central

Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

2015-01-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

An improved reversible data hiding algorithm based on modification of prediction errors

NASA Astrophysics Data System (ADS)

Jafar, Iyad F.; Hiary, Sawsan A.; Darabkh, Khalid A.

2014-04-01

Reversible data hiding algorithms are concerned with the ability of hiding data and recovering the original digital image upon extraction. This issue is of interest in medical and military imaging applications. One particular class of such algorithms relies on the idea of histogram shifting of prediction errors. In this paper, we propose an improvement over one popular algorithm in this class. The improvement is achieved by employing a different predictor, the use of more bins in the prediction error histogram in addition to multilevel embedding. The proposed extension shows significant improvement over the original algorithm and its variations.

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

PubMed Central

Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

2014-01-01

Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

Ensemble Learning of QTL Models Improves Prediction of Complex Traits

PubMed Central

Bian, Yang; Holland, James B.

2015-01-01

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects. PMID:26276383

The relationship between change in cognition and change in functional ability in schizophrenia during cognitive and psychosocial rehabilitation.

PubMed

Rispaud, Samuel G; Rose, Jennifer; Kurtz, Matthew M

2016-10-30

While a wealth of studies have evaluated cross-sectional links between cognition and functioning in schizophrenia, few have investigated the relationship between change in cognition and change in functioning in the context of treatment trials targeted at cognition. Identifying cognitive skills that, when improved, predict improvement in functioning will guide the development of more targeted rehabilitation for this population. The present study identifies the relationship between change in specific cognitive skills and change in functional ability during one year of cognitive rehabilitation. Ninety-six individuals with schizophrenia were assessed with a battery of cognitive measures and a measure of performance-based functioning before and after cognitive training consisting of either drill-and-practice cognitive remediation or computer skills training. Results revealed that while working and episodic memory, problem-solving, and processing speed skills all improved during the trial, only improved working memory and processing speed skills predicted improvement in functional ability. Secondary analyses revealed these relationships were driven by individuals who showed a moderate level (SD≥0.5) of cognitive improvement during the trial. These findings suggest that while a variety of cognitive skills may improve during training targeted at cognition, only improvements in a subset of cognitive functions may translate into functional gains. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

EPA Science Inventory

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

Eighteen-month-olds' ability to make gaze predictions following distraction or a long delay.

PubMed

Forssman, Linda; Bohlin, Gunilla; von Hofsten, Claes

2014-05-01

The abilities to flexibly allocate attention, select between conflicting stimuli, and make anticipatory gaze movements are important for young children's exploration and learning about their environment. These abilities constitute voluntary control of attention and show marked improvements in the second year of a child's life. Here we investigate the effects of visual distraction and delay on 18-month-olds' ability to predict the location of an occluded target in an experiment that requires switching of attention, and compare their performance to that of adults. Our results demonstrate that by 18 months of age children can readily overcome a previously learned response, even under a condition that involves visual distraction, but have difficulties with correctly updating their prediction when presented with a longer time delay. Further, the experiment shows that, overall, the 18-month-olds' allocation of visual attention is similar to that of adults, the primary difference being that adults demonstrate a superior ability to maintain attention on task and update their predictions over a longer time period. Copyright © 2014 Elsevier Inc. All rights reserved.

Parent Praise to 1-3 Year-Olds Predicts Children’s Motivational Frameworks 5 Years Later

PubMed Central

Gunderson, Elizabeth A.; Gripshover, Sarah J.; Romero, Carissa; Dweck, Carol S.; Goldin-Meadow, Susan; Levine, Susan C.

2013-01-01

In laboratory studies, praising children’s effort encourages them to adopt incremental motivational frameworks—they believe ability is malleable, attribute success to hard work, enjoy challenges, and generate strategies for improvement. In contrast, praising children’s inherent abilities encourages them to adopt fixed-ability frameworks. Does the praise parents spontaneously give children at home show the same effects? Although parents’ early praise of inherent characteristics was not associated with children’s later fixed-ability frameworks, parents’ praise of children’s effort at 14-38 months (N=53) did predict incremental frameworks at 7-8 years, suggesting that causal mechanisms identified in experimental work may be operating in home environments. PMID:23397904

[Changes in psychopathological symptoms during the waiting period for outpatient psychotherapy].

PubMed

Huckert, Thomas Frank; Hank, Petra; Krampen, Günter

2012-08-01

This study empirically tests symptom changes in a sample of 106 psychotherapy outpatients during a 6-month waiting period before treatment commencement. Using indirect measurement of change, the patients improve in psychopathological symptoms. Using direct measurement of change, 48% of the outpatients show no significant change in psychopathological symptoms. However, the symptoms of 29% improve and 23% worsen. Using multinomial logistic regression, group membership (no change, positive change, negative change) can be predicted by personality traits for 60% of the patients. Social trust negatively predicts changes for the worse. Liberal gender-role orientation positively predicts improvement. A positive self-concept of ability positively predicts changes for the worse. Moreover sociodemographic variables correctly predict group membership for 57% of the patients. Age positively predicts changes for the worse. Female gender negatively predicts improvement. © Georg Thieme Verlag KG Stuttgart · New York.

Predicting absenteeism: screening for work ability or burnout.

PubMed

Schouteten, R

2017-01-01

In determining the predictors of occupational health problems, two factors can be distinguished: personal (work ability) factors and work-related factors (burnout, job characteristics). However, these risk factors are hardly ever combined and it is not clear whether burnout or work ability best predicts absenteeism. To relate measures of work ability, burnout and job characteristics to absenteeism as the indicators of occupational health problems. Survey data on work ability, burnout and job characteristics from a Dutch university were related to the absenteeism data from the university's occupational health and safety database in the year following the survey study. The survey contained the Work Ability Index (WAI), Utrecht Burnout Scale (UBOS) and seven job characteristics from the Questionnaire on Experience and Evaluation of Work (QEEW). There were 242 employees in the study group. Logistic regression analyses revealed that job characteristics did not predict absenteeism. Exceptional absenteeism was most consistently predicted by the WAI dimensions 'employees' own prognosis of work ability in two years from now' and 'mental resources/vitality' and the burnout dimension 'emotional exhaustion'. Other significant predictors of exceptional absenteeism frequency included estimated work impairment due to diseases (WAI) and feelings of depersonalization or emotional distance from the work (burnout). Absenteeism among university personnel was best predicted by a combination of work ability and burnout. As a result, measures to prevent absenteeism and health problems may best be aimed at improving an individual's work ability and/or preventing the occurrence of burnout. © The Author 2016. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Intrinsic motivation towards sports in Singaporean students: the role of sport ability beliefs.

PubMed

Wang, C K John; Biddle, Stuart J H

2003-09-01

This study investigated determinants of active lifestyles in Singaporean university students. Using confirmatory factor analysis, a measure of lay beliefs concerning athletic ability was confirmed. Other results confirmed hypotheses that beliefs reflecting that athletic ability can be developed over time (incremental beliefs) predict an achievement task (self-referenced) orientation, while beliefs reflecting that athletic ability is relatively stable (entity beliefs) predict an ego (other-person, comparative) orientation. Goal orientations directly affect perceived competence which, in turn, influence intrinsic motivation to be physically active. A task orientation had a direct link to intrinsic motivation. Results suggest that intrinsic motivation towards sport and physical activity might be enhanced through interventions that focus on self-referenced and self-improvement notions of ability as well as perceived competence.

Improved prediction of antibody VL–VH orientation

PubMed Central

Marze, Nicholas A.; Lyskov, Sergey; Gray, Jeffrey J.

2016-01-01

Antibodies are important immune molecules with high commercial value and therapeutic interest because of their ability to bind diverse antigens. Computational prediction of antibody structure can quickly reveal valuable information about the nature of these antigen-binding interactions, but only if the models are of sufficient quality. To achieve high model quality during complementarity-determining region (CDR) structural prediction, one must account for the VL–VH orientation. We developed a novel four-metric VL–VH orientation coordinate frame. Additionally, we extended the CDR grafting protocol in RosettaAntibody with a new method that diversifies VL–VH orientation by using 10 VL–VH orientation templates rather than a single one. We tested the multiple-template grafting protocol on two datasets of known antibody crystal structures. During the template-grafting phase, the new protocol improved the fraction of accurate VL–VH orientation predictions from only 26% (12/46) to 72% (33/46) of targets. After the full RosettaAntibody protocol, including CDR H3 remodeling and VL–VH re-orientation, the new protocol produced more candidate structures with accurate VL–VH orientation than the standard protocol in 43/46 targets (93%). The improved ability to predict VL–VH orientation will bolster predictions of other parts of the paratope, including the conformation of CDR H3, a grand challenge of antibody homology modeling. PMID:27276984

Length of stay, discharge destination, and functional improvement: utility of the Australian National Subacute and Nonacute Patient Casemix Classification.

PubMed

Tooth, Leigh; McKenna, Kryss; Goh, Kong; Varghese, Paul

2005-07-01

Although implemented in 1998, no research has examined how well the Australian National Subacute and Nonacute Patient (AN-SNAP) Casemix Classification predicts length of stay (LOS), discharge destination, and functional improvement in public hospital stroke rehabilitation units in Australia. 406 consecutive admissions to 3 stroke rehabilitation units in Queensland, Australia were studied. Sociodemographic, clinical, and functional data were collected. General linear modeling and logistic regression were used to assess the ability of AN-SNAP to predict outcomes. AN-SNAP significantly predicted each outcome. There were clear relationships between the outcomes of longer LOS, poorer functional improvement and discharge into care, and the AN-SNAP classes that reflected poorer functional ability and older age. Other predictors included living situation, acute LOS, comorbidity, and stroke type. AN-SNAP is a consistent predictor of LOS, functional change and discharge destination, and has utility in assisting clinicians to set rehabilitation goals and plan discharge.

Genomic prediction in a nuclear population of layers using single-step models.

PubMed

Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

2018-02-01

Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Executive function predicts artificial language learning

PubMed Central

Kapa, Leah L.; Colombo, John

2017-01-01

Previous research suggests executive function (EF) advantages among bilinguals compared to monolingual peers, and these advantages are generally attributed to experience controlling two linguistic systems. However, the possibility that the relationship between bilingualism and EF might be bidirectional has not been widely considered; while experience with two languages might improve EF, better EF skills might also facilitate language learning. In the current studies, we tested whether adults’ and preschool children’s EF abilities predicted success in learning a novel artificial language. After controlling for working memory and English receptive vocabulary, adults’ artificial language performance was predicted by their inhibitory control ability (Study 1) and children’s performance was predicted by their attentional monitoring and shifting ability (Study 2). These findings provide preliminary evidence suggesting that EF processes may be employed during initial stages of language learning, particularly vocabulary acquisition, and support the possibility of a bidirectional relationship between EF and language acquisition. PMID:29129958

Characterizing Attention with Predictive Network Models.

PubMed

Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

2017-04-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving Learners' Ability to Recognize Emergence with Embedded Assessment in a Virtual Watershed

ERIC Educational Resources Information Center

Erlandson, Benjamin E.

2014-01-01

Measures of participants' water cycle knowledge and ability to recognize emergence were taken at various points throughout a 2-h experience with the Cloverdale virtual watershed socioecological simulation. Multilevel growth models were estimated for analysis of hypothesized predictive relationships between measured variables. Significant…

Improving Working Memory Efficiency by Reframing Metacognitive Interpretation of Task Difficulty

ERIC Educational Resources Information Center

Autin, Frederique; Croizet, Jean-Claude

2012-01-01

Working memory capacity, our ability to manage incoming information for processing purposes, predicts achievement on a wide range of intellectual abilities. Three randomized experiments (N = 310) tested the effectiveness of a brief psychological intervention designed to boost working memory efficiency (i.e., state working memory capacity) by…

Genetic Predisposition to Ischemic Stroke

PubMed Central

Kamatani, Yoichiro; Takahashi, Atsushi; Hata, Jun; Furukawa, Ryohei; Shiwa, Yuh; Yamaji, Taiki; Hara, Megumi; Tanno, Kozo; Ohmomo, Hideki; Ono, Kanako; Takashima, Naoyuki; Matsuda, Koichi; Wakai, Kenji; Sawada, Norie; Iwasaki, Motoki; Yamagishi, Kazumasa; Ago, Tetsuro; Ninomiya, Toshiharu; Fukushima, Akimune; Hozawa, Atsushi; Minegishi, Naoko; Satoh, Mamoru; Endo, Ryujin; Sasaki, Makoto; Sakata, Kiyomi; Kobayashi, Seiichiro; Ogasawara, Kuniaki; Nakamura, Motoyuki; Hitomi, Jiro; Kita, Yoshikuni; Tanaka, Keitaro; Iso, Hiroyasu; Kitazono, Takanari; Kubo, Michiaki; Tanaka, Hideo; Tsugane, Shoichiro; Kiyohara, Yutaka; Yamamoto, Masayuki; Sobue, Kenji; Shimizu, Atsushi

2017-01-01

Background and Purpose— The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk. Methods— We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets). Results— In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33–2.31) and 1.99 (95% confidence interval, 1.19–3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001). Conclusions— The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors. PMID:28034966

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

PubMed

Hüsing, Anika; Canzian, Federico; Beckmann, Lars; Garcia-Closas, Montserrat; Diver, W Ryan; Thun, Michael J; Berg, Christine D; Hoover, Robert N; Ziegler, Regina G; Figueroa, Jonine D; Isaacs, Claudine; Olsen, Anja; Viallon, Vivian; Boeing, Heiner; Masala, Giovanna; Trichopoulos, Dimitrios; Peeters, Petra H M; Lund, Eiliv; Ardanaz, Eva; Khaw, Kay-Tee; Lenner, Per; Kolonel, Laurence N; Stram, Daniel O; Le Marchand, Loïc; McCarty, Catherine A; Buring, Julie E; Lee, I-Min; Zhang, Shumin; Lindström, Sara; Hankinson, Susan E; Riboli, Elio; Hunter, David J; Henderson, Brian E; Chanock, Stephen J; Haiman, Christopher A; Kraft, Peter; Kaaks, Rudolf

2012-09-01

There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

Genomic Selection in Multi-environment Crop Trials.

PubMed

Oakey, Helena; Cullis, Brian; Thompson, Robin; Comadran, Jordi; Halpin, Claire; Waugh, Robbie

2016-05-03

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers. Copyright © 2016 Oakey et al.

The Work Ability of Hong Kong Construction Workers in Relation to Individual and Work-Related Factors.

PubMed

Ng, Jacky Y K; Chan, Alan H S

2018-05-14

The shortage in Hong Kong of construction workers is expected to worsen in future due to the aging population and increasing construction activity. Construction work is dangerous and to help reduce the premature loss of construction workers due to work-related disabilities, this study measured the work ability of 420 Hong Kong construction workers with a Work Ability Index (WAI) which can be used to predict present and future work performance. Given the importance of WAI, in this study the effects of individual and work-related factors on WAI were examined to develop and validate a WAI model to predict how individual and work-related factors affect work ability. The findings will be useful for formulating a pragmatic intervention program to improve the work ability of construction workers and keep them in the work force.

Hyperspectral Remote Sensing of New England Coastal Waters to Predict Seagrass Distribution

EPA Science Inventory

The U.S. Environmental Protection Agency is working to improve its ability to quantify and predict aquatic (freshwater, estuarine, marine) ecosystem response and recovery to changing nutrient loads. The objective of this research is to quantify the relationship of nutrients with...

The Predicted Cross Value for Genetic Introgression of Multiple Alleles

PubMed Central

Han, Ye; Cameron, John N.; Wang, Lizhi; Beavis, William D.

2017-01-01

We consider the plant genetic improvement challenge of introgressing multiple alleles from a homozygous donor to a recipient. First, we frame the project as an algorithmic process that can be mathematically formulated. We then introduce a novel metric for selecting breeding parents that we refer to as the predicted cross value (PCV). Unlike estimated breeding values, which represent predictions of general combining ability, the PCV predicts specific combining ability. The PCV takes estimates of recombination frequencies as an input vector and calculates the probability that a pair of parents will produce a gamete with desirable alleles at all specified loci. We compared the PCV approach with existing estimated-breeding-value approaches in two simulation experiments, in which 7 and 20 desirable alleles were to be introgressed from a donor line into a recipient line. Results suggest that the PCV is more efficient and effective for multi-allelic trait introgression. We also discuss how operations research can be used for other crop genetic improvement projects and suggest several future research directions. PMID:28122824

Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature.

PubMed

Kluth, Luis A; Black, Peter C; Bochner, Bernard H; Catto, James; Lerner, Seth P; Stenzl, Arnulf; Sylvester, Richard; Vickers, Andrew J; Xylinas, Evanguelos; Shariat, Shahrokh F

2015-08-01

This review focuses on risk assessment and prediction tools for bladder cancer (BCa). To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Discriminative ability of commonly used indices to predict adverse outcomes after poster lumbar fusion: a comparison of demographics, ASA, the modified Charlson Comorbidity Index, and the modified Frailty Index.

PubMed

Ondeck, Nathaniel T; Bohl, Daniel D; Bovonratwet, Patawut; McLynn, Ryan P; Cui, Jonathan J; Shultz, Blake N; Lukasiewicz, Adam M; Grauer, Jonathan N

2018-01-01

As research tools, the American Society of Anesthesiologists (ASA) physical status classification system, the modified Charlson Comorbidity Index (mCCI), and the modified Frailty Index (mFI) have been associated with complications following spine procedures. However, with respect to clinical use for various adverse outcomes, no known study has compared the predictive performance of these indices specifically following posterior lumbar fusion (PLF). This study aimed to compare the discriminative ability of ASA, mCCI, and mFI, as well as demographic factors including age, body mass index, and gender for perioperative adverse outcomes following PLF. A retrospective review of prospectively collected data was performed. Patients undergoing elective PLF with or without interbody fusion were extracted from the 2011-2014 American College of Surgeons National Surgical Quality Improvement Program (NSQIP). Perioperative adverse outcome variables assessed included the occurrence of minor adverse events, severe adverse events, infectious adverse events, any adverse event, extended length of hospital stay, and discharge to higher-level care. Patient comorbidity indices and characteristics were delineated and assessed for discriminative ability in predicting perioperative adverse outcomes using an area under the curve analysis from the receiver operating characteristics curves. In total, 16,495 patients were identified who met the inclusion criteria. The most predictive comorbidity index was ASA and demographic factor was age. Of these two factors, age had the larger discriminative ability for three out of the six adverse outcomes and ASA was the most predictive for one out of six adverse outcomes. A combination of the most predictive demographic factor and comorbidity index resulted in improvements in discriminative ability over the individual components for five of the six outcome variables. For PLF, easily obtained patient ASA and age have overall similar or better discriminative abilities for perioperative adverse outcomes than numerically tabulated indices that have multiple inputs and are harder to implement in clinical practice. Copyright © 2017 Elsevier Inc. All rights reserved.

A multilevel modeling approach to examining individual differences in skill acquisition for a computer-based task.

PubMed

Nair, Sankaran N; Czaja, Sara J; Sharit, Joseph

2007-06-01

This article explores the role of age, cognitive abilities, prior experience, and knowledge in skill acquisition for a computer-based simulated customer service task. Fifty-two participants aged 50-80 performed the task over 4 consecutive days following training. They also completed a battery that assessed prior computer experience and cognitive abilities. The data indicated that overall quality and efficiency of performance improved with practice. The predictors of initial level of performance and rate of change in performance varied according to the performance parameter assessed. Age and fluid intelligence predicted initial level and rate of improvement in overall quality, whereas crystallized intelligence and age predicted initial e-mail processing time, and crystallized intelligence predicted rate of change in e-mail processing time over days. We discuss the implications of these findings for the design of intervention strategies.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Annoyance due to simulated blade-slap noise

NASA Technical Reports Server (NTRS)

Powell, C. A.

1978-01-01

The effects of several characteristics of blade slap noise on annoyance response were studied. These characteristics or parameters were the sound pressure level of the continuous noise used to simulate helicopter broadband noise, the ratio of impulse peak to broadband noise or crest factor, the number of pressure excursions comprising an impulse event, the rise and fall time of the individual impulses, and the repetition frequency of the impulses. Analyses were conducted to determine the correlation between subjective response and various physical measures for the range of parameters studied. A small but significant improvement in the predictive ability of PNL was provided by an A-weighted crest factor correlation. No significant improvement in predictive ability was provided by a rate correction.

Prediction of stock markets by the evolutionary mix-game model

NASA Astrophysics Data System (ADS)

Chen, Fang; Gou, Chengling; Guo, Xiaoqian; Gao, Jieping

2008-06-01

This paper presents the efforts of using the evolutionary mix-game model, which is a modified form of the agent-based mix-game model, to predict financial time series. Here, we have carried out three methods to improve the original mix-game model by adding the abilities of strategy evolution to agents, and then applying the new model referred to as the evolutionary mix-game model to forecast the Shanghai Stock Exchange Composite Index. The results show that these modifications can improve the accuracy of prediction greatly when proper parameters are chosen.

Description and prediction of individual tree biomass on pinon (Pinus edulis) in northern New Mexico

Treesearch

Mark Loveall; John T. Harrington

2008-01-01

The purpose of this study was to gain reliable information on the distribution of aboveground biomass of an important component of the woodlands of north-central New Mexico, and to develop prediction equations that may be used to quickly compute biomass from relatively simple field measurements. Improved understanding of and ability to predict aboveground biomass...

The ability to tap to a beat relates to cognitive, linguistic, and perceptual skills

PubMed Central

Tierney, Adam T.; Kraus, Nina

2013-01-01

Reading-impaired children have difficulty tapping to a beat. Here we tested whether this relationship between reading ability and synchronized tapping holds in typically-developing adolescents. We also hypothesized that tapping relates to two other abilities. First, since auditory-motor synchronization requires monitoring of the relationship between motor output and auditory input, we predicted that subjects better able to tap to the beat would perform better on attention tests. Second, since auditory-motor synchronization requires fine temporal precision within the auditory system for the extraction of a sound’s onset time, we predicted that subjects better able to tap to the beat would be less affected by backward masking, a measure of temporal precision within the auditory system. As predicted, tapping performance related to reading, attention, and backward masking. These results motivate future research investigating whether beat synchronization training can improve not only reading ability, but potentially executive function and basic auditory processing as well. PMID:23400117

The Work Ability of Hong Kong Construction Workers in Relation to Individual and Work-Related Factors

PubMed Central

Ng, Jacky Y. K.

2018-01-01

The shortage in Hong Kong of construction workers is expected to worsen in future due to the aging population and increasing construction activity. Construction work is dangerous and to help reduce the premature loss of construction workers due to work-related disabilities, this study measured the work ability of 420 Hong Kong construction workers with a Work Ability Index (WAI) which can be used to predict present and future work performance. Given the importance of WAI, in this study the effects of individual and work-related factors on WAI were examined to develop and validate a WAI model to predict how individual and work-related factors affect work ability. The findings will be useful for formulating a pragmatic intervention program to improve the work ability of construction workers and keep them in the work force. PMID:29758018

Identifying potential dropouts from college physics classes

NASA Astrophysics Data System (ADS)

Wollman, Warren; Lawrenz, Frances

Hudson and Rottman (1981) established that mathematics ability is probably a secondary factor influencing dropout from college physics courses. Other factors remain to be found for predicting who will drop out or at least have difficulty with the course. When mathematics ability is coupled with general indicators of performance (total GPA and ACT natural science), prediction of performance for those who complete the course is substantially improved. Moreover, discriminant analyses reveal who will have at least some difficulty, but not who will drop out. The problem of isolating specific weaknesses of students who have difficulty persists. Physics achievement appears to depend on mathematics ability only to the extent that students possess the ability to utilize mathematics knowledge for solving physics problems. Identification of the specific aspects of this ability as well as the specific deficiencies leading to dropout should be the object of future research. For the present, interviews might be more revealing than group testing methods.

Development of a Melanoma Risk Prediction Model Incorporating MC1R Genotype and Indoor Tanning Exposure: Impact of Mole Phenotype on Model Performance

PubMed Central

Penn, Lauren A.; Qian, Meng; Zhang, Enhan; Ng, Elise; Shao, Yongzhao; Berwick, Marianne; Lazovich, DeAnn; Polsky, David

2014-01-01

Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals. PMID:25003831

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection.

PubMed

Zipkin, Elise F; Grant, Evan H Campbell; Fagan, William F

2012-10-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multispecies hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions about species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation data set. We found that wetland hydroperiod (the length of time that a wetland holds water), as well as the occurrence state in the prior year, were generally the most important factors in determining occupancy. The model with habitat-only covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multispecies models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

USGS Publications Warehouse

Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

2012-01-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

Can we predict functional decline in hospitalized older people admitted through the emergency department? Reanalysis of a predictive tool ten years after its conception.

PubMed

De Brauwer, Isabelle; Cornette, Pascale; Boland, Benoît; Verschuren, Franck; D'Hoore, William

2017-05-12

In the Emergency Department (ED), early and rapid identification of older people at risk of adverse outcomes, who could best benefit from complex geriatric intervention, would avoid wasting time, especially in terms of prevention of adverse outcomes, and ensure optimal orientation of vulnerable patients. We wanted to test the predictive ability of a screening tool assessing risk of functional decline (FD), named SHERPA, 10 years after its conception, and to assess the added value of other clinical or biological factors associated with FD. A prospective cohort study of older patients (n = 305, ≥ 75 years) admitted through the emergency department, for at least 48 h in non-geriatric wards (mean age 82.5 ± 4.9, 55% women). SHERPA variables (i.e. age, pre-admission instrumental Activity of Daily Living (ADL) status, falls within a year, self-rated health and 21-point MMSE) were collected within 48 h of admission, along with socio-demographic, medical and biological data. Functional status was followed at 3 months by phone. FD was defined as a decrease at 3 months of at least one point in the pre-admission basic ADL score. Predictive ability of SHERPA was assessed using c-statistic, predictive values and likelihood ratios. Measures of discrimination improvement were Net Reclassification Improvement and Integrated Discrimination Improvement. One hundred and five patients (34%) developed 3-month FD. Predictive ability of SHERPA decreased dramatically over 10 years (c = 0.73 vs. 0.64). Only two of its constitutive variables, i.e. falls and instrumental ADL, were significant in logistic regression analysis for functional decline, while 21-point MMSE was kept in the model for clinical relevance. Demographic, comorbidity or laboratory data available upon admission did not improve the SHERPA predictive yield. Prediction of FD with SHERPA is difficult, but predictive factors, i.e. falls, pre-existing functional limitation and cognitive impairment, stay consistent across time and with literature. As accuracy of SHERPA and others existing screening tools for FD is moderate, using these predictors as flags instead of using composite scales can be a way to screen for high-risk patients.

Preschoolers' precision of the approximate number system predicts later school mathematics performance.

PubMed

Mazzocco, Michèle M M; Feigenson, Lisa; Halberda, Justin

2011-01-01

The Approximate Number System (ANS) is a primitive mental system of nonverbal representations that supports an intuitive sense of number in human adults, children, infants, and other animal species. The numerical approximations produced by the ANS are characteristically imprecise and, in humans, this precision gradually improves from infancy to adulthood. Throughout development, wide ranging individual differences in ANS precision are evident within age groups. These individual differences have been linked to formal mathematics outcomes, based on concurrent, retrospective, or short-term longitudinal correlations observed during the school age years. However, it remains unknown whether this approximate number sense actually serves as a foundation for these school mathematics abilities. Here we show that ANS precision measured at preschool, prior to formal instruction in mathematics, selectively predicts performance on school mathematics at 6 years of age. In contrast, ANS precision does not predict non-numerical cognitive abilities. To our knowledge, these results provide the first evidence for early ANS precision, measured before the onset of formal education, predicting later mathematical abilities.

Preschoolers' Precision of the Approximate Number System Predicts Later School Mathematics Performance

PubMed Central

Mazzocco, Michèle M. M.; Feigenson, Lisa; Halberda, Justin

2011-01-01

The Approximate Number System (ANS) is a primitive mental system of nonverbal representations that supports an intuitive sense of number in human adults, children, infants, and other animal species. The numerical approximations produced by the ANS are characteristically imprecise and, in humans, this precision gradually improves from infancy to adulthood. Throughout development, wide ranging individual differences in ANS precision are evident within age groups. These individual differences have been linked to formal mathematics outcomes, based on concurrent, retrospective, or short-term longitudinal correlations observed during the school age years. However, it remains unknown whether this approximate number sense actually serves as a foundation for these school mathematics abilities. Here we show that ANS precision measured at preschool, prior to formal instruction in mathematics, selectively predicts performance on school mathematics at 6 years of age. In contrast, ANS precision does not predict non-numerical cognitive abilities. To our knowledge, these results provide the first evidence for early ANS precision, measured before the onset of formal education, predicting later mathematical abilities. PMID:21935362

The proposed 'concordance-statistic for benefit' provided a useful metric when modeling heterogeneous treatment effects.

PubMed

van Klaveren, David; Steyerberg, Ewout W; Serruys, Patrick W; Kent, David M

2018-02-01

Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit-the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The 'c-for-benefit' represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke-thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics. Copyright © 2017 Elsevier Inc. All rights reserved.

Parent Praise to 1- to 3-Year-Olds Predicts Children's Motivational Frameworks 5 Years Later

ERIC Educational Resources Information Center

Gunderson, Elizabeth A.; Gripshover, Sarah J.; Romero, Carissa; Dweck, Carol S.; Goldin-Meadow, Susan; Levine, Susan C.

2013-01-01

In laboratory studies, praising children's effort encourages them to adopt incremental motivational frameworks--they believe ability is malleable, attribute success to hard work, enjoy challenges, and generate strategies for improvement. In contrast, praising children's inherent abilities encourages them to adopt fixed-ability…

Elicited Imitation Performance at 20 Months Predicts Memory Abilities in School-Aged Children

ERIC Educational Resources Information Center

Riggins, Tracy; Cheatham, Carol L.; Stark, Emily; Bauer, Patricia J.

2013-01-01

During the first decade of life, there are marked improvements in mnemonic abilities. An important question from both a theoretical and applied perspective is the extent of continuity in the nature of memory during this period. The present longitudinal investigation examined declarative memory during the transition from toddlerhood to school age…

Modified Augmented Renal Clearance Score Predicts Rapid Piperacillin and Tazobactam Clearance in Critically Ill Surgery and Trauma Patients

DTIC Science & Technology

2014-04-24

intermittent dosing regimens. CONCLUSION: Given its ability to predict antimicrobial clearance above populationmedians, which could compromise therapy, the...campaign dedicated to improve out- comes.1,2 In the era ofmultiply drug- resistant pathogens and rising antimicrobial minimum inhibitory concentrations (MICs...urinary creatinine clearance significantly exceeds what is predicted by the serum creatinine concentration according to various mathematical

Predictive Measures of Locomotor Performance on an Unstable Walking Surface

NASA Technical Reports Server (NTRS)

Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Caldwell, E. E.; Batson, C. D.; De Dios, Y. E.; Gadd, N. E.; Goel, R.; Wood, S. J.; Cohen, H. S.;

Tall fescue forage mass in a grass-legume mixture: predicted efficiency of indirect selection

USDA-ARS?s Scientific Manuscript database

High fertilizer prices and improved environmental stewardship have increased interest in grass-legume mixed pastures. It has been hypothesized, but not validated, that the ecological combining ability between grasses and legumes can be improved by breeding specifically for mixture performance. Thi...

Critical analysis of 3-D organoid in vitro cell culture models for high-throughput drug candidate toxicity assessments.

PubMed

Astashkina, Anna; Grainger, David W

2014-04-01

Drug failure due to toxicity indicators remains among the primary reasons for staggering drug attrition rates during clinical studies and post-marketing surveillance. Broader validation and use of next-generation 3-D improved cell culture models are expected to improve predictive power and effectiveness of drug toxicological predictions. However, after decades of promising research significant gaps remain in our collective ability to extract quality human toxicity information from in vitro data using 3-D cell and tissue models. Issues, challenges and future directions for the field to improve drug assay predictive power and reliability of 3-D models are reviewed. Copyright © 2014 Elsevier B.V. All rights reserved.

A Physiologically-Based Description of the Inhalation Pharmacokinetics of Styrene in Rats and Humans

DTIC Science & Technology

1983-01-01

model for rat were scaled to give a description of human kinetics and the predictions agreed closely with available data from the literature (Fig. 4...for predicting human kinetics from a data base in other mammalian species. The ability to anticipate kinetic behavior in humans could very much improve

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

PubMed

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Social learning and evolution: the cultural intelligence hypothesis

PubMed Central

van Schaik, Carel P.; Burkart, Judith M.

2011-01-01

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer. PMID:21357223

Social learning and evolution: the cultural intelligence hypothesis.

PubMed

van Schaik, Carel P; Burkart, Judith M

2011-04-12

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer.

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data

PubMed Central

Krumme, Alexis A; Sanfélix-Gimeno, Gabriel; Franklin, Jessica M; Isaman, Danielle L; Mahesri, Mufaddal; Matlin, Olga S; Shrank, William H; Brennan, Troyen A; Brill, Gregory; Choudhry, Niteesh K

2016-01-01

Objective The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. Design Retrospective. Setting and participants A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. Outcome We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. Results Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. Conclusions While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions. PMID:28186924

Learning temporal statistics for sensory predictions in mild cognitive impairment.

PubMed

Di Bernardi Luft, Caroline; Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe

2015-08-01

Training is known to improve performance in a variety of perceptual and cognitive skills. However, there is accumulating evidence that mere exposure (i.e. without supervised training) to regularities (i.e. patterns that co-occur in the environment) facilitates our ability to learn contingencies that allow us to interpret the current scene and make predictions about future events. Recent neuroimaging studies have implicated fronto-striatal and medial temporal lobe brain regions in the learning of spatial and temporal statistics. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are characterized by hippocampal dysfunction are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards orientated gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. However, our fMRI results demonstrate that MCI-AD patients recruit an alternate circuit to hippocampus to succeed in learning of predictive structures. In particular, we observed stronger learning-dependent activations for structured sequences in frontal, subcortical and cerebellar regions for patients compared to age-matched controls. Thus, our findings suggest a cortico-striatal-cerebellar network that may mediate the ability for predictive learning despite hippocampal dysfunction in MCI-AD. Copyright © 2015 Elsevier Ltd. All rights reserved.

Music training, cognition, and personality.

PubMed

Corrigall, Kathleen A; Schellenberg, E Glenn; Misura, Nicole M

2013-01-01

Although most studies that examined associations between music training and cognitive abilities had correlational designs, the prevailing bias is that music training causes improvements in cognition. It is also possible, however, that high-functioning children are more likely than other children to take music lessons, and that they also differ in personality. We asked whether individual differences in cognition and personality predict who takes music lessons and for how long. The participants were 118 adults (Study 1) and 167 10- to 12-year-old children (Study 2). We collected demographic information and measured cognitive ability and the Big Five personality dimensions. As in previous research, cognitive ability was associated with musical involvement even when demographic variables were controlled statistically. Novel findings indicated that personality was associated with musical involvement when demographics and cognitive ability were held constant, and that openness-to-experience was the personality dimension with the best predictive power. These findings reveal that: (1) individual differences influence who takes music lessons and for how long, (2) personality variables are at least as good as cognitive variables at predicting music training, and (3) future correlational studies of links between music training and non-musical ability should account for individual differences in personality.

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

PubMed

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

2014-11-03

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

Neurocognitive Predictors of Academic Outcomes among Childhood Leukemia Survivors

PubMed Central

(Ki) Moore, Ida M.; Lupo, Philip J.; Insel, Kathleen; Harris, Lynnette L.; Pasvogel, Alice; Koerner, Kari M.; Adkins, Kristin B.; Taylor, Olga A.; Hockenberry, Marilyn J.

2015-01-01

Background Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer and survival approaches 90%. ALL survivors are more likely than healthy peers or siblings to experience academic underachievement yet little is known about neurocognitive predictors of academic outcomes. Objective Objectives were to compare neurocognitive abilities to age-adjusted standardized norms; to examine change over time in neurocognitive abilities; and to establish neurocognitive predictors of academic outcomes. Methods Seventy-one children were followed over the course of therapy. Cognitive abilities were assessed during Induction when the child was in remission (Baseline) and annually for 3 years (Year 1, Year 2, Year 3). Reading and mathematics abilities were assessed at Year 3. Results Fine motor dexterity was significantly below age-adjusted norms at all data points, but showed improvement over time. Baseline visual-motor integration was within the normal range but significantly declined by Year 3, and mean scores at Years 2 and 3 were significantly below age-adjusted norms. Verbal short-term memory was significantly below age-adjusted norms at all assessments. Visual-motor integration predicted reading and mathematic abilities. Verbal short-term memory predicted reading abilities, and visual short-term memory predicted mathematic abilities. Conclusions CNS-directed therapy is associated with specific neurocognitive problems. Visual spatial skills, verbal and visual short term memory predict academic outcomes. Implications for practice Early assessment of visual spatial perception and short-term memory can identify children at risk for academic problems. Children who are at risk for academic problems could benefit from a school based Individual Educational Program and/or educational intervention. PMID:26166361

Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments

USDA-ARS?s Scientific Manuscript database

As a key component of the carbon cycle, soil respiration (Rsoil) is being excessively studied with the aim of improving our understanding as well as our ability to predict Rsoil when climate changes. Many manipulation experiments have been performed to test how Rsoil and other carbon fluxes and ecos...

Using partial least squares regression as a predictive tool in describing equine third metacarpal bone shape.

PubMed

Liley, Helen; Zhang, Ju; Firth, Elwyn; Fernandez, Justin; Besier, Thor

2017-11-01

Population variance in bone shape is an important consideration when applying the results of subject-specific computational models to a population. In this letter, we demonstrate the ability of partial least squares regression to provide an improved shape prediction of the equine third metacarpal epiphysis, using two easily obtained measurements.

Estimating verbal fluency and naming ability from the test of premorbid functioning and demographic variables: Regression equations derived from a regional UK sample.

PubMed

Jenkinson, Toni-Marie; Muncer, Steven; Wheeler, Miranda; Brechin, Don; Evans, Stephen

2018-06-01

Neuropsychological assessment requires accurate estimation of an individual's premorbid cognitive abilities. Oral word reading tests, such as the test of premorbid functioning (TOPF), and demographic variables, such as age, sex, and level of education, provide a reasonable indication of premorbid intelligence, but their ability to predict other related cognitive abilities is less well understood. This study aimed to develop regression equations, based on the TOPF and demographic variables, to predict scores on tests of verbal fluency and naming ability. A sample of 119 healthy adults provided demographic information and were tested using the TOPF, FAS, animal naming test (ANT), and graded naming test (GNT). Multiple regression analyses, using the TOPF and demographics as predictor variables, were used to estimate verbal fluency and naming ability test scores. Change scores and cases of significant impairment were calculated for two clinical samples with diagnosed neurological conditions (TBI and meningioma) using the method in Knight, McMahon, Green, and Skeaff (). Demographic variables provided a significant contribution to the prediction of all verbal fluency and naming ability test scores; however, adding TOPF score to the equation considerably improved prediction beyond that afforded by demographic variables alone. The percentage of variance accounted for by demographic variables and/or TOPF score varied from 19 per cent (FAS), 28 per cent (ANT), and 41 per cent (GNT). Change scores revealed significant differences in performance in the clinical groups, particularity the TBI group. Demographic variables, particularly education level, and scores on the TOPF should be taken into consideration when interpreting performance on tests of verbal fluency and naming ability. © 2017 The British Psychological Society.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel. PMID:27672112

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

PubMed Central

Jo, Booil; Adamson, Maheen M.; Kennedy, Quinn; Noda, Art; Hernandez, Beatriz; Zeitzer, Jamie M.; Friedman, Leah F.; Fairchild, Kaci; Scanlon, Blake K.; Murphy, Greer M.; Taylor, Joy L.

2011-01-01

Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age. Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40–77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1–13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise. Results. Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age. Discussion. These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities. PMID:21586627

Neural predictors of individual differences in response to math tutoring in primary-grade school children

PubMed Central

Supekar, Kaustubh; Swigart, Anna G.; Tenison, Caitlin; Jolles, Dietsje D.; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-01-01

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8–9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. PMID:23630286

Neural predictors of individual differences in response to math tutoring in primary-grade school children.

PubMed

Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-05-14

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.

Development of Mathematical Models in Support of AFGL Atmospheric Studies.

DTIC Science & Technology

1980-05-15

can systematically design a sensor and predict its sensitivity, false alarm rate and detection probability. When one considers a mosaic staring sensor...data base to improve the Air Force’s ability to specify and predict geomagnetic activity. This information is very useful in the studying of propagation...storms or proton showers which cause these disturbances cannot be predicted without a knowledge of the solar activity which causes them. During periods

The Modified Telephone Interview for Cognitive Status is More Predictive of Memory Abilities Than the Mini-Mental State Examination.

PubMed

Duff, Kevin; Tometich, Danielle; Dennett, Kathryn

2015-09-01

Although not as popular as the Mini-Mental State Examination (MMSE), the modified Telephone Interview for Cognitive Status (mTICS) has some distinct advantages when screening cognitive functioning in older adults. The current study compared these 2 cognitive screening measures in their ability to predict performance on a memory composite (ie, delayed recall of verbal and visual information) in a cohort of 121 community-dwelling older adults, both at baseline and after 1 year. Both the MMSE and the mTICS significantly correlated with the memory composite at baseline (r's of .41 and .62, respectively) and at 1 year (r's of .36 and .50, respectively). At baseline, stepwise linear regression indicated that the mTICS and gender best predicted the memory composite score (R (2) = .45, P < .001), and the MMSE and other demographic variables did not significantly improve the prediction. At 1 year, the results were very similar. Despite its lesser popularity, the mTICS may be a more attractive option when screening for cognitive abilities in this age range. © The Author(s) 2015.

Role of learning potential in cognitive remediation: Construct and predictive validity.

PubMed

Davidson, Charlie A; Johannesen, Jason K; Fiszdon, Joanna M

2016-03-01

The construct, convergent, discriminant, and predictive validity of Learning Potential (LP) was evaluated in a trial of cognitive remediation for adults with schizophrenia-spectrum disorders. LP utilizes a dynamic assessment approach to prospectively estimate an individual's learning capacity if provided the opportunity for specific related learning. LP was assessed in 75 participants at study entry, of whom 41 completed an eight-week cognitive remediation (CR) intervention, and 22 received treatment-as-usual (TAU). LP was assessed in a "test-train-test" verbal learning paradigm. Incremental predictive validity was assessed as the degree to which LP predicted memory skill acquisition above and beyond prediction by static verbal learning ability. Examination of construct validity confirmed that LP scores reflected use of trained semantic clustering strategy. LP scores correlated with executive functioning and education history, but not other demographics or symptom severity. Following the eight-week active phase, TAU evidenced little substantial change in skill acquisition outcomes, which related to static baseline verbal learning ability but not LP. For the CR group, LP significantly predicted skill acquisition in domains of verbal and visuospatial memory, but not auditory working memory. Furthermore, LP predicted skill acquisition incrementally beyond relevant background characteristics, symptoms, and neurocognitive abilities. Results suggest that LP assessment can significantly improve prediction of specific skill acquisition with cognitive training, particularly for the domain assessed, and thereby may prove useful in individualization of treatment. Published by Elsevier B.V.

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

The nature and development of hypothetico-predictive argumentation with implications for science teaching

NASA Astrophysics Data System (ADS)

Lawson, Anton E.

2003-11-01

This paper explicates a pattern of scientific argumentation in which scientists respond to causal questions with the generation and test of alternative hypotheses through cycles of hypothetico-predictive argumentation. Hypothetico-predictive arguments are employed to test causal claims that exist on at least two levels (designated stage 4 in which the causal claims are perceptible, and stage 5 in which the causal claims are imperceptible). Origins of the ability to construct and comprehend hypothetico-predictive arguments at the highest level can be traced to pre-verbal reasoning of the sensory-motor child and the gradual internalization of verbally mediated arguments involving nominal, categorical, causal and, finally, theoretical propositions. Presumably, the ability to construct and comprehend hypothetico-predictive arguments (an aspect of procedural knowledge) is necessary for the construction of conceptual knowledge (an aspect of declarative knowledge) because such arguments are used during concept construction and conceptual change. Science instruction that focuses on the generation and debate of hypothetico-predictive arguments should improve students' conceptual understanding and their argumentative/reasoning skills.

Age effects on sensory-processing abilities and their impact on handwriting.

PubMed

Engel-Yeger, Batya; Hus, Sari; Rosenblum, Sara

2012-12-01

Sensory-processing abilities are known to deteriorate in the elderly. As a result, daily activities such as handwriting may be impaired. Yet, knowledge about sensory-processing involvement in handwriting characteristics among older persons is limited. To examine how age influences sensory-processing abilities and the impact on handwriting as a daily performance. The study participants were 118 healthy, independently functioning adults divided into four age groups: 31-45, 46-60, 61-75 and 76+ years. All participants completed the Adolescent/ Adult Sensory Profile (AASP). Handwriting process was documented using the Computerized Handwriting Penmanship Evaluation Tool (ComPET). Age significantly affects sensory processing and handwriting pressure as well as temporal and spatial measures. Both handwriting time and spatial organization of the written product were predicted by sensory seeking. When examining age contribution to the prediction of handwriting by sensory processing, sensory seeking showed a tendency for predicting handwriting pressure (p = .06), while sensory sensitivity significantly predicted handwriting velocity. Age appears to influence sensory-processing abilities and affect daily performance tasks, such as handwriting, for which sensitivity and seeking for sensations are essential. Awareness of clinicians to sensory-processing deficits among older adults and examining their impact on broader daily activities are essential to improve daily performance and quality of life.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

Innate biology versus lifestyle behaviour in the aetiology of obesity and type 2 diabetes: the GLACIER Study.

PubMed

Poveda, Alaitz; Koivula, Robert W; Ahmad, Shafqat; Barroso, Inês; Hallmans, Göran; Johansson, Ingegerd; Renström, Frida; Franks, Paul W

2016-03-01

We compared the ability of genetic (established type 2 diabetes, fasting glucose, 2 h glucose and obesity variants) and modifiable lifestyle (diet, physical activity, smoking, alcohol and education) risk factors to predict incident type 2 diabetes and obesity in a population-based prospective cohort of 3,444 Swedish adults studied sequentially at baseline and 10 years later. Multivariable logistic regression analyses were used to assess the predictive ability of genetic and lifestyle risk factors on incident obesity and type 2 diabetes by calculating the AUC. The predictive accuracy of lifestyle risk factors was similar to that yielded by genetic information for incident type 2 diabetes (AUC 75% and 74%, respectively) and obesity (AUC 68% and 73%, respectively) in models adjusted for age, age(2) and sex. The addition of genetic information to the lifestyle model significantly improved the prediction of type 2 diabetes (AUC 80%; p = 0.0003) and obesity (AUC 79%; p < 0.0001) and resulted in a net reclassification improvement of 58% for type 2 diabetes and 64% for obesity. These findings illustrate that lifestyle and genetic information separately provide a similarly high degree of long-range predictive accuracy for obesity and type 2 diabetes.

Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers' Academic Success upon Entering Graduate Education

ERIC Educational Resources Information Center

Bahadir, Elif

2016-01-01

The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic…

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Separating predictable and unpredictable work to manage interruptions and promote safe and effective work flow.

PubMed

Kowinsky, Amy M; Shovel, Judith; McLaughlin, Maribeth; Vertacnik, Lisa; Greenhouse, Pamela K; Martin, Susan Christie; Minnier, Tamra E

2012-01-01

Predictable and unpredictable patient care tasks compete for caregiver time and attention, making it difficult for patient care staff to reliably and consistently meet patient needs. We have piloted a redesigned care model that separates the work of patient care technicians based on task predictability and creates role specificity. This care model shows promise in improving the ability of staff to reliably complete tasks in a more consistent and timely manner.

Burnout and Engagement: Relative Importance of Predictors and Outcomes in Two Health Care Worker Samples.

PubMed

Fragoso, Zachary L; Holcombe, Kyla J; McCluney, Courtney L; Fisher, Gwenith G; McGonagle, Alyssa K; Friebe, Susan J

2016-06-09

This study's purpose was twofold: first, to examine the relative importance of job demands and resources as predictors of burnout and engagement, and second, the relative importance of engagement and burnout related to health, depressive symptoms, work ability, organizational commitment, and turnover intentions in two samples of health care workers. Nurse leaders (n = 162) and licensed emergency medical technicians (EMTs; n = 102) completed surveys. In both samples, job demands predicted burnout more strongly than job resources, and job resources predicted engagement more strongly than job demands. Engagement held more weight than burnout for predicting commitment, and burnout held more weight for predicting health outcomes, depressive symptoms, and work ability. Results have implications for the design, evaluation, and effectiveness of workplace interventions to reduce burnout and improve engagement among health care workers. Actionable recommendations for increasing engagement and decreasing burnout in health care organizations are provided. © 2016 The Author(s).

Minding the gaps: literacy enhances lexical segmentation in children learning to read.

PubMed

Havron, Naomi; Arnon, Inbal

2017-11-01

Can emergent literacy impact the size of the linguistic units children attend to? We examined children's ability to segment multiword sequences before and after they learned to read, in order to disentangle the effect of literacy and age on segmentation. We found that early readers were better at segmenting multiword units (after controlling for age, cognitive, and linguistic variables), and that improvement in literacy skills between the two sessions predicted improvement in segmentation abilities. Together, these findings suggest that literacy acquisition, rather than age, enhanced segmentation. We discuss implications for models of language learning.

Accurate Descriptions of Hot Flow Behaviors Across β Transus of Ti-6Al-4V Alloy by Intelligence Algorithm GA-SVR

NASA Astrophysics Data System (ADS)

Wang, Li-yong; Li, Le; Zhang, Zhi-hua

2016-09-01

Hot compression tests of Ti-6Al-4V alloy in a wide temperature range of 1023-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively characterize the highly nonlinear flow behaviors, support vector regression (SVR) which is a machine learning method was combined with genetic algorithm (GA) for characterizing the flow behaviors, namely, the GA-SVR. The prominent character of GA-SVR is that it with identical training parameters will keep training accuracy and prediction accuracy at a stable level in different attempts for a certain dataset. The learning abilities, generalization abilities, and modeling efficiencies of the mathematical regression model, ANN, and GA-SVR for Ti-6Al-4V alloy were detailedly compared. Comparison results show that the learning ability of the GA-SVR is stronger than the mathematical regression model. The generalization abilities and modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR. The stress-strain data outside experimental conditions were predicted by the well-trained GA-SVR, which improved simulation accuracy of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as speculating work hardening and dynamic recovery, characterizing dynamic recrystallization evolution, and improving processing maps.

Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models.

PubMed

Azevedo Peixoto, Leonardo de; Laviola, Bruno Galvêas; Alves, Alexandre Alonso; Rosado, Tatiana Barbosa; Bhering, Leonardo Lopes

2017-01-01

Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

Subjective field study of response to impulsive helicopter noise

NASA Technical Reports Server (NTRS)

Powell, C. A.

1981-01-01

Subjects, located outdoors and indoors, judged the noisiness and other subjective noise characteristics of flyovers of two helicopters and a propeller driven airplane as part of a study of the effects of impulsiveness on the subjective response to helicopter noise. In the first experiment, the impulsive characteristics of one helicopter was controlled by varying the main rotor speed while maintaining a constant airspeed in level flight. The second experiment which utilized only the helicopters, included descent and level flight operations. The more impulsive helicopter was consistently judged less noisy than the less impulsive helicopter at equal effective perceived noise levels (EPNL). The ability of EPNL to predict noisiness was not improved by the addition of either of two proposed impulse corrections. A subjective measure of impulsiveness, however, which was not significantly related to the proposed impulse corrections, was found to improve the predictive ability of EPNL.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Intelligent tutoring systems as tools for investigating individual differences in learning

NASA Technical Reports Server (NTRS)

Shute, Valerie J.

1987-01-01

The ultimate goal of this research is to build an improved model-based selection and classification system for the United States Air Force. Researchers are developing innovative approaches to ability testing. The Learning Abilities Measurement Program (LAMP) examines individual differences in learning abilities, seeking answers to the questions of why some people learn more and better than others and whether there are basic cognitive processes applicable across tasks and domains that are predictive of successful performance (or whether there are more complex problem solving behaviors involved).

Comparison between urine albumin-to-creatinine ratio and urine protein dipstick testing for prevalence and ability to predict the risk for chronic kidney disease in the general population (Iwate-KENCO study): a prospective community-based cohort study.

PubMed

Koeda, Yorihiko; Tanaka, Fumitaka; Segawa, Toshie; Ohta, Mutsuko; Ohsawa, Masaki; Tanno, Kozo; Makita, Shinji; Ishibashi, Yasuhiro; Itai, Kazuyoshi; Omama, Shin-Ichi; Onoda, Toshiyuki; Sakata, Kiyomi; Ogasawara, Kuniaki; Okayama, Akira; Nakamura, Motoyuki

2016-05-12

This study compared the combination of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) vs. eGFR and urine protein reagent strip testing to determine chronic kidney disease (CKD) prevalence, and each method's ability to predict the risk for cardiovascular events in the general Japanese population. Baseline data including eGFR, UACR, and urine dipstick tests were obtained from the general population (n = 22 975). Dipstick test results (negative, trace, positive) were allocated to three levels of UACR (<30, 30-300, >300), respectively. In accordance with Kidney Disease Improving Global Outcomes CKD prognosis heat mapping, the cohort was classified into four risk grades (green: grade 1; yellow: grade 2; orange: grade 3, red: grade 4) based on baseline eGFR and UACR levels or dipstick tests. During the mean follow-up period of 5.6 years, 708 new onset cardiovascular events were recorded. For CKD identified by eGFR and dipstick testing (dipstick test ≥ trace and eGFR <60 mL/min/1.73 m(2)), the incidence of CKD was found to be 9 % in the general population. In comparison to non-CKD (grade 1), although cardiovascular risk was significantly higher in risk grades ≥3 (relative risk (RR) = 1.70; 95 % CI: 1.28-2.26), risk predictive ability was not significant in risk grade 2 (RR = 1.20; 95 % CI: 0.95-1.52). When CKD was defined by eGFR and UACR (UACR ≥30 mg/g Cr and eGFR <60 mL/min/1.73 m(2)), prevalence was found to be 29 %. Predictive ability in risk grade 2 (RR = 1.41; 95 % CI: 1.19-1.66) and risk grade ≥3 (RR = 1.76; 95 % CI: 1.37-2.28) were both significantly greater than for non-CKD. Reclassification analysis showed a significant improvement in risk predictive abilities when CKD risk grading was based on UACR rather than on dipstick testing in this population (p < 0.001). Although prevalence of CKD was higher when detected by UACR rather than urine dipstick testing, the predictive ability for cardiovascular events from UACR-based risk grading was superior to that of dipstick-based risk grading in the general population.

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.

Automated selection of stabilizing mutations in designed and natural proteins.

PubMed

Borgo, Benjamin; Havranek, James J

2012-01-31

The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins.

Automated selection of stabilizing mutations in designed and natural proteins

PubMed Central

Borgo, Benjamin; Havranek, James J.

2012-01-01

The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins. PMID:22307603

Life prediction and constitutive behavior

NASA Technical Reports Server (NTRS)

Halford, G. R.

1983-01-01

One of the primary drivers that prompted the initiation of the hot section technology (HOST) program was the recognized need for improved cyclic durability of costly hot section components. All too frequently, fatigue in one form or another was directly responsible for the less than desired durability, and prospects for the future weren't going to improve unless a significant effort was mounted to increase our knowledge and understanding of the elements governing cyclic crack initiation and propagation lifetime. Certainly one of the important factors is the ability to perform accurate structural stress-strain analyses on a routine basis to determine the magnitudes of the localized stresses and strains since it is these localized conditions that govern the initiation and crack growth processes. Developing the ability to more accurately predict crack initiation lifetimes and cyclic crack growth rates for the complex loading conditions found in turbine engine hot sections is of course the ultimate goal of the life prediction research efforts. It has been found convenient to divide the research efforts into those dealing with nominally isotropic and anisotropic alloys; the latter for application to directionally solidified and single crystal turbine blades.

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens

PubMed Central

Nagarajan, Mahesh B.; De, Titas; Lochmüller, Eva-Maria; Eckstein, Felix; Wismüller, Axel

2017-01-01

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk. PMID:29170581

Self-generated sounds of locomotion and ventilation and the evolution of human rhythmic abilities.

PubMed

Larsson, Matz

2014-01-01

It has been suggested that the basic building blocks of music mimic sounds of moving humans, and because the brain was primed to exploit such sounds, they eventually became incorporated in human culture. However, that raises further questions. Why do genetically close, culturally well-developed apes lack musical abilities? Did our switch to bipedalism influence the origins of music? Four hypotheses are raised: (1) Human locomotion and ventilation can mask critical sounds in the environment. (2) Synchronization of locomotion reduces that problem. (3) Predictable sounds of locomotion may stimulate the evolution of synchronized behavior. (4) Bipedal gait and the associated sounds of locomotion influenced the evolution of human rhythmic abilities. Theoretical models and research data suggest that noise of locomotion and ventilation may mask critical auditory information. People often synchronize steps subconsciously. Human locomotion is likely to produce more predictable sounds than those of non-human primates. Predictable locomotion sounds may have improved our capacity of entrainment to external rhythms and to feel the beat in music. A sense of rhythm could aid the brain in distinguishing among sounds arising from discrete sources and also help individuals to synchronize their movements with one another. Synchronization of group movement may improve perception by providing periods of relative silence and by facilitating auditory processing. The adaptive value of such skills to early ancestors may have been keener detection of prey or stalkers and enhanced communication. Bipedal walking may have influenced the development of entrainment in humans and thereby the evolution of rhythmic abilities.

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

DOE PAGES

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; ...

2016-02-24

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

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

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed Central

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J. M.; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C.; Glanville, Helen C.; Jones, Davey L.; Angel, Roey; Salminen, Janne; Newton, Ryan J.; Bürgmann, Helmut; Ingram, Lachlan J.; Hamer, Ute; Siljanen, Henri M. P.; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C.; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C.; Lopes, Ana R.; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S.; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S.; Basiliko, Nathan; Nemergut, Diana R.

2016-01-01

Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. PMID:26941732

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed

Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

2016-01-01

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

Route Prediction on Tracking Data to Location-Based Services

NASA Astrophysics Data System (ADS)

Petróczi, Attila István; Gáspár-Papanek, Csaba

Wireless networks have become so widespread, it is beneficial to determine the ability of cellular networks for localization. This property enables the development of location-based services, providing useful information. These services can be improved by route prediction under the condition of using simple algorithms, because of the limited capabilities of mobile stations. This study gives alternative solutions for this problem of route prediction based on a specific graph model. Our models provide the opportunity to reach our destinations with less effort.

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

Dealing with feeling: Specific emotion regulation skills predict responses to stress in psychosis.

PubMed

Lincoln, Tania M; Hartmann, Maike; Köther, Ulf; Moritz, Steffen

2015-08-15

Elevated negative affect is an established link between minor stressors and psychotic symptoms. Less clear is why people with psychosis fail to regulate distressing emotions effectively. This study tests whether subjective, psychophysiological and symptomatic responses to stress can be predicted by specific emotion regulation (ER) difficulties. Participants with psychotic disorders (n=35) and healthy controls (n=28) were assessed for ER-skills at baseline. They were then exposed to a noise versus no stressor on different days, during which self-reported stress responses, state paranoia and skin conductance levels (SCL) were assessed. Participants with psychosis showed a stronger increase in self-reported stress and SCL in response to the stressor than healthy controls. Stronger increases in self-reported stress were predicted by a reduced ability to be aware of and tolerate distressing emotions, whereas increases in SCL were predicted by a reduced ability to be aware of, tolerate, accept and modify them. Although paranoid symptoms were not significantly affected by the stressors, individual variation in paranoid responses was also predicted by a reduced ability to be aware of and tolerate emotions. Differences in stress responses in the samples were no longer significant after controlling for ER skills. Thus, interventions that improve ER-skills could reduce stress-sensitivity in psychosis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Prediction of situational awareness in F-15 pilots.

PubMed

Carretta, T R; Perry, D C; Ree, M J

1996-01-01

Situational awareness (SA) is a skill often deemed essential to pilot performance in both combat and noncombat flying. A study was conducted to determine if SA in U.S. Air Force F-15 pilots could be predicted. The participants were 171 active duty F-15 A/C pilots who completed a test battery representative of various psychological constructs proposed or demonstrated to be valid for the prediction of performance in a wide variety of military and civilian jobs. These predictors encompassed measures of cognitive ability, psychomotor ability, and personality. Supervisor and peer ratings of SA were collected. Supervisors and peers showed substantial agreement on the SA ratings of the pilots. The first unrotated principle component extracted from the supervisor and peer ratings accounted for 92.5% of the variability of ratings. The unrotated first principal component served as the SA criterion. Flying experience measured in number of F-15 hours was the best predictor of SA. After controlling for the effects of F-15 flying hours, the measures of general cognitive ability based on working memory, spatial reasoning, and divided attention were found to be predictive of SA. Psychomotor and personality measures were not predictive. With additional F-15 flying hours it is expected that pilots would improve their ratings of SA.

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

PubMed Central

Ihlen, Espen A. F.; van Schooten, Kimberley S.; Bruijn, Sjoerd M.; van Dieën, Jaap H.; Vereijken, Beatrix; Helbostad, Jorunn L.; Pijnappels, Mirjam

2018-01-01

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers. PMID:29556188

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking.

PubMed

Ihlen, Espen A F; van Schooten, Kimberley S; Bruijn, Sjoerd M; van Dieën, Jaap H; Vereijken, Beatrix; Helbostad, Jorunn L; Pijnappels, Mirjam

2018-01-01

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME ( p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Testing the utility of the 3-PG model for growth of Eucalyptus grandis x urophylla with natural and manipulated supplies of water and nutrients

Treesearch

Jose Luiz Stape; Michael G. Ryan; Dan Binkley

2004-01-01

The productivity of fast-growing tropical plantations depends, in part, on the ability of trees to obtain and utilize site resources, and the allocation of fixed carbon (C) to wood production. Simulation models can represent these processes and interactions, but the value of these models depends on their ability to improve predictions of stand growth relative to...

Accuracy of Predicted Genomic Breeding Values in Purebred and Crossbred Pigs.

PubMed

Hidalgo, André M; Bastiaansen, John W M; Lopes, Marcos S; Harlizius, Barbara; Groenen, Martien A M; de Koning, Dirk-Jan

2015-05-26

Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Within-population predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent. Copyright © 2015 Hidalgo et al.

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

NASA Astrophysics Data System (ADS)

Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

2017-09-01

Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

Computerized training of non-verbal reasoning and working memory in children with intellectual disability

PubMed Central

Söderqvist, Stina; Nutley, Sissela B.; Ottersen, Jon; Grill, Katja M.; Klingberg, Torkel

2012-01-01

Children with intellectual disabilities show deficits in both reasoning ability and working memory (WM) that impact everyday functioning and academic achievement. In this study we investigated the feasibility of cognitive training for improving WM and non-verbal reasoning (NVR) ability in children with intellectual disability. Participants were randomized to a 5-week adaptive training program (intervention group) or non-adaptive version of the program (active control group). Cognitive assessments were conducted prior to and directly after training and 1 year later to examine effects of the training. Improvements during training varied largely and amount of progress during training predicted transfer to WM and comprehension of instructions, with higher training progress being associated with greater transfer improvements. The strongest predictors for training progress were found to be gender, co-morbidity, and baseline capacity on verbal WM. In particular, females without an additional diagnosis and with higher baseline performance showed greater progress. No significant effects of training were observed at the 1-year follow-up, suggesting that training should be more intense or repeated in order for effects to persist in children with intellectual disabilities. A major finding of this study is that cognitive training is feasible in this clinical sample and can help improve their cognitive performance. However, a minimum cognitive capacity or training ability seems necessary for the training to be beneficial, with some individuals showing little improvement in performance. Future studies of cognitive training should take into consideration how inter-individual differences in training progress influence transfer effects and further investigate how baseline capacities predict training outcome. PMID:23060775

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

USGS Publications Warehouse

Loman, Zachary G.; DeLuca, William; Harrison, Daniel J.; Loftin, Cynthia S.; Rolek, Brian W.; Wood, Petra B.

2018-01-01

ContextSpecies-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning.ObjectivesWe tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats.MethodsWe compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling.ResultsBlackpoll warbler (Setophaga striata), wood thrush (Hylocichla mustelina), and Louisiana (Parkesia motacilla) and northern waterthrush (P. noveboracensis) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird (Seiurus aurocapilla), blackburnian (Setophaga fusca) and cerulean warbler (Setophaga cerulea)], as all were positively related to occupancy data.ConclusionsLC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.

Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides.

PubMed

Lu, Qingzhang; Shen, Guoli; Yu, Ruqin

2002-11-15

The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples. Copyright 2002 Wiley Periodicals, Inc.

Improving pandemic influenza risk assessment

USDA-ARS?s Scientific Manuscript database

Assessing the pandemic risk posed by specific non-human influenza A viruses remains a complex challenge. As influenza virus genome sequencing becomes cheaper, faster and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk asses...

NATIONAL URBAN DATABASE AND ACCESS PROTAL TOOL

EPA Science Inventory

Current mesoscale weather prediction and microscale dispersion models are limited in their ability to perform accurate assessments in urban areas. A project called the National Urban Database with Access Portal Tool (NUDAPT) is beginning to provide urban data and improve the para...

Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors

PubMed Central

Zhan, Zehui; Zhang, Lei; Mei, Hu; Fong, Patrick S. W.

2016-01-01

The detection of university online learners’ reading ability is generally problematic and time-consuming. Thus the eye-tracking sensors have been employed in this study, to record temporal and spatial human eye movements. Learners’ pupils, blinks, fixation, saccade, and regression are recognized as primary indicators for detecting reading abilities. A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint. The model presents good generalization ability with an error of only 4.9% when randomly running 100 times. It has obvious advantages in saving time and improving precision, with only 20 min of testing required for prediction of an individual learner’s reading ability. PMID:27626418

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data.

PubMed

Krumme, Alexis A; Sanfélix-Gimeno, Gabriel; Franklin, Jessica M; Isaman, Danielle L; Mahesri, Mufaddal; Matlin, Olga S; Shrank, William H; Brennan, Troyen A; Brill, Gregory; Choudhry, Niteesh K

2016-11-09

The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. Retrospective. A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

PubMed

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

TIME-INTEGRATED EXPOSURE MEASURES TO IMPROVE THE PREDICTIVE POWER OF EXPOSURE CLASSIFICATION FOR EPIDEMIOLOGIC STUDIES

EPA Science Inventory

Accurate exposure classification tools are required to link exposure with health effects in epidemiological studies. Although long-term integrated exposure measurements are a critical component of exposure assessment, the ability to include these measurements into epidemiologic...

Evidence of counter-gradient growth in western pond turtles (Actinemys marmorata) across thermal gradients

USGS Publications Warehouse

Snover, Melissa; Adams, Michael J.; Ashton, Donald T.; Bettaso, Jamie B.; Welsh, Hartwell H.

2015-01-01

Given the importance of size and age at reproductive maturity to population dynamics, this information on counter-gradient growth will improve our ability to understand and predict the consequences of dam operations for downstream turtle populations.

Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects

PubMed Central

Sun, Chuanyu; VanRaden, Paul M.; Cole, John B.; O'Connell, Jeffrey R.

2014-01-01

Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield. PMID:25084281

Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea.

PubMed

Kainer, David; Stone, Eric A; Padovan, Amanda; Foley, William J; Külheim, Carsten

2018-06-11

Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in Eucalyptus polybractea , a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration (0.59 and 0.73, respectively), than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height (0.25 and 0.19, respectively). Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species. Copyright © 2018, G3: Genes, Genomes, Genetics.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Genetically informed ecological niche models improve climate change predictions.

PubMed

Ikeda, Dana H; Max, Tamara L; Allan, Gerard J; Lau, Matthew K; Shuster, Stephen M; Whitham, Thomas G

2017-01-01

We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change. © 2016 John Wiley & Sons Ltd.

Sensory-guided motor tasks benefit from mental training based on serial prediction

PubMed Central

Binder, Ellen; Hagelweide, Klara; Wang, Ling E.; Kornysheva, Katja; Grefkes, Christian; Fink, Gereon R.; Schubotz, Ricarda I.

2017-01-01

Mental strategies have been suggested to constitute a promising approach to improve motor abilities in both healthy subjects and patients. This behavioural effect has been shown to be associated with changes of neural activity in premotor areas, not only during movement execution, but also while performing motor imagery or action observation. However, how well such mental tasks are performed is often difficult to assess, especially in patients. We here used a novel mental training paradigm based on the serial prediction task (SPT) in order to activate premotor circuits in the absence of a motor task. We then tested whether this intervention improves motor-related performance such as sensorimotor transformation. Two groups of healthy young participants underwent a single-blinded five-day cognitive training schedule and were tested in four different motor tests on the day before and after training. One group (N = 22) received the SPT-training and the other one (N = 21) received a control training based on a serial match-to-sample task. The results revealed significant improvements of the SPT-group in a sensorimotor timing task, i.e. synchronization of finger tapping to a visually presented rhythm, as well as improved visuomotor coordination in a sensory-guided pointing task compared to the group that received the control training. However, mental training did not show transfer effects on motor abilities in healthy subjects beyond the trained modalities as evident by non-significant changes in the Jebsen–Taylor handfunctiontest. In summary, the data suggest that mental training based on the serial prediction task effectively engages sensorimotor circuits and thereby improves motor behaviour. PMID:24321273

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2017-01-01

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

A Method to Estimate Fabric Particle Penetration Performance

DTIC Science & Technology

2014-09-08

may be needed to improve the correlation between wind tunnel component sleeve tests and bench top swatch test. The ability to predict multi-layered...within the fabric/component gap may be needed to improve the correlation between wind tunnel component sleeve tests and bench top swatch test...impermeable garment . Heat stress becomes a major problem with this approach however, as normal physiological heat loss mechanisms (especially sweat

Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes.

PubMed

Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M

2012-10-01

The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.

Strategies to predict rheumatoid arthritis development in at-risk populations

PubMed Central

van der Helm-van Mil, Annette H.

2016-01-01

The development of RA is conceived as a multiple hit process and the more hits that are acquired, the greater the risk of developing clinically apparent RA. Several at-risk phases have been described, including the presence of genetic and environmental factors, RA-related autoantibodies and biomarkers and symptoms. Intervention in these preclinical phases may be more effective compared with intervention in the clinical phase. One prerequisite for preventive strategies is the ability to estimate an individual’s risk adequately. This review evaluates the ability to predict the risk of RA in the various preclinical stages. Present data suggest that a combination of genetic and environmental factors is helpful to identify persons at high risk of RA among first-degree relatives. Furthermore, a combination of symptoms, antibody characteristics and environmental factors has been shown to be relevant for risk prediction in seropositive arthralgia patients. Large prospective studies are needed to validate and improve risk prediction in preclinical disease stages. PMID:25096602

Making Predictions in a Changing World: The Benefits of Individual-Based Ecology

PubMed Central

Stillman, Richard A.; Railsback, Steven F.; Giske, Jarl; Berger, Uta; Grimm, Volker

2014-01-01

Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions—fitness maximization by individual organisms—is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research. PMID:26955076

Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules

PubMed Central

Park, Hahnbeom; Bradley, Philip; Greisen, Per; Liu, Yuan; Mulligan, Vikram Khipple; Kim, David E.; Baker, David; DiMaio, Frank

2017-01-01

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties. PMID:27766851

Predictive factors of depression among Asian female marriage immigrants in Korea.

PubMed

Kim, Jung A; Yang, Sook Ja; Kwon, Kyoung Ja; Kim, Jee Hee

2011-09-01

This study investigated the prevailing rate of depression in female marriage immigrants in Korea and the predictive factors of their rates of depression. The study included 316 foreign female marriage immigrant participants. Four instruments yielded the data: the Center for Epidemiologic Studies Depression Scale and Multidimensional Scale of Perceived Social Support and questionnaires regarding the participants' Korean language ability and demographic data. The survey scales were translated into Korean, Vietnamese, Chinese, and English. The data collection was conducted by a face-to-face interview and translators were used when needed. The female marriage immigrants were found to have higher depression rates than women in the general Korean population. The predictive factors of depression for the female marriage immigrants included their country of origin, Korean speaking ability, and family support. Far more depression was found to occur in the Chinese participants, while the rate of depression was lower in those with competent Korean speaking ability and family support. An exploration of strategies to improve the speaking ability and family support of female marriage immigrants will be necessary in order to decrease their incidence of depression and the strategies should be differentiated based on the female marriage immigrants' country of origin. © 2011 Blackwell Publishing Asia Pty Ltd.

Beyond Climate and Weather Science: Expanding the Forecasting Family to Serve Societal Needs

NASA Astrophysics Data System (ADS)

Barron, E. J.

2009-05-01

The ability to "anticipate" the future is what makes information from the Earth sciences valuable to society - whether it is the prediction of severe weather or the future availability of water resources in response to climate change. An improved ability to anticipate or forecast has the potential to serve society by simultaneously improving our ability to (1) promote economic vitality, (2) enable environmental stewardship, (3) protect life and property, as well as (4) improve our fundamental knowledge of the earth system. The potential is enormous, yet many appear ready to move quickly toward specific mitigation and adaptation strategies assuming that the science is settled. Five important weakness must be addressed first: (1) the formation of a true "climate services" function and capability, (2) the deliberate investment in expanding the family of forecasting elements to incorporate a broader array of environmental factors and impacts, (3) the investment in the sciences that connect climate to society, (4) a deliberate focus on the problems associated with scale, in particular the difference between the scale of predictive models and the scale associated with societal decisions, and (5) the evolution from climate services and model predictions to the equivalent of "environmental intelligence centers." The objective is to bring the discipline of forecasting to a broader array of environmental challenges. Assessments of the potential impacts of global climate change on societal sectors such as water, human health, and agriculture provide good examples of this challenge. We have the potential to move from a largely reactive mode in addressing adverse health outcomes, for example, to one in which the ties between climate, land cover, infectious disease vectors, and human health are used to forecast and predict adverse human health conditions. The potential exists for a revolution in forecasting, that entrains a much broader set of societal needs and solutions. The argument is made that (for example) the current capabilities in the prediction of environmental health is similar to the capabilities (and potential) of weather forecasting in the 1960's.

Improving Software Engineering on NASA Projects

NASA Technical Reports Server (NTRS)

Crumbley, Tim; Kelly, John C.

2010-01-01

Software Engineering Initiative: Reduces risk of software failure -Increases mission safety. More predictable software cost estimates and delivery schedules. Smarter buyer of contracted out software. More defects found and removed earlier. Reduces duplication of efforts between projects. Increases ability to meet the challenges of evolving software technology.

Predictive Analytics for Safer Food Supply

USDA-ARS?s Scientific Manuscript database

Science based risk analysis improves the USDA Food Safety Inspection Service’s ability to combat threats to public health from food-borne illness by allowing the Agency to focus resources on hazards that pose the greatest risk. Innovative algorithms enable detection and containment of threat by an...

Autism as a disorder of prediction

PubMed Central

Sinha, Pawan; Kjelgaard, Margaret M.; Gandhi, Tapan K.; Tsourides, Kleovoulos; Cardinaux, Annie L.; Pantazis, Dimitrios; Diamond, Sidney P.; Held, Richard M.

2014-01-01

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy. PMID:25288765

Riddle appreciation and reading comprehension in Cantonese-speaking children.

PubMed

Tang, Ivy N Y; To, Carol K S; Weekes, Brendan S

2013-10-01

Inference-making skills are necessary for reading comprehension. Training in riddle appreciation is an effective way to improve reading comprehension among English-speaking children. However, it is not clear whether these methods generalize to other writing systems. The goal of the present study was to investigate the relationship between inference-making skills, as measured by riddle appreciation ability, and reading comprehension performance in typically developing Cantonese-speaking children in the 4th grade. Forty Cantonese-speaking children between the ages of 9;1 (years;months) and 11;0 were given tests of riddle appreciation ability and reading comprehension. Chinese character reading and auditory comprehension abilities were also assessed using tests that had been standardized in Hong Kong. Regression analyses revealed that riddle appreciation ability explained a significant amount of variance in reading comprehension after variance due to character reading skills and auditory comprehension skills were first considered. Orthographic, lexical, morphological, and syntactic riddles were also significantly correlated with reading comprehension. Riddle appreciation ability predicts reading comprehension in Cantonese-speaking 4th-grade children. Therefore, training Cantonese speakers in riddle appreciation should improve their reading comprehension.

SEC proton prediction model: verification and analysis.

PubMed

Balch, C C

1999-06-01

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Engineering the earth system

NASA Astrophysics Data System (ADS)

Keith, D. W.

2005-12-01

The post-war growth of the earth sciences has been fueled, in part, by a drive to quantify environmental insults in order to support arguments for their reduction, yet paradoxically the knowledge gained is grants us ever greater capability to deliberately engineer environmental processes on a planetary scale. Increased capability can arises though seemingly unconnected scientific advances. Improvements in numerical weather prediction such as the use of adjoint models in analysis/forecast systems, for example, means that weather modification can be accomplished with smaller control inputs. Purely technological constraints on our ability to engineer earth systems arise from our limited ability to measure and predict system responses and from limits on our ability to manage large engineering projects. Trends in all three constraints suggest a rapid growth in our ability to engineer the planet. What are the implications of our growing ability to geoengineer? Will we see a reemergence of proposals to engineer our way out of the climate problem? How can we avoid the moral hazard posed by the knowledge that geoengineering might provide a backstop to climate damages? I will speculate about these issues, and suggest some institutional factors that may provide a stronger constraint on the use of geoengineering than is provided by any purely technological limit.

Socio-cognitive profiles for visual learning in young and older adults

PubMed Central

Christian, Julie; Goldstone, Aimee; Kuai, Shu-Guang; Chin, Wynne; Abrams, Dominic; Kourtzi, Zoe

2015-01-01

It is common wisdom that practice makes perfect; but why do some adults learn better than others? Here, we investigate individuals’ cognitive and social profiles to test which variables account for variability in learning ability across the lifespan. In particular, we focused on visual learning using tasks that test the ability to inhibit distractors and select task-relevant features. We tested the ability of young and older adults to improve through training in the discrimination of visual global forms embedded in a cluttered background. Further, we used a battery of cognitive tasks and psycho-social measures to examine which of these variables predict training-induced improvement in perceptual tasks and may account for individual variability in learning ability. Using partial least squares regression modeling, we show that visual learning is influenced by cognitive (i.e., cognitive inhibition, attention) and social (strategic and deep learning) factors rather than an individual’s age alone. Further, our results show that independent of age, strong learners rely on cognitive factors such as attention, while weaker learners use more general cognitive strategies. Our findings suggest an important role for higher-cognitive circuits involving executive functions that contribute to our ability to improve in perceptual tasks after training across the lifespan. PMID:26113820

Learning strategies and general cognitive ability as predictors of gender- specific academic achievement

PubMed Central

Ruffing, Stephanie; Wach, F. -Sophie; Spinath, Frank M.; Brünken, Roland; Karbach, Julia

2015-01-01

Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability in the prediction of academic achievement. The relationship between these variables was examined by correlation analyses. A set of t-tests was used to test for gender differences in learning strategies, whereas structural equation modeling as well as multi-group analyses were applied to investigate the incremental contribution of learning strategies for male and female students’ academic performance. The sample consisted of 461 students (mean age = 21.2 years, SD = 3.2). Correlation analyses revealed that general cognitive ability as well as the learning strategies effort, attention, and learning environment were positively correlated with academic achievement. Gender differences were found in the reported application of many learning strategies. Importantly, the prediction of achievement in structural equation modeling revealed that only effort explained incremental variance (10%) over general cognitive ability. Results of multi-group analyses showed no gender differences in this prediction model. This finding provides further knowledge regarding gender differences in learning research and the specific role of learning strategies for academic achievement. The incremental assessment of learning strategy use as well as gender-differences in their predictive value contributes to the understanding and improvement of successful academic development. PMID:26347698

Learning strategies and general cognitive ability as predictors of gender- specific academic achievement.

PubMed

Ruffing, Stephanie; Wach, F-Sophie; Spinath, Frank M; Brünken, Roland; Karbach, Julia

2015-01-01

Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability in the prediction of academic achievement. The relationship between these variables was examined by correlation analyses. A set of t-tests was used to test for gender differences in learning strategies, whereas structural equation modeling as well as multi-group analyses were applied to investigate the incremental contribution of learning strategies for male and female students' academic performance. The sample consisted of 461 students (mean age = 21.2 years, SD = 3.2). Correlation analyses revealed that general cognitive ability as well as the learning strategies effort, attention, and learning environment were positively correlated with academic achievement. Gender differences were found in the reported application of many learning strategies. Importantly, the prediction of achievement in structural equation modeling revealed that only effort explained incremental variance (10%) over general cognitive ability. Results of multi-group analyses showed no gender differences in this prediction model. This finding provides further knowledge regarding gender differences in learning research and the specific role of learning strategies for academic achievement. The incremental assessment of learning strategy use as well as gender-differences in their predictive value contributes to the understanding and improvement of successful academic development.

Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model.

PubMed

Jin, H; Wu, S; Vidyanti, I; Di Capua, P; Wu, B

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression. This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes. Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression. Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions. The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.

Examining the "Matthew Effect" on the motivation and ability to make lifestyle changes in 217 heart rehabilitation patients.

PubMed

Mildestvedt, Thomas; Meland, Eivind

2007-01-01

Those who are socioeconomically disadvantaged and people with emotional problems have a poorer prognosis for cardiovascular disease. The authors wanted to examine: (1) what effect household income, emotional status, high-risk smoking status, and severity of heart disease had on the ability of individuals to make dietary and exercise improvements after heart disease and (2) to what extent unfavourable lifestyle outcomes among disadvantaged people were mediated by motivational problems. A two-year follow-up study of the combined cohorts of a randomized controlled trial. Level of exercise and present dietary habits were measured at inclusion and after 6 and 24 months. Different motivational factors and emotional distress were measured during rehabilitation. Autonomous self-regulation was lowest among smokers (b = -0.31, p = 0.02) and female participants (b = 0.39, p = 0.004). Participants with high scores of emotional distress predicted lower motivation for all the measures. We found no association between socioeconomic status (household income) and the ability to perform lifestyle changes. Current smoking status predicted lower ability to obtain lifestyle changes on all measures. Emotional distress was related to lower ability to increase physical activity at 6 months' but not at 24 months' follow-up. The mediating effects of motivational factors were insignificant. The results of this study do not support the suspicion that preventive efforts accentuate the socioeconomic differences in cardiovascular health. Health-promotive efforts after heart disease should safeguard that high-risk groups such as smokers are not discouraged from improving their lifestyle in other areas.

A-Priori Tuning of Modified Magnussen Combustion Model

NASA Technical Reports Server (NTRS)

Norris, A. T.

2016-01-01

In the application of CFD to turbulent reacting flows, one of the main limitations to predictive accuracy is the chemistry model. Using a full or skeletal kinetics model may provide good predictive ability, however, at considerable computational cost. Adding the ability to account for the interaction between turbulence and chemistry improves the overall fidelity of a simulation but adds to this cost. An alternative is the use of simple models, such as the Magnussen model, which has negligible computational overhead, but lacks general predictive ability except for cases that can be tuned to the flow being solved. In this paper, a technique will be described that allows the tuning of the Magnussen model for an arbitrary fuel and flow geometry without the need to have experimental data for that particular case. The tuning is based on comparing the results of the Magnussen model and full finite-rate chemistry when applied to perfectly and partially stirred reactor simulations. In addition, a modification to the Magnussen model is proposed that allows the upper kinetic limit for the reaction rate to be set, giving better physical agreement with full kinetic mechanisms. This procedure allows a simple reacting model to be used in a predictive manner, and affords significant savings in computational costs for simulations.

Limitations of BCC_CSM's ability to predict summer precipitation over East Asia and the Northwestern Pacific

NASA Astrophysics Data System (ADS)

Gong, Zhiqiang; Dogar, Muhammad Mubashar Ahmad; Qiao, Shaobo; Hu, Po; Feng, Guolin

2017-09-01

This study examines the ability of the Beijing Climate Center Climate System Model (BCC_CSM) to predict the meridional pattern of summer precipitation over East Asia-Northwest Pacific (EA-NWP) and its East Asia-Pacific (EAP) teleconnection. The differences of summer precipitation modes of the empirical orthogonal function and the bias of atmospheric circulations over EA-NWP are analyzed to determine the reason for the precipitation prediction errors. Results indicate that the BCC_CSM could not reproduce the positive-negative-positive meridional tripole pattern from south to north that differs markedly from that observed over the last 20 years. This failure can be attributed to the bias of the BCC_CSM hindcasts of the summer EAP teleconnection and the low predictability of 500 hPa at the mid-high latitude lobe of the EAP. Meanwhile, the BCC_CSM hindcasts' deficiencies of atmospheric responses to SST anomalies over the Indonesia maritime continent (IMC) resulted in opposite and geographically shifted geopotential anomalies at 500 hPa as well as wind and vorticity anomalies at 850 hPa, rendering the BCC_CSM unable to correctly reproduce the EAP teleconnection pattern. Understanding these two problems will help further improve BCC_CSM's summer precipitation forecasting ability over EA-NWP.

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

PubMed

Zhu, Fan; Panwar, Bharat; Dodge, Hiroko H; Li, Hongdong; Hampstead, Benjamin M; Albin, Roger L; Paulson, Henry L; Guan, Yuanfang

2016-10-05

We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

Usefulness of Midregional Proadrenomedullin to Predict Poor Outcome in Patients with Community Acquired Pneumonia

PubMed Central

Gordo-Remartínez, Susana; Sevillano-Fernández, José A.; Álvarez-Sala, Luis A.; Andueza-Lillo, Juan A.; de Miguel-Yanes, José M.

2015-01-01

Background midregional proadrenomedullin (MR-proADM) is a prognostic biomarker in patients with community-acquired pneumonia (CAP). We sought to confirm whether MR-proADM added to Pneumonia Severity Index (PSI) improves the potential prognostic value of PSI alone, and tested to what extent this combination could be useful in predicting poor outcome of patients with CAP in an Emergency Department (ED). Methods Consecutive patients diagnosed with CAP were enrolled in this prospective, single-centre, observational study. We analyzed the ability of MR-proADM added to PSI to predict poor outcome using receiver operating characteristic (ROC) curves, logistic regression and risk reclassification and comparing it with the ability of PSI alone. The primary outcome was “poor outcome”, defined as the incidence of an adverse event (ICU admission, hospital readmission, or mortality at 30 days after CAP diagnosis). Results 226 patients were included; 33 patients (14.6%) reached primary outcome. To predict primary outcome the highest area under curve (AUC) was found for PSI (0.74 [0.64-0.85]), which was not significantly higher than for MR-proADM (AUC 0.72 [0.63-0.81, p > 0.05]). The combination of PSI and MR-proADM failed to improve the predictive potential of PSI alone (AUC 0.75 [0.65-0.85, p=0.56]). Ten patients were appropriately reclassified when the combined PSI and MR-proADM model was used as compared with the model of PSI alone. Net reclassification improvement (NRI) index was statistically significant (7.69%, p = 0.03) with an improvement percentage of 3.03% (p = 0.32) for adverse event, and 4.66% (P = 0.02) for no adverse event. Conclusion MR-proADM in combination with PSI may be helpful in individual risk stratification for short-term poor outcome of CAP patients, allowing a better reclassification of patients compared with PSI alone. PMID:26030588

Graphics Processing Units (GPU) and the Goddard Earth Observing System atmospheric model (GEOS-5): Implementation and Potential Applications

NASA Technical Reports Server (NTRS)

Putnam, William M.

2011-01-01

Earth system models like the Goddard Earth Observing System model (GEOS-5) have been pushing the limits of large clusters of multi-core microprocessors, producing breath-taking fidelity in resolving cloud systems at a global scale. GPU computing presents an opportunity for improving the efficiency of these leading edge models. A GPU implementation of GEOS-5 will facilitate the use of cloud-system resolving resolutions in data assimilation and weather prediction, at resolutions near 3.5 km, improving our ability to extract detailed information from high-resolution satellite observations and ultimately produce better weather and climate predictions

Genome wide selection in Citrus breeding.

PubMed

Gois, I B; Borém, A; Cristofani-Yaly, M; de Resende, M D V; Azevedo, C F; Bastianel, M; Novelli, V M; Machado, M A

2016-10-17

Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seq TM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.

Enhanced backgrounds in scene rendering with GTSIMS

NASA Astrophysics Data System (ADS)

Prussing, Keith F.; Pierson, Oliver; Cordell, Chris; Stewart, John; Nielson, Kevin

2018-05-01

A core component to modeling visible and infrared sensor responses is the ability to faithfully recreate background noise and clutter in a synthetic image. Most tracking and detection algorithms use a combination of signal to noise or clutter to noise ratios to determine if a signature is of interest. A primary source of clutter is the background that defines the environment in which a target is placed. Over the past few years, the Electro-Optical Systems Laboratory (EOSL) at the Georgia Tech Research Institute has made significant improvements to its in house simulation framework GTSIMS. First, we have expanded our terrain models to include the effects of terrain orientation on emission and reflection. Second, we have included the ability to model dynamic reflections with full BRDF support. Third, we have added the ability to render physically accurate cirrus clouds. And finally, we have updated the overall rendering procedure to reduce the time necessary to generate a single frame by taking advantage of hardware acceleration. Here, we present the updates to GTSIMS to better predict clutter and noise doe to non-uniform backgrounds. Specifically, we show how the addition of clouds, terrain, and improved non-uniform sky rendering improve our ability to represent clutter during scene generation.

Video games as a means to reduce age-related cognitive decline: attitudes, compliance, and effectiveness.

PubMed

Boot, Walter R; Champion, Michael; Blakely, Daniel P; Wright, Timothy; Souders, Dustin J; Charness, Neil

2013-01-01

Recent research has demonstrated broad benefits of video game play to perceptual and cognitive abilities. These broad improvements suggest that video game-based cognitive interventions may be ideal to combat the many perceptual and cognitive declines associated with advancing age. Furthermore, game interventions have the potential to induce higher rates of intervention compliance compared to other cognitive interventions as they are assumed to be inherently enjoyable and motivating. We explored these issues in an intervention that tested the ability of an action game and a "brain fitness" game to improve a variety of abilities. Cognitive abilities did not significantly improve, suggesting caution when recommending video game interventions as a means to reduce the effects of cognitive aging. However, the game expected to produce the largest benefit based on previous literature (an action game) induced the lowest intervention compliance. We explain this low compliance by participants' ratings of the action game as less enjoyable and by their prediction that training would have few meaningful benefits. Despite null cognitive results, data provide valuable insights into the types of video games older adults are willing to play and why.

Video Games as a Means to Reduce Age-Related Cognitive Decline: Attitudes, Compliance, and Effectiveness

PubMed Central

Boot, Walter R.; Champion, Michael; Blakely, Daniel P.; Wright, Timothy; Souders, Dustin J.; Charness, Neil

2013-01-01

Recent research has demonstrated broad benefits of video game play to perceptual and cognitive abilities. These broad improvements suggest that video game-based cognitive interventions may be ideal to combat the many perceptual and cognitive declines associated with advancing age. Furthermore, game interventions have the potential to induce higher rates of intervention compliance compared to other cognitive interventions as they are assumed to be inherently enjoyable and motivating. We explored these issues in an intervention that tested the ability of an action game and a “brain fitness” game to improve a variety of abilities. Cognitive abilities did not significantly improve, suggesting caution when recommending video game interventions as a means to reduce the effects of cognitive aging. However, the game expected to produce the largest benefit based on previous literature (an action game) induced the lowest intervention compliance. We explain this low compliance by participants’ ratings of the action game as less enjoyable and by their prediction that training would have few meaningful benefits. Despite null cognitive results, data provide valuable insights into the types of video games older adults are willing to play and why. PMID:23378841

Efficient depth intraprediction method for H.264/AVC-based three-dimensional video coding

NASA Astrophysics Data System (ADS)

Oh, Kwan-Jung; Oh, Byung Tae

2015-04-01

We present an intracoding method that is applicable to depth map coding in multiview plus depth systems. Our approach combines skip prediction and plane segmentation-based prediction. The proposed depth intraskip prediction uses the estimated direction at both the encoder and decoder, and does not need to encode residual data. Our plane segmentation-based intraprediction divides the current block into biregions, and applies a different prediction scheme for each segmented region. This method avoids incorrect estimations across different regions, resulting in higher prediction accuracy. Simulation results demonstrate that the proposed scheme is superior to H.264/advanced video coding intraprediction and has the ability to improve the subjective rendering quality.

At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration

DOE PAGES

Stegen, James C.

2018-04-10

To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modelingmore » frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. Here, we can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.« less

At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration

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

Stegen, James C.

To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modelingmore » frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. Here, we can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.« less

At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration.

PubMed

Stegen, James C

2018-01-01

To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modeling frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. We can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

Study of Earthquake Disaster Prediction System of Langfang city Based on GIS

NASA Astrophysics Data System (ADS)

Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei

2017-07-01

In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.

Improvement in intelligence test scores from 6 to 10 years in children of teenage mothers.

PubMed

Cornelius, Marie D; Goldschmidt, Lidush; De Genna, Natacha M; Richardson, Gale A; Leech, Sharon L; Day, Richard

2010-06-01

This study investigates change in IQ scores among 290 children born to teenage mothers and identifies social, economic, and environmental variables that may be associated with change in intelligence test performance. The children of 290 teenage mothers (72% African-American and 28% European American) were assessed with the Stanford-Binet Intelligence Scale-4th Edition at ages 6 and 10. The mean composite score at age 6 was 84.8 and 91.2 at age 10, an improvement of 6.4 points. Significant cross-sectional predictors at both ages 6 and 10 of higher Stanford-Binet Intelligence Scale scores were maternal cognitive ability, school grade, white ethnicity, and caregiver education. Having more children in the household significantly predicted lower Stanford-Binet Intelligence Scale scores at age 6. Higher satisfaction with maternal social support predicted higher Stanford-Binet Intelligence Scale scores at age 10. Change in IQ scores was not related to maternal socioeconomic status, social support, home environment, ethnicity, or family interactions. Custodial stability was associated with an improvement in IQ scores, whereas increase in caregiver depression was related to decline in IQ scores. Our findings suggest that improvement in IQ scores of offspring of teenage mothers may be related to stability of maternal custody. More research is needed to determine the impact of the maturation of adolescent mothers' parenting and the role of early education on improvement in cognitive abilities.

The prediction of the level of personality organization on reduction of psychiatric symptoms and improvement of work ability in short- versus long-term psychotherapies during a 5-year follow-up.

PubMed

Knekt, Paul; Lindfors, Olavi; Keinänen, Matti; Heinonen, Erkki; Virtala, Esa; Härkänen, Tommi

2017-09-01

How level of personality organization (LPO) predicts psychiatric symptoms and work ability in short- versus long-term psychotherapies is poorly known. We investigated the importance of the LPO on the benefits of short-term versus long-term psychotherapies. A cohort study based on 326 outpatients with mood or anxiety disorder was allocated to long-term (LPP) and short-term (SPP) psychodynamic psychotherapy, and solution-focused therapy (SFT). The LPO was assessed by interview at baseline and categorized into neuroses and higher level borderline. Outcome was assessed at baseline and 4-9 times during a 5-year follow-up, using self-report and interview-based measures of symptoms and work ability. For patients receiving SPP, improvement in work ability, symptom reduction, and the remission rate were more considerable in patients with neuroses than in higher level borderline patients, whereas LPP or SFT showed no notable differences in effectiveness in the two LPO groups. In patients with neuroses, improvement was more considerable in the short-term therapy groups during the first year of follow-up, and in higher level borderline patients LPP was more effective after 3 years of follow-up. The remission rate, defined as both symptom reduction and lack of auxiliary treatment, was higher in LPP than in SPP for both the LPO groups considered. In neuroses, short-term psychotherapy was associated with a more rapid reduction of symptoms and increase in work ability, whereas LPP was more effective for longer follow-ups in both LPO groups. Further large-scale studies are needed. Level of personality organization is relevant for selection between short- and long-term psychotherapies. Short-term therapy gives faster benefits for neurotic patients but not for patients with higher level borderline personality organization. Sustained remission from symptoms is more probable after long-term than short-term therapy. © 2016 The British Psychological Society.

Longitudinal Monitoring of Patients With Chronic Low Back Pain During Physical Therapy Treatment Using the STarT Back Screening Tool.

PubMed

Medeiros, Flávia Cordeiro; Costa, Leonardo Oliveira Pena; Added, Marco Aurélio Nemitalla; Salomão, Evelyn Cassia; Costa, Lucíola da Cunha Menezes

2017-05-01

Study Design Preplanned secondary analysis of a randomized clinical trial. Background The STarT Back Screening Tool (SBST) was developed to screen and to classify patients with low back pain into subgroups for the risk of having a poor prognosis. However, this classification at baseline does not take into account variables that can influence the prognosis during treatment or over time. Objectives (1) To investigate the changes in risk subgroup measured by the SBST over a period of 6 months, and (2) to assess the long-term predictive ability of the SBST when administered at different time points. Methods Patients with chronic nonspecific low back pain (n = 148) receiving physical therapy care as part of a randomized trial were analyzed. Pain intensity, disability, global perceived effect, and the SBST were collected at baseline, 5 weeks, 3 months, and 6 months. Changes in SBST risk classification were calculated. Hierarchical linear regression models adjusted for potential confounders were built to analyze the predictive capabilities of the SBST when administered at different time points. Results A large proportion of patients (60.8%) changed their risk subgroup after receiving physical therapy care. The SBST improved the prediction for all 6-month outcomes when using the 5-week risk subgroup and the difference between baseline and 5-week subgroup, after controlling for potential confounders. The SBST at baseline did not improve the predictive ability of the models after adjusting for confounders. Conclusion This study shows that many patients change SBST risk subgroup after receiving physical therapy care, and that the predictive ability of the SBST in patients with chronic low back pain increases when administered at different time points. Level of Evidence Prognosis, 2b. J Orthop Sports Phys Ther 2017;47(5):314-323. Epub 29 Mar 2017. doi:10.2519/jospt.2017.7199.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

PubMed Central

Ganesh, Gowrishankar

2017-01-01

Abstract The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions. PMID:29340300

Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?

PubMed

Fangmann, A; Bergfelder-Drüing, S; Tholen, E; Simianer, H; Erbe, M

2015-12-01

In most countries and for most livestock species, genomic evaluations are obtained from within-breed analyses. To achieve reliable breeding values, however, a sufficient reference sample size is essential. To increase this size, the use of multibreed reference populations for small populations is considered a suitable option in other species. Over decades, the separate breeding work of different pig breeding organizations in Germany has led to stratified subpopulations in the breed German Large White. Due to this fact and the limited number of Large White animals available in each organization, there was a pressing need for ascertaining if multi-subpopulation genomic prediction is superior compared with within-subpopulation prediction in pigs. Direct genomic breeding values were estimated with genomic BLUP for the trait "number of piglets born alive" using genotype data (Illumina Porcine 60K SNP BeadChip) from 2,053 German Large White animals from five different commercial pig breeding companies. To assess the prediction accuracy of within- and multi-subpopulation reference sets, a random 5-fold cross-validation with 20 replications was performed. The five subpopulations considered were only slightly differentiated from each other. However, the prediction accuracy of the multi-subpopulations approach was not better than that of the within-subpopulation evaluation, for which the predictive ability was already high. Reference sets composed of closely related multi-subpopulation sets performed better than sets of distantly related subpopulations but not better than the within-subpopulation approach. Despite the low differentiation of the five subpopulations, the genetic connectedness between these different subpopulations seems to be too small to improve the prediction accuracy by applying multi-subpopulation reference sets. Consequently, resources should be used for enlarging the reference population within subpopulation, for example, by adding genotyped females.

Introduction to Agricultural Marketing.

ERIC Educational Resources Information Center

Futrell, Gene; And Others

This marketing unit focuses on the importance of forecasting in order for a farm family to develop marketing plans. It describes sources of information and includes a glossary of marketing terms and exercises using both fundamental and technical methods to predict prices in order to improve forecasting ability. The unit is organized in the…

Cost-Effective Prediction of Reading Difficulties.

ERIC Educational Resources Information Center

Heath, Steve M.; Hogben, John H.

2004-01-01

This study addressed 2 questions: (a) Can preschoolers who will fail at reading be more efficiently identified by targeting those at highest risk for reading problems? and (b) will auditory temporal processing (ATP) improve the accuracy of identification derived from phonological processing and oral language ability? A sample of 227 preschoolers…

APPLICATION OF A SIMPLE CIRCULATING MARKER OF OXIDATIVE STRESS FOR CLINICAL AND EPIDEMIOLOGICAL STUDIES

EPA Science Inventory

Biomarker development has improved our ability to detect early changes at the molecular, cellular and pre-clinical level that are often predictive of adverse cancer and non cancer related health outcomes. The role of reactive oxygen species (ROS) is implicated in many disease pr...

Improving General Chemistry Course Performance through Online Homework-Based Metacognitive Training

ERIC Educational Resources Information Center

Casselman, Brock L.; Atwood, Charles H.

2017-01-01

In a first-semester general chemistry course, metacognitive training was implemented as part of an online homework system. Students completed weekly quizzes and multiple practice tests to regularly assess their abilities on the chemistry principles. Before taking these assessments, students predicted their score, receiving feedback after…

Bilateral versus unilateral cochlear implants in children: a study of spoken language outcomes.

PubMed

Sarant, Julia; Harris, David; Bennet, Lisa; Bant, Sharyn

2014-01-01

Although it has been established that bilateral cochlear implants (CIs) offer additional speech perception and localization benefits to many children with severe to profound hearing loss, whether these improved perceptual abilities facilitate significantly better language development has not yet been clearly established. The aims of this study were to compare language abilities of children having unilateral and bilateral CIs to quantify the rate of any improvement in language attributable to bilateral CIs and to document other predictors of language development in children with CIs. The receptive vocabulary and language development of 91 children was assessed when they were aged either 5 or 8 years old by using the Peabody Picture Vocabulary Test (fourth edition), and either the Preschool Language Scales (fourth edition) or the Clinical Evaluation of Language Fundamentals (fourth edition), respectively. Cognitive ability, parent involvement in children's intervention or education programs, and family reading habits were also evaluated. Language outcomes were examined by using linear regression analyses. The influence of elements of parenting style, child characteristics, and family background as predictors of outcomes were examined. Children using bilateral CIs achieved significantly better vocabulary outcomes and significantly higher scores on the Core and Expressive Language subscales of the Clinical Evaluation of Language Fundamentals (fourth edition) than did comparable children with unilateral CIs. Scores on the Preschool Language Scales (fourth edition) did not differ significantly between children with unilateral and bilateral CIs. Bilateral CI use was found to predict significantly faster rates of vocabulary and language development than unilateral CI use; the magnitude of this effect was moderated by child age at activation of the bilateral CI. In terms of parenting style, high levels of parental involvement, low amounts of screen time, and more time spent by adults reading to children facilitated significantly better vocabulary and language outcomes. In terms of child characteristics, higher cognitive ability and female sex were predictive of significantly better language outcomes. When family background factors were examined, having tertiary-educated primary caregivers and a family history of hearing loss were significantly predictive of better outcomes. Birth order was also found to have a significant negative effect on both vocabulary and language outcomes, with each older sibling predicting a 5 to 10% decrease in scores. Children with bilateral CIs achieved significantly better vocabulary outcomes, and 8-year-old children with bilateral CIs had significantly better language outcomes than did children with unilateral CIs. These improvements were moderated by children's ages at both first and second CIs. The outcomes were also significantly predicted by a number of factors related to parenting, child characteristics, and family background. Fifty-one percent of the variance in vocabulary outcomes and between 59 to 69% of the variance in language outcomes was predicted by the regression models.

Assessment of the Relationship between Spiritual and Social Health and the Self-Care Ability of Elderly People Referred to Community Health Centers.

PubMed

Mohammadi, Mahboobeh; Alavi, Mousa; Bahrami, Masoud; Zandieh, Zahra

2017-01-01

Promotion of self-care ability among older people is an essential means to help maintain and improve their health. However, the role of spiritual and social health has not yet been considered in detail in the context of self-care ability among elderly. The aim of this study was to assess the relationship between spiritual and social health and self-care ability of older people referred to community health centers in Isfahan. In this cross-sectional correlation study, 200 people, aged 60 years and older, referred to healthcare centers in 2016 were recruited through convenience sampling method. Data were collected by four-part tool comprising of: (a) demographics, (b) Ellison and Palotzin's spiritual well-being scale, (c) Kees's "social health" scale, and (d) self-care ability scale for the elderly by Soderhamn's; data were analyzed by descriptive and inferential (independent t -test, analysis of variance - ANOVA, Pearson's coefficient tests, and multiple regression analysis) statistics by SPSS16 software. Findings showed that the entered predictor variables were accounted for 41% of total variance ( R 2 ) of the two self-care ability in the model ( p < 0.001, F 3, 199 = 46.02). Two out of the three predictor variables including religious well-being and social health, significantly predicted the self-care ability of older people. The results of this study emphasized on the relationship between spiritual and social health of the elderly people and their ability to self-care. Therefore, it would be recommended to keep the focus of the service resources towards improving social and spiritual health to improve self-care ability in elderly people.

Relation between aerobic capacity and walking ability in older adults with a lower-limb amputation.

PubMed

Wezenberg, Daphne; van der Woude, Lucas H; Faber, Willemijn X; de Haan, Arnold; Houdijk, Han

2013-09-01

To determine the relative aerobic load, walking speed, and walking economy of older adults with a lower-limb prosthesis, and to predict the effect of an increased aerobic capacity on their walking ability. Cross-sectional. Human motion laboratory at a rehabilitation center. Convenience sample of older adults (n=36) who underwent lower-limb amputation because of vascular deficiency or trauma and able-bodied controls (n=21). Not applicable. Peak aerobic capacity and oxygen consumption while walking were determined. The relative aerobic load and walking economy were assessed as a function of walking speed, and a data-based model was constructed to predict the effect of an increased aerobic capacity on walking ability. People with a vascular amputation walked at a substantially higher (45.2%) relative aerobic load than people with an amputation because of trauma. The preferred walking speed in both groups of amputees was slower than that of able-bodied controls and below their most economical walking speed. We predicted that a 10% increase in peak aerobic capacity could potentially result in a reduction in the relative aerobic load of 9.1%, an increase in walking speed of 17.3% and 13.9%, and an improvement in the walking economy of 6.8% and 2.9%, for people after a vascular or traumatic amputation, respectively. Current findings corroborate the notion that, especially in people with a vascular amputation, the peak aerobic capacity is an important determinant for walking ability. The data provide quantitative predictions on the effect of aerobic training; however, future research is needed to experimentally confirm these predictions. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Improving prediction of fall risk among nursing home residents using electronic medical records.

PubMed

Marier, Allison; Olsho, Lauren E W; Rhodes, William; Spector, William D

2016-03-01

Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls. The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data. In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification. Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Stroboscopic Training Enhances Anticipatory Timing.

PubMed

Smith, Trevor Q; Mitroff, Stephen R

The dynamic aspects of sports often place heavy demands on visual processing. As such, an important goal for sports training should be to enhance visual abilities. Recent research has suggested that training in a stroboscopic environment, where visual experiences alternate between visible and obscured, may provide a means of improving attentional and visual abilities. The current study explored whether stroboscopic training could impact anticipatory timing - the ability to predict where a moving stimulus will be at a specific point in time. Anticipatory timing is a critical skill for both sports and non-sports activities, and thus finding training improvements could have broad impacts. Participants completed a pre-training assessment that used a Bassin Anticipation Timer to measure their abilities to accurately predict the timing of a moving visual stimulus. Immediately after this initial assessment, the participants completed training trials, but in one of two conditions. Those in the Control condition proceeded as before with no change. Those in the Strobe condition completed the training trials while wearing specialized eyewear that had lenses that alternated between transparent and opaque (rate of 100ms visible to 150ms opaque). Post-training assessments were administered immediately after training, 10-minutes after training, and 10-days after training. Compared to the Control group, the Strobe group was significantly more accurate immediately after training, was more likely to respond early than to respond late immediately after training and 10 minutes later, and was more consistent in their timing estimates immediately after training and 10 minutes later.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

PubMed Central

Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients. PMID:29904574

Predictive displays for a process-control schematic interface.

PubMed

Yin, Shanqing; Wickens, Christopher D; Helander, Martin; Laberge, Jason C

2015-02-01

Our objective was to examine the extent to which increasing precision of predictive (rate of change) information in process control will improve performance on a simulated process-control task. Predictive displays have been found to be useful in process control (as well as aviation and maritime industries). However, authors of prior research have not examined the extent to which predictive value is increased by increasing predictor resolution, nor has such research tied potential improvements to changes in process control strategy. Fifty nonprofessional participants each controlled a simulated chemical mixture process (honey mixer simulation) that simulated the operations found in process control. Participants in each of five groups controlled with either no predictor or a predictor ranging in the resolution of prediction of the process. Increasing detail resolution generally increased the benefit of prediction over the control condition although not monotonically so. The best overall performance, combining quality and predictive ability, was obtained by the display of intermediate resolution. The two displays with the lowest resolution were clearly inferior. Predictors with higher resolution are of value but may trade off enhanced sensitivity to variable change (lower-resolution discrete state predictor) with smoother control action (higher-resolution continuous predictors). The research provides guidelines to the process-control industry regarding displays that can most improve operator performance.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

PubMed

Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Modeling of temperature-induced near-infrared and low-field time-domain nuclear magnetic resonance spectral variation: chemometric prediction of limonene and water content in spray-dried delivery systems.

PubMed

Andrade, Letícia; Farhat, Imad A; Aeberhardt, Kasia; Bro, Rasmus; Engelsen, Søren Balling

2009-02-01

The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 degrees C to 60 degrees C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.

PubMed

Liu, Yun; Scirica, Benjamin M; Stultz, Collin M; Guttag, John V

2016-10-06

Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.

Development and experimental validation of computational methods to simulate abnormal thermal and structural environments

NASA Astrophysics Data System (ADS)

Moya, J. L.; Skocypec, R. D.; Thomas, R. K.

1993-09-01

Over the past 40 years, Sandia National Laboratories (SNL) has been actively engaged in research to improve the ability to accurately predict the response of engineered systems to abnormal thermal and structural environments. These engineered systems contain very hazardous materials. Assessing the degree of safety/risk afforded the public and environment by these engineered systems, therefore, is of upmost importance. The ability to accurately predict the response of these systems to accidents (to abnormal environments) is required to assess the degree of safety. Before the effect of the abnormal environment on these systems can be determined, it is necessary to ascertain the nature of the environment. Ascertaining the nature of the environment, in turn, requires the ability to physically characterize and numerically simulate the abnormal environment. Historically, SNL has demonstrated the level of safety provided by these engineered systems by either of two approaches: a purely regulatory approach, or by a probabilistic risk assessment (PRA). This paper will address the latter of the two approaches.

Developing Personalized Sensorimotor Adaptability Countermeasures for Spaceflight

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Seidler, R. D.; Peters, B.; Cohen, H. S.; Wood, S.; Bloomberg, J. J.

2016-01-01

Astronauts experience sensorimotor disturbances during their initial exposure to microgravity and during the re-adaptation phase following a return to an Earth-gravitational environment. Interestingly, astronauts who return from spaceflight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. In this paper we will be presenting results from our ground-based study that show how behavioral, brain imaging and genomic data may be used to predict individual differences in sensorimotor adaptability to novel sensorimotor environments. This approach will allow us to better design and implement sensorimotor adaptability training countermeasures against decrements in post-mission adaptive capability that are customized for each crewmember's sensory biases, adaptive capacity, brain structure, functional capacities, and genetic predispositions. The ability to customize adaptability training will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach.

PubMed

Canela-Xandri, Oriol; Rawlik, Konrad; Woolliams, John A; Tenesa, Albert

2016-01-01

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.

Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?

PubMed

Lamers, L M

Adequate risk adjustment is critical to the success of market-oriented health care reforms in many countries. Currently used risk adjusters based on demographic and diagnostic cost groups (DCGs) do not reflect expected costs accurately. This study examines the simultaneous predictive accuracy of inpatient and outpatient morbidity measures and prior costs. DCGs, pharmacy cost groups (PCGs), and prior year's costs improve the predictive accuracy of the demographic model substantially. DCGs and PCGs seem complementary in their ability to predict future costs. However, this study shows that the combination of DCGs and PCGs still leaves room for cream skimming.

Snow on Sea Ice Workshop - An Icy Meeting of the Minds: Modelers and Measurers

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Snow on Sea Ice Workshop - An Icy Meeting of the Minds...workshop was to promote more seamless and better integration between measurements and modeling of snow on sea ice , thereby improving our predictive...capabilities for sea ice . OBJECTIVES The key objective was to improve the ability of modelers and measurers work together closely. To that end, we

Predicting student performance in sonographic scanning using spatial ability as an ability determinent of skill acquisition

NASA Astrophysics Data System (ADS)

Clem, Douglas Wayne

Spatial ability refers to an individual's capacity to visualize and mentally manipulate three dimensional objects. Since sonographers manually manipulate 2D and 3D sonographic images to generate multi-viewed, logical, sequential renderings of an anatomical structure, it can be assumed that spatial ability is central to the perception and interpretation of these medical images. Using Ackerman's theory of ability determinants of skilled performance as a conceptual framework, this study explored the relationship of spatial ability and learning sonographic scanning. Beginning first year sonography students from four different educational institutions were administered a spatial abilities test prior to their initial scanning lab coursework. The students' spatial test scores were compared with their scanning competency performance scores. A significant relationship between the students' spatial ability scores and their scanning performance scores was found. This result suggests that the use of spatial ability tests for admission to sonography programs may improve candidate selection, as well as assist programs in adjusting instruction and curriculum for students who demonstrate low spatial ability.

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

USGS Publications Warehouse

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

2016-01-01

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

Revised Framingham Stroke Risk Score, Nontraditional Risk Markers, and Incident Stroke in a Multiethnic Cohort.

PubMed

Flueckiger, Peter; Longstreth, Will; Herrington, David; Yeboah, Joseph

2018-02-01

Limited data exist on the performance of the revised Framingham Stroke Risk Score (R-FSRS) and the R-FSRS in conjunction with nontraditional risk markers. We compared the R-FSRS, original FSRS, and the Pooled Cohort Equation for stroke prediction and assessed the improvement in discrimination by nontraditional risk markers. Six thousand seven hundred twelve of 6814 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) were included. Cox proportional hazard, area under the curve, net reclassification improvement, and integrated discrimination increment analysis were used to assess and compare each stroke prediction risk score. Stroke was defined as fatal/nonfatal strokes (hemorrhagic or ischemic). After mean follow-up of 10.7 years, 231 of 6712 (3.4%) strokes were adjudicated (2.7% ischemic strokes). Mean stroke risks using the R-FSRS, original FSRS, and Pooled Cohort Equation were 4.7%, 5.9%, and 13.5%. The R-FSRS had the best calibration (Hosmer-Lemeshow goodness-of-fit, χ 2 =6.55; P =0.59). All risk scores were predictive of incident stroke. C statistics of R-FSRS (0.716) was similar to Pooled Cohort Equation (0.716), but significantly higher than the original FSRS (0.653; P =0.01 for comparison with R-FSRS). Adding nontraditional risk markers individually to the R-FSRS did not improve discrimination of the R-FSRS in the area under the curve analysis, but did improve category-less net reclassification improvement and integrated discrimination increment for incident stroke. The addition of coronary artery calcium to R-FSRS produced the highest category-less net reclassification improvement (0.36) and integrated discrimination increment (0.0027). Similar results were obtained when ischemic strokes were used as the outcome. The R-FSRS downgraded stroke risk but had better calibration and discriminative ability for incident stroke compared with the original FSRS. Nontraditional risk markers modestly improved the discriminative ability of the R-FSRS, with coronary artery calcium performing the best. © 2018 American Heart Association, Inc.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-06-01

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-06-28

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Current gaps in understanding and predicting space weather: An operations perspective

NASA Astrophysics Data System (ADS)

Murtagh, W. J.

2016-12-01

The NOAA Space Weather Prediction Center (SWPC), one of the nine National Weather Service (NWS) National Centers for Environmental Prediction, is the Nation's official source for space weather alerts and warnings. Space weather effects the technology that forms the backbone of global economic vitality and national security, including satellite and airline operations, communications networks, and the electric power grid. Many of SWPC's over 48,000 subscribers rely on space weather forecasts for critical decision making. But extraordinary gaps still exist in our ability to meet customer needs for accurate and timely space weather forecasts and warnings. The 2015 National Space Weather Strategy recognizes that it is imperative that we improve the fundamental understanding of space weather and increase the accuracy, reliability, and timeliness of space-weather observations and forecasts in support of the growing demands. In this talk we provide a broad perspective of the key challenges that currently limit the forecaster's ability to better understand and predict space weather. We also examine the impact of these limitations on the end-user community.

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions.

PubMed

Wuchty, S; Rajagopala, S V; Blazie, S M; Parrish, J R; Khuri, S; Finley, R L; Uetz, P

2017-01-01

The functions of roughly a third of all proteins in Streptococcus pneumoniae , a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae . We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae , the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Applicability of a panel method, which includes nonlinear effects, to a forward-swept-wing aircraft

NASA Technical Reports Server (NTRS)

Ross, J. C.

1984-01-01

The ability of a lower order panel method VSAERO, to accurately predict the lift and pitching moment of a complete forward-swept-wing/canard configuration was investigated. The program can simulate nonlinear effects including boundary-layer displacement thickness, wake roll up, and to a limited extent, separated wakes. The predictions were compared with experimental data obtained using a small-scale model in the 7- by 10- Foot Wind Tunnel at NASA Ames Research Center. For the particular configuration under investigation, wake roll up had only a small effect on the force and moment predictions. The effect of the displacement thickness modeling was to reduce the lift curve slope slightly, thus bringing the predicted lift into good agreement with the measured value. Pitching moment predictions were also improved by the boundary-layer simulation. The separation modeling was found to be sensitive to user inputs, but appears to give a reasonable representation of a separated wake. In general, the nonlinear capabilities of the code were found to improve the agreement with experimental data. The usefullness of the code would be enhanced by improving the reliability of the separated wake modeling and by the addition of a leading edge separation model.

INTEGRATED CHEMICAL INFORMATION TECHNOLOGIES ...

EPA Pesticide Factsheets

A central regulatory mandate of the Environmental Protection Agency, spanning many Program Offices and issues, is to assess the potential health and environmental risks of large numbers of chemicals released into the environment, often in the absence of relevant test data. Models for predicting potential adverse effects of chemicals based primarily on chemical structure play a central role in prioritization and screening strategies yet are highly dependent and conditional upon the data used for developing such models. Hence, limits on data quantity, quality, and availability are considered by many to be the largest hurdles to improving prediction models in diverse areas of toxicology. Generation of new toxicity data for additional chemicals and endpoints, development of new high-throughput, mechanistically relevant bioassays, and increased generation of genomics and proteomics data that can clarify relevant mechanisms will all play important roles in improving future SAR prediction models. The potential for much greater immediate gains, across large domains of chemical and toxicity space, comes from maximizing the ability to mine and model useful information from existing toxicity data, data that represent huge past investment in research and testing expenditures. In addition, the ability to place newer “omics” data, data that potentially span many possible domains of toxicological effects, in the broader context of historical data is the means for opti

Dietary diversity scores can be improved through the use of portion requirements: an analysis in young Filipino children.

PubMed

Daniels, M C; Adair, L S; Popkin, B M; Truong, Y K

2009-02-01

Early childhood malnutrition is a pressing international concern which dietary diversity scores (summary scores of food groups in the diet) may be helpful in addressing. We explored three current research needs surrounding diversity scores: the impact of portion size on score function, the relationship of scores to nutrient adequacy and density and the ability of scores to function as screening tools. 1810 children, age 24 months. Cross sectional study of a birth cohort. We evaluated two nine food group dietary diversity scores based on 0 and 10 g minimum food group requirements for their relationship to nutrient adequacy and nutrient density. Both scores were significantly correlated with nutrient adequacy and density and predicted statistically significant increases (P<0.05) in the probability of adequacy for all nutrients. However, correlations and predicted increases were somewhat larger for the 10 g score. We also considered the sensitivity and specificity of each score for detecting low and high nutrient adequacy in the population. The 10 g cutoff improved score ability to predict low nutrient adequacy, and reduced the misclassification of subjects for all comparisons. This research suggests that the score without portion requirements reflects dietary adequacy, but when feasible, further refinement of diversity scores is desirable through the application of minimum portion requirements.

Behavior and neuroimaging at baseline predict individual response to combined mathematical and working memory training in children.

PubMed

Nemmi, Federico; Helander, Elin; Helenius, Ola; Almeida, Rita; Hassler, Martin; Räsänen, Pekka; Klingberg, Torkel

2016-08-01

Mathematical performance is highly correlated with several general cognitive abilities, including working memory (WM) capacity. Here we investigated the effect of numerical training using a number-line (NLT), WM training (WMT), or the combination of the two on a composite score of mathematical ability. The aim was to investigate if the combination contributed to the outcome, and determine if baseline performance or neuroimaging predict the magnitude of improvement. We randomly assigned 308, 6-year-old children to WMT, NLT, WMT+NLT or a control intervention. Overall, there was a significant effect of NLT but not WMT. The WMT+NLT was the only group that improved significantly more than the controls, although the interaction NLTxWM was non-significant. Higher WM and maths performance predicted larger benefits for WMT and NLT, respectively. Neuroimaging at baseline also contributed significant information about training gain. Different individuals showed as much as a three-fold difference in their responses to the same intervention. These results show that the impact of an intervention is highly dependent on individual characteristics of the child. If differences in responses could be used to optimize the intervention for each child, future interventions could be substantially more effective. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.).

PubMed

Schulthess, Albert W; Zhao, Yusheng; Longin, C Friedrich H; Reif, Jochen C

2018-03-01

Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.

Do in-training evaluation reports deserve their bad reputations? A study of the reliability and predictive ability of ITER scores and narrative comments.

PubMed

Ginsburg, Shiphra; Eva, Kevin; Regehr, Glenn

2013-10-01

Although scores on in-training evaluation reports (ITERs) are often criticized for poor reliability and validity, ITER comments may yield valuable information. The authors assessed across-rotation reliability of ITER scores in one internal medicine program, ability of ITER scores and comments to predict postgraduate year three (PGY3) performance, and reliability and incremental predictive validity of attendings' analysis of written comments. Numeric and narrative data from the first two years of ITERs for one cohort of residents at the University of Toronto Faculty of Medicine (2009-2011) were assessed for reliability and predictive validity of third-year performance. Twenty-four faculty attendings rank-ordered comments (without scores) such that each resident was ranked by three faculty. Mean ITER scores and comment rankings were submitted to regression analyses; dependent variables were PGY3 ITER scores and program directors' rankings. Reliabilities of ITER scores across nine rotations for 63 residents were 0.53 for both postgraduate year one (PGY1) and postgraduate year two (PGY2). Interrater reliabilities across three attendings' rankings were 0.83 for PGY1 and 0.79 for PGY2. There were strong correlations between ITER scores and comments within each year (0.72 and 0.70). Regressions revealed that PGY1 and PGY2 ITER scores collectively explained 25% of variance in PGY3 scores and 46% of variance in PGY3 rankings. Comment rankings did not improve predictions. ITER scores across multiple rotations showed decent reliability and predictive validity. Comment ranks did not add to the predictive ability, but correlation analyses suggest that trainee performance can be measured through these comments.

Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm.

PubMed

Cuthbertson, Carmen C; Kucharska-Newton, Anna; Faurot, Keturah R; Stürmer, Til; Jonsson Funk, Michele; Palta, Priya; Windham, B Gwen; Thai, Sydney; Lund, Jennifer L

2018-07-01

Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data. Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011-2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality. The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds. The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

Early Predictors of Middle School Fraction Knowledge

PubMed Central

Bailey, Drew H.; Siegler, Robert S.; Geary, David C.

2014-01-01

Recent findings that earlier fraction knowledge predicts later mathematics achievement raise the question of what predicts later fraction knowledge. Analyses of longitudinal data indicated that whole number magnitude knowledge in first grade predicted knowledge of fraction magnitudes in middle school, controlling for whole number arithmetic proficiency, domain general cognitive abilities, parental income and education, race, and gender. Similarly, knowledge of whole number arithmetic in first grade predicted knowledge of fraction arithmetic in middle school, controlling for whole number magnitude knowledge in first grade and the other control variables. In contrast, neither type of early whole number knowledge uniquely predicted middle school reading achievement. We discuss the implications of these findings for theories of numerical development and for improving mathematics learning. PMID:24576209

Enhancing inferential abilities in adolescence: new hope for students in poverty

PubMed Central

Gamino, Jacquelyn F.; Motes, Michael M.; Riddle, Russell; Lyon, G. Reid; Spence, Jeffrey S.; Chapman, Sandra B.

2014-01-01

The ability to extrapolate essential gist through the analysis and synthesis of information, prediction of potential outcomes, abstraction of ideas, and integration of relationships with world knowledge is critical for higher-order learning. The present study investigated the efficacy of cognitive training to elicit improvements in gist reasoning and fact recall ability in 556 public middle school students (grades seven and eight), vs. a sample of 357 middle school students who served as a comparison group, to determine if changes in gist reasoning and fact recall were demonstrated without cognitive training. The results showed that, in general, cognitive training increased gist reasoning and fact recall abilities in students from families in poverty as well as students from families living above poverty. However, the magnitude of gains in gist reasoning varied as a function of gender and grade level. Our primary findings were that seventh and eighth grade girls and eighth grade boys showed significant increases in gist reasoning after training regardless of socioeconomic status (SES). There were no significant increases in gist reasoning or fact recall ability for the 357 middle school students who served as a comparison group. We postulate that cognitive training in middle school is efficacious for improving gist reasoning ability and fact recall in students from all socioeconomic levels. PMID:25505393

Enhancing inferential abilities in adolescence: new hope for students in poverty.

PubMed

Gamino, Jacquelyn F; Motes, Michael M; Riddle, Russell; Lyon, G Reid; Spence, Jeffrey S; Chapman, Sandra B

2014-01-01

The ability to extrapolate essential gist through the analysis and synthesis of information, prediction of potential outcomes, abstraction of ideas, and integration of relationships with world knowledge is critical for higher-order learning. The present study investigated the efficacy of cognitive training to elicit improvements in gist reasoning and fact recall ability in 556 public middle school students (grades seven and eight), vs. a sample of 357 middle school students who served as a comparison group, to determine if changes in gist reasoning and fact recall were demonstrated without cognitive training. The results showed that, in general, cognitive training increased gist reasoning and fact recall abilities in students from families in poverty as well as students from families living above poverty. However, the magnitude of gains in gist reasoning varied as a function of gender and grade level. Our primary findings were that seventh and eighth grade girls and eighth grade boys showed significant increases in gist reasoning after training regardless of socioeconomic status (SES). There were no significant increases in gist reasoning or fact recall ability for the 357 middle school students who served as a comparison group. We postulate that cognitive training in middle school is efficacious for improving gist reasoning ability and fact recall in students from all socioeconomic levels.

Development of a bedside viable ultrasound protocol to quantify appendicular lean tissue mass.

PubMed

Paris, Michael T; Lafleur, Benoit; Dubin, Joel A; Mourtzakis, Marina

2017-10-01

Ultrasound is a non-invasive and readily available tool that can be prospectively applied at the bedside to assess muscle mass in clinical settings. The four-site protocol, which images two anatomical sites on each quadriceps, may be a viable bedside method, but its ability to predict musculature has not been compared against whole-body reference methods. Our primary objectives were to (i) compare the four-site protocol's ability to predict appendicular lean tissue mass from dual-energy X-ray absorptiometry; (ii) optimize the predictability of the four-site protocol with additional anatomical muscle thicknesses and easily obtained covariates; and (iii) assess the ability of the optimized protocol to identify individuals with low lean tissue mass. This observational cross-sectional study recruited 96 university and community dwelling adults. Participants underwent ultrasound scans for assessment of muscle thickness and whole-body dual-energy X-ray absorptiometry scans for assessment of appendicular lean tissue. Ultrasound protocols included (i) the nine-site protocol, which images nine anterior and posterior muscle groups in supine and prone positions, and (ii) the four-site protocol, which images two anterior sites on each quadriceps muscle group in a supine position. The four-site protocol was strongly associated (R 2  = 0.72) with appendicular lean tissue mass, but Bland-Altman analysis displayed wide limits of agreement (-5.67, 5.67 kg). Incorporating the anterior upper arm muscle thickness, and covariates age and sex, alongside the four-site protocol, improved the association (R 2  = 0.91) with appendicular lean tissue and displayed narrower limits of agreement (-3.18, 3.18 kg). The optimized protocol demonstrated a strong ability to identify low lean tissue mass (area under the curve = 0.89). The four-site protocol can be improved with the addition of the anterior upper arm muscle thickness, sex, and age when predicting appendicular lean tissue mass. This optimized protocol can accurately identify low lean tissue mass, while still being easily applied at the bedside. © 2017 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders.

Development of a bedside viable ultrasound protocol to quantify appendicular lean tissue mass

PubMed Central

Paris, Michael T.; Lafleur, Benoit; Dubin, Joel A.

2017-01-01

Abstract Background Ultrasound is a non‐invasive and readily available tool that can be prospectively applied at the bedside to assess muscle mass in clinical settings. The four‐site protocol, which images two anatomical sites on each quadriceps, may be a viable bedside method, but its ability to predict musculature has not been compared against whole‐body reference methods. Our primary objectives were to (i) compare the four‐site protocol's ability to predict appendicular lean tissue mass from dual‐energy X‐ray absorptiometry; (ii) optimize the predictability of the four‐site protocol with additional anatomical muscle thicknesses and easily obtained covariates; and (iii) assess the ability of the optimized protocol to identify individuals with low lean tissue mass. Methods This observational cross‐sectional study recruited 96 university and community dwelling adults. Participants underwent ultrasound scans for assessment of muscle thickness and whole‐body dual‐energy X‐ray absorptiometry scans for assessment of appendicular lean tissue. Ultrasound protocols included (i) the nine‐site protocol, which images nine anterior and posterior muscle groups in supine and prone positions, and (ii) the four‐site protocol, which images two anterior sites on each quadriceps muscle group in a supine position. Results The four‐site protocol was strongly associated (R 2 = 0.72) with appendicular lean tissue mass, but Bland–Altman analysis displayed wide limits of agreement (−5.67, 5.67 kg). Incorporating the anterior upper arm muscle thickness, and covariates age and sex, alongside the four‐site protocol, improved the association (R 2 = 0.91) with appendicular lean tissue and displayed narrower limits of agreement (−3.18, 3.18 kg). The optimized protocol demonstrated a strong ability to identify low lean tissue mass (area under the curve = 0.89). Conclusions The four‐site protocol can be improved with the addition of the anterior upper arm muscle thickness, sex, and age when predicting appendicular lean tissue mass. This optimized protocol can accurately identify low lean tissue mass, while still being easily applied at the bedside. PMID:28722298

New equations for predicting postoperative risk in patients with hip fracture.

PubMed

Hirose, Jun; Ide, Junji; Irie, Hiroki; Kikukawa, Kenshi; Mizuta, Hiroshi

2009-12-01

Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

Network of listed companies based on common shareholders and the prediction of market volatility

NASA Astrophysics Data System (ADS)

Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

2016-11-01

In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

Canine Hip Dysplasia: Diagnostic Imaging.

PubMed

Butler, J Ryan; Gambino, Jennifer

2017-07-01

Diagnostic imaging is the principal method used to screen for and diagnose hip dysplasia in the canine patient. Multiple techniques are available, each having advantages, disadvantages, and limitations. Hip-extended radiography is the most used method and is best used as a screening tool and for assessment for osteoarthritis. Distraction radiographic methods such as the PennHip method allow for improved detection of laxity and improved ability to predict future osteoarthritis development. More advanced techniques such as MRI, although expensive and not widely available, may improve patient screening and allow for improved assessment of cartilage health. Copyright © 2017 Elsevier Inc. All rights reserved.

Acoustic sorting models for improved log segregation

Treesearch

Xiping Wang; Steve Verrill; Eini Lowell; Robert J. Ross; Vicki L. Herian

2013-01-01

In this study, we examined three individual log measures (acoustic velocity, log diameter, and log vertical position in a tree) for their ability to predict average modulus of elasticity (MOE) and grade yield of structural lumber obtained from Douglas-fir (Pseudotsuga menziesii [Mirb. Franco]) logs. We found that log acoustic velocity only had a...

Integration of gene expression, clinical, and demographic information in relation to asthma status to identify biomarkers associated with subtypes of childhood asthma

EPA Science Inventory

Advances in biomarker development have improved our ability to detect early changes at the molecular, cellular, and pre-clinical level that are often predictive of adverse health outcomes. Biomarkers for monitoring the underlying molecular mechanisms of disease are of increasing...

Statistical Properties of Differences between Low and High Resolution CMAQ Runs with Matched Initial and Boundary Conditions

EPA Science Inventory

The difficulty in assessing errors in numerical models of air quality is a major obstacle to improving their ability to predict and retrospectively map air quality. In this paper, using simulation outputs from the Community Multi-scale Air Quality Model (CMAQ), the statistic...

Cognitive Learning Strategy as a Partial Effect on Major Field Test in Business Results

ERIC Educational Resources Information Center

Strang, Kenneth David

2014-01-01

An experiment was developed to determine if cognitive learning strategies improved standardized university business exam results. Previous studies revealed that factors such as prior ability, age, gender, and culture predicted a student's Major Field Test in Business (MFTB) score better than course content. The experiment control consisted of…

An improved null model for assessing the net effects of multiple stressors on communities.

PubMed

Thompson, Patrick L; MacLennan, Megan M; Vinebrooke, Rolf D

2018-01-01

Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our compositional null model. © 2017 John Wiley & Sons Ltd.

Theory-of-mind development influences suggestibility and source monitoring.

PubMed

Bright-Paul, Alexandra; Jarrold, Christopher; Wright, Daniel B

2008-07-01

According to the mental-state reasoning model of suggestibility, 2 components of theory of mind mediate reductions in suggestibility across the preschool years. The authors examined whether theory-of-mind performance may be legitimately separated into 2 components and explored the memory processes underlying the associations between theory of mind and suggestibility, independent of verbal ability. Children 3 to 6 years old completed 6 theory-of-mind tasks and a postevent misinformation procedure. Contrary to the model's prediction, a single latent theory-of-mind factor emerged, suggesting a single-component rather than a dual-component conceptualization of theory-of-mind performance. This factor provided statistical justification for computing a single composite theory-of-mind score. Improvements in theory of mind predicted reductions in suggestibility, independent of verbal ability (Study 1, n = 72). Furthermore, once attribution biases were controlled (Study 2, n = 45), there was also a positive relationship between theory of mind and source memory, but not recognition performance. The findings suggest a substantial, and possibly causal, association between theory-of-mind development and resistance to suggestion, driven specifically by improvements in source monitoring.

Predictability and Prediction of Low-Frequency Rainfall Over the Lower Reaches of the Yangtze River Valley on the Time Scale of 20 to 30 days

NASA Astrophysics Data System (ADS)

Yang, Qiuming

2018-01-01

This paper presents a predictability study of the 20-30-day low-frequency rainfall over the lower reaches of the Yangtze River valley (LYRV). This study relies on an extended complex autoregressive (ECAR) model method, which is based on the principal components of the global 850 hPa low-frequency meridional wind. ECAR is a recently advanced climate forecast method, based on data-driven models. It not only reflects the lagged variations information between the leading low-frequency components of the global circulation and rainfall in a complex space, but also displays the ability to describe the synergy variations of low-frequency components of a climate system in a low dimensional space. A 6-year forecast experiment is conducted on the low-frequency rainfall over the LYRV for the extended-range daily forecasts during 2009-2014, based on the time-varying high-order ECAR. These experimental results demonstrate that the useful skills of the real-time forecasts are achieved for an extended lead-time up to 28 days with a fifth-order model, and are also shown to be 27-day lead for forecasts which are initiated from weak intraseasonal oscillation (ISO). This high-order ECAR displays the ability to significantly improve the predictions of the ISO. The analysis of the 20-30-day ISO predictability reveals a predictability limit of about 28-40 days. Therefore, the forecast framework used in this study is determined to have the potential to assist in improving the real-time forecasts for the 20-30-day oscillations related to the heavy rainfall over the LYRV in summer.

DRREP: deep ridge regressed epitope predictor.

PubMed

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Critical joints in large composite aircraft structure

NASA Technical Reports Server (NTRS)

Nelson, W. D.; Bunin, B. L.; Hart-Smith, L. J.

1983-01-01

A program was conducted at Douglas Aircraft Company to develop the technology for critical structural joints of composite wing structure that meets design requirements for a 1990 commercial transport aircraft. The prime objective of the program was to demonstrate the ability to reliably predict the strength of large bolted composite joints. Ancillary testing of 180 specimens generated data on strength and load-deflection characteristics which provided input to the joint analysis. Load-sharing between fasteners in multirow bolted joints was computed by the nonlinear analysis program A4EJ. This program was used to predict strengths of 20 additional large subcomponents representing strips from a wing root chordwise splice. In most cases, the predictions were accurate to within a few percent of the test results. In some cases, the observed mode of failure was different than anticipated. The highlight of the subcomponent testing was the consistent ability to achieve gross-section failure strains close to 0.005. That represents a considerable improvement over the state of the art.

Genetic variants in SLC22A17 and SLC22A7 are associated with anthracycline-induced cardiotoxicity in children.

PubMed

Visscher, Henk; Rassekh, S Rod; Sandor, George S; Caron, Huib N; van Dalen, Elvira C; Kremer, Leontien C; van der Pal, Helena J; Rogers, Paul C; Rieder, Michael J; Carleton, Bruce C; Hayden, Michael R; Ross, Colin J

2015-01-01

To identify novel variants associated with anthracycline-induced cardiotoxicity and to assess these in a genotype-guided risk prediction model. Two cohorts treated for childhood cancer (n = 344 and 218, respectively) were genotyped for 4578 SNPs in drug ADME and toxicity genes. Significant associations were identified in SLC22A17 (rs4982753; p = 0.0078) and SLC22A7 (rs4149178; p = 0.0034), with replication in the second cohort (p = 0.0071 and 0.047, respectively). Additional evidence was found for SULT2B1 and several genes related to oxidative stress. Adding the SLC22 variants to the prediction model improved its discriminative ability (AUC 0.78 vs 0.75 [p = 0.029]). Two novel variants in SLC22A17 and SLC22A7 were significantly associated with anthracycline-induced cardiotoxicity and improved a genotype-guided risk prediction model, which could improve patient risk stratification.

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

Prediction of Soil pH Hyperspectral Spectrum in Guanzhong Area of Shaanxi Province Based on PLS

NASA Astrophysics Data System (ADS)

Liu, Jinbao; Zhang, Yang; Wang, Huanyuan; Cheng, Jie; Tong, Wei; Wei, Jing

2017-12-01

The soil pH of Fufeng County, Yangling County and Wugong County in Shaanxi Province was studied. The spectral reflectance was measured by ASD Field Spec HR portable terrain spectrum, and its spectral characteristics were analyzed. The first deviation of the original spectral reflectance of the soil, the second deviation, the logarithm of the reciprocal logarithm, the first order differential of the reciprocal logarithm and the second order differential of the reciprocal logarithm were used to establish the soil pH Spectral prediction model. The results showed that the correlation between the reflectance spectra after SNV pre-treatment and the soil pH was significantly improved. The optimal prediction model of soil pH established by partial least squares method was a prediction model based on the first order differential of the reciprocal logarithm of spectral reflectance. The principal component factor was 10, the decision coefficient Rc2 = 0.9959, the model root means square error RMSEC = 0.0076, the correction deviation SEC = 0.0077; the verification decision coefficient Rv2 = 0.9893, the predicted root mean square error RMSEP = 0.0157, The deviation of SEP = 0.0160, the model was stable, the fitting ability and the prediction ability were high, and the soil pH can be measured quickly.

Development of the Metacognitive Skills of Prediction and Evaluation in Children With or Without Math Disability

PubMed Central

Garrett, Adia J.; Mazzocco, Michèle M. M.; Baker, Linda

2009-01-01

Metacognition refers to knowledge about one’s own cognition. The present study was designed to assess metacognitive skills that either precede or follow task engagement, rather than the processes that occur during a task. Specifically, we examined prediction and evaluation skills among children with (n = 17) or without (n = 179) mathematics learning disability (MLD), from grades 2 to 4. Children were asked to predict which of several math problems they could solve correctly; later, they were asked to solve those problems. They were asked to evaluate whether their solution to each of another set of problems was correct. Children’s ability to evaluate their answers to math problems improved from grade 2 to grade 3, whereas there was no change over time in the children’s ability to predict which problems they could solve correctly. Children with MLD were less accurate than children without MLD in evaluating both their correct and incorrect solutions, and they were less accurate at predicting which problems they could solve correctly. However, children with MLD were as accurate as their peers in correctly predicting that they could not solve specific math problems. The findings have implications for the usefulness of children’s self-review during mathematics problem solving. PMID:20084181

Immersive Virtual Reality to Improve Walking Abilities in Cerebral Palsy: A Pilot Study.

PubMed

Gagliardi, Chiara; Turconi, Anna Carla; Biffi, Emilia; Maghini, Cristina; Marelli, Alessia; Cesareo, Ambra; Diella, Eleonora; Panzeri, Daniele

2018-04-27

Immersive virtual reality (IVR) offers new possibilities to perform treatments in an ecological and interactive environment with multimodal online feedbacks. Sixteen school-aged children (mean age 11 ± 2.4 years) with Bilateral CP-diplegia, attending mainstream schools were recruited for a pilot study in a pre-post treatment experimental design. The intervention was focused on walking competences and endurance and performed by the Gait Real-time Analysis Interactive Lab (GRAIL), an innovative treadmill platform based on IVR. The participants underwent eighteen therapy sessions in 4 weeks. Functional evaluations, instrumental measures including GAIT analysis and parental questionnaire were utilized to assess the treatment effects. Walking pattern (stride length left and right side, respectively p = 0.001 and 0.003; walking speed p = 0.001), endurance (6MWT, p = 0.026), gross motor abilities (GMFM-88, p = 0.041) and most kinematic and kinetic parameters significantly improved after the intervention. The changes were mainly predicted by age and cognitive abilities. The effect could have been due to the possibility of IVR to foster integration of motor/perceptual competences beyond the training of the walking ability, giving a chance of improvement also to older and already treated children.

An evaluation of the lamb vision system as a predictor of lamb carcass red meat yield percentage.

PubMed

Brady, A S; Belk, K E; LeValley, S B; Dalsted, N L; Scanga, J A; Tatum, J D; Smith, G C

2003-06-01

An objective method for predicting red meat yield in lamb carcasses is needed to accurately assess true carcass value. This study was performed to evaluate the ability of the lamb vision system (LVS; Research Management Systems USA, Fort Collins, CO) to predict fabrication yields of lamb carcasses. Lamb carcasses (n = 246) were evaluated using LVS and hot carcass weight (HCW), as well as by USDA expert and on-line graders, before fabrication of carcass sides to either bone-in or boneless cuts. On-line whole number, expert whole-number, and expert nearest-tenth USDA yield grades and LVS + HCW estimates accounted for 53, 52, 58, and 60%, respectively, of the observed variability in boneless, saleable meat yields, and accounted for 56, 57, 62, and 62%, respectively, of the variation in bone-in, saleable meat yields. The LVS + HCW system predicted 77, 65, 70, and 87% of the variation in weights of boneless shoulders, racks, loins, and legs, respectively, and 85, 72, 75, and 86% of the variation in weights of bone-in shoulders, racks, loins, and legs, respectively. Addition of longissimus muscle area (REA), adjusted fat thickness (AFT), or both REA and AFT to LVS + HCW models resulted in improved prediction of boneless saleable meat yields by 5, 3, and 5 percentage points, respectively. Bone-in, saleable meat yield estimations were improved in predictive accuracy by 7.7, 6.6, and 10.1 percentage points, and in precision, when REA alone, AFT alone, or both REA and AFT, respectively, were added to the LVS + HCW output models. Use of LVS + HCW to predict boneless red meat yields of lamb carcasses was more accurate than use of current on-line whole-number, expert whole-number, or expert nearest-tenth USDA yield grades. Thus, LVS + HCW output, when used alone or in combination with AFT and/or REA, improved on-line estimation of boneless cut yields from lamb carcasses. The ability of LVS + HCW to predict yields of wholesale cuts suggests that LVS could be used as an objective means for pricing carcasses in a value-based marketing system.

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

PubMed

Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T

2001-02-01

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data.

PubMed

Samarakoon, Pubudu Saneth; Sorte, Hanne Sørmo; Stray-Pedersen, Asbjørg; Rødningen, Olaug Kristin; Rognes, Torbjørn; Lyle, Robert

2016-01-14

With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies. We have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families. In summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data.

Generating highly accurate prediction hypotheses through collaborative ensemble learning

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Fifty Years of Space Weather Forecasting from Boulder

NASA Astrophysics Data System (ADS)

Berger, T. E.

2015-12-01

The first official space weather forecast was issued by the Space Disturbances Laboratory in Boulder, Colorado, in 1965, ushering in an era of operational prediction that continues to this day. Today, the National Oceanic and Atmospheric Administration (NOAA) charters the Space Weather Prediction Center (SWPC) as one of the nine National Centers for Environmental Prediction (NCEP) to provide the nation's official watches, warnings, and alerts of space weather phenomena. SWPC is now integral to national and international efforts to predict space weather events, from the common and mild, to the rare and extreme, that can impact critical technological infrastructure. In 2012, the Strategic National Risk Assessment included extreme space weather events as low-to-medium probability phenomena that could, unlike any other meteorogical phenomena, have an impact on the government's ability to function. Recognizing this, the White House chartered the Office of Science and Technology Policy (OSTP) to produce the first comprehensive national strategy for the prediction, mitigation, and response to an extreme space weather event. The implementation of the National Strategy is ongoing with NOAA, its partners, and stakeholders concentrating on the goal of improving our ability to observe, model, and predict the onset and severity of space weather events. In addition, work continues with the research community to improve our understanding of the physical mechanisms - on the Sun, in the heliosphere, and in the Earth's magnetic field and upper atmosphere - of space weather as well as the effects on critical infrastructure such as electrical power transmission systems. In fifty years, people will hopefully look back at the history of operational space weather prediction and credit our efforts today with solidifying the necessary developments in observational systems, full-physics models of the entire Sun-Earth system, and tools for predicting the impacts to infrastructure to protect against any and all forms of space weather.

Training the approximate number system improves math proficiency.

PubMed

Park, Joonkoo; Brannon, Elizabeth M

2013-10-01

Humans and nonhuman animals share an approximate number system (ANS) that permits estimation and rough calculation of quantities without symbols. Recent studies show a correlation between the acuity of the ANS and performance in symbolic math throughout development and into adulthood, which suggests that the ANS may serve as a cognitive foundation for the uniquely human capacity for symbolic math. Such a proposition leads to the untested prediction that training aimed at improving ANS performance will transfer to improvement in symbolic-math ability. In the two experiments reported here, we showed that ANS training on approximate addition and subtraction of arrays of dots selectively improved symbolic addition and subtraction. This finding strongly supports the hypothesis that complex math skills are fundamentally linked to rudimentary preverbal quantitative abilities and provides the first direct evidence that the ANS and symbolic math may be causally related. It also raises the possibility that interventions aimed at the ANS could benefit children and adults who struggle with math.

Recreational benefits of urban forests: explaining visitors' willingness to pay in the context of the theory of planned behavior.

PubMed

Bernath, Katrin; Roschewitz, Anna

2008-11-01

The extension of contingent valuation models with an attitude-behavior based framework has been proposed in order to improve the descriptive and predictive ability of the models. This study examines the potential of the theory of planned behavior to explain willingness to pay (WTP) in a contingent valuation survey of the recreational benefits of the Zurich city forests. Two aspects of WTP responses, protest votes and bid levels, were analyzed separately. In both steps, models with and without the psychological predictors proposed by the theory of planned behavior were compared. Whereas the inclusion of the psychological predictors significantly improved explanations of protest votes, their ability to improve the performance of the model explaining bid levels was limited. The results indicate that the interpretation of bid levels as behavioral intention may not be appropriate and that the potential of the theory of planned behavior to improve contingent valuation models depends on which aspect of WTP responses is examined.

Severity of specific language impairment predicts delayed development in number skills

PubMed Central

Durkin, Kevin; Mok, Pearl L. H.; Conti-Ramsden, Gina

2013-01-01

The extent to which mathematical development is dependent upon language is controversial. This longitudinal study investigates the role of language ability in children's development of number skills. Participants were 229 children with specific language impairment (SLI) who were assessed initially at age 7 and again 1 year later. All participants completed measures of psycholinguistic development (expressive and receptive), performance IQ, and the Basic Number Skills subtest of the British Ability Scales. Number skills data for this sample were compared with normative population data. Consistent with predictions that language impairment would impact on numerical development, average standard scores were more than 1 SD below the population mean at both ages. Although the children showed improvements in raw scores at the second wave of the study, the discrepancy between their scores and the population data nonetheless increased over time. Regression analyses showed that, after controlling for the effect of PIQ, language skills explained an additional 19 and 17% of the variance in number skills for ages 7 and 8, respectively. Furthermore, logistic regression analyses revealed that less improvement in the child's language ability over the course of the year was associated with a greater odds of a drop in performance in basic number skills from 7 to 8 years. The results are discussed in relation to the interaction of linguistic and cognitive factors in numerical development and the implications for mathematical education. PMID:24027548

Renal Tumor Anatomic Complexity: Clinical Implications for Urologists.

PubMed

Joshi, Shreyas S; Uzzo, Robert G

2017-05-01

Anatomic tumor complexity can be objectively measured and reported using nephrometry. Various scoring systems have been developed in an attempt to correlate tumor complexity with intraoperative and postoperative outcomes. Nephrometry may also predict tumor biology in a noninvasive, reproducible manner. Other scoring systems can help predict surgical complexity and the likelihood of complications, independent of tumor characteristics. The accumulated data in this new field provide provocative evidence that objectifying anatomic complexity can consolidate reporting mechanisms and improve metrics of comparisons. Further prospective validation is needed to understand the full descriptive and predictive ability of the various nephrometry scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Uncertainty Quantification to Guide Development and Improvements of a Regional-Scale Model of the Coastal Lowlands Aquifer System Spanning Texas, Louisiana, Mississippi, Alabama and Florida

NASA Astrophysics Data System (ADS)

Foster, L. K.; Clark, B. R.; Duncan, L. L.; Tebo, D. T.; White, J.

2017-12-01

Several historical groundwater models exist within the Coastal Lowlands Aquifer System (CLAS), which spans the Gulf Coastal Plain in Texas, Louisiana, Mississippi, Alabama, and Florida. The largest of these models, called the Gulf Coast Regional Aquifer System Analysis (RASA) model, has been brought into a new framework using the Newton formulation for MODFLOW-2005 (MODFLOW-NWT) and serves as the starting point of a new investigation underway by the U.S. Geological Survey to improve understanding of the CLAS and provide predictions of future groundwater availability within an uncertainty quantification (UQ) framework. The use of an UQ framework will not only provide estimates of water-level observation worth, hydraulic parameter uncertainty, boundary-condition uncertainty, and uncertainty of future potential predictions, but it will also guide the model development process. Traditionally, model development proceeds from dataset construction to the process of deterministic history matching, followed by deterministic predictions using the model. This investigation will combine the use of UQ with existing historical models of the study area to assess in a quantitative framework the effect model package and property improvements have on the ability to represent past-system states, as well as the effect on the model's ability to make certain predictions of water levels, water budgets, and base-flow estimates. Estimates of hydraulic property information and boundary conditions from the existing models and literature, forming the prior, will be used to make initial estimates of model forecasts and their corresponding uncertainty, along with an uncalibrated groundwater model run within an unconstrained Monte Carlo analysis. First-Order Second-Moment (FOSM) analysis will also be used to investigate parameter and predictive uncertainty, and guide next steps in model development prior to rigorous history matching by using PEST++ parameter estimation code.

A new pathological scoring system by the Japanese classification to predict renal outcome in diabetic nephropathy.

PubMed

Hoshino, Junichi; Furuichi, Kengo; Yamanouchi, Masayuki; Mise, Koki; Sekine, Akinari; Kawada, Masahiro; Sumida, Keiichi; Hiramatsu, Rikako; Hasegawa, Eiko; Hayami, Noriko; Suwabe, Tatsuya; Sawa, Naoki; Hara, Shigeko; Fujii, Takeshi; Ohashi, Kenichi; Kitagawa, Kiyoki; Toyama, Tadashi; Shimizu, Miho; Takaichi, Kenmei; Ubara, Yoshifumi; Wada, Takashi

2018-01-01

The impact of the newly proposed pathological classification by the Japan Renal Pathology Society (JRPS) on renal outcome is unclear. So we evaluated that impact and created a new pathological scoring to predict outcome using this classification. A multicenter cohort of 493 biopsy-proven Japanese patients with diabetic nephropathy (DN) were analyzed. The association between each pathological factor-Tervaert' and JRPS classifications-and renal outcome (dialysis initiation or 50% eGFR decline) was estimated by adjusted Cox regression. The overall pathological risk score (J-score) was calculated, whereupon its predictive ability for 10-year risk of renal outcome was evaluated. The J-scores of diffuse lesion classes 2 or 3, GBM doubling class 3, presence of mesangiolysis, polar vasculosis, and arteriolar hyalinosis were, respectively, 1, 2, 4, 1, and 2. The scores of IFTA classes 1, 2, and 3 were, respectively, 3, 4, and 4, and those of interstitial inflammation classes 1, 2, and 3 were 5, 5, and 4 (J-score range, 0-19). Renal survival curves, when dividing into four J-score grades (0-5, 6-10, 11-15, and 16-19), were significantly different from each other (p<0.01, log-rank test). After adjusting clinical factors, the J-score was a significant predictor of renal outcome. Ability to predict 10-year renal outcome was improved when the J-score was added to the basic model: c-statistics from 0.661 to 0.685; category-free net reclassification improvement, 0.154 (-0.040, 0.349, p = 0.12); and integrated discrimination improvement, 0.015 (0.003, 0.028, p = 0.02). Mesangiolysis, polar vasculosis, and doubling of GBM-features of the JRPS system-were significantly associated with renal outcome. Prediction of DN patients' renal outcome was better with the J-score than without it.

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley.

PubMed

Li, Zuo; Philipp, Norman; Spiller, Monika; Stiewe, Gunther; Reif, Jochen C; Zhao, Yusheng

2017-03-01

Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L.) and maize ( L.) adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP) and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP). Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups. Copyright © 2017 Crop Science Society of America.

Lung function parameters improve prediction of VO2peak in an elderly population: The Generation 100 study.

PubMed

Hassel, Erlend; Stensvold, Dorthe; Halvorsen, Thomas; Wisløff, Ulrik; Langhammer, Arnulf; Steinshamn, Sigurd

2017-01-01

Peak oxygen uptake (VO2peak) is an indicator of cardiovascular health and a useful tool for risk stratification. Direct measurement of VO2peak is resource-demanding and may be contraindicated. There exist several non-exercise models to estimate VO2peak that utilize easily obtainable health parameters, but none of them includes lung function measures or hemoglobin concentrations. We aimed to test whether addition of these parameters could improve prediction of VO2peak compared to an established model that includes age, waist circumference, self-reported physical activity and resting heart rate. We included 1431 subjects aged 69-77 years that completed a laboratory test of VO2peak, spirometry, and a gas diffusion test. Prediction models for VO2peak were developed with multiple linear regression, and goodness of fit was evaluated. Forced expiratory volume in one second (FEV1), diffusing capacity of the lung for carbon monoxide and blood hemoglobin concentration significantly improved the ability of the established model to predict VO2peak. The explained variance of the model increased from 31% to 48% for men and from 32% to 38% for women (p<0.001). FEV1, diffusing capacity of the lungs for carbon monoxide and hemoglobin concentration substantially improved the accuracy of VO2peak prediction when added to an established model in an elderly population.

Bilateral Versus Unilateral Cochlear Implants in Children: A Study of Spoken Language Outcomes

PubMed Central

Harris, David; Bennet, Lisa; Bant, Sharyn

2014-01-01

Objectives: Although it has been established that bilateral cochlear implants (CIs) offer additional speech perception and localization benefits to many children with severe to profound hearing loss, whether these improved perceptual abilities facilitate significantly better language development has not yet been clearly established. The aims of this study were to compare language abilities of children having unilateral and bilateral CIs to quantify the rate of any improvement in language attributable to bilateral CIs and to document other predictors of language development in children with CIs. Design: The receptive vocabulary and language development of 91 children was assessed when they were aged either 5 or 8 years old by using the Peabody Picture Vocabulary Test (fourth edition), and either the Preschool Language Scales (fourth edition) or the Clinical Evaluation of Language Fundamentals (fourth edition), respectively. Cognitive ability, parent involvement in children’s intervention or education programs, and family reading habits were also evaluated. Language outcomes were examined by using linear regression analyses. The influence of elements of parenting style, child characteristics, and family background as predictors of outcomes were examined. Results: Children using bilateral CIs achieved significantly better vocabulary outcomes and significantly higher scores on the Core and Expressive Language subscales of the Clinical Evaluation of Language Fundamentals (fourth edition) than did comparable children with unilateral CIs. Scores on the Preschool Language Scales (fourth edition) did not differ significantly between children with unilateral and bilateral CIs. Bilateral CI use was found to predict significantly faster rates of vocabulary and language development than unilateral CI use; the magnitude of this effect was moderated by child age at activation of the bilateral CI. In terms of parenting style, high levels of parental involvement, low amounts of screen time, and more time spent by adults reading to children facilitated significantly better vocabulary and language outcomes. In terms of child characteristics, higher cognitive ability and female sex were predictive of significantly better language outcomes. When family background factors were examined, having tertiary-educated primary caregivers and a family history of hearing loss were significantly predictive of better outcomes. Birth order was also found to have a significant negative effect on both vocabulary and language outcomes, with each older sibling predicting a 5 to 10% decrease in scores. Conclusions: Children with bilateral CIs achieved significantly better vocabulary outcomes, and 8-year-old children with bilateral CIs had significantly better language outcomes than did children with unilateral CIs. These improvements were moderated by children’s ages at both first and second CIs. The outcomes were also significantly predicted by a number of factors related to parenting, child characteristics, and family background. Fifty-one percent of the variance in vocabulary outcomes and between 59 to 69% of the variance in language outcomes was predicted by the regression models. PMID:24557003

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

NASA Astrophysics Data System (ADS)

Wang, Shan; Jiang, Zhi-Qiang; Li, Sai-Ping; Zhou, Wei-Xing

2015-12-01

Technical trading rules have a long history of being used by practitioners in financial markets. The profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousand traditional technical trading rules on the Shanghai Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and China Securities Index 300 (CSI 300) from April 8, 2005 through June 30, 2013 to check whether an effective trading strategy could be found by using the performance measurements based on the return and Sharpe ratio. To correct for the influence of the data-snooping effect, we adopt the Superior Predictive Ability test to evaluate if there exists a trading rule that can significantly outperform the benchmark. The result shows that for SSCI, technical trading rules offer significant profitability, while for CSI 300, this ability is lost. We further partition the SSCI into two sub-series and find that the efficiency of technical trading in sub-series, which have exactly the same spanning period as that of CSI 300, is severely weakened. By testing the trading rules on both indexes with a five-year moving window, we find that during the financial bubble from 2005 to 2007, the effectiveness of technical trading rules is greatly improved. This is consistent with the predictive ability of technical trading rules which appears when the market is less efficient.

Age matters: The effect of onset age of video game play on task-switching abilities.

PubMed

Hartanto, Andree; Toh, Wei Xing; Yang, Hwajin

2016-05-01

Although prior research suggests that playing video games can improve cognitive abilities, recent empirical studies cast doubt on such findings (Unsworth et al., 2015). To reconcile these inconsistent findings, we focused on the link between video games and task switching. Furthermore, we conceptualized video-game expertise as the onset age of active video-game play rather than the frequency of recent gameplay, as it captures both how long a person has played video games and whether the individual began playing during periods of high cognitive plasticity. We found that the age of active onset better predicted switch and mixing costs than did frequency of recent gameplay; specifically, players who commenced playing video games at an earlier age reaped greater benefits in terms of task switching than did those who started at a later age. Moreover, improving switch costs required a more extensive period of video-game experience than did mixing costs; this finding suggests that certain cognitive abilities benefit from different amounts of video game experience.

Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

NASA Technical Reports Server (NTRS)

Yang, Chao-Yuan; Liu, Jiping (Inventor); Hu, Yongyun; Horton, Radley M.; Chen, Liqi; Cheng, Xiao

2016-01-01

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.

Medicine is not science: guessing the future, predicting the past.

PubMed

Miller, Clifford

2014-12-01

Irregularity limits human ability to know, understand and predict. A better understanding of irregularity may improve the reliability of knowledge. Irregularity and its consequences for knowledge are considered. Reliable predictive empirical knowledge of the physical world has always been obtained by observation of regularities, without needing science or theory. Prediction from observational knowledge can remain reliable despite some theories based on it proving false. A naïve theory of irregularity is outlined. Reducing irregularity and/or increasing regularity can increase the reliability of knowledge. Beyond long experience and specialization, improvements include implementing supporting knowledge systems of libraries of appropriately classified prior cases and clinical histories and education about expertise, intuition and professional judgement. A consequence of irregularity and complexity is that classical reductionist science cannot provide reliable predictions of the behaviour of complex systems found in nature, including of the human body. Expertise, expert judgement and their exercise appear overarching. Diagnosis involves predicting the past will recur in the current patient applying expertise and intuition from knowledge and experience of previous cases and probabilistic medical theory. Treatment decisions are an educated guess about the future (prognosis). Benefits of the improvements suggested here are likely in fields where paucity of feedback for practitioners limits development of reliable expert diagnostic intuition. Further analysis, definition and classification of irregularity is appropriate. Observing and recording irregularities are initial steps in developing irregularity theory to improve the reliability and extent of knowledge, albeit some forms of irregularity present inherent difficulties. © 2014 John Wiley & Sons, Ltd.

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

NASA Astrophysics Data System (ADS)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

Long-Term Impact of Parental Well-Being on Adult Outcomes and Dementia Status in Individuals with Down Syndrome

ERIC Educational Resources Information Center

Esbensen, Anna J.; Mailick, Marsha R.; Silverman, Wayne

2013-01-01

Parental characteristics were significant predictors of health, functional abilities, and behavior problems in adults with Down syndrome ("n" ?=? 75) over a 22-year time span, controlling for initial levels and earlier changes in these outcomes. Lower levels of behavior problems were predicted by improvements in maternal depressive…

Preexposure to Objects That Contrast in Familiarity Improves Young Children's Lexical Knowledge Judgment

ERIC Educational Resources Information Center

Hartin, Travis L.; Stevenson, Colleen M.; Merriman, William E.

2016-01-01

The ability to judge the limits of one's own knowledge may play an important role in knowledge acquisition. The current study tested the prediction that preschoolers would judge the limits of their lexical knowledge more accurately if they were first exposed to a few objects of contrasting familiarity. Such preexposure was hypothesized to increase…

Evaluating population connectivity for species of conservation concern in the American Great Plains

Treesearch

Samuel A. Cushman; Erin L. Landguth; Curtis H. Flather

2013-01-01

Habitat loss and fragmentation are widely recognized as among the most important threats to global biodiversity. New analytical approaches are providing an improved ability to predict the effects of landscape change on population connectivity at vast spatial extents. This paper presents an analysis of population connectivity for three species of conservation concern [...

Predictive Ability of Variables Related to the Aspects of School Principals' Management

ERIC Educational Resources Information Center

Lukaš, Mirko; Jankovic, Boris

2014-01-01

The authors of this research paper believe that school principals play an irreplaceable role in raising the school efficiency. Their role is rather neglected in the Croatian academic debates on improving the quality of school system. This research intends to enhance the scientific level of their position as irreplaceable factors in a school…

Using Dirichlet Priors to Improve Model Parameter Plausibility

ERIC Educational Resources Information Center

Rai, Dovan; Gong, Yue; Beck, Joseph E.

2009-01-01

Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its…

Choice-Stimulus Preference Assessment for Students At-Risk for Emotional Disturbance in Educational Settings: An Improvement for Practice?

ERIC Educational Resources Information Center

King, Seth Andrew

2013-01-01

The ability of educators to identify consequences that act as reinforcers may predict the success of behavior change strategies predicated on the use of reinforcement. Although well supported for children with severe disabilities research concerning the effectiveness of choice-stimulus assessment for children with emotional disturbance (ED)…

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

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

He, Yupeng; Gorkin, David U.; Dickel, Diane E.

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

DOE PAGES

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; ...

2017-02-13

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Functional status and mortality prediction in community-acquired pneumonia.

PubMed

Jeon, Kyeongman; Yoo, Hongseok; Jeong, Byeong-Ho; Park, Hye Yun; Koh, Won-Jung; Suh, Gee Young; Guallar, Eliseo

2017-10-01

Poor functional status (FS) has been suggested as a poor prognostic factor in both pneumonia and severe pneumonia in elderly patients. However, it is still unclear whether FS is associated with outcomes and improves survival prediction in community-acquired pneumonia (CAP) in the general population. Data on hospitalized patients with CAP and FS, assessed by the Eastern Cooperative Oncology Group (ECOG) scale were prospectively collected between January 2008 and December 2012. The independent association of FS with 30-day mortality in CAP patients was evaluated using multivariable logistic regression. Improvement in mortality prediction when FS was added to the CRB-65 (confusion, respiratory rate, blood pressure and age 65) score was evaluated for discrimination, reclassification and calibration. The 30-day mortality of study participants (n = 1526) was 10%. Mortality significantly increased with higher ECOG score (P for trend <0.001). In multivariable analysis, ECOG ≥3 was strongly associated with 30-day mortality (adjusted OR: 5.70; 95% CI: 3.82-8.50). Adding ECOG ≥3 significantly improved the discriminatory power of CRB-65. Reclassification indices also confirmed the improvement in discrimination ability when FS was combined with the CRB-65, with a categorized net reclassification index (NRI) of 0.561 (0.437-0.686), a continuous NRI of 0.858 (0.696-1.019) and a relative integrated discrimination improvement in the discrimination slope of 139.8 % (110.8-154.6). FS predicted 30-day mortality and improved discrimination and reclassification in consecutive CAP patients. Assessment of premorbid FS should be considered in mortality prediction in patients with CAP. © 2017 Asian Pacific Society of Respirology.

Explaining resource consumption among non-normal neonates

PubMed Central

Schwartz, Rachel M.; Michelman, Thomas; Pezzullo, John; Phibbs, Ciaran S.

1991-01-01

The adoption by Medicare in 1983 of prospective payment using diagnosis-related groups (DRGs) has stimulated research to develop case-mix grouping schemes that more accurately predict resource consumption by patients. In this article, the authors explore a new method designed to improve case-mix classification for newborns through the use of birth weight in combination with DRGs to adjust the unexplained case-mix severity. Although the findings are developmental in nature, they reveal that the model significantly improves our ability to explain resource use. PMID:10122360

Propeller flow visualization techniques

NASA Technical Reports Server (NTRS)

Stefko, G. L.; Paulovich, F. J.; Greissing, J. P.; Walker, E. D.

1982-01-01

Propeller flow visualization techniques were tested. The actual operating blade shape as it determines the actual propeller performance and noise was established. The ability to photographically determine the advanced propeller blade tip deflections, local flow field conditions, and gain insight into aeroelastic instability is demonstrated. The analytical prediction methods which are being developed can be compared with experimental data. These comparisons contribute to the verification of these improved methods and give improved capability for designing future advanced propellers with enhanced performance and noise characteristics.

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Hurricane and Monsoon Tracking with Driftsondes

NASA Astrophysics Data System (ADS)

Drobinski, Philippe; Cocquerez, Philippe; Doerenbecher, A.; Hock, Terrence; Lavaysse, C.; Parsons, D.; Redelsperger, J. L.

Tropical cyclones (TCs) are a typical weather threat. The threat can apply to humans, their properties, and activities. Their prediction, particularly their trajectory and intensity, remains difficult. In addition, TCs develop above the tropical oceans where the coverage by in situ observations is poor and within cloud clusters (mesoscale convective systems MCS) that limit the ability of numerical weather prediction (NWP) models to assimilate satellite data [18]. Improved forecast of TCs trajectories is a huge benefit in terms of material costs of evacuations and damage, not being able to quantify saved life.

Spatial and Visual Reasoning: Do These Abilities Improve in First-Year Veterinary Medical Students Exposed to an Integrated Curriculum?

PubMed

Gutierrez, J Claudio; Chigerwe, Munashe; Ilkiw, Jan E; Youngblood, Patricia; Holladay, Steven D; Srivastava, Sakti

Spatial visualization ability refers to the human cognitive ability to form, retrieve, and manipulate mental models of spatial nature. Visual reasoning ability has been linked to spatial ability. There is currently limited information about how entry-level spatial and visual reasoning abilities may predict veterinary anatomy performance or may be enhanced with progression through the veterinary anatomy content in an integrated curriculum. The present study made use of two tests that measure spatial ability and one test that measures visual reasoning ability in veterinary students: Guay's Visualization of Views Test, adapted version (GVVT), the Mental Rotations Test (MRT), and Raven's Advanced Progressive Matrices Test, short form (RavenT). The tests were given to the entering class of veterinary students during their orientation week and at week 32 in the veterinary medical curriculum. Mean score on the MRT significantly increased from 15.2 to 20.1, and on the RavenT significantly increased from 7.5 to 8.8. When females only were evaluated, results were similar to the total class outcome; however, all three tests showed significant increases in mean scores. A positive correlation between the pre- and post-test scores was found for all three tests. The present results should be considered preliminary at best for associating anatomic learning in an integrated curriculum with spatial and visual reasoning abilities. Other components of the curriculum, for instance histology or physiology, could also influence the improved spatial visualization and visual reasoning test scores at week 32.

Regional Seismic Travel-Time Prediction, Uncertainty, and Location Improvement in Western Eurasia

NASA Astrophysics Data System (ADS)

Flanagan, M. P.; Myers, S. C.

2004-12-01

We investigate our ability to improve regional travel-time prediction and seismic event location using an a priori, three-dimensional velocity model of Western Eurasia and North Africa: WENA1.0 [Pasyanos et al., 2004]. Our objective is to improve the accuracy of seismic location estimates and calculate representative location uncertainty estimates. As we focus on the geographic region of Western Eurasia, the Middle East, and North Africa, we develop, test, and validate 3D model-based travel-time prediction models for 30 stations in the study region. Three principal results are presented. First, the 3D WENA1.0 velocity model improves travel-time prediction over the iasp91 model, as measured by variance reduction, for regional Pg, Pn, and P phases recorded at the 30 stations. Second, a distance-dependent uncertainty model is developed and tested for the WENA1.0 model. Third, an end-to-end validation test based on 500 event relocations demonstrates improved location performance over the 1-dimensional iasp91 model. Validation of the 3D model is based on a comparison of approximately 11,000 Pg, Pn, and P travel-time predictions and empirical observations from ground truth (GT) events. Ray coverage for the validation dataset is chosen to provide representative, regional-distance sampling across Eurasia and North Africa. The WENA1.0 model markedly improves travel-time predictions for most stations with an average variance reduction of 25% for all ray paths. We find that improvement is station dependent, with some stations benefiting greatly from WENA1.0 predictions (52% at APA, 33% at BKR, and 32% at NIL), some stations showing moderate improvement (12% at KEV, 14% at BOM, and 12% at TAM), some benefiting only slightly (6% at MOX, and 4% at SVE), and some are degraded (-6% at MLR and -18% at QUE). We further test WENA1.0 by comparing location accuracy with results obtained using the iasp91 model. Again, relocation of these events is dependent on ray paths that evenly sample WENA1.0 and therefore provide an unbiased assessment of location performance. A statistically significant sample is achieved by generating 500 location realizations based on 5 events with location accuracy between 1 km and 5 km. Each realization is a randomly selected event with location determined by randomly selecting 5 stations from the available network. In 340 cases (68% of the instances), locations are improved, and average mislocation is reduced from 31 km to 26 km. Preliminary test of uncertainty estimates suggest that our uncertainty model produces location uncertainty ellipses that are representative of location accuracy. These results highlight the importance of accurate GT datasets in assessing regional travel-time models and demonstrate that an a priori 3D model can markedly improve our ability to locate small magnitude events in a regional monitoring context. This work was performed under the auspices of the U.S. Department of Energy by the University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48, Contribution UCRL-CONF-206386.

Visual prediction and perceptual expertise

PubMed Central

Cheung, Olivia S.; Bar, Moshe

2012-01-01

Making accurate predictions about what may happen in the environment requires analogies between perceptual input and associations in memory. These elements of predictions are based on cortical representations, but little is known about how these processes can be enhanced by experience and training. On the other hand, studies on perceptual expertise have revealed that the acquisition of expertise leads to strengthened associative processing among features or objects, suggesting that predictions and expertise may be tightly connected. Here we review the behavioral and neural findings regarding the mechanisms involving prediction and expert processing, and highlight important possible overlaps between them. Future investigation should examine the relations among perception, memory and prediction skills as a function of expertise. The knowledge gained by this line of research will have implications for visual cognition research, and will advance our understanding of how the human brain can improve its ability to predict by learning from experience. PMID:22123523

Analysis of the predictive qualities of betting odds and FIFA World Ranking: evidence from the 2006, 2010 and 2014 Football World Cups.

PubMed

Wunderlich, Fabian; Memmert, Daniel

2016-12-01

The present study aims to investigate the ability of a new framework enabling to derive more detailed model-based predictions from ranking systems. These were compared to predictions from the bet market including data from the World Cups 2006, 2010, and 2014. The results revealed that the FIFA World Ranking has essentially improved its predictive qualities compared to the bet market since the mode of calculation was changed in 2006. While both predictors were useful to obtain accurate predictions in general, the world ranking was able to outperform the bet market significantly for the World Cup 2014 and when the data from the World Cups 2010 and 2014 were pooled. Our new framework can be extended in future research to more detailed prediction tasks (i.e., predicting the final scores of a match or the tournament progress of a team).

Investigating the running abilities of Tyrannosaurus rex using stress-constrained multibody dynamic analysis.

PubMed

Sellers, William I; Pond, Stuart B; Brassey, Charlotte A; Manning, Philip L; Bates, Karl T

2017-01-01

The running ability of Tyrannosaurus rex has been intensively studied due to its relevance to interpretations of feeding behaviour and the biomechanics of scaling in giant predatory dinosaurs. Different studies using differing methodologies have produced a very wide range of top speed estimates and there is therefore a need to develop techniques that can improve these predictions. Here we present a new approach that combines two separate biomechanical techniques (multibody dynamic analysis and skeletal stress analysis) to demonstrate that true running gaits would probably lead to unacceptably high skeletal loads in T. rex . Combining these two approaches reduces the high-level of uncertainty in previous predictions associated with unknown soft tissue parameters in dinosaurs, and demonstrates that the relatively long limb segments of T. rex -long argued to indicate competent running ability-would actually have mechanically limited this species to walking gaits. Being limited to walking speeds contradicts arguments of high-speed pursuit predation for the largest bipedal dinosaurs like T. rex , and demonstrates the power of multiphysics approaches for locomotor reconstructions of extinct animals.

Working Group 5: Measurements technology and active experiments

NASA Technical Reports Server (NTRS)

Whipple, E.; Barfield, J. N.; Faelthammar, C.-G.; Feynman, J.; Quinn, J. N.; Roberts, W.; Stone, N.; Taylor, W. L.

1986-01-01

Technology issues identified by working groups 5 are listed. (1) New instruments are needed to upgrade the ability to measure plasma properties in space. (2) Facilities should be developed for conducting a broad range of plasma experiments in space. (3) The ability to predict plasma weather within magnetospheres should be improved and a capability to modify plasma weather developed. (4) Methods of control of plasma spacecraft and spacecraft plasma interference should be upgraded. (5) The space station laboratory facilities should be designed with attention to problems of flexibility to allow for future growth. These issues are discussed.

Theories of willpower affect sustained learning.

PubMed

Miller, Eric M; Walton, Gregory M; Dweck, Carol S; Job, Veronika; Trzesniewski, Kali H; McClure, Samuel M

2012-01-01

Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower-whether willpower is viewed as a limited or non-limited resource-impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people's ability to recruit their cognitive resources to sustain learning over time.

Theories of Willpower Affect Sustained Learning

PubMed Central

Miller, Eric M.; Walton, Gregory M.; Dweck, Carol S.; Job, Veronika; Trzesniewski, Kali H.; McClure, Samuel M.

2012-01-01

Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower–whether willpower is viewed as a limited or non-limited resource–impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people’s ability to recruit their cognitive resources to sustain learning over time. PMID:22745675

Dual-component video image analysis system (VIASCAN) as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades.

PubMed

Cannell, R C; Tatum, J D; Belk, K E; Wise, J W; Clayton, R P; Smith, G C

1999-11-01

An improved ability to quantify differences in the fabrication yields of beef carcasses would facilitate the application of value-based marketing. This study was conducted to evaluate the ability of the Dual-Component Australian VIASCAN to 1) predict fabricated beef subprimal yields as a percentage of carcass weight at each of three fat-trim levels and 2) augment USDA yield grading, thereby improving accuracy of grade placement. Steer and heifer carcasses (n = 240) were evaluated using VIASCAN, as well as by USDA expert and online graders, before fabrication of carcasses to each of three fat-trim levels. Expert yield grade (YG), online YG, VIASCAN estimates, and VIASCAN estimated ribeye area used to augment actual and expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, and hot carcass weight), respectively, 1) accounted for 51, 37, 46, and 55% of the variation in fabricated yields of commodity-trimmed subprimals, 2) accounted for 74, 54, 66, and 75% of the variation in fabricated yields of closely trimmed subprimals, and 3) accounted for 74, 54, 71, and 75% of the variation in fabricated yields of very closely trimmed subprimals. The VIASCAN system predicted fabrication yields more accurately than current online yield grading and, when certain VIASCAN-measured traits were combined with some USDA yield grade factors in an augmentation system, the accuracy of cutability prediction was improved, at packing plant line speeds, to a level matching that of expert graders applying grades at a comfortable rate.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

PubMed Central

Rajagopala, S. V.; Blazie, S. M.; Parrish, J. R.; Khuri, S.; Finley, R. L.

2017-01-01

ABSTRACT The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. PMID:28744484

Mapping Tamarix: New techniques for field measurements, spatial modeling and remote sensing

NASA Astrophysics Data System (ADS)

Evangelista, Paul H.

Native riparian ecosystems throughout the southwestern United States are being altered by the rapid invasion of Tamarix species, commonly known as tamarisk. The effects that tamarisk has on ecosystem processes have been poorly quantified largely due to inadequate survey methods. I tested new approaches for field measurements, spatial models and remote sensing to improve our ability measure and to map tamarisk occurrence, and provide new methods that will assist in management and control efforts. Examining allometric relationships between basal cover and height measurements collected in the field, I was able to produce several models to accurately estimate aboveground biomass. The best two models were explained 97% of the variance (R 2 = 0.97). Next, I tested five commonly used predictive spatial models to identify which methods performed best for tamarisk using different types of data collected in the field. Most spatial models performed well for tamarisk, with logistic regression performing best with an Area Under the receiver-operating characteristic Curve (AUC) of 0.89 and overall accuracy of 85%. The results of this study also suggested that models may not perform equally with different invasive species, and that results may be influenced by species traits and their interaction with environmental factors. Lastly, I tested several approaches to improve the ability to remotely sense tamarisk occurrence. Using Landsat7 ETM+ satellite scenes and derived vegetation indices for six different months of the growing season, I examined their ability to detect tamarisk individually (single-scene analyses) and collectively (time-series). My results showed that time-series analyses were best suited to distinguish tamarisk from other vegetation and landscape features (AUC = 0.96, overall accuracy = 90%). June, August and September were the best months to detect unique phenological attributes that are likely related to the species' extended growing season and green-up during peak growing months. These studies demonstrate that new techniques can further our understanding of tamarisk's impacts on ecosystem processes, predict potential distribution and new invasions, and improve our ability to detect occurrence using remote sensing techniques. Collectively, the results of my studies may increase our ability to map tamarisk distributions and better quantify its impacts over multiple spatial and temporal scales.

Predicting functional ability in mild cognitive impairment with the Dementia Rating Scale-2.

PubMed

Greenaway, Melanie C; Duncan, Noah L; Hanna, Sherrie; Smith, Glenn E

2012-06-01

We examined the utility of cognitive evaluation to predict instrumental activities of daily living (IADLs) and decisional ability in Mild Cognitive Impairment (MCI). Sixty-seven individuals with single-domain amnestic MCI were administered the Dementia Rating Scale-2 (DRS-2) as well as the Everyday Cognition assessment form to assess functional ability. The DRS-2 Total Scores and Initiation/Perseveration and Memory subscales were found to be predictive of IADLs, with Total Scores accounting for 19% of the variance in IADL performance on average. In addition, the DRS-2 Initiation/Perseveration and Total Scores were predictive of ability to understand information, and the DRS-2 Conceptualization helped predict ability to communicate with others, both key variables in decision-making ability. These findings suggest that performance on the DRS-2, and specific subscales related to executive function and memory, is significantly related to IADLs in individuals with MCI. These cognitive measures are also associated with decision-making-related abilities in MCI.

Improved Broadband Liner Optimization Applied to the Advanced Noise Control Fan

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Jones, Michael G.; Sutliff, Daniel L.; Ayle, Earl; Ichihashi, Fumitaka

2014-01-01

The broadband component of fan noise has grown in relevance with the utilization of increased bypass ratio and advanced fan designs. Thus, while the attenuation of fan tones remains paramount, the ability to simultaneously reduce broadband fan noise levels has become more desirable. This paper describes improvements to a previously established broadband acoustic liner optimization process using the Advanced Noise Control Fan rig as a demonstrator. Specifically, in-duct attenuation predictions with a statistical source model are used to obtain optimum impedance spectra over the conditions of interest. The predicted optimum impedance information is then used with acoustic liner modeling tools to design liners aimed at producing impedance spectra that most closely match the predicted optimum values. Design selection is based on an acceptance criterion that provides the ability to apply increased weighting to specific frequencies and/or operating conditions. Constant-depth, double-degree of freedom and variable-depth, multi-degree of freedom designs are carried through design, fabrication, and testing to validate the efficacy of the design process. Results illustrate the value of the design process in concurrently evaluating the relative costs/benefits of these liner designs. This study also provides an application for demonstrating the integrated use of duct acoustic propagation/radiation and liner modeling tools in the design and evaluation of novel broadband liner concepts for complex engine configurations.

Predictive value of repeated measurements of luteal progesterone and estradiol levels in patients with intrauterine insemination and controlled ovarian stimulation.

PubMed

Bakas, Panagiotis; Simopoulou, Maria; Giner, Maria; Drakakis, Petros; Panagopoulos, Perikles; Vlahos, Nikolaos

2017-10-01

The objective of this study is to assess if the difference of repeated measurements of estradiol and progesterone during luteal phase predict the outcome of intrauterine insemination. Prospective study. Reproductive clinic. 126 patients with infertility. Patients underwent controlled ovarian stimulation with recombinant FSH (50-150 IU/d). The day of IUI patients were given p.o natural micronized progesterone in a dose of 100 mg/tds. The area under the receiver characteristic operating curve (ROC curve) in predicting clinical pregnancy for % change of estradiol level on days 6 and 10 was 0.892 with 95% CI: 0.82-0.94. A cutoff value of change > -29.5% had a sensitivity of 85.7 with a specificity of 90.2. The corresponding ROC curve for % change of progesterone level was 0.839 with 95% CI: 0.76-0.90. A cutoff value of change > -33% had a sensitivity of 85 with a specificity of 75. The % change of estradiol and progesterone between days 6 and 10 has a predictive ability of pregnancy after IUI with COS of 89.2% and 83.4%, respectively. The addition of % of progesterone to % change of estradiol does not improve the predictive ability of % estradiol and should not be used.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Preschool Predictors of School-Age Academic Achievement in Autism Spectrum Disorder

PubMed Central

Miller, Lauren E.; Burke, Jeffrey D.; Troyb, Eva; Knoch, Kelley; Herlihy, Lauren E.; Fein, Deborah A.

2017-01-01

Objective Characterization of academic functioning in children with autism spectrum disorder (ASD), particularly predictors of achievement, may have important implications for intervention. The current study aimed to characterize achievement profiles, confirm associations between academic ability and concurrent intellectual and social skills, and explore preschool predictors of school-age academic achievement in a sample of children with ASD. Method Children with ASD (N = 26) were evaluated at the approximate ages of two, four, and ten years. Multiple regression was used to predict school-age academic achievement in reading and mathematics from both concurrent (i.e., school-age) and preschool variables. Results Children with ASD demonstrated a weakness in reading comprehension relative to word reading. There was a smaller difference between mathematics skills; math reasoning was lower than numerical operations, but this did not quite reach trend level significance. Concurrent IQ and social skills were associated with school-age academic achievement across domains. Preschool verbal abilities significantly predicted school-age reading comprehension, above and beyond concurrent IQ, and early motor functioning predicted later math skills. Conclusions Specific developmental features of early ASD predict specific aspects of school-age achievement. Early intervention targeting language and motor skills may improve later achievement in this population. PMID:27705180

Preschool predictors of school-age academic achievement in autism spectrum disorder.

PubMed

Miller, Lauren E; Burke, Jeffrey D; Troyb, Eva; Knoch, Kelley; Herlihy, Lauren E; Fein, Deborah A

2017-02-01

Characterization of academic functioning in children with autism spectrum disorder (ASD), particularly predictors of achievement, may have important implications for intervention. The current study aimed to characterize achievement profiles, confirm associations between academic ability and concurrent intellectual and social skills, and explore preschool predictors of school-age academic achievement in a sample of children with ASD. Children with ASD (n = 26) were evaluated at the approximate ages of two, four, and ten. Multiple regression was used to predict school-age academic achievement in reading and mathematics from both concurrent (i.e. school-age) and preschool variables. Children with ASD demonstrated a weakness in reading comprehension relative to word reading. There was a smaller difference between mathematics skills; math reasoning was lower than numerical operations, but this did not quite reach trend level significance. Concurrent IQ and social skills were associated with school-age academic achievement across domains. Preschool verbal abilities significantly predicted school-age reading comprehension, above and beyond concurrent IQ, and early motor functioning predicted later math skills. Specific developmental features of early ASD predict specific aspects of school-age achievement. Early intervention targeting language and motor skills may improve later achievement in this population.

INVASIVE AND NON-INVASIVE TECHNIQUES FOR DETECTING PORTAL HYPERTENSION AND PREDICTING VARICEAL BLEEDING IN CIRRHOSIS: A REVIEW

PubMed Central

Zardi, Enrico Maria; Di Matteo, Francesco Maria; Pacella, Claudio Maurizio; Sanyal, Arun J

2016-01-01

Portal hypertension is a severe syndrome that may derive from pre-sinusoidal, sinusoidal and post-sinusoidal causes. As a consequence, several complications (i.e., ascites, oesophageal varices) may develop. In sinusoidal portal hypertension, hepatic venous pressure gradient (HVPG) is a reliable method for defining the grade of portal pressure, establishing the effectiveness of the treatment and predicting the occurrence of complications; however, some questions exist regarding its ability to discriminate bleeding from nonbleeding varices in cirrhotic patients. Other imaging techniques (transient elastography, endoscopy, endosonography and duplex Doppler sonography) for assessing causes and complications of portal hypertensive syndrome are available and may be valuable for the management of these patients. In this review, we evaluate invasive and non-invasive techniques currently employed to obtain a clinical prediction of deadly complications, such as variceal bleeding in patients affected by sinusoidal portal hypertension, in order to create a diagnostic algorithm to manage them. Again, HVPG appears to be the reference standard to evaluate portal hypertension and monitor the response to treatment, but its ability to predict several complications and support management decisions might be further improved through the diagnostic combination with other imaging techniques. PMID:24328372

The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

NASA Astrophysics Data System (ADS)

Lu, Z. L.; Li, D. C.; Lu, B. H.; Zhang, A. F.; Zhu, G. X.; Pi, G.

2010-05-01

Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.

Invasive and non-invasive techniques for detecting portal hypertension and predicting variceal bleeding in cirrhosis: a review.

PubMed

Zardi, Enrico Maria; Di Matteo, Francesco Maria; Pacella, Claudio Maurizio; Sanyal, Arun J

2014-02-01

Portal hypertension is a severe syndrome that may derive from pre-sinusoidal, sinusoidal, and post-sinusoidal causes. As a consequence, several complications (i.e. ascites, oesophageal varices) may develop. In sinusoidal portal hypertension, hepatic venous pressure gradient (HVPG) is a reliable method for defining the grade of portal pressure, establishing the effectiveness of the treatment, and predicting the occurrence of complications; however, some questions exist regarding its ability to discriminate bleeding from non-bleeding varices in cirrhotic patients. Other imaging techniques (transient elastography, endoscopy, endosonography, and duplex Doppler sonography) for assessing causes and complications of portal hypertensive syndrome are available and may be valuable for the management of these patients. In this review, we evaluate invasive and non-invasive techniques currently employed to obtain a clinical prediction of deadly complications, such as variceal bleeding in patients affected by sinusoidal portal hypertension, in order to create a diagnostic algorithm to manage them. Again, HVPG appears to be the reference standard to evaluate portal hypertension and monitor the response to treatment, but its ability to predict several complications and support management decisions might be further improved through the diagnostic combination with other imaging techniques.

Neuroprediction, Violence, and the Law: Setting the Stage.

PubMed

Nadelhoffer, Thomas; Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2012-04-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In "Violence Risk Assessment and the Law", we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing ("Violence Risk Assessment and Capital Sentencing"), civil commitment hearings ("Violence Risk Assessment and Civil Commitment"), and "sexual predator" statutes ("Violence Risk Assessment and Sexual Predator Statutes"). In "Clinical vs. Actuarial Violence Risk Assessment", we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In "The Neural Correlates of Psychopathy", we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection ("Cutting-Edge Data Collection: Genetically Informed Neuroimaging") and data analysis ("Cutting-Edge Data Analysis: Pattern Classification") that we believe will play an important role when it comes to future neuroscientific research on violence. In "The Potential Promise of Neuroprediction", we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in "The Potential Perils of Neuroprediction", we explore some potential evidentiary ("Evidentiary Issues"), constitutional ("Constitutional Issues"), and moral ("Moral Issues") issues that may arise in the context of the neuroprediction of violence.

Reactive stepping behaviour in response to forward loss of balance predicts future falls in community-dwelling older adults.

PubMed

Carty, Christopher P; Cronin, Neil J; Nicholson, Deanne; Lichtwark, Glen A; Mills, Peter M; Kerr, Graham; Cresswell, Andrew G; Barrett, Rod S

2015-01-01

a fall occurs when an individual experiences a loss of balance from which they are unable to recover. Assessment of balance recovery ability in older adults may therefore help to identify individuals at risk of falls. The purpose of this 12-month prospective study was to assess whether the ability to recover from a forward loss of balance with a single step across a range of lean magnitudes was predictive of falls. two hundred and one community-dwelling older adults, aged 65-90 years, underwent baseline testing of sensori-motor function and balance recovery ability followed by 12-month prospective falls evaluation. Balance recovery ability was defined by whether participants required either single or multiple steps to recover from forward loss of balance from three lean magnitudes, as well as the maximum lean magnitude participants could recover from with a single step. forty-four (22%) participants experienced one or more falls during the follow-up period. Maximal recoverable lean magnitude and use of multiple steps to recover at the 15% body weight (BW) and 25%BW lean magnitudes significantly predicted a future fall (odds ratios 1.08-1.26). The Physiological Profile Assessment, an established tool that assesses variety of sensori-motor aspects of falls risk, was also predictive of falls (Odds ratios 1.22 and 1.27, respectively), whereas age, sex, postural sway and timed up and go were not predictive. reactive stepping behaviour in response to forward loss of balance and physiological profile assessment are independent predictors of a future fall in community-dwelling older adults. Exercise interventions designed to improve reactive stepping behaviour may protect against future falls. © The Author 2014. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Improved Slip Casting Of Ceramic Models

NASA Technical Reports Server (NTRS)

Buck, Gregory M.; Vasquez, Peter; Hicks, Lana P.

1994-01-01

Improved technique of investment slip casting developed for making precise ceramic wind-tunnel models. Needed in wind-tunnel experiments to verify predictions of aerothermodynamical computer codes. Ceramic materials used because of their low heat conductivities and ability to survive high temperatures. Present improved slip-casting technique enables casting of highly detailed models from aqueous or nonaqueous solutions. Wet shell molds peeled off models to ensure precise and undamaged details. Used at NASA Langley Research Center to form superconducting ceramic components from nonaqueous slip solutions. Technique has many more applications when ceramic materials developed further for such high-strength/ temperature components as engine parts.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change.

PubMed

Garrard, Georgia E; McCarthy, Michael A; Vesk, Peter A; Radford, James Q; Bennett, Andrew F

2012-01-01

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature. 2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable. 3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation. 4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation. 5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon. 6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

Changes in actual and perceived physical abilities in clinically obese children: a 9-month multi-component intervention study.

PubMed

Morano, Milena; Colella, Dario; Rutigliano, Irene; Fiore, Pietro; Pettoello-Mantovani, Massimo; Campanozzi, Angelo

2012-01-01

(1) To examine relationships among changes in physical activity, physical fitness and some psychosocial determinants of activity behavior in a clinical sample of obese children involved in a multi-component program; (2) to investigate the causal relationship over time between physical activity and one of its strongest correlates (i.e. perceived physical ability). Self-reported physical activity and health-related fitness tests were administered before and after a 9-month intervention in 24 boys and 20 girls aged 8 to 11 years. Individuals' perceptions of strength, speed and agility were assessed using the Perceived Physical Ability Scale, while body image was measured using Collins' Child Figure Drawings. Findings showed that body mass index, physical activity, performances on throwing and weight-bearing tasks, perceived physical ability and body image significantly improved after treatment among obese children. Gender differences were found in the correlational analyses, showing a link between actual and perceived physical abilities in boys, but not in girls. For the specific measurement interval of this study, perception of physical ability was an antecedent and not a potential consequence of physical activity. Results indicate that a multi-component activity program not based merely on a dose-effect approach enhances adherence of the participants and has the potential to increase the lifelong exercise skills of obese children. Rather than focusing entirely on diet and weight loss, findings support the inclusion of interventions directed toward improving perceived physical ability that is predictive of subsequent physical activity.

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

PubMed

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

Nuclear charge radii: density functional theory meets Bayesian neural networks

NASA Astrophysics Data System (ADS)

Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

2016-11-01

The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

PubMed

Wang, Duolin; Zeng, Shuai; Xu, Chunhui; Qiu, Wangren; Liang, Yanchun; Joshi, Trupti; Xu, Dong

2017-12-15

Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. xudong@missouri.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Viewing Marine Bacteria, Their Activity and Response to Environmental Drivers from Orbit

PubMed Central

Grimes, D. Jay; Ford, Tim E.; Colwell, Rita R.; Baker-Austin, Craig; Martinez-Urtaza, Jaime; Subramaniam, Ajit; Capone, Douglas G.

2014-01-01

Satellite-based remote sensing of marine microorganisms has become a useful tool in predicting human health risks associated with these microscopic targets. Early applications were focused on harmful algal blooms, but more recently methods have been developed to interrogate the ocean for bacteria. As satellite-based sensors have become more sophisticated and our ability to interpret information derived from these sensors has advanced, we have progressed from merely making fascinating pictures from space to developing process models with predictive capability. Our understanding of the role of marine microorganisms in primary production and global elemental cycles has been vastly improved as has our ability to use the combination of remote sensing data and models to provide early warning systems for disease outbreaks. This manuscript will discuss current approaches to monitoring cyanobacteria and vibrios, their activity and response to environmental drivers, and will also suggest future directions. PMID:24477922

Viewing marine bacteria, their activity and response to environmental drivers from orbit: satellite remote sensing of bacteria.

PubMed

Grimes, D Jay; Ford, Tim E; Colwell, Rita R; Baker-Austin, Craig; Martinez-Urtaza, Jaime; Subramaniam, Ajit; Capone, Douglas G

2014-04-01

Satellite-based remote sensing of marine microorganisms has become a useful tool in predicting human health risks associated with these microscopic targets. Early applications were focused on harmful algal blooms, but more recently methods have been developed to interrogate the ocean for bacteria. As satellite-based sensors have become more sophisticated and our ability to interpret information derived from these sensors has advanced, we have progressed from merely making fascinating pictures from space to developing process models with predictive capability. Our understanding of the role of marine microorganisms in primary production and global elemental cycles has been vastly improved as has our ability to use the combination of remote sensing data and models to provide early warning systems for disease outbreaks. This manuscript will discuss current approaches to monitoring cyanobacteria and vibrios, their activity and response to environmental drivers, and will also suggest future directions.

Factors influencing the degree of eating ability among people with dementia.

PubMed

Lee, Kyoung Min; Song, Jun-Ah

2015-06-01

To explore the degree of eating ability in people with dementia and identify what factors affect their eating ability. Appropriate food consumption is important to human life. Although eating difficulties are common among people with dementia, little is known about what factors might influence their eating ability. Descriptive, cross-sectional study. A total of 149 people with dementia residing in nursing facilities in Seoul or the Gyeonggi area of Korea were evaluated using the Korean Mini-Mental State Examination, Korean Activities of Daily Living Scale and Eating Behaviour Scale. Data were analysed using descriptive statistics, one-way analysis of variance, Pearson correlation coefficient and multiple regression analysis. The participants showed a moderate level of dependency with respect to eating ability and were most dependent on the use of utensils. There were significant differences in eating ability according to general characteristics such as duration of residence, duration of illness, degree of visual impairment, eating place, and diet type. The eating ability of the participants was significantly correlated with cognitive function and physical function. Cognitive function, physical function, duration of illness, eating place (living room or dining room), and diet type (soft or liquid) significantly predicted eating ability in people with dementia. The findings of this study suggest that it is necessary to thoroughly assess the eating ability of people with dementia and to develop appropriate training programs to maintain or improve their remaining eating ability. The creation of a pleasurable physical and social environment for eating might also be helpful. These findings would be able to serve a useful basis in the development of materials for nursing intervention programs for people with dementia during mealtimes by improving the techniques and care qualities of nursing caregivers. © 2015 John Wiley & Sons Ltd.

Predicting Gender-Role Attitudes in Adolescent Females: Ability, Agency, and Parental Factors.

ERIC Educational Resources Information Center

Ahrens, Julia A.; O'Brien, Karen M.

1996-01-01

Investigated the contribution of ability, agency, and parental factors to the prediction of gender-role attitudes of 409 adolescent females in a private, college-preparatory high school. Findings indicate that ability and agency were predictive of gender-role attitudes, whereas parental factors were not significant contributors. Recommendations…

Physical Activity Predicts Performance in an Unpracticed Bimanual Coordination Task.

PubMed

Boisgontier, Matthieu P; Serbruyns, Leen; Swinnen, Stephan P

2017-01-01

Practice of a given physical activity is known to improve the motor skills related to this activity. However, whether unrelated skills are also improved is still unclear. To test the impact of physical activity on an unpracticed motor task, 26 young adults completed the international physical activity questionnaire and performed a bimanual coordination task they had never practiced before. Results showed that higher total physical activity predicted higher performance in the bimanual task, controlling for multiple factors such as age, physical inactivity, music practice, and computer games practice. Linear mixed models allowed this effect of physical activity to be generalized to a large population of bimanual coordination conditions. This finding runs counter to the notion that generalized motor abilities do not exist and supports the existence of a "learning to learn" skill that could be improved through physical activity and that impacts performance in tasks that are not necessarily related to the practiced activity.

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

PubMed Central

Vo, Loan T. K.; Walther, Dirk B.; Kramer, Arthur F.; Erickson, Kirk I.; Boot, Walter R.; Voss, Michelle W.; Prakash, Ruchika S.; Lee, Hyunkyu; Fabiani, Monica; Gratton, Gabriele; Simons, Daniel J.; Sutton, Bradley P.; Wang, Michelle Y.

2011-01-01

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills. PMID:21264257

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

Is Approximate Number Precision a Stable Predictor of Math Ability?

ERIC Educational Resources Information Center

Libertus, Melissa E.; Feigenson, Lisa; Halberda, Justin

2013-01-01

Previous research shows that children's ability to estimate numbers of items using their Approximate Number System (ANS) predicts later math ability. To more closely examine the predictive role of early ANS acuity on later abilities, we assessed the ANS acuity, math ability, and expressive vocabulary of preschoolers twice, six months apart. We…

Predicting Arithmetic Abilities: The Role of Preparatory Arithmetic Markers and Intelligence

ERIC Educational Resources Information Center

Stock, Pieter; Desoete, Annemie; Roeyers, Herbert

2009-01-01

Arithmetic abilities acquired in kindergarten are found to be strong predictors for later deficient arithmetic abilities. This longitudinal study (N = 684) was designed to examine if it was possible to predict the level of children's arithmetic abilities in first and second grade from their performance on preparatory arithmetic abilities in…

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

PubMed

Cho, Woon; Jang, Jinbeum; Koschan, Andreas; Abidi, Mongi A; Paik, Joonki

2016-11-28

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

Urinary biomarkers predict advanced acute kidney injury after cardiovascular surgery.

PubMed

Wang, Jian-Jhong; Chi, Nai-Hsin; Huang, Tao-Min; Connolly, Rory; Chen, Liang Wen; Chueh, Shih-Chieh Jeff; Kan, Wei-Chih; Lai, Chih-Cheng; Wu, Vin-Cent; Fang, Ji-Tseng; Chu, Tzong-Shinn; Wu, Kwan-Dun

2018-04-26

Acute kidney injury (AKI) after cardiovascular surgery is a serious complication. Little is known about the ability of novel biomarkers in combination with clinical risk scores for prediction of advanced AKI. In this prospectively conducted multicenter study, urine samples were collected from 149 adults at 0, 3, 6, 12 and 24 h after cardiovascular surgery. We measured urinary hemojuvelin (uHJV), kidney injury molecule-1 (uKIM-1), neutrophil gelatinase-associated lipocalin (uNGAL), α-glutathione S-transferase (uα-GST) and π-glutathione S-transferase (uπ-GST). The primary outcome was advanced AKI, under the definition of Kidney Disease: Improving Global Outcomes (KDIGO) stage 2, 3 and composite outcomes were KDIGO stage 2, 3 or 90-day mortality after hospital discharge. Patients with advanced AKI had significantly higher levels of uHJV and uKIM-1 at 3, 6 and 12 h after surgery. When normalized by urinary creatinine level, uKIM-1 in combination with uHJV at 3 h post-surgery had a high predictive ability for advanced AKI and composite outcome (AUC = 0.898 and 0.905, respectively). The combination of this biomarker panel (normalized uKIM-1, uHJV at 3 h post-operation) and Liano's score was superior in predicting advanced AKI (AUC = 0.931, category-free net reclassification improvement of 1.149, and p <  0.001). When added to Liano's score, normalized uHJV and uKIM-1 levels at 3 h after cardiovascular surgery enhanced the identification of patients at higher risk of progression to advanced AKI and composite outcomes.

A Limiting Feature of the Mozart Effect: Listening Enhances Mental Rotation Abilities in Non-Musicians but Not Musicians

ERIC Educational Resources Information Center

Aheadi, Afshin; Dixon, Peter; Glover, Scott

2010-01-01

The "Mozart effect" occurs when performance on spatial cognitive tasks improves following exposure to Mozart. It is hypothesized that the Mozart effect arises because listening to complex music activates similar regions of the right cerebral hemisphere as are involved in spatial cognition. A counter-intuitive prediction of this hypothesis (and one…

An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior

Treesearch

W. J. Massman; J. M. Forthofer; M. A. Finney

2017-01-01

The ability to rapidly estimate wind speed beneath a forest canopy or near the ground surface in any vegetation is critical to practical wildland fire behavior models. The common metric of this wind speed is the "mid-flame" wind speed, UMF. However, the existing approach for estimating UMF has some significant shortcomings. These include the assumptions that...

Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...

Modeling the spatial and temporal dynamics of isolated emerald ash borer populations

Treesearch

Nathan W. Siegert; Andrew M. Liebhold; Deborah G. McCullough

2008-01-01

The ability to predict the distance and rate of emerald ash borer (EAB) spread in outlier populations is needed to continue development of effective management strategies for improved EAB control. We have developed a coupled map lattice model to estimate the spread and dispersal of isolated emerald ash borer populations. This model creates an artificial environment in...

Design and Implementation of an Intelligent System to Predict the Student Graduation AGPA

ERIC Educational Resources Information Center

Ismail, Sameh; Abdulla, Shubair

2015-01-01

Since Accumulated Grad-Point Average (AGPA) is crucial in the professional life of students, it is an interesting and challenging problem to create profiles for those students who are likely to graduate with low AGPA. Identifying this kind of students accurately will enable the university staff to help them improve their ability by providing them…

Patients with lower activation associated with higher costs; delivery systems should know their patients' 'scores'.

PubMed

Hibbard, Judith H; Greene, Jessica; Overton, Valerie

2013-02-01

Patient activation is a term that describes the skills and confidence that equip patients to become actively engaged in their health care. Health care delivery systems are turning to patient activation as yet another tool to help them and their patients improve outcomes and influence costs. In this article we examine the relationship between patient activation levels and billed care costs. In an analysis of 33,163 patients of Fairview Health Services, a large health care delivery system in Minnesota, we found that patients with the lowest activation levels had predicted average costs that were 8 percent higher in the base year and 21 percent higher in the first half of the next year than the costs of patients with the highest activation levels, both significant differences. What's more, patient activation was a significant predictor of cost even after adjustment for a commonly used "risk score" specifically designed to predict future costs. As health care delivery systems move toward assuming greater accountability for costs and outcomes for defined patient populations, knowing patients' ability and willingness to manage their health will be a relevant piece of information integral to health care providers' ability to improve outcomes and lower costs.

Genotype-specific relationships among phosphorus use, growth and abundance in Daphnia pulicaria

PubMed Central

Chowdhury, Priyanka Roy; Baker, Kristina D.; Weider, Lawrence J.; Jeyasingh, Punidan D.

2017-01-01

The framework ecological stoichiometry uses elemental composition of species to make predictions about growth and competitive ability in defined elemental supply conditions. Although intraspecific differences in stoichiometry have been observed, we have yet to understand the mechanisms generating and maintaining such variation. We used variation in phosphorus (P) content within a Daphnia species to test the extent to which %P can explain variation in growth and competition. Further, we measured 33P kinetics (acquisition, assimilation, incorporation and retention) to understand the extent to which such variables improved predictions. Genotypes showed significant variation in P content, 33P kinetics and growth rate. P content alone was a poor predictor of growth rate and competitive ability. While most genotypes exhibited the typical growth penalty under P limitation, a few varied little in growth between P diets. These observations indicate that some genotypes can maintain growth under P-limited conditions by altering P use, suggesting that decomposing P content of an individual into physiological components of P kinetics will improve stoichiometric models. More generally, attention to the interplay between nutrient content and nutrient-use is required to make inferences regarding the success of genotypes in defined conditions of nutrient supply. PMID:29308224

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

Evaluation and comparison of the ability of online available prediction programs to predict true linear B-cell epitopes.

PubMed

Costa, Juan G; Faccendini, Pablo L; Sferco, Silvano J; Lagier, Claudia M; Marcipar, Iván S

2013-06-01

This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.

The circadian profile of epilepsy improves seizure forecasting.

PubMed

Karoly, Philippa J; Ung, Hoameng; Grayden, David B; Kuhlmann, Levin; Leyde, Kent; Cook, Mark J; Freestone, Dean R

2017-08-01

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Using genetics to predict the natural history of asthma?

PubMed

Holloway, John W; Arshad, Syed H; Holgate, Stephen T

2010-08-01

Clinical practice reminds us that there is considerable variability in the course of asthma over time. Treatment of patients with asthma would be considerably improved if one could accurately predict the likely course of disease over the life course. Recently, with the advent of the era of genome-wide association studies, there has been a monumental shift in our understanding of the genetic factors that underlie inherited susceptibility to asthma. Genes have been identified that modulate many aspects of the natural history of asthma, such as susceptibility to atopy, altered lung development, and susceptibility to more severe disease. Heritability studies have even suggested a role for genetic factors in remission of asthma. However, although the discovery of novel genetic factors underlying disease susceptibility has undoubtedly improved our understanding of disease pathogenesis, whether these advances have improved the ability to predict the natural history in individual patients is questionable, and the application of genetic testing to clinical practice remains some way off. Copyright 2010 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Comprehending 3D Diagrams: Sketching to Support Spatial Reasoning.

PubMed

Gagnier, Kristin M; Atit, Kinnari; Ormand, Carol J; Shipley, Thomas F

2017-10-01

Science, technology, engineering, and mathematics (STEM) disciplines commonly illustrate 3D relationships in diagrams, yet these are often challenging for students. Failing to understand diagrams can hinder success in STEM because scientific practice requires understanding and creating diagrammatic representations. We explore a new approach to improving student understanding of diagrams that convey 3D relations that is based on students generating their own predictive diagrams. Participants' comprehension of 3D spatial diagrams was measured in a pre- and post-design where students selected the correct 2D slice through 3D geologic block diagrams. Generating sketches that predicated the internal structure of a model led to greater improvement in diagram understanding than visualizing the interior of the model without sketching, or sketching the model without attempting to predict unseen spatial relations. In addition, we found a positive correlation between sketched diagram accuracy and improvement on the diagram comprehension measure. Results suggest that generating a predictive diagram facilitates students' abilities to make inferences about spatial relationships in diagrams. Implications for use of sketching in supporting STEM learning are discussed. Copyright © 2016 Cognitive Science Society, Inc.

Editorial Commentary: Role of Synovial Biomarkers in Patient Outcomes After Knee Arthroscopy.

PubMed

Brand, Jefferson C

2016-03-01

Humans are notably poor at predicting event outcomes. In "Correlation of Synovial Fluid Biomarkers With Cartilage Pathology and Associated Outcomes in Knee Arthroscopy," Cuellar, Cuellar, Kirsch, and Strauss show that some synovial fluid biomarkers (20 were sampled for the investigation) may predict operative findings at the time of arthroscopy and patient-reported outcome measures at follow-up. Further research will clarify the role of synovial biomarkers in knee pathology and, hopefully, narrow the choices to one or two pertinent markers that can be used to improve our ability to predict outcomes from arthroscopic knee surgery. Copyright © 2016 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Novel biomarkers for cardiovascular risk assessment: current status and future directions.

PubMed

MacNamara, James; Eapen, Danny J; Quyyumi, Arshed; Sperling, Laurence

2015-09-01

Cardiovascular disease (CVD) is the leading cause of mortality in the modern world. Traditional risk algorithms may miss up to 20% of CVD events. Therefore, there is a need for new cardiac biomarkers. Many fields of research are dedicated to improving cardiac risk prediction, including genomics, transcriptomics and proteomics. To date, even the most promising biomarkers have only demonstrated modest associations and predictive ability. Few have undergone randomized control trials. A number of biomarkers are targets to new therapies aimed to reduce cardiovascular risk. Currently, some of the most promising risk prediction has been demonstrated with panels of multiple biomarkers. This article reviews the current state and future of proteomic biomarkers and aggregate biomarker panels.

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

DNA methylation-based age prediction from various tissues and body fluids

PubMed Central

Jung, Sang-Eun; Shin, Kyoung-Jin; Lee, Hwan Young

2017-01-01

Aging is a natural and gradual process in human life. It is influenced by heredity, environment, lifestyle, and disease. DNA methylation varies with age, and the ability to predict the age of donor using DNA from evidence materials at a crime scene is of considerable value in forensic investigations. Recently, many studies have reported age prediction models based on DNA methylation from various tissues and body fluids. Those models seem to be very promising because of their high prediction accuracies. In this review, the changes of age-associated DNA methylation and the age prediction models for various tissues and body fluids were examined, and then the applicability of the DNA methylation-based age prediction method to the forensic investigations was discussed. This will improve the understandings about DNA methylation markers and their potential to be used as biomarkers in the forensic field, as well as the clinical field. PMID:28946940

Effect of shaping sensor data on pilot response

NASA Technical Reports Server (NTRS)

Bailey, Roger M.

1990-01-01

The pilot of a modern jet aircraft is subjected to varying workloads while being responsible for multiple, ongoing tasks. The ability to associate the pilot's responses with the task/situation, by modifying the way information is presented relative to the task, could provide a means of reducing workload. To examine the feasibility of this concept, a real time simulation study was undertaken to determine whether preprocessing of sensor data would affect pilot response. Results indicated that preprocessing could be an effective way to tailor the pilot's response to displayed data. The effects of three transformations or shaping functions were evaluated with respect to the pilot's ability to predict and detect out-of-tolerance conditions while monitoring an electronic engine display. Two nonlinear transformations, on being the inverse of the other, were compared to a linear transformation. Results indicate that a nonlinear transformation that increases the rate-or-change of output relative to input tends to advance the prediction response and improve the detection response, while a nonlinear transformation that decreases the rate-of-change of output relative to input tends to lengthen the prediction response and make detection more difficult.

Pulmonary hypertension due to left heart disease: diagnostic and prognostic value of CT in chronic systolic heart failure.

PubMed

Colin, Geoffrey C; Gerber, Bernhard L; de Meester de Ravenstein, Christophe; Byl, David; Dietz, Anna; Kamga, Michele; Pasquet, Agnes; Vancraeynest, David; Vanoverschelde, Jean-Louis; D'Hondt, Anne-Marie; Ghaye, Benoit; Pouleur, Anne-Catherine

2018-05-14

To evaluate the ability of chest computed tomography (CT) to predict pulmonary hypertension (PH) and outcome in chronic heart failure with reduced ejection fraction (HFrEF). We reviewed 119 consecutive patients with HFrEF by CT, transthoracic echocardiography (TTE) and right heart catheterization (RHC). CT-derived pulmonary artery (PA) diameter and PA to ascending aorta diameter ratio (PA:A ratio), left atrial, right atrial, right ventricular (RV) and left ventricular volumes were correlated with RHC mean pulmonary arterial pressure (mPAP) . Diagnostic accuracy to predict PH and ability to predict primary composite endpoint of all-cause mortality and HF events were evaluated. RV volume was significantly higher in 81 patients with PH compared to 38 patients without PH (133 ml/m 2 vs. 79 ml/m 2 , p < 0.001) and was moderately correlated with mPAP (r=0.55, p < 0.001). Also, RV volume had higher ability to predict PH (area under the curve: 0.88) than PA diameter (0.79), PA:A ratio (0.76) by CT and tricuspid regurgitation gradient (0.83) and RV basal diameter by TTE (0.84, all p < 0.001). During the follow-up period (median: 3.4 years), 51 patients (43%) had HF events or died. After correction for important clinical, TTE and RHC parameters, RV volume (adjusted hazard ratio [HR]: 1.71, 95% CI 1.31-2.23, p < 0.001) and PA diameter (HR: 1.61, 95% CI 1.18-2.22, p = 0.003) were independent predictors of the primary endpoint. In patients with HFrEF, measurement of RV volume and PA diameter on ungated CT are non-invasive markers of PH and may help to predict the patient outcome. • Right ventricular (RV) volume measured by chest CT has good ability to identify pulmonary hypertension (PH) in patients with chronic heart failure (HF) and reduced ejection fraction (HFrEF). • The accuracy of pulmonary artery (PA) diameter and PA to ascending aorta diameter ratio (PA:A ratio) to predict PH was similar to previous studies, however, with lower cut-offs (28.1 mm and 0.92, respectively). • Chest CT-derived PA diameter and RV volume independently predict all-cause mortality and HF events and improve outcome prediction in patients with advanced HFrEF.

Predictive validity of the Work Ability Index and its individual items in the general population.

PubMed

Lundin, Andreas; Leijon, Ola; Vaez, Marjan; Hallgren, Mats; Torgén, Margareta

2017-06-01

This study assesses the predictive ability of the full Work Ability Index (WAI) as well as its individual items in the general population. The Work, Health and Retirement Study (WHRS) is a stratified random national sample of 25-75-year-olds living in Sweden in 2000 that received a postal questionnaire ( n = 6637, response rate = 53%). Current and subsequent sickness absence was obtained from registers. The ability of the WAI to predict long-term sickness absence (LTSA; ⩾ 90 consecutive days) during a period of four years was analysed by logistic regression, from which the Area Under the Receiver Operating Characteristic curve (AUC) was computed. There were 313 incident LTSA cases among 1786 employed individuals. The full WAI had acceptable ability to predict LTSA during the 4-year follow-up (AUC = 0.79; 95% CI 0.76 to 0.82). Individual items were less stable in their predictive ability. However, three of the individual items: current work ability compared with lifetime best, estimated work impairment due to diseases, and number of diagnosed current diseases, exceeded AUC > 0.70. Excluding the WAI item on number of days on sickness absence did not result in an inferior predictive ability of the WAI. The full WAI has acceptable predictive validity, and is superior to its individual items. For public health surveys, three items may be suitable proxies of the full WAI; current work ability compared with lifetime best, estimated work impairment due to diseases, and number of current diseases diagnosed by a physician.

Driving improvements in emerging disease surveillance through locally relevant capacity strengthening.

PubMed

Halliday, Jo E B; Hampson, Katie; Hanley, Nick; Lembo, Tiziana; Sharp, Joanne P; Haydon, Daniel T; Cleaveland, Sarah

2017-07-14

Emerging infectious diseases (EIDs) threaten the health of people, animals, and crops globally, but our ability to predict their occurrence is limited. Current public health capacity and ability to detect and respond to EIDs is typically weakest in low- and middle-income countries (LMICs). Many known drivers of EID emergence also converge in LMICs. Strengthening capacity for surveillance of diseases of relevance to local populations can provide a mechanism for building the cross-cutting and flexible capacities needed to tackle both the burden of existing diseases and EID threats. A focus on locally relevant diseases in LMICs and the economic, social, and cultural contexts of surveillance can help address existing inequalities in health systems, improve the capacity to detect and contain EIDs, and contribute to broader global goals for development. Copyright © 2017, American Association for the Advancement of Science.

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

The association between cognition and academic performance in Ugandan children surviving malaria with neurological involvement.

PubMed

Bangirana, Paul; Menk, Jeremiah; John, Chandy C; Boivin, Michael J; Hodges, James S

2013-01-01

The contribution of different cognitive abilities to academic performance in children surviving cerebral insult can guide the choice of interventions to improve cognitive and academic outcomes. This study's objective was to identify which cognitive abilities are associated with academic performance in children after malaria with neurological involvement. 62 Ugandan children with a history of malaria with neurological involvement were assessed for cognitive ability (working memory, reasoning, learning, visual spatial skills, attention) and academic performance (reading, spelling, arithmetic) three months after the illness. Linear regressions were fit for each academic score with the five cognitive outcomes entered as predictors. Adjusters in the analysis were age, sex, education, nutrition, and home environment. Exploratory factor analysis (EFA) and structural equation models (SEM) were used to determine the nature of the association between cognition and academic performance. Predictive residual sum of squares was used to determine which combination of cognitive scores was needed to predict academic performance. In regressions of a single academic score on all five cognitive outcomes and adjusters, only Working Memory was associated with Reading (coefficient estimate = 0.36, 95% confidence interval = 0.10 to 0.63, p<0.01) and Spelling (0.46, 0.13 to 0.78, p<0.01), Visual Spatial Skills was associated with Arithmetic (0.15, 0.03 to 0.26, p<0.05), and Learning was associated with Reading (0.06, 0.00 to 0.11, p<0.05). One latent cognitive factor was identified using EFA. The SEM found a strong association between this latent cognitive ability and each academic performance measure (P<0.0001). Working memory, visual spatial ability and learning were the best predictors of academic performance. Academic performance is strongly associated with the latent variable labelled "cognitive ability" which captures most of the variation in the individual specific cognitive outcome measures. Working memory, visual spatial skills, and learning together stood out as the best combination to predict academic performance.

Determinants of work ability and its predictive value for disability.

PubMed

Alavinia, S M; de Boer, A G E M; van Duivenbooden, J C; Frings-Dresen, M H W; Burdorf, A

2009-01-01

Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of work ability on receiving a work-related disability pension. A longitudinal study was conducted among 850 construction workers aged 40 years and older, with average follow-up period of 23 months. Disability was defined as receiving a disability pension, granted to workers unable to continue working in their regular job. Work ability was assessed using the work ability index (WAI). Associations between work-related factors and individual characteristics with work ability at baseline were evaluated using linear regression analysis, and Cox regression analysis was used to evaluate the predictive value of work ability for disability. Work-related factors were associated with a lower work ability at baseline, but had little prognostic value for disability during follow-up. The hazard ratios for disability among workers with a moderate and poor work ability at baseline were 8 and 32, respectively. All separate scales in the WAI had predictive power for future disability with the highest influence of current work ability in relation to job demands and lowest influence of diseases diagnosed by a physician. A moderate or poor work ability was highly predictive for receiving a disability pension. Preventive measures should facilitate a good balance between work performance and health in order to prevent quitting labour participation.

75 FR 80354 - Satellite Television Extension and Localism Act of 2010 and Satellite Home Viewer Extension and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-22

... Commission, adopts a point-to-point predictive model for determining the ability of individual locations to... predictive model for reliably and presumptively determining the ability of individual locations, through the... adopted a point-to-point predictive model for determining the ability of individual locations to receive...

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

A suicide education programme for nurses to educate the family caregivers of suicidal individuals: a longitudinal study.

PubMed

Sun, Fan-Ko; Chiang, Chun-Ying; Yu, Pei-Jane; Lin, Ching-Hsing

2013-10-01

Family members lack the ability to care for suicidal relatives. Nurses have a responsibility to improve family members' ability to care for their suicidal relatives. The aims of this study were to design a suicide education programme for nurses to educate family caregivers and to evaluate the longitudinal (12 months after the educational programme) effects of a suicide care education programme on the ability of families to care for suicidal relatives. A randomised controlled trial was conducted. The study population (n=61) was composed of the family caregivers of suicidal individuals. Several caregivers (n=26) were randomly allocated to an experimental group who attended a two-hour suicide care education programme, and the other caregivers (n=35) represented a control group who did not attend the education programme. All of the participants were given a questionnaire at baseline, 3 months, and 12 months during the period from 2009 to 2011. The results of the longitudinal effects of the suicide care education programme demonstrated that there were statistically significant differences after the educational programme as compared to before the programme with regard to "seeking assistance from resources" and the ability to care for those who were once suicidal. The longitudinal results of both groups showed that there was a significant difference in terms of "caring ability" at 12 months. The results of a multiple linear regression analysis indicated that evaluations performed at the three-month time point were able to effectively predict success in "seeking assistance from resources", "caring ability"; caring ability was also significantly improved among those who engaged in the educational programme at the 12-month time point. The suicide care education programme had long-term effects for family caregivers caring for their suicidal relatives. Nurses could employ this suicide care education programme to improve the ability of family caregivers to care for their suicidal relatives. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

Numerical predictors of arithmetic success in grades 1-6.

PubMed

Lyons, Ian M; Price, Gavin R; Vaessen, Anniek; Blomert, Leo; Ansari, Daniel

2014-09-01

Math relies on mastery and integration of a wide range of simpler numerical processes and concepts. Recent work has identified several numerical competencies that predict variation in math ability. We examined the unique relations between eight basic numerical skills and early arithmetic ability in a large sample (N = 1391) of children across grades 1-6. In grades 1-2, children's ability to judge the relative magnitude of numerical symbols was most predictive of early arithmetic skills. The unique contribution of children's ability to assess ordinality in numerical symbols steadily increased across grades, overtaking all other predictors by grade 6. We found no evidence that children's ability to judge the relative magnitude of approximate, nonsymbolic numbers was uniquely predictive of arithmetic ability at any grade. Overall, symbolic number processing was more predictive of arithmetic ability than nonsymbolic number processing, though the relative importance of symbolic number ability appears to shift from cardinal to ordinal processing. © 2014 John Wiley & Sons Ltd.

The role of ability, motivation, and opportunity to work in the transition from work to early retirement--testing and optimizing the Early Retirement Model.

PubMed

de Wind, Astrid; Geuskens, Goedele A; Ybema, Jan Fekke; Bongers, Paulien M; van der Beek, Allard J

2015-01-01

Determinants in the domains health, job characteristics, skills, and social and financial factors may influence early retirement through three central explanatory variables, namely, the ability, motivation, and opportunity to work. Based on the literature, we created the Early Retirement Model. This study aims to investigate whether data support the model and how it could be improved. Employees aged 58-62 years (N=1862), who participated in the first three waves of the Dutch Study on Transitions in Employment, Ability and Motivation (STREAM) were included. Determinants were assessed at baseline, central explanatory variables after one year, and early retirement after two years. Structural equation modeling was applied. Testing the Early Retirement Model resulted in a model with good fit. Health, job characteristics, skills, and social and financial factors were related to the ability, motivation and/or opportunity to work (significant β range: 0.05-0.31). Lower work ability (β=-0.13) and less opportunity to work (attitude colleagues and supervisor about working until age 65: β=-0.24) predicted early retirement, whereas the motivation to work (work engagement) did not. The model could be improved by adding direct effects of three determinants on early retirement, ie, support of colleagues and supervisor (β=0.14), positive attitude of the partner with respect to early retirement (β=0.15), and not having a partner (β=-0.13). The Early Retirement Model was largely supported by the data but could be improved. The prolongation of working life might be promoted by work-related interventions focusing on health, work ability, the social work climate, social norms on prolonged careers, and the learning environment.

Jump neural network for real-time prediction of glucose concentration.

PubMed

Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

2015-01-01

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

Prediction of changes in memory performance by plasma homovanillic acid levels in clozapine-treated patients with schizophrenia.

PubMed

Sumiyoshi, Tomiki; Roy, A; Kim, C-H; Jayathilake, K; Lee, M A; Sumiyoshi, C; Meltzer, H Y

2004-12-01

Cognitive dysfunction in schizophrenia has been demonstrated to be dependent, in part, on dopaminergic activity. Clozapine has been found to improve some domains of cognition, including verbal memory, in patients with schizophrenia. This study tested the hypothesis that plasma homovanillic acid (pHVA) levels, a peripheral measure of central dopaminergic activity, would predict the change in memory performance in patients with schizophrenia treated with clozapine. Twenty-seven male patients with schizophrenia received clozapine treatment for 6 weeks. Verbal list learning (VLL)-Delayed Recall (VLL-DR), a test of secondary verbal memory, was administered before and after clozapine treatment. Blood samples to measure pHVA levels were collected at baseline. Baseline pHVA levels were negatively correlated with change in performance on VLL-DR; the lower baseline pHVA level was associated with greater improvement in performance on VLL-DR during treatment with clozapine. Baseline pHVA levels in subjects who showed improvement in verbal memory during clozapine treatment ( n=13) were significantly lower than those in subjects whose memory performance did not improve ( n=14). The results of this study indicate that baseline pHVA levels predict the ability of clozapine to improve memory performance in patients with schizophrenia.

Left ventricular hypertrophy by ECG versus cardiac MRI as a predictor for heart failure.

PubMed

Oseni, Abdullahi O; Qureshi, Waqas T; Almahmoud, Mohamed F; Bertoni, Alain G; Bluemke, David A; Hundley, William G; Lima, Joao A C; Herrington, David M; Soliman, Elsayed Z

2017-01-01

To determine if there is a significant difference in the predictive abilities of left ventricular hypertrophy (LVH) detected by ECG-LVH versus LVH ascertained by cardiac MRI-LVH in a model similar to the Framingham Heart Failure Risk Score (FHFRS). This study included 4745 (mean age 61±10 years, 53.5% women, 61.7% non-whites) participants in the Multi-Ethnic Study of Atherosclerosis. ECG-LVH was defined using Cornell voltage product while MRI-LVH was derived from left ventricular mass. Cox proportional hazard regression was used to examine the association between ECG-LVH and MRI-LVH with incident heart failure (HF). Harrell's concordance C-index was used to estimate the predictive ability of the model when either ECG-LVH or MRI-LVH was included as one of its components. ECG-LVH was present in 291 (6.1%), while MRI-LVH was present in 499 (10.5%) of the participants. Both ECG-LVH (HR 2.25, 95% CI 1.38 to 3.69) and MRI-LVH (HR 3.80, 95% CI 1.56 to 5.63) were predictive of HF. The absolute risk of developing HF was 8.81% for MRI-LVH versus 2.26% for absence of MRI-LVH with a relative risk of 3.9. With ECG-LVH, the absolute risk of developing HF 6.87% compared with 2.69% for absence of ECG-LVH with a relative risk of 2.55. The ability of the model to predict HF was better with MRI-LVH (C-index 0.871, 95% CI 0.842 to 0.899) than with ECG-LVH (C-index 0.860, 95% CI 0.833 to 0.888) (p<0.0001). ECG-LVH and MRI-LVH are predictive of HF. Substituting MRI-LVH for ECG-LVH improves the predictive ability of a model similar to the FHFRS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.

PubMed

Su, Jiandong; Barbera, Lisa; Sutradhar, Rinku

2015-06-01

Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status. © The Author(s) 2015.

Two baselines are better than one: Improving the reliability of computerized testing in sports neuropsychology.

PubMed

Bruce, Jared; Echemendia, Ruben; Tangeman, Lindy; Meeuwisse, Willem; Comper, Paul; Hutchison, Michael; Aubry, Mark

2016-01-01

Computerized neuropsychological tests are frequently used to assist in return-to-play decisions following sports concussion. However, due to concerns about test reliability, the Centers for Disease Control and Prevention recommends yearly baseline testing. The standard practice that has developed in baseline/postinjury comparisons is to examine the difference between the most recent baseline test and postconcussion performance. Drawing from classical test theory, the present study investigated whether temporal stability could be improved by taking an alternate approach that uses the aggregate of 2 baselines to more accurately estimate baseline cognitive ability. One hundred fifteen English-speaking professional hockey players with 3 consecutive Immediate Postconcussion Assessment and Testing (ImPACT) baseline tests were extracted from a clinical program evaluation database overseen by the National Hockey League and National Hockey League Players' Association. The temporal stability of ImPACT composite scores was significantly increased by aggregating test performance during Sessions 1 and 2 to predict performance during Session 3. Using this approach, the 2-factor Memory (r = .72) and Speed (r = .79) composites of ImPACT showed acceptable long-term reliability. Using the aggregate of 2 baseline scores significantly improves temporal stability and allows for more accurate predictions of cognitive change following concussion. Clinicians are encouraged to estimate baseline abilities by taking into account all of an athlete's previous baseline scores.

Changes in ventromedial prefrontal and insular cortex support the development of metamemory from childhood into adolescence.

PubMed

Fandakova, Yana; Selmeczy, Diana; Leckey, Sarah; Grimm, Kevin J; Wendelken, Carter; Bunge, Silvia A; Ghetti, Simona

2017-07-18

Metamemory monitoring, or the ability to introspect on the accuracy of one's memories, improves considerably during childhood, but the underlying neural changes and implications for intellectual development are largely unknown. The present study examined whether cortical changes in key brain areas hypothesized to support metacognition contribute to the development of metamemory monitoring from late childhood into early adolescence. Metamemory monitoring was assessed among 7- to 12-y-old children ( n = 145) and adults ( n = 31). Children returned for up to two additional assessments at 8 to 14 y of age ( n = 120) and at 9 to 15 y of age ( n = 107) ( n = 347 longitudinal scans). Results showed that metamemory monitoring continues to improve from childhood into adolescence. More pronounced cortical thinning in the anterior insula and a greater increase in the thickness of the ventromedial prefrontal cortex over the three assessment points predicted these improvements. Thus, performance benefits are linked to the unique patterns of regional cortical change during development. Metamemory monitoring at the first time point predicted intelligence at the third time point and vice versa, suggesting parallel development of these abilities and their reciprocal influence. Together, these results provide insights into the neuroanatomical correlates supporting the development of the capacity to self-reflect, and highlight the role of this capacity for general intellectual development.

Baseline Performance Predicts tDCS-Mediated Improvements in Language Symptoms in Primary Progressive Aphasia

PubMed Central

McConathey, Eric M.; White, Nicole C.; Gervits, Felix; Ash, Sherry; Coslett, H. Branch; Grossman, Murray; Hamilton, Roy H.

2017-01-01

Primary Progressive Aphasia (PPA) is a neurodegenerative condition characterized by insidious irreversible loss of language abilities. Prior studies suggest that transcranial direct current stimulation (tDCS) directed toward language areas of the brain may help to ameliorate symptoms of PPA. In the present sham-controlled study, we examined whether tDCS could be used to enhance language abilities (e.g., picture naming) in individuals with PPA variants primarily characterized by difficulties with speech production (non-fluent and logopenic). Participants were recruited from the Penn Frontotemporal Dementia Center to receive 10 days of both real and sham tDCS (counter-balanced, full-crossover design; participants were naïve to stimulation condition). A battery of language tests was administered at baseline, immediately post-tDCS (real and sham), and 6 weeks and 12 weeks following stimulation. When we accounted for individuals’ baseline performance, our analyses demonstrated a stratification of tDCS effects. Individuals who performed worse at baseline showed tDCS-related improvements in global language performance, grammatical comprehension and semantic processing. Individuals who performed better at baseline showed a slight tDCS-related benefit on our speech repetition metric. Real tDCS may improve language performance in some individuals with PPA. Severity of deficits at baseline may be an important factor in predicting which patients will respond positively to language-targeted tDCS therapies. Clinicaltrials.gov ID: NCT02928848 PMID:28713256

Changes in ventromedial prefrontal and insular cortex support the development of metamemory from childhood into adolescence

PubMed Central

Selmeczy, Diana; Leckey, Sarah; Grimm, Kevin J.; Wendelken, Carter; Bunge, Silvia A.; Ghetti, Simona

2017-01-01

Metamemory monitoring, or the ability to introspect on the accuracy of one’s memories, improves considerably during childhood, but the underlying neural changes and implications for intellectual development are largely unknown. The present study examined whether cortical changes in key brain areas hypothesized to support metacognition contribute to the development of metamemory monitoring from late childhood into early adolescence. Metamemory monitoring was assessed among 7- to 12-y-old children (n = 145) and adults (n = 31). Children returned for up to two additional assessments at 8 to 14 y of age (n = 120) and at 9 to 15 y of age (n = 107) (n = 347 longitudinal scans). Results showed that metamemory monitoring continues to improve from childhood into adolescence. More pronounced cortical thinning in the anterior insula and a greater increase in the thickness of the ventromedial prefrontal cortex over the three assessment points predicted these improvements. Thus, performance benefits are linked to the unique patterns of regional cortical change during development. Metamemory monitoring at the first time point predicted intelligence at the third time point and vice versa, suggesting parallel development of these abilities and their reciprocal influence. Together, these results provide insights into the neuroanatomical correlates supporting the development of the capacity to self-reflect, and highlight the role of this capacity for general intellectual development. PMID:28673976

Comparing Mammography Abnormality Features and Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

PubMed Central

Burnside, Elizabeth S.; Liu, Jie; Wu, Yirong; Onitilo, Adedayo A.; McCarty, Catherine; Page, C. David; Peissig, Peggy; Trentham-Dietz, Amy; Kitchner, Terrie; Fan, Jun; Yuan, Ming

2015-01-01

Rationale and Objectives The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. Materials and Methods Our IRB-approved, HIPAA-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature including: demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to BI-RADS. We developed predictive models using logistic regression to determine the predictive ability of: 1) demographic variables, 2) 10 selected genetic variants, or 3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross validation; used this risk estimate to construct ROC curves; and compared the AUC of each using the DeLong method. Results The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (p=0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; p<0.001) and the genetic model (AUC = .601; p<0.001). Conclusion BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy. PMID:26514439

Universal gestational age effects on cognitive and basic mathematic processing: 2 cohorts in 2 countries.

PubMed

Wolke, Dieter; Strauss, Vicky Yu-Chun; Johnson, Samantha; Gilmore, Camilla; Marlow, Neil; Jaekel, Julia

2015-06-01

To determine whether general cognitive ability, basic mathematic processing, and mathematic attainment are universally affected by gestation at birth, as well as whether mathematic attainment is more strongly associated with cohort-specific factors such as schooling than basic cognitive and mathematical abilities. The Bavarian Longitudinal Study (BLS, 1289 children, 27-41 weeks gestational age [GA]) was used to estimate effects of GA on IQ, basic mathematic processing, and mathematic attainment. These estimations were used to predict IQ, mathematic processing, and mathematic attainment in the EPICure Study (171 children <26 weeks GA). For children born <34 weeks GA, each lower week decreased IQ and mathematic attainment scores by 2.34 (95% CI: -2.99, -1.70) and 2.76 (95% CI: -3.40, -2.11) points, respectively. There were no differences among children born 34-41 weeks GA. Similarly, for children born <36 weeks GA, mathematic processing scores decreased by 1.77 (95% CI: -2.20, -1.34) points with each lower GA week. The prediction function generated using BLS data accurately predicted the effect of GA on IQ and mathematic processing among EPICure children. However, these children had better attainment than predicted by BLS. Prematurity has adverse effects on basic mathematic processing following birth at all gestations <36 weeks and on IQ and mathematic attainment <34 weeks GA. The ability to predict IQ and mathematic processing scores from one cohort to another among children cared for in different eras and countries suggests that universal neurodevelopmental factors may explain the effects of gestation at birth. In contrast, mathematic attainment may be improved by schooling. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Universal Gestational Age Effects on Cognitive and Basic Mathematic Processing: 2 Cohorts in 2 Countries

PubMed Central

Wolke, Dieter; Strauss, Vicky Yu-Chun; Johnson, Samantha; Gilmore, Camilla; Marlow, Neil; Jaekel, Julia

2015-01-01

Objective To determine whether general cognitive ability, basic mathematic processing, and mathematic attainment are universally affected by gestation at birth, as well as whether mathematic attainment is more strongly associated with cohort-specific factors such as schooling than basic cognitive and mathematical abilities. Study design The Bavarian Longitudinal Study (BLS, 1289 children, 27-41 weeks gestational age [GA]) was used to estimate effects of GA on IQ, basic mathematic processing, and mathematic attainment. These estimations were used to predict IQ, mathematic processing, and mathematic attainment in the EPICure Study (171 children <26 weeks GA). Results For children born <34 weeks GA, each lower week decreased IQ and mathematic attainment scores by 2.34 (95% CI: −2.99, −1.70) and 2.76 (95% CI: −3.40, −2.11) points, respectively. There were no differences among children born 34-41 weeks GA. Similarly, for children born <36 weeks GA, mathematic processing scores decreased by 1.77 (95% CI: −2.20, −1.34) points with each lower GA week. The prediction function generated using BLS data accurately predicted the effect of GA on IQ and mathematic processing among EPICure children. However, these children had better attainment than predicted by BLS. Conclusions Prematurity has adverse effects on basic mathematic processing following birth at all gestations <36 weeks and on IQ and mathematic attainment <34 weeks GA. The ability to predict IQ and mathematic processing scores from one cohort to another among children cared for in different eras and countries suggests that universal neurodevelopmental factors may explain the effects of gestation at birth. In contrast, mathematic attainment may be improved by schooling. PMID:25842966

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

PubMed

Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of a High-Lift Multi-Element Airfoil using a Navier-Stokes Code

NASA Technical Reports Server (NTRS)

Whitlock, Mark E.

1995-01-01

A thin-layer Navier-Stokes code, CFL3D, was utilized to compute the flow over a high-lift multi-element airfoil. This study was conducted to improve the prediction of high-lift flowfields using various turbulence models and improved glidding techniques. An overset Chimera grid system is used to model the three element airfoil geometry. The effects of wind tunnel wall modeling, changes to the grid density and distribution, and embedded grids are discussed. Computed pressure and lift coefficients using Spalart-Allmaras, Baldwin-Barth, and Menter's kappa-omega - Shear Stress Transport (SST) turbulence models are compared with experimental data. The ability of CFL3D to predict the effects on lift coefficient due to changes in Reynolds number changes is also discussed.

Two-hour post-challenge glucose is a better predictor of adverse outcome after myocardial infarction than fasting or admission glucose in patients without diabetes.

PubMed

Chattopadhyay, Sudipta; George, Anish; John, Joseph; Sathyapalan, Thozhukat

2018-05-01

We evaluate prevalence of new abnormal glucose tolerance (AGT) in post-MI survivors without known diabetes (DM) if guidelines are followed and compare the ability of admission (APG), fasting (FPG) and 2-h post-load plasma glucose (2h-PG) to predict prognosis. A total of 674 patients were followed up for 4 years for incidence of major adverse cardiovascular events (MACE) of cardiovascular death, non-fatal re-infarction or non-haemorrhagic stroke. Ability of models including APG, FPG and 2h-PG to predict MACE was compared. Of the total, 93-96% of impaired glucose tolerance and 64-75% of DM would be missed with current guidelines. MACE was higher in the upper quartiles of 2h-PG. When 2h-PG and FPG were included simultaneously in models, only 2h-PG predicted MACE (HR 1.12, CI 1.04-1.20, p = 0.0012), all cause mortality (HR 1.17, CI 1.05-1.30, p = 0.0039), cardiovascular mortality (HR 1.17, CI 1.02-1.33, p = 0.0205) and non-fatal MI (HR 1.10, CI 1.01-1.20, p = 0.0291). Adding 2h-PG significantly improved ability of models including FPG (χ 2  = 16.01, df = 1, p = 0.0001) or FPG and APG (χ 2  = 17.36, df = 1, p = 0.000) to predict MACE. Model including 2h-PG only had the lowest Akaike's information criteria and highest Akaike weights suggesting that this was the best in predicting events. Adding 2h-PG to models including FPG or APG with other co-variates yielded continuous net reclassification improvement (NRI) of 0.22 (p = 0.026) and 0.27 (p = 0.005) and categorical NRI of 0.09 (p = 0.032) and 0.12 (p = 0.014), respectively. Adding 2 h-PG to models including only FPG, only APG and both yielded integrated discrimination improvement of 0.012 (p = 0.015), 0.022 (p = 0.001) and 0.013 (p = 0.014), respectively. AGT is under-diagnosed on current guidelines. 2h-PG is a better predictor of prognosis compared to APG and FPG.

Genomic selection in a commercial winter wheat population.

PubMed

He, Sang; Schulthess, Albert Wilhelm; Mirdita, Vilson; Zhao, Yusheng; Korzun, Viktor; Bothe, Reiner; Ebmeyer, Erhard; Reif, Jochen C; Jiang, Yong

2016-03-01

Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Peters, B.; De Dios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Oddsson, L.; Kreutzberg, G.; Zanello, S.;

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

I-TASSER: fully automated protein structure prediction in CASP8.

PubMed

Zhang, Yang

2009-01-01

The I-TASSER algorithm for 3D protein structure prediction was tested in CASP8, with the procedure fully automated in both the Server and Human sections. The quality of the server models is close to that of human ones but the human predictions incorporate more diverse templates from other servers which improve the human predictions in some of the distant homology targets. For the first time, the sequence-based contact predictions from machine learning techniques are found helpful for both template-based modeling (TBM) and template-free modeling (FM). In TBM, although the accuracy of the sequence based contact predictions is on average lower than that from template-based ones, the novel contacts in the sequence-based predictions, which are complementary to the threading templates in the weakly or unaligned regions, are important to improve the global and local packing in these regions. Moreover, the newly developed atomic structural refinement algorithm was tested in CASP8 and found to improve the hydrogen-bonding networks and the overall TM-score, which is mainly due to its ability of removing steric clashes so that the models can be generated from cluster centroids. Nevertheless, one of the major issues of the I-TASSER pipeline is the model selection where the best models could not be appropriately recognized when the correct templates are detected only by the minority of the threading algorithms. There are also problems related with domain-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM-based structure predictions. Copyright 2009 Wiley-Liss, Inc.

Pulse Wave Velocity Predicts the Progression of Blood Pressure and Development of Hypertension in Young Adults.

PubMed

Koivistoinen, Teemu; Lyytikäinen, Leo-Pekka; Aatola, Heikki; Luukkaala, Tiina; Juonala, Markus; Viikari, Jorma; Lehtimäki, Terho; Raitakari, Olli T; Kähönen, Mika; Hutri-Kähönen, Nina

2018-03-01

The aim of the present study was to examine whether pulse wave velocity (PWV) predicts the progression of blood pressure and the development of hypertension in young adults. In addition, we studied whether PWV improves the risk prediction of incident hypertension beyond traditional cardiovascular risk factors. Systolic and diastolic blood pressures were measured in 2007 and 2011 for 1449 Finnish adults (aged 30-45 years). In addition, PWV and other cardiovascular risk factors were measured in 2007. The association between PWV (in 2007) and blood pressure (in 2011) was studied in the whole population (n=1449) and in a normotensive subpopulation (n=1183). The ability of PWV measured in 2007 to predict incident hypertension in 2011 was investigated in the subpopulation (n=1183). PWV measured in 2007 was directly and independently associated with systolic and diastolic blood pressures measured in 2011 ( P <0.001 for both). PWV measured in 2007 was also an independent predictor of incident hypertension in 2011 (odds ratio, 1.96 per 1-SDincrease; 95% confidence interval, 1.51-2.57; P <0.001). The extended prediction model (including PWV) improved the incident hypertension risk prediction beyond traditional cardiovascular risk factors, the area under receiver operating characteristics curve being 0.833 versus 0.809 ( P =0.040), and the continuous net reclassification improvement 59.4% ( P <0.001). These findings suggest that PWV predicts the progression of blood pressure and could provide a valuable tool in hypertension risk prediction in young adults. © 2018 American Heart Association, Inc.

External validation of preexisting first trimester preeclampsia prediction models.

PubMed

Allen, Rebecca E; Zamora, Javier; Arroyo-Manzano, David; Velauthar, Luxmilar; Allotey, John; Thangaratinam, Shakila; Aquilina, Joseph

2017-10-01

To validate the increasing number of prognostic models being developed for preeclampsia using our own prospective study. A systematic review of literature that assessed biomarkers, uterine artery Doppler and maternal characteristics in the first trimester for the prediction of preeclampsia was performed and models selected based on predefined criteria. Validation was performed by applying the regression coefficients that were published in the different derivation studies to our cohort. We assessed the models discrimination ability and calibration. Twenty models were identified for validation. The discrimination ability observed in derivation studies (Area Under the Curves) ranged from 0.70 to 0.96 when these models were validated against the validation cohort, these AUC varied importantly, ranging from 0.504 to 0.833. Comparing Area Under the Curves obtained in the derivation study to those in the validation cohort we found statistically significant differences in several studies. There currently isn't a definitive prediction model with adequate ability to discriminate for preeclampsia, which performs as well when applied to a different population and can differentiate well between the highest and lowest risk groups within the tested population. The pre-existing large number of models limits the value of further model development and future research should be focussed on further attempts to validate existing models and assessing whether implementation of these improves patient care. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

PubMed

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-05-12

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.

Biological effects: Marine mammals and sea turtles (chapter 14). Book chapter

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

Haebler, R.

1994-01-01

All spills are different, varying in type and amount of oil spilled, species exposed, and geographic and atmospheric conditions. It is important to understand as much as possible about both the natural history and characteristics of various species and the specific effects oil has on wildlife. Doing so improves the ability to extrapolate from one spill to another and improves prediction of types and severity of effects to wildlife. This chapter presents an overview of the biological effects of oil on marine mammals and sea turtles.

Narrative Fiction and Expository Nonfiction Differentially Predict Verbal Ability

ERIC Educational Resources Information Center

Mar, Raymond A.; Rain, Marina

2015-01-01

Although reading is known to be an important contributor to language abilities, it is not yet well established whether different text genres are uniquely associated with verbal abilities. We examined how exposure to narrative fiction and expository nonfiction predict language ability among university students. Exposure was measured both with…

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

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

2014-07-01

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

Towards Improving Sea Ice Predictabiity: Evaluating Climate Models Against Satellite Sea Ice Observations

NASA Astrophysics Data System (ADS)

Stroeve, J. C.

2014-12-01

The last four decades have seen a remarkable decline in the spatial extent of the Arctic sea ice cover, presenting both challenges and opportunities to Arctic residents, government agencies and industry. After the record low extent in September 2007 effort has increased to improve seasonal, decadal-scale and longer-term predictions of the sea ice cover. Coupled global climate models (GCMs) consistently project that if greenhouse gas concentrations continue to rise, the eventual outcome will be a complete loss of the multiyear ice cover. However, confidence in these projections depends o HoHoweon the models ability to reproduce features of the present-day climate. Comparison between models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5) and observations of sea ice extent and thickness show that (1) historical trends from 85% of the model ensemble members remain smaller than observed, and (2) spatial patterns of sea ice thickness are poorly represented in most models. Part of the explanation lies with a failure of models to represent details of the mean atmospheric circulation pattern that governs the transport and spatial distribution of sea ice. These results raise concerns regarding the ability of CMIP5 models to realistically represent the processes driving the decline of Arctic sea ice and to project the timing of when a seasonally ice-free Arctic may be realized. On shorter time-scales, seasonal sea ice prediction has been challenged to predict the sea ice extent from Arctic conditions a few months to a year in advance. Efforts such as the Sea Ice Outlook (SIO) project, originally organized through the Study of Environmental Change (SEARCH) and now managed by the Sea Ice Prediction Network project (SIPN) synthesize predictions of the September sea ice extent based on a variety of approaches, including heuristic, statistical and dynamical modeling. Analysis of SIO contributions reveals that when the September sea ice extent is near the long-term trend, contributions tend to be accurate. Years when the observed extent departs from the trend have proven harder to predict. Predictability skill does not appear to be more accurate for dynamical models over statistical ones, nor is there a measurable improvement in skill as the summer progresses.

Persistence of soil organic matter as an ecosystem property.

PubMed

Schmidt, Michael W I; Torn, Margaret S; Abiven, Samuel; Dittmar, Thorsten; Guggenberger, Georg; Janssens, Ivan A; Kleber, Markus; Kögel-Knabner, Ingrid; Lehmann, Johannes; Manning, David A C; Nannipieri, Paolo; Rasse, Daniel P; Weiner, Steve; Trumbore, Susan E

2011-10-05

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily--and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Persistence of soil organic matter as an ecosystem property

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

Schmidt, M.W.; Torn, M. S.; Abiven, S.

2011-08-15

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily—and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.

Landmark lecture on cardiac intensive care and anaesthesia: continuum and conundrums.

PubMed

Laussen, Peter C

2017-12-01

Cardiac anesthesia and critical care provide an important continuum of care for patients with congenital heart disease. Clinicians in both areas work in complex environments in which the interactions between humans and technology is critical. Understanding our contributions to outcomes (modifiable risk) and our ability to perceive and predict an evolving clinical state (low failure-to-predict rate) are important performance metrics. Improved methods for capturing continuous physiologic signals will allow for new and interactive approaches to data visualization, and for sophisticated and iterative data modeling that will help define a patient's phenotype and response to treatment (precision physiology).

Comparison and relationship of thyroid hormones, IL-6, IL-10 and albumin as mortality predictors in case-mix critically ill patients.

PubMed

Quispe E, Álvaro; Li, Xiang-Min; Yi, Hong

2016-05-01

To compare the ability of thyroid hormones, IL-6, IL-10, and albumin to predict mortality, and to assess their relationship in case-mix acute critically ill patients. APACHE II scores and serum thyroid hormones (FT3, FT4, and TSH), IL-6, IL-10, and albumin were obtained at EICU admission for 79 cases of mix acute critically ill patients without previous history of thyroid disease. Patients were followed for 28 days with patient's death as the primary outcome. All mean values were compared, correlations assessed with Pearson' test, and mortality prediction assessed by multivariate logistic regression and ROC. Non survivors were older, with higher APACHE II score (p=0.000), IL-6 (p<0.05), IL-10 (p=0.000) levels, and lower albumin (p=0.000) levels compared to survivors at 28 days. IL-6 and IL-10 had significant negative correlation with albumin (p=0.001) and FT3 (p ⩽ 0.05) respectively, while low albumin had a direct correlation with FT3 (p<0.05). In the mortality prediction assessment, IL-10, albumin and APACHE II were independent morality predictors and showed to have a good (0.70-0.79) AUC-ROC (p<0.05). Despite that the entire cohort showed low FT3 serum levels (p=0.000), there was not statistical difference between survivors and non-survivors; neither showed any significance as mortality predictor. IL-6 and IL-10 are correlated with Low FT3 and hypoalbuminemia. Thyroid hormones assessed at EICU admission did not have any predictive value in our study. And finally, high levels of IL-6 and IL-10 in conjunction with albumin could improve our ability to evaluate disease's severity and predict mortality in the critically ill patients. When use in combination with APACHE II scores, our model showed improved mortality prediction. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Work-related stress, education and work ability among hospital nurses.

PubMed

Golubic, Rajna; Milosevic, Milan; Knezevic, Bojana; Mustajbegovic, Jadranka

2009-10-01

This paper is a report of a study conducted to determine which occupational stressors are present in nurses' working environment; to describe and compare occupational stress between two educational groups of nurses; to estimate which stressors and to what extent predict nurses' work ability; and to determine if educational level predicts nurses' work ability. Nurses' occupational stress adversely affects their health and nursing quality. Higher educational level has been shown to have positive effects on the preservation of good work ability. A cross-sectional study was conducted in 2006-2007. Questionnaires were distributed to a convenience sample of 1392 (59%) nurses employed at four university hospitals in Croatia (n = 2364). The response rate was 78% (n = 1086). Data were collected using the Occupational Stress Assessment Questionnaire and Work Ability Index Questionnaire. We identified six major groups of occupational stressors: 'Organization of work and financial issues', 'public criticism', 'hazards at workplace', 'interpersonal conflicts at workplace', 'shift work' and 'professional and intellectual demands'. Nurses with secondary school qualifications perceived Hazards at workplace and Shift work as statistically significantly more stressful than nurses a with college degree. Predictors statistically significantly related with low work ability were: Organization of work and financial issues (odds ratio = 1.69, 95% confidence interval 122-236), lower educational level (odds ratio = 1.69, 95% confidence interval 122-236) and older age (odds ratio = 1.07, 95% confidence interval 1.05-1.09). Hospital managers should develop strategies to address and improve the quality of working conditions for nurses in Croatian hospitals. Providing educational and career prospects can contribute to decreasing nurses' occupational stress levels, thus maintaining their work ability.

Embedded CMOS basecalling for nanopore DNA sequencing.

PubMed

Chengjie Wang; Junli Zheng; Magierowski, Sebastian; Ghafar-Zadeh, Ebrahim

2016-08-01

DNA sequencing based on nanopore sensors is now entering the marketplace. The ability to interface this technology to established CMOS microelectronics promises significant improvements in functionality and miniaturization. Among the key functions to benefit from this interface will be basecalling, the conversion of raw electronic molecular signatures to nucleotide sequence predictions. This paper presents the design and performance potential of custom CMOS base-callers embedded alongside nanopore sensors. A basecalliing architecture implemented in 32-nm technology is discussed with the ability to process the equivalent of 20 human genomes per day in real-time at a power density of 5 W/cm2 assuming a 3-mer nanopore sensor.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Establishment and validation of the scoring system for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.

PubMed

Liu, Wen; Cheng, Ruochuan; Ma, Yunhai; Wang, Dan; Su, Yanjun; Diao, Chang; Zhang, Jianming; Qian, Jun; Liu, Jin

2018-05-03

Early preoperative diagnosis of central lymph node metastasis (CNM) is crucial to improve survival rates among patients with papillary thyroid carcinoma (PTC). Here, we analyzed clinical data from 2862 PTC patients and developed a scoring system using multivariable logistic regression and testified by the validation group. The predictive diagnostic effectiveness of the scoring system was evaluated based on consistency, discrimination ability, and accuracy. The scoring system considered seven variables: gender, age, tumor size, microcalcification, resistance index >0.7, multiple nodular lesions, and extrathyroid extension. The area under the receiver operating characteristic curve (AUC) was 0.742, indicating a good discrimination. Using 5 points as a diagnostic threshold, the validation results for validation group had an AUC of 0.758, indicating good discrimination and consistency in the scoring system. The sensitivity of this predictive model for preoperative diagnosis of CNM was 4 times higher than a direct ultrasound diagnosis. These data indicate that the CNM prediction model would improve preoperative diagnostic sensitivity for CNM in patients with papillary thyroid carcinoma.

Prospective memory, retrospective memory, and individual differences in cognitive abilities, personality, and psychopathology.

PubMed

Uttl, Bob; White, Carmela A; Cnudde, Kelsey; Grant, Laura M

2018-01-01

Although individual differences in processing speed, working memory, intelligence, and other cognitive functions were found to explain individual differences in retrospective memory (RetM), much less is known about their relationship with prospective memory (ProM). Moreover, the studies that investigated the relationship between ProM and cognitive functions arrived to contradictory conclusions. The relationship between ProM, personality, and psychopathology is similarly unsettled. Meta-analytic reviews of the relationships of ProM with aging and personality suggest that the contradictory findings may be due to widespread methodological problems plaguing ProM research including the prevalent use of inefficient, unreliable binary measures; widespread ceiling effects; failure to distinguish between various ProM subdomains (e.g., episodic ProM versus vigilance/monitoring); various confounds; and, importantly, small sample sizes, resulting in insufficient statistical power. Accordingly, in a large scale study with nearly 1,200 participants, we investigated the relationship between episodic event-cued ProM, episodic RetM, and fundamental cognitive functions including intelligence, personality, and psychopathology, using reliable continuous measures of episodic event-cued ProM. Our findings show that (a) continuous measures of episodic event-cued ProM were much more reliable than binary measures, (b) episodic event-cued ProM was associated with measures of processing speed, working memory, crystallized and fluid intelligence, as well as RetM, and that such associations were similar for ProM and RetM, (c) personality factors did not improve prediction of neither ProM nor RetM beyond the variance predicted by cognitive ability, (d) symptoms of psychopathology did not improve the prediction of ProM although they slightly improved the prediction of RetM, and (e) participants' sex was not associated with ProM but showed small correlations with RetM. In addition to advancing our theoretical understanding of ProM, our findings highlight the need to avoid common pitfalls plaguing ProM research.

The Added Value of Collecting Information on Pain Experience When Predicting Time on Benefits for Injured Workers with Back Pain.

PubMed

Steenstra, Ivan A; Franche, Renée-Louise; Furlan, Andrea D; Amick, Ben; Hogg-Johnson, Sheilah

2016-06-01

Objectives Some injured workers with work-related, compensated back pain experience a troubling course in return to work. A prediction tool was developed in an earlier study, using administrative data only. This study explored the added value of worker reported data in identifying those workers with back pain at higher risk of being on benefits for a longer period of time. Methods This was a cohort study of workers with compensated back pain in 2005 in Ontario. Workplace Safety and Insurance Board (WSIB) data was used. As well, we examined the added value of patient-reported prognostic factors obtained from a prospective cohort study. Improvement of model fit was determined by comparing area under the curve (AUC) statistics. The outcome measure was time on benefits during a first workers' compensation claim for back pain. Follow-up was 2 years. Results Among 1442 workers with WSIB data still on full benefits at 4 weeks, 113 were also part of the prospective cohort study. Model fit of an established rule in the smaller dataset of 113 workers was comparable to the fit previously established in the larger dataset. Adding worker rating of pain at baseline improved the rule substantially (AUC = 0.80, 95 % CI 0.68, 0.91 compared to benefit status at 180 days, AUC = 0.88, 95 % CI 0.74, 1.00 compared to benefits status at 360 days). Conclusion Although data routinely collected by workers' compensation boards show some ability to predict prolonged time on benefits, adding information on experienced pain reported by the worker improves the predictive ability of the model from 'fairly good' to 'good'. In this study, a combination of prognostic factors, reported by multiple stakeholders, including the worker, could identify those at high risk of extended duration on disability benefits and in potentially in need of additional support at the individual level.

Effectiveness of the CANRISK tool in the identification of dysglycemia in First Nations and Métis in Canada

PubMed Central

Gina, Agarwal; Ying, Jiang; Susan, Rogers Van Katwyk; Chantal, Lemieux; Heather, Orpana; Yang, Mao; Brandan, Hanley; Karen, Davis; Laurel, Leuschen; Howard, Morrison

2018-01-01

Abstract Introduction: First Nations/Métis populations develop diabetes earlier and at higher rates than other Canadians. The Canadian diabetes risk questionnaire (CANRISK) was developed as a diabetes screening tool for Canadians aged 40 years or over. The primary aim of this paper is to assess the effectiveness of the existing CANRISK tool and risk scores in detecting dysglycemia in First Nations/Métis participants, including among those under the age of 40. A secondary aim was to determine whether alternative waist circumference (WC) and body mass index (BMI) cut-off points improved the predictive ability of logistic regression models using CANRISK variables to predict dysglycemia. Methods: Information from a self-administered CANRISK questionnaire, anthropometric measurements, and results of a standard oral glucose tolerance test (OGTT) were collected from First Nations and Métis participants (n = 1479). Sensitivity and specificity of CANRISK scores using published risk score cut-off points were calculated. Logistic regression was conducted with alternative ethnicity-specific BMI and WC cut-off points to predict dysglycemia using CANRISK variables. Results: Compared with OGTT results, using a CANRISK score cut-off point of 33, the sensitivity and specificity of CANRISK was 68% and 63% among individuals aged 40 or over; it was 27% and 87%, respectively among those under 40. Using a lower cut-off point of 21, the sensitivity for individuals under 40 improved to 77% with a specificity of 44%. Though specificity at this threshold was low, the higher level of sensitivity reflects the importance of the identification of high risk individuals in this population. Despite altered cut-off points of BMI and WC, logistic regression models demonstrated similar predictive ability. Conclusion: CANRISK functioned well as a preliminary step for diabetes screening in a broad age range of First Nations and Métis in Canada, with an adjusted CANRISK cutoff point for individuals under 40, and with no incremental improvement from using alternative BMI/WC cut-off points. PMID:29443485

Prospective memory, retrospective memory, and individual differences in cognitive abilities, personality, and psychopathology

PubMed Central

White, Carmela A.; Cnudde, Kelsey; Grant, Laura M.

2018-01-01

Although individual differences in processing speed, working memory, intelligence, and other cognitive functions were found to explain individual differences in retrospective memory (RetM), much less is known about their relationship with prospective memory (ProM). Moreover, the studies that investigated the relationship between ProM and cognitive functions arrived to contradictory conclusions. The relationship between ProM, personality, and psychopathology is similarly unsettled. Meta-analytic reviews of the relationships of ProM with aging and personality suggest that the contradictory findings may be due to widespread methodological problems plaguing ProM research including the prevalent use of inefficient, unreliable binary measures; widespread ceiling effects; failure to distinguish between various ProM subdomains (e.g., episodic ProM versus vigilance/monitoring); various confounds; and, importantly, small sample sizes, resulting in insufficient statistical power. Accordingly, in a large scale study with nearly 1,200 participants, we investigated the relationship between episodic event-cued ProM, episodic RetM, and fundamental cognitive functions including intelligence, personality, and psychopathology, using reliable continuous measures of episodic event-cued ProM. Our findings show that (a) continuous measures of episodic event-cued ProM were much more reliable than binary measures, (b) episodic event-cued ProM was associated with measures of processing speed, working memory, crystallized and fluid intelligence, as well as RetM, and that such associations were similar for ProM and RetM, (c) personality factors did not improve prediction of neither ProM nor RetM beyond the variance predicted by cognitive ability, (d) symptoms of psychopathology did not improve the prediction of ProM although they slightly improved the prediction of RetM, and (e) participants' sex was not associated with ProM but showed small correlations with RetM. In addition to advancing our theoretical understanding of ProM, our findings highlight the need to avoid common pitfalls plaguing ProM research. PMID:29584735

Plaque Structural Stress Estimations Improve Prediction of Future Major Adverse Cardiovascular Events After Intracoronary Imaging.

PubMed

Brown, Adam J; Teng, Zhongzhao; Calvert, Patrick A; Rajani, Nikil K; Hennessy, Orla; Nerlekar, Nitesh; Obaid, Daniel R; Costopoulos, Charis; Huang, Yuan; Hoole, Stephen P; Goddard, Martin; West, Nick E J; Gillard, Jonathan H; Bennett, Martin R

2016-06-01

Although plaque rupture is responsible for most myocardial infarctions, few high-risk plaques identified by intracoronary imaging actually result in future major adverse cardiovascular events (MACE). Nonimaging markers of individual plaque behavior are therefore required. Rupture occurs when plaque structural stress (PSS) exceeds material strength. We therefore assessed whether PSS could predict future MACE in high-risk nonculprit lesions identified on virtual-histology intravascular ultrasound. Baseline nonculprit lesion features associated with MACE during long-term follow-up (median: 1115 days) were determined in 170 patients undergoing 3-vessel virtual-histology intravascular ultrasound. MACE was associated with plaque burden ≥70% (hazard ratio: 8.6; 95% confidence interval, 2.5-30.6; P<0.001) and minimal luminal area ≤4 mm(2) (hazard ratio: 6.6; 95% confidence interval, 2.1-20.1; P=0.036), although absolute event rates for high-risk lesions remained <10%. PSS derived from virtual-histology intravascular ultrasound was subsequently estimated in nonculprit lesions responsible for MACE (n=22) versus matched control lesions (n=22). PSS showed marked heterogeneity across and between similar lesions but was significantly increased in MACE lesions at high-risk regions, including plaque burden ≥70% (13.9±11.5 versus 10.2±4.7; P<0.001) and thin-cap fibroatheroma (14.0±8.9 versus 11.6±4.5; P=0.02). Furthermore, PSS improved the ability of virtual-histology intravascular ultrasound to predict MACE in plaques with plaque burden ≥70% (adjusted log-rank, P=0.003) and minimal luminal area ≤4 mm(2) (P=0.002). Plaques responsible for MACE had larger superficial calcium inclusions, which acted to increase PSS (P<0.05). Baseline PSS is increased in plaques responsible for MACE and improves the ability of intracoronary imaging to predict events. Biomechanical modeling may complement plaque imaging for risk stratification of coronary nonculprit lesions. © 2016 American Heart Association, Inc.

Multiple model analysis with discriminatory data collection (MMA-DDC): A new method for improving measurement selection

NASA Astrophysics Data System (ADS)

Kikuchi, C.; Ferre, P. A.; Vrugt, J. A.

2011-12-01

Hydrologic models are developed, tested, and refined based on the ability of those models to explain available hydrologic data. The optimization of model performance based upon mismatch between model outputs and real world observations has been extensively studied. However, identification of plausible models is sensitive not only to the models themselves - including model structure and model parameters - but also to the location, timing, type, and number of observations used in model calibration. Therefore, careful selection of hydrologic observations has the potential to significantly improve the performance of hydrologic models. In this research, we seek to reduce prediction uncertainty through optimization of the data collection process. A new tool - multiple model analysis with discriminatory data collection (MMA-DDC) - was developed to address this challenge. In this approach, multiple hydrologic models are developed and treated as competing hypotheses. Potential new data are then evaluated on their ability to discriminate between competing hypotheses. MMA-DDC is well-suited for use in recursive mode, in which new observations are continuously used in the optimization of subsequent observations. This new approach was applied to a synthetic solute transport experiment, in which ranges of parameter values constitute the multiple hydrologic models, and model predictions are calculated using likelihood-weighted model averaging. MMA-DDC was used to determine the optimal location, timing, number, and type of new observations. From comparison with an exhaustive search of all possible observation sequences, we find that MMA-DDC consistently selects observations which lead to the highest reduction in model prediction uncertainty. We conclude that using MMA-DDC to evaluate potential observations may significantly improve the performance of hydrologic models while reducing the cost associated with collecting new data.

Status and Preliminary Evaluation for Chinese Re-Analysis Datasets

NASA Astrophysics Data System (ADS)

bin, zhao; chunxiang, shi; tianbao, zhao; dong, si; jingwei, liu

2016-04-01

Based on operational T639L60 spectral model, combined with Hybird_GSI assimilation system by using meteorological observations including radiosondes, buoyes, satellites el al., a set of Chinese Re-Analysis (CRA) datasets is developing by Chinese National Meteorological Information Center (NMIC) of Chinese Meteorological Administration (CMA). The datasets are run at 30km (0.28°latitude / longitude) resolution which holds higher resolution than most of the existing reanalysis dataset. The reanalysis is done in an effort to enhance the accuracy of historical synoptic analysis and aid to find out detailed investigation of various weather and climate systems. The current status of reanalysis is in a stage of preliminary experimental analysis. One-year forecast data during Jun 2013 and May 2014 has been simulated and used in synoptic and climate evaluation. We first examine the model prediction ability with the new assimilation system, and find out that it represents significant improvement in Northern and Southern hemisphere, due to addition of new satellite data, compared with operational T639L60 model, the effect of upper-level prediction is improved obviously and overall prediction stability is enhanced. In climatological analysis, compared with ERA-40, NCEP/NCAR and NCEP/DOE reanalyses, the results show that surface temperature simulates a bit lower in land and higher over ocean, 850-hPa specific humidity reflects weakened anomaly and the zonal wind value anomaly is focus on equatorial tropics. Meanwhile, the reanalysis dataset shows good ability for various climate index, such as subtropical high index, ESMI (East-Asia subtropical Summer Monsoon Index) et al., especially for the Indian and western North Pacific monsoon index. Latter we will further improve the assimilation system and dynamical simulating performance, and obtain 40-years (1979-2018) reanalysis datasets. It will provide a more comprehensive analysis for synoptic and climate diagnosis.

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

PubMed Central

Siddharthan, Rahul

2008-01-01

PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. First, PhyloGibbs-MP can localise predictions to small, undetermined regions of a large input sequence, thus effectively predicting cis-regulatory modules (CRMs) ab initio while simultaneously predicting binding sites in those modules—tasks that are usually done by two separate programs. PhyloGibbs-MP's performance at such ab initio CRM prediction is comparable with or superior to dedicated module-prediction software that use prior knowledge of previously characterised transcription factors. Second, PhyloGibbs-MP can predict motifs that differentiate between two (or more) different groups of regulatory regions, that is, motifs that occur preferentially in one group over the others. While other “discriminative motif-finders” have been published in the literature, PhyloGibbs-MP's implementation has some unique features and flexibility. Benchmarks on synthetic and actual genomic data show that this algorithm is successful at enhancing predictions of differentiating sites and suppressing predictions of common sites and compares with or outperforms other discriminative motif-finders on actual genomic data. Additional enhancements include significant performance and speed improvements, the ability to use “informative priors” on known transcription factors, and the ability to output annotations in a format that can be visualised with the Generic Genome Browser. In stand-alone motif-finding, PhyloGibbs-MP remains competitive, outperforming PhyloGibbs-1.0 and other programs on benchmark data. PMID:18769735

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition

PubMed Central

Elias, Ani A.; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-01

Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance. PMID:29358232

Improving Genomic Prediction in Cassava Field Experiments by Accounting for Interplot Competition.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-03-02

Plants competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava ( Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur at two levels: (i) the genotypic level, which we call interclonal, and (ii) the plot level irrespective of the type of genotype, which we call interplot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection (GS) model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that <10% increase in accuracy was achieved with GS-interclonal competition model, but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations, we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance. Copyright © 2018 Elias et al.

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

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

Huang, Shengzhi; Ming, Bo; Huang, Qiang

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less

Exercise program-induced mood improvement and improved eating in severely obese adults.

PubMed

Annesi, James J; Tennant, Gisèle A

Using a practical setting, this study aimed to test exercise and nutrition interventions' effects on negative mood, self-regulation, and self-efficacy to control eating; and to assess the ability of mood change to predict changes in eating behavior, while accounting for changes in self-regulation and self-efficacy. Severely obese adults participated in a cognitive-behavioral exercise support treatment paired with either nutrition education (n = 140) or cognitive-behavioral methods applied to improved eating (n = 146). They were assessed on measures of overall negative mood, self-regulatory skill usage, self-efficacy to control eating when negative moods are present, and fruit and vegetable consumption at baseline and Week 26. Significant improvements in each psychosocial variable and fruit and vegetable intake were found. Improved mood significantly predicted fruit and vegetable consumption change, R2 = 0.12, P < 0.001. Entry of changes in self-regulation and self-efficacy into the multiple regression equation significantly strengthened the variance explained, R2 = 0.18, P < 0.001. Findings suggest that exercise-induced improvements in mood improve eating behaviors, with increases in self-regulation and self-efficacy adding to this effect.

Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm.

PubMed

Visentin, G; Penasa, M; Gottardo, P; Cassandro, M; De Marchi, M

2016-10-01

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm(-1) was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Changes In Actual And Perceived Physical Abilities In Clinically Obese Children: A 9-Month Multi-Component Intervention Study

PubMed Central

Morano, Milena; Colella, Dario; Rutigliano, Irene; Fiore, Pietro; Pettoello-Mantovani, Massimo; Campanozzi, Angelo

2012-01-01

Objectives (1) To examine relationships among changes in physical activity, physical fitness and some psychosocial determinants of activity behavior in a clinical sample of obese children involved in a multi-component program; (2) to investigate the causal relationship over time between physical activity and one of its strongest correlates (i.e. perceived physical ability). Methods Self-reported physical activity and health-related fitness tests were administered before and after a 9-month intervention in 24 boys and 20 girls aged 8 to 11 years. Individuals’ perceptions of strength, speed and agility were assessed using the Perceived Physical Ability Scale, while body image was measured using Collins’ Child Figure Drawings. Results Findings showed that body mass index, physical activity, performances on throwing and weight-bearing tasks, perceived physical ability and body image significantly improved after treatment among obese children. Gender differences were found in the correlational analyses, showing a link between actual and perceived physical abilities in boys, but not in girls. For the specific measurement interval of this study, perception of physical ability was an antecedent and not a potential consequence of physical activity. Conclusions Results indicate that a multi-component activity program not based merely on a dose-effect approach enhances adherence of the participants and has the potential to increase the lifelong exercise skills of obese children. Rather than focusing entirely on diet and weight loss, findings support the inclusion of interventions directed toward improving perceived physical ability that is predictive of subsequent physical activity. PMID:23239985

Predicting Tropical Cyclogenesis with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis(2008)

NASA Technical Reports Server (NTRS)

Shen, B.-W.; Tao, W.-K.; Lau, W. K.; Atlas, R.

2010-01-01

Very severe cyclonic storm Nargis devastated Burma (Myanmar) in May 2008, caused tremendous damage and numerous fatalities, and became one of the 10 deadliest tropical cyclones (TCs) of all time. To increase the warning time in order to save lives and reduce economic damage, it is important to extend the lead time in the prediction of TCs like Nargis. As recent advances in high-resolution global models and supercomputing technology have shown the potential for improving TC track and intensity forecasts, the ability of a global mesoscale model to predict TC genesis in the Indian Ocean is examined in this study with the aim of improving simulations of TC climate. High-resolution global simulations with real data show that the initial formation and intensity variations of TC Nargis can be realistically predicted up to 5 days in advance. Preliminary analysis suggests that improved representations of the following environmental conditions and their hierarchical multiscale interactions were the key to achieving this lead time: (1) a westerly wind burst and equatorial trough, (2) an enhanced monsoon circulation with a zero wind shear line, (3) good upper-level outflow with anti-cyclonic wind shear between 200 and 850 hPa, and (4) low-level moisture convergence.

Can the Theory of Planned Behavior Predict Dietary Intention and Future Dieting in an Ethnically Diverse Sample of Overweight and Obese Veterans Attending Medical Clinics?

PubMed Central

Lash, Denise N.; Smith, Jane Ellen; Rinehart, Jenny K.

2016-01-01

Obesity has become a world-wide epidemic; in the United States (U.S.) approximately two-thirds of adults are classified as overweight or obese. Military veterans’ numbers are even higher, with 77% of retired or discharged U.S. veterans falling in these weight categories. One of the most common methods of changing one’s weight is through dieting, yet little is known regarding the factors that facilitate successful dieting behavior. The current investigation tested the Theory of Planned Behavior’s (TPB) ability to predict dietary intention and future dieting in a sample of 84 overweight and obese patients attending medical clinics at a Veterans Affairs Hospital in the southwestern part of the U.S. Participants primarily were male (92%) and ethnic/racial minorities (58%). Perceived need and anticipated regret were added to the standard TPB model. While the TPB predicted dietary intention, it did not significantly account for improved dietary behaviors. Anticipated regret significantly enhanced the basic TPB’s ability to predict intention to diet, while perceived need did not. These findings highlight the difficulty in predicting sustained change in a complex behavior such as dieting to lose weight. The need for more work with older, overweight/obese medical patients attending veterans’ facilities is stressed, as is the need for such work with male patients and ethnic minorities in particular. PMID:26792774

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

PubMed Central

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; Nery, Joseph R.; Castanon, Rosa G.; Lee, Ah Young; Shen, Yin; Visel, Axel; Pennacchio, Len A.; Ren, Bing; Ecker, Joseph R.

2017-01-01

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types. REPTILE is available at https://github.com/yupenghe/REPTILE/. PMID:28193886

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Improvement of Progressive Damage Model to Predicting Crashworthy Composite Corrugated Plate

NASA Astrophysics Data System (ADS)

Ren, Yiru; Jiang, Hongyong; Ji, Wenyuan; Zhang, Hanyu; Xiang, Jinwu; Yuan, Fuh-Gwo

2018-02-01

To predict the crashworthy composite corrugated plate, different single and stacked shell models are evaluated and compared, and a stacked shell progressive damage model combined with continuum damage mechanics is proposed and investigated. To simulate and predict the failure behavior, both of the intra- and inter- laminar failure behavior are considered. The tiebreak contact method, 1D spot weld element and cohesive element are adopted in stacked shell model, and a surface-based cohesive behavior is used to capture delamination in the proposed model. The impact load and failure behavior of purposed and conventional progressive damage models are demonstrated. Results show that the single shell could simulate the impact load curve without the delamination simulation ability. The general stacked shell model could simulate the interlaminar failure behavior. The improved stacked shell model with continuum damage mechanics and cohesive element not only agree well with the impact load, but also capture the fiber, matrix debonding, and interlaminar failure of composite structure.

Customizing Countermeasure Prescriptions using Predictive Measures of Sensorimotor Adaptability

NASA Technical Reports Server (NTRS)

Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Miller, C. A.; Batson, C. D.; Wood, S. J.; Guined, J. R.; Cohen, H. S.; Buccello-Stout, R.; DeDios, Y. E.;

Fracture Prediction by Computed Tomography and Finite Element Analysis: Current and Future Perspectives.

PubMed

Johannesdottir, Fjola; Allaire, Brett; Bouxsein, Mary L

2018-05-30

This review critiques the ability of CT-based methods to predict incident hip and vertebral fractures. CT-based techniques with concurrent calibration all show strong associations with incident hip and vertebral fracture, predicting hip and vertebral fractures as well as, and sometimes better than, dual-energy X-ray absorptiometry areal biomass density (DXA aBMD). There is growing evidence for use of routine CT scans for bone health assessment. CT-based techniques provide a robust approach for osteoporosis diagnosis and fracture prediction. It remains to be seen if further technical advances will improve fracture prediction compared to DXA aBMD. Future work should include more standardization in CT analyses, establishment of treatment intervention thresholds, and more studies to determine whether routine CT scans can be efficiently used to expand the number of individuals who undergo evaluation for fracture risk.

A dynamic multi-scale Markov model based methodology for remaining life prediction

NASA Astrophysics Data System (ADS)

Yan, Jihong; Guo, Chaozhong; Wang, Xing

2011-05-01

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.

Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

2011-01-01

Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Predicting electrocardiogram and arterial blood pressure waveforms with different Echo State Network architectures.

PubMed

Fong, Allan; Mittu, Ranjeev; Ratwani, Raj; Reggia, James

2014-01-01

Alarm fatigue caused by false alarms and alerts is an extremely important issue for the medical staff in Intensive Care Units. The ability to predict electrocardiogram and arterial blood pressure waveforms can potentially help the staff and hospital systems better classify a patient's waveforms and subsequent alarms. This paper explores the use of Echo State Networks, a specific type of neural network for mining, understanding, and predicting electrocardiogram and arterial blood pressure waveforms. Several network architectures are designed and evaluated. The results show the utility of these echo state networks, particularly ones with larger integrated reservoirs, for predicting electrocardiogram waveforms and the adaptability of such models across individuals. The work presented here offers a unique approach for understanding and predicting a patient's waveforms in order to potentially improve alarm generation. We conclude with a brief discussion of future extensions of this research.

Analysis backpropagation methods with neural network for prediction of children's ability in psychomotoric

NASA Astrophysics Data System (ADS)

Izhari, F.; Dhany, H. W.; Zarlis, M.; Sutarman

2018-03-01

A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.

Capturing patients' needs in casemix: a systematic literature review on the value of adding functioning information in reimbursement systems.

PubMed

Hopfe, Maren; Stucki, Gerold; Marshall, Ric; Twomey, Conal D; Üstün, T Bedirhan; Prodinger, Birgit

2016-02-03

Contemporary casemix systems for health services need to ensure that payment rates adequately account for actual resource consumption based on patients' needs for services. It has been argued that functioning information, as one important determinant of health service provision and resource use, should be taken into account when developing casemix systems. However, there has to date been little systematic collation of the evidence on the extent to which the addition of functioning information into existing casemix systems adds value to those systems with regard to the predictive power and resource variation explained by the groupings of these systems. Thus, the objective of this research was to examine the value of adding functioning information into casemix systems with respect to the prediction of resource use as measured by costs and length of stay. A systematic literature review was performed. Peer-reviewed studies, published before May 2014 were retrieved from CINAHL, EconLit, Embase, JSTOR, PubMed and Sociological Abstracts using keywords related to functioning ('Functioning', 'Functional status', 'Function*, 'ICF', 'International Classification of Functioning, Disability and Health', 'Activities of Daily Living' or 'ADL') and casemix systems ('Casemix', 'case mix', 'Diagnosis Related Groups', 'Function Related Groups', 'Resource Utilization Groups' or 'AN-SNAP'). In addition, a hand search of reference lists of included articles was conducted. Information about study aims, design, country, setting, methods, outcome variables, study results, and information regarding the authors' discussion of results, study limitations and implications was extracted. Ten included studies provided evidence demonstrating that adding functioning information into casemix systems improves predictive ability and fosters homogeneity in casemix groups with regard to costs and length of stay. Collection and integration of functioning information varied across studies. Results suggest that, in particular, DRG casemix systems can be improved in predicting resource use and capturing outcomes for frail elderly or severely functioning-impaired patients. Further exploration of the value of adding functioning information into casemix systems is one promising approach to improve casemix systems ability to adequately capture the differences in patient's needs for services and to better predict resource use.

Predicting risk of coronary events and all-cause mortality: role of B-type natriuretic peptide above traditional risk factors and coronary artery calcium scoring in the general population: the Heinz Nixdorf Recall Study.

PubMed

Kara, Kaffer; Mahabadi, Amir A; Berg, Marie H; Lehmann, Nils; Möhlenkamp, Stefan; Kälsch, Hagen; Bauer, Marcus; Moebus, Susanne; Dragano, Nico; Jöckel, Karl-Heinz; Neumann, Till; Erbel, Raimund

2014-09-01

Several biomarkers including B-type natriuretic peptide (BNP) have been suggested to improve prediction of coronary events and all-cause mortality. Moreover, coronary artery calcium (CAC) as marker of subclinical atherosclerosis is a strong predictor for cardiovascular mortality and morbidity. We aimed to evaluate the predictive ability of BNP and CAC for all-cause mortality and coronary events above traditional cardiovascular risk factors (TRF) in the general population. We followed 3782 participants of the population-based Heinz Nixdorf Recall cohort study without coronary artery disease at baseline for 7.3 ± 1.3 years. Associations of BNP and CAC with incident coronary events and all-cause mortality were assessed using Cox regression, Harrell's c, and time-dependent integrated discrimination improvement (IDI(t), increase in explained variance). Subjects with high BNP levels had increased frequency of coronary events and death (coronary events/mortality: 14.1/28.2% for BNP ≥100 pg/ml vs. 2.7/5.5% for BNP < 100 pg/ml, respectively). Subjects with a BNP ≥100 pg/ml had increased incidence of hard endpoints sustaining adjustment for CAC and TRF (for coronary events: hazard ratio (HR) (95% confidence interval (CI)) 3.41(1.78-6.53); for all-cause mortality: HR 3.35(2.15-5.23)). Adding BNP to TRF and CAC increased measures of predictive ability: coronary events (Harrell's c, for coronary events, 0.775-0.784, p = 0.09; for all-cause mortality 0.733-0.740, p = 0.04; and IDI(t) (95% CI), for coronary events: 2.79% (0.33-5.65%) and for all-cause mortality 1.78% (0.73-3.10%). Elevated levels of BNP are associated with excess incident coronary events and all-cause mortality rates, with BNP and CAC significantly and complementary improving prediction of risk in the general population above TRF. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

GUMICS-4 Year Run: Ground Magnetic Field Predictions

NASA Astrophysics Data System (ADS)

Honkonen, I. J.; Viljanen, A.; Juusola, L.; Facsko, G.; Vanhamäki, H.

2013-12-01

Space weather can have severe effects even at ground level when Geomagnetically Induced Currents (GIC) disrupt power transmission networks, the worst case being a complete blackout affecting millions of people. The importance of space weather forecasting as well as the need for model improvement and validation has been recognized internationally. The recently concluded GUMICS-4 one year run, in which solar wind observations obtained from OMNIWeb for the period 2002-01-29 to 2003-02-02 were given as input to the model, will allow GUMICS to be validated against observations on an unprecedented scale. The performance of GUMICS can be quantified statistically, as a function of, for example, the solar wind driver, various geomagnetic indices, magnetic local time and other parameters. Here we concentrate on the ability of GUMICS to predict ground magnetic field observations for one year of simulated results. The ground magnetic field predictions are compared to observations of the mainland IMAGE magnetometer stations located at CGM latitudes 54-68 N. Furthermore the GIC derived from ground magnetic field predictions are compared to observations along the natural gas pipeline at Mäntsälä, South Finland. Various metrics are used to objectively evaluate the performance of GUMICS as a function of different parameters, thereby providing significant insight into the space weather forecasting ability of models based on first principles.

Numerical assessment affects aggression and competitive ability: a team-fighting strategy for the ant Formica xerophila.

PubMed

Tanner, Colby J

2006-11-07

The relationship between numerical advantage and competitive ability is a fundamental component in contests between groups of social animals. An individual's ability to correctly assess the numerical state of its group is of vital importance. In addition to numerical dominance, the group's fighting ability also plays an important role in competitive interactions. By staging experimental fights between two Formica ant species, I show that Formica xerophila are able to assess their own group's strength prior to any competitive encounter. Ants that perceive themselves as part of a large group act more aggressively toward a competitor than ants that perceive themselves as isolated individuals. This increase in aggression improves F. xerophila's competitive ability. Furthermore, the number of individuals in a contest was found to affect competitive ability. In contests with equal number of competitors, groups of F. xerophila were more successful than individual F. xerophila. Contrary to previous predictions using Lanchester's laws of fighting, F. xerophila's ability to kill competitors increased nonlinearly with group size. This nonlinearity was due to the collective fighting strategy of an F. xerophila group isolating and engaging a single Formica integroides competitors.

Numerical assessment affects aggression and competitive ability: a team-fighting strategy for the ant Formica xerophila

PubMed Central

Tanner, Colby J

2006-01-01

The relationship between numerical advantage and competitive ability is a fundamental component in contests between groups of social animals. An individual's ability to correctly assess the numerical state of its group is of vital importance. In addition to numerical dominance, the group's fighting ability also plays an important role in competitive interactions. By staging experimental fights between two Formica ant species, I show that Formica xerophila are able to assess their own group's strength prior to any competitive encounter. Ants that perceive themselves as part of a large group act more aggressively toward a competitor than ants that perceive themselves as isolated individuals. This increase in aggression improves F. xerophila's competitive ability. Furthermore, the number of individuals in a contest was found to affect competitive ability. In contests with equal number of competitors, groups of F. xerophila were more successful than individual F. xerophila. Contrary to previous predictions using Lanchester's laws of fighting, F. xerophila's ability to kill competitors increased nonlinearly with group size. This nonlinearity was due to the collective fighting strategy of an F. xerophila group isolating and engaging a single Formica integroides competitors. PMID:17015327

Money Affects Theory of Mind Differently by Gender

PubMed Central

Ridinger, Garret; McBride, Michael

2015-01-01

Theory of Mind (ToM) ─ the ability to understand other’s thoughts, intentions, and emotions ─ is important for navigating interpersonal relationships, avoiding conflict, and empathizing. Prior research has identified many factors that affect one’s ToM ability, but little work has examined how different kinds of monetary incentives affect ToM ability. We ask: Does money affect ToM ability? If so, how does the effect depend on the structure of monetary incentives? How do the differences depend on gender? We hypothesize that money will affect ToM ability differently by gender: monetary rewards increase males’ motivation to express ToM ability while simultaneously crowding out females’ motivation. This prediction is confirmed in an experiment that varies the structure of monetary rewards for correct answers in the Reading the Mind in the Eyes Test (RMET). RMET scores decrease for females and increase for males with individual payments, and this effect is stronger with competitively-structured payments. RMET scores do not significantly change when monetary earnings go to a charity. Whether money improves or hinders ToM ability, and, hence, success in social interactions, thus depends on the interaction of gender and monetary incentive structure. PMID:26633171

Money Affects Theory of Mind Differently by Gender.

PubMed

Ridinger, Garret; McBride, Michael

2015-01-01

Theory of Mind (ToM)--the ability to understand other's thoughts, intentions, and emotions--is important for navigating interpersonal relationships, avoiding conflict, and empathizing. Prior research has identified many factors that affect one's ToM ability, but little work has examined how different kinds of monetary incentives affect ToM ability. We ask: Does money affect ToM ability? If so, how does the effect depend on the structure of monetary incentives? How do the differences depend on gender? We hypothesize that money will affect ToM ability differently by gender: monetary rewards increase males' motivation to express ToM ability while simultaneously crowding out females' motivation. This prediction is confirmed in an experiment that varies the structure of monetary rewards for correct answers in the Reading the Mind in the Eyes Test (RMET). RMET scores decrease for females and increase for males with individual payments, and this effect is stronger with competitively-structured payments. RMET scores do not significantly change when monetary earnings go to a charity. Whether money improves or hinders ToM ability, and, hence, success in social interactions, thus depends on the interaction of gender and monetary incentive structure.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

PubMed Central

Walter, Jonathan P.; Kinney, Allison L.; Banks, Scott A.; D'Lima, Darryl D.; Besier, Thor F.; Lloyd, David G.; Fregly, Benjamin J.

2014-01-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values. PMID:24402438

Muscle synergies may improve optimization prediction of knee contact forces during walking.

PubMed

Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J

2014-02-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Song and speech: examining the link between singing talent and speech imitation ability.

PubMed

Christiner, Markus; Reiterer, Susanne M

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of "speech" on the productive level and "music" on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory.

Song and speech: examining the link between singing talent and speech imitation ability

PubMed Central

Christiner, Markus; Reiterer, Susanne M.

2013-01-01

In previous research on speech imitation, musicality, and an ability to sing were isolated as the strongest indicators of good pronunciation skills in foreign languages. We, therefore, wanted to take a closer look at the nature of the ability to sing, which shares a common ground with the ability to imitate speech. This study focuses on whether good singing performance predicts good speech imitation. Forty-one singers of different levels of proficiency were selected for the study and their ability to sing, to imitate speech, their musical talent and working memory were tested. Results indicated that singing performance is a better indicator of the ability to imitate speech than the playing of a musical instrument. A multiple regression revealed that 64% of the speech imitation score variance could be explained by working memory together with educational background and singing performance. A second multiple regression showed that 66% of the speech imitation variance of completely unintelligible and unfamiliar language stimuli (Hindi) could be explained by working memory together with a singer's sense of rhythm and quality of voice. This supports the idea that both vocal behaviors have a common grounding in terms of vocal and motor flexibility, ontogenetic and phylogenetic development, neural orchestration and auditory memory with singing fitting better into the category of “speech” on the productive level and “music” on the acoustic level. As a result, good singers benefit from vocal and motor flexibility, productively and cognitively, in three ways. (1) Motor flexibility and the ability to sing improve language and musical function. (2) Good singers retain a certain plasticity and are open to new and unusual sound combinations during adulthood both perceptually and productively. (3) The ability to sing improves the memory span of the auditory working memory. PMID:24319438

Short-term wind speed prediction based on the wavelet transformation and Adaboost neural network

NASA Astrophysics Data System (ADS)

Hai, Zhou; Xiang, Zhu; Haijian, Shao; Ji, Wu

2018-03-01

The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.

Unraveling the mechanisms underlying postural instability in Parkinson's disease using dynamic posturography.

PubMed

Nonnekes, Jorik; de Kam, Digna; Geurts, Alexander C H; Weerdesteyn, Vivian; Bloem, Bastiaan R

2013-12-01

Postural instability, one of the cardinal symptoms of Parkinson's disease (PD), has devastating consequences for affected patients. Better strategies to prevent falls are needed, but this calls for an improved understanding of the complex mechanisms underlying postural instability. We must also improve our ability to timely identify patients at risk of falling. Dynamic posturography is a promising avenue to achieve these goals. The latest moveable platforms can deliver 'real-life' balance perturbations, permitting study of everyday fall circumstances. Dynamic posturography studies have shown that PD patients have fundamental problems in scaling their postural responses in accordance with the need of the actual balance task at hand. On-going studies evaluate the predictive ability of impaired posturography performance for daily life falls. We also review recent work aimed at exploring balance correcting steps in PD, and the presumed interaction between startle pathways and postural responses.

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

PubMed

Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Predictive Factors in the Outcome of Surgical Repair of Abdominal Rectus Diastasis.

PubMed

Strigård, Karin; Clay, Leonard; Stark, Birgit; Gunnarsson, Ulf

2016-05-01

The aim of this study was to define the indicators predicting improved abdominal wall function after surgical repair of abdominal rectus diastasis (ARD). Preoperative subjective assessment quantified by the validated Ventral Hernia Pain Questionnaire (VHPQ) was related to relative postoperative functional improvement in abdominal muscle strength. Fifty-seven patients undergoing surgery for ARD completed the VHPQ before surgery. Preoperative pain assessment results were compared with the relative improvement in muscle strength measured with the BioDex system 4. There was a correlation between the relative improvement in muscle strength measured by the BioDex System 4 for flexion at 30 degrees (P = 0.046) and 60 degrees per second (P = 0.004) and the preoperative question, "Do you find it painful to sit for more than 30 minutes?" There was also a correlation between BioDex improvement for flexion at 30 degrees (P = 0.022) and for isometric work load (P = 0.038) and the preoperative question, "Has abdominal pain limited your ability to perform sports activities?" The VHPQ responses also formed a pattern with a fairly good correlation between other BioDex modalities (with the exception of extension at 60 degrees per second) and the response to the question regarding complaints when performing sports. Postoperative visual analog scale ratings of abdominal wall stability correlated to the questions regarding complaints when sitting (P = 0.040) and standing (P = 0.047). No other correlation was seen. VHPQ ratings concerning pain while being seated for more than 30 minutes and pain limiting the ability to perform sports are promising indicators in the identification of patients likely to benefit from surgical correction of their ARD.

A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.

PubMed

Elnady, Ahmed Mohamed; Zhang, Xin; Xiao, Zhen Gang; Yong, Xinyi; Randhawa, Bubblepreet Kaur; Boyd, Lara; Menon, Carlo

2015-01-01

Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain-computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants' ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke.

A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform

PubMed Central

Elnady, Ahmed Mohamed; Zhang, Xin; Xiao, Zhen Gang; Yong, Xinyi; Randhawa, Bubblepreet Kaur; Boyd, Lara; Menon, Carlo

2015-01-01

Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain–computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants’ ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke. PMID:25870554

Ability of preoperative 3.0-Tesla magnetic resonance imaging to predict the absence of side-specific extracapsular extension of prostate cancer.

PubMed

Hara, Tomohiko; Nakanishi, Hiroyuki; Nakagawa, Tohru; Komiyama, Motokiyo; Kawahara, Takashi; Manabe, Tomoko; Miyake, Mototaka; Arai, Eri; Kanai, Yae; Fujimoto, Hiroyuki

2013-10-01

Recent studies have shown an improvement in prostate cancer diagnosis with the use of 3.0-Tesla magnetic resonance imaging. We retrospectively assessed the ability of this imaging technique to predict side-specific extracapsular extension of prostate cancer. From October 2007 to August 2011, prostatectomy was carried out in 396 patients after preoperative 3.0-Tesla magnetic resonance imaging. Among these, 132 (primary sample) and 134 patients (validation sample) underwent 12-core prostate biopsy at the National Cancer Center Hospital of Tokyo, Japan, and at other institutions, respectively. In the primary dataset, univariate and multivariate analyses were carried out to predict side-specific extracapsular extension using variables determined preoperatively, including 3.0-Tesla magnetic resonance imaging findings (T2-weighted and diffusion-weighted imaging). A prediction model was then constructed and applied to the validation study sample. Multivariate analysis identified four significant independent predictors (P < 0.05), including a biopsy Gleason score of ≥8, positive 3.0-Tesla diffusion-weighted magnetic resonance imaging findings, ≥2 positive biopsy cores on each side and a maximum percentage of positive cores ≥31% on each side. The negative predictive value was 93.9% in the combination model with these four predictors, meanwhile the positive predictive value was 33.8%. Good reproducibility of these four significant predictors and the combination model was observed in the validation study sample. The side-specific extracapsular extension prediction by the biopsy Gleason score and factors associated with tumor location, including a positive 3.0-Tesla diffusion-weighted magnetic resonance imaging finding, have a high negative predictive value, but a low positive predictive value. © 2013 The Japanese Urological Association.

Long-term changes in neurocognition and behavior following treatment of sleep disordered breathing in school-aged children.

PubMed

Biggs, Sarah N; Vlahandonis, Anna; Anderson, Vicki; Bourke, Robert; Nixon, Gillian M; Davey, Margot J; Horne, Rosemary S C

2014-01-01

Sleep disordered breathing (SDB) in children is associated with detrimental neurocognitive and behavioral consequences. The long term impact of treatment on these outcomes is unknown. This study examined the long-term effect of treatment of SDB on neurocognition, academic ability, and behavior in a cohort of school-aged children. Four-year longitudinal study. Children originally diagnosed with SDB and healthy non-snoring controls underwent repeat polysomnography and age-standardized neurocognitive and behavioral assessment 4y following initial testing. Melbourne Children's Sleep Centre, Melbourne, Australia. Children 12-16 years of age, originally assessed at 7-12 years, were categorized into Treated (N = 12), Untreated (N = 26), and Control (N = 18) groups. Adenotonsillectomy, Tonsillectomy, Nasal Steroids. Decision to treat was independent of this study. Changes in sleep and respiratory parameters over time were assessed. A decrease in obstructive apnea hypopnea index (OAHI) from Time 1 to Time 2 was seen in 63% and 100% of the Untreated and Treated groups, respectively. The predictive relationship between change in OAHI and standardized neurocognitive, academic, and behavioral scores over time was examined. Improvements in OAHI were predictive of improvements in Performance IQ, but not Verbal IQ or academic measures. Initial group differences in behavioral assessment on the Child Behavior Checklist did not change over time. Children with SDB at baseline continued to exhibit significantly poorer behavior than Controls at follow-up, irrespective of treatment. After four years, improvements in SDB are concomitant with improvements in some areas of neurocognition, but not academic ability or behavior in school-aged children.

Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors

PubMed Central

Milot, Marie-Hélène; Spencer, Steven J.; Chan, Vicky; Allington, James P.; Klein, Julius; Chou, Cathy; Pearson-Fuhrhop, Kristin; Bobrow, James E.; Reinkensmeyer, David J.; Cramer, Steven C.

2014-01-01

Background Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective Determine the predictive ability of behavioral and brain measures to improve selection of individuals for robotic training. Methods Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, fMRI, diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results Training was associated with an average gain of 6±5 blocks on the BBT (p<0.0001). Bivariate analysis revealed that lower baseline motor evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains. PMID:24642382

Advancing Drug Safety Through Prospective Pharmacovigilance.

PubMed

Pitts, Peter J; Le Louet, Hervé

2018-01-01

Much has changed in a relatively short period of time. There is a raging debate over the level of evidence expected to first introduce a treatment to patients based on smaller, more adaptive data sets. Some argue for less data followed by postapproval follow-up, others for more adaptive clinical trial designs and end-point modification driven by patient-focused drug development and use of real-world evidence. The transition in both the review and postmarketing regulatory framework is happening in front of our eyes in real time. To improve the ability of patients to receive high-quality, safe, effective, and timely care, better information via pharmacovigilance must be a priority as the world's many regulatory systems build the capacity to harness electronic health information to improve health, care quality, and safety. Globally, the widely variable ability of nations to build reliable regulatory systems (from precise review to robust pharmacovigilance) is a dangerous source of health care inequality. Developing validated tools and techniques for "predictive pharmacovigilance" will assist all health systems in better understanding the risks and benefits of the medicines they regulate by understanding what should be happening once a new medicine moves from risk-benefit regulatory efficacy to real-world risk-effectiveness. This will be of particular utility for smaller regulatory agencies with fewer resources. By comparing preapproval predictive pharmacovigilance data, developing regulatory authorities will be able to better understand the potential gap between what was predicted and what was actually measured (via more traditional pharmacovigilance methodologies). Predictive pharmacovigilance recognizes the value of understanding the imperfect reporting of real-world clinical use and that the absence of reporting is, in itself, an important postmarketing signal.

Functional test of pedotransfer functions to predict water flow and solute transport with the dual-permeability model MACRO

NASA Astrophysics Data System (ADS)

Moeys, J.; Larsbo, M.; Bergström, L.; Brown, C. D.; Coquet, Y.; Jarvis, N. J.

2012-07-01

Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = -0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the estimation of water flow parameters. Overall, the results are encouraging for the use of this modelling set-up to estimate pesticide leaching risks at the regional-scale, especially where the objective is to identify vulnerable soils and "source" areas of contamination.

The Association of CHA2DS2-VASc Score and Blood Biomarkers with Ischemic Stroke Outcomes: The Belgrade Stroke Study

PubMed Central

Potpara, Tatjana S.; Polovina, Marija M.; Djikic, Dijana; Marinkovic, Jelena M.; Kocev, Nikola; Lip, Gregory Y. H.

2014-01-01

Background Many blood biomarkers have a positive association with stroke outcome, but adding blood biomarkers to the National Institutes of Health Stroke Scale (NIHSS) did not significantly improve its discriminatory ability. We investigated the association of the CHA2DS2-VASc score with unfavourable functional outcome (defined as a 30-day modified Rankin Scale [mRS] ≥3) in patients presenting with acute ischemic stroke (AIS), and examined whether the addition of blood biomarkers (troponin I [TnI], fibrinogen, C-reactive protein [CRP]) affects the model discriminatory ability. Methods We conducted an observational single-centre study of consecutive patients with AIS. All patients were admitted to hospital within 24 hours from the neurological symptoms onset. Results Of 240 patients (mean age 70.0±8.9 years), unfavourable 30-day outcome occurred in 92 (38.3%). Patients with mRS≥3 were older and more likely to have atrial fibrillation or other comorbidities (all p<0.001). They had higher levels of CRP, fibrinogen, TnI and higher CHA2DS2-VASc and CHADS2 scores (all p<0.05). The adjusted CHA2DS2-VASc score had excellent predictive ability for poor stroke outcome (c-statistic 0.982;95%CI,0.964–1.000, p<0.001). Whilst CRP had the highest sensitivity (83.7%), cardiac TnI was the most specific (97.3%) for prediction of poor stroke outcome (cut-off: >0.09µg/L). Compared with each of these biomarkers, CHA2DS2-VASc score had significantly better predictive ability for poor stroke outcome (c-statistic for CRP, Fibrinogen and TnI was 0.853;95%CI,0.802–0.895, 0.848;95%CI,0.796–0.891, and 0.792;95%CI,0.736–0.842, all p<0.001, respectively, versus 0.932;95%CI,0.892–0.960, p<0.001 for the CHA2DS2-VASc, all p for the comparisons<0.01). There was no significant difference in the predictive ability of the CHA2DS2-VASc score vs. combinations of the CHA2DS2-VASc and TnI or TnI, fibrinogen and CRP (z statistic 0.369, p = 0.7119; integrated discrimination index 0.00801 and 0.00172, respectively, both p>0.05). Conclusions The CHA2DS2-VASc score alone reliably predicts 30-day unfavourable outcome of stroke. Adding blood biomarkers to the CHA2DS2-VASc score did not significantly increase the predictive ability of the model. PMID:25184809

Frailty Versus Stopping Elderly Accidents, Deaths and Injuries Initiative Fall Risk Score: Ability to Predict Future Falls.

PubMed

Crow, Rebecca S; Lohman, Matthew C; Pidgeon, Dawna; Bruce, Martha L; Bartels, Stephen J; Batsis, John A

2018-03-01

To compare the ability of frailty status to predict fall risk with that of community fall risk screening tools. Analysis of cross-sectional and longitudinal data from NHATS. National Health and Aging Trend Study (NHATS) 2011-2015. Individuals aged 65 and older (N = 7,392). Fall risk was defined according to the Stopping Elderly Accidents, Deaths and Injuries (STEADI) initiative. Frailty was defined as exhaustion, weight loss, low activity, slow gait speed, and weak grip strength. Robust was defined as meeting 0 criteria, prefrailty as 1 or 2 criteria, and frailty as 3 or more criteria. Falls were self-reported and ascertained using NHATS subsequent rounds (2012-2015). We compared the ability of frailty to predict future falls with that of STEADI score, adjusting for age, race, sex, education, comorbidities, hearing and vision impairment, and disability. Of the 7,392 participants (58.5% female), there 3,545 (48.0%) were classified as being at low risk of falling, 2,966 (40.1%) as being at moderate risk, and 881 (11.9%) as being at high risk. The adjusted risk of falling over the 4 subsequent years was 2.5 times as great for the moderate-risk group (hazard ratio (HR) = 2.50, 95% confidence interval (CI) = 2.16-2.89) and almost 4 times as great (HR = 3.79, 95% CI = 2.76-5.21) for the high-risk group as for the low-risk group. Risk of falling was greater for those who were prefrail (HR = 1.34, 95% CI 1.16-1.55) and frail (HR = 1.20, 95% CI = 0.94-1.54) than for those who were robust. STEADI score is a strong predictor of future falls. Addition of frailty status does not improve the ability of the STEADI measure to predict future falls. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Musical Expertise and the Ability to Imagine Loudness

PubMed Central

Bishop, Laura; Bailes, Freya; Dean, Roger T.

2013-01-01

Most perceived parameters of sound (e.g. pitch, duration, timbre) can also be imagined in the absence of sound. These parameters are imagined more veridically by expert musicians than non-experts. Evidence for whether loudness is imagined, however, is conflicting. In music, the question of whether loudness is imagined is particularly relevant due to its role as a principal parameter of performance expression. This study addressed the hypothesis that the veridicality of imagined loudness improves with increasing musical expertise. Experts, novices and non-musicians imagined short passages of well-known classical music under two counterbalanced conditions: 1) while adjusting a slider to indicate imagined loudness of the music and 2) while tapping out the rhythm to indicate imagined timing. Subtests assessed music listening abilities and working memory span to determine whether these factors, also hypothesised to improve with increasing musical expertise, could account for imagery task performance. Similarity between each participant’s imagined and listening loudness profiles and reference recording intensity profiles was assessed using time series analysis and dynamic time warping. The results suggest a widespread ability to imagine the loudness of familiar music. The veridicality of imagined loudness tended to be greatest for the expert musicians, supporting the predicted relationship between musical expertise and musical imagery ability. PMID:23460791

Experience with external pump trial prior to implantation for intrathecal baclofen in ambulatory patients with spastic cerebral palsy.

PubMed

Bleyenheuft, C; Filipetti, P; Caldas, C; Lejeune, T

2007-01-01

To evaluate effectiveness and safety of intrathecal baclofen administration (ITB) testing with continuous infusion via an external pump before the implantation of an internal one in ambulatory spastic patients with cerebral palsy (CP). Seven CP patients (3 diplegic, 4 quadriplegic - 18.4+/-7.0 years) with a progressive decrease in walking ability were included. Assessments included: Ashworth's scale, Observational Gait Scale (OGS), and GMFM-66. During the ITB test (45-150 microg/24h), spasticity decreased by more than two points on Ashworth's scale (p<0.001) and walking ability improved (median OGS increased from 7 to 9, p

Musical expertise and the ability to imagine loudness.

PubMed

Bishop, Laura; Bailes, Freya; Dean, Roger T

2013-01-01

Most perceived parameters of sound (e.g. pitch, duration, timbre) can also be imagined in the absence of sound. These parameters are imagined more veridically by expert musicians than non-experts. Evidence for whether loudness is imagined, however, is conflicting. In music, the question of whether loudness is imagined is particularly relevant due to its role as a principal parameter of performance expression. This study addressed the hypothesis that the veridicality of imagined loudness improves with increasing musical expertise. Experts, novices and non-musicians imagined short passages of well-known classical music under two counterbalanced conditions: 1) while adjusting a slider to indicate imagined loudness of the music and 2) while tapping out the rhythm to indicate imagined timing. Subtests assessed music listening abilities and working memory span to determine whether these factors, also hypothesised to improve with increasing musical expertise, could account for imagery task performance. Similarity between each participant's imagined and listening loudness profiles and reference recording intensity profiles was assessed using time series analysis and dynamic time warping. The results suggest a widespread ability to imagine the loudness of familiar music. The veridicality of imagined loudness tended to be greatest for the expert musicians, supporting the predicted relationship between musical expertise and musical imagery ability.

Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

PubMed

Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

2017-06-01

As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient statistical mapping of avian count data

USGS Publications Warehouse

Royle, J. Andrew; Wikle, C.K.

2005-01-01

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

Predicting paclitaxel-induced neutropenia using the DMET platform.

PubMed

Nieuweboer, Annemieke J M; Smid, Marcel; de Graan, Anne-Joy M; Elbouazzaoui, Samira; de Bruijn, Peter; Martens, John W; Mathijssen, Ron H J; van Schaik, Ron H N

2015-01-01

The use of paclitaxel in cancer treatment is limited by paclitaxel-induced neutropenia. We investigated the ability of genetic variation in drug-metabolizing enzymes and transporters to predict hematological toxicity. Using a discovery and validation approach, we identified a pharmacogenetic predictive model for neutropenia. For this, a drug-metabolizing enzymes and transporters plus DNA chip was used, which contains 1936 SNPs in 225 metabolic enzyme and drug-transporter genes. Our 10-SNP model in 279 paclitaxel-dosed patients reached 43% sensitivity in the validation cohort. Analysis in 3-weekly treated patients only resulted in improved sensitivity of 79%, with a specificity of 33%. None of our models reached statistical significance. Our drug-metabolizing enzymes and transporters-based SNP-models are currently of limited value for predicting paclitaxel-induced neutropenia in clinical practice. Original submitted 9 March 2015; Revision submitted 20 May 2015.

Health Literacy, Cognitive Abilities, and Mortality Among Elderly Persons

PubMed Central

Wolf, Michael S.; Feinglass, Joseph; Thompson, Jason A.

2008-01-01

Background Low health literacy and low cognitive abilities both predict mortality, but no study has jointly examined these relationships. Methods We conducted a prospective cohort study of 3,260 community-dwelling adults age 65 and older. Participants were interviewed in 1997 and administered the Short Test of Functional Health Literacy in Adults and the Mini Mental Status Examination. Mortality was determined using the National Death Index through 2003. Measurements and Main Results In multivariate models with only literacy (not cognition), the adjusted hazard ratio was 1.50 (95% confidence of interval [CI] 1.24–1.81) for inadequate versus adequate literacy. In multivariate models without literacy, delayed recall of 3 items and the ability to serial subtract numbers were associated with higher mortality (e.g., adjusted hazard ratios [AHR] 1.74 [95% CI 1.30–2.34] for recall of zero versus 3 items, and 1.32 [95% CI 1.09–1.60] for 0–2 vs 5 correct subtractions). In multivariate analysis with both literacy and cognition, the AHRs for the cognition items were similar, but the AHR for inadequate literacy decreased to 1.27 (95% CI 1.03 – 1.57). Conclusions Both health literacy and cognitive abilities independently predict mortality. Interventions to improve patient knowledge and self-management skills should consider both the reading level and cognitive demands of the materials. PMID:18330654

Executive Function Buffers the Association between Early Math and Later Academic Skills.

PubMed

Ribner, Andrew D; Willoughby, Michael T; Blair, Clancy B

2017-01-01

Extensive evidence has suggested that early academic skills are a robust indicator of later academic achievement; however, there is mixed evidence of the effectiveness of intervention on academic skills in early years to improve later outcomes. As such, it is clear there are other contributing factors to the development of academic skills. The present study tests the role of executive function (EF) (a construct made up of skills complicit in the achievement of goal-directed tasks) in predicting 5th grade math and reading ability above and beyond math and reading ability prior to school entry, and net of other cognitive covariates including processing speed, vocabulary, and IQ. Using a longitudinal dataset of N = 1292 participants representative of rural areas in two distinctive geographical parts of the United States, the present investigation finds EF at age 5 strongly predicts 5th grade academic skills, as do cognitive covariates. Additionally, investigation of an interaction between early math ability and EF reveals the magnitude of the association between early math and later math varies as a function of early EF, such that participants who have high levels of EF can "catch up" to peers who perform better on assessments of early math ability. These results suggest EF is pivotal to the development of academic skills throughout elementary school. Implications for further research and practice are discussed.

A Critical Review of Validation, Blind Testing, and Real- World Use of Alchemical Protein-Ligand Binding Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Mobley, David L; Friesner, Richard A

2017-01-01

Protein-ligand binding is among the most fundamental phenomena underlying all molecular biology, and a greater ability to more accurately and robustly predict the binding free energy of a small molecule ligand for its cognate protein is expected to have vast consequences for improving the efficiency of pharmaceutical drug discovery. We briefly reviewed a number of scientific and technical advances that have enabled alchemical free energy calculations to recently emerge as a preferred approach, and critically considered proper validation and effective use of these techniques. In particular, we characterized a selection bias effect which may be important in prospective free energy calculations, and introduced a strategy to improve the accuracy of the free energy predictions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Reading Comprehension Deficits in Adolescents: Addressing Underlying Language Abilities.

PubMed

Nippold, Marilyn A

2017-04-20

The purpose of this article is to discuss reading comprehension deficits in adolescents in relation to their word reading skills and lexical and syntactic development. Although reading comprehension strategies (e.g., "Find the main idea") are often recommended, it is argued that before these can be effective, students' underlying language deficits should be addressed. Data from a longitudinal study are analyzed to determine the relationship between reading comprehension, word reading, and lexical and syntactic development in adolescents. The findings indicate that poor reading comprehension in adolescents is predicted by concurrent deficits in word reading ability, lexical development, and syntactic development. When poor comprehension is accompanied by deficits in word reading ability and/or lexical and syntactic development, intervention should target the underlying areas of deficiency. Studies designed to improve reading comprehension in adolescents are needed.

Reading Comprehension Deficits in Adolescents: Addressing Underlying Language Abilities

PubMed Central

2017-01-01

Purpose The purpose of this article is to discuss reading comprehension deficits in adolescents in relation to their word reading skills and lexical and syntactic development. Although reading comprehension strategies (e.g., “Find the main idea”) are often recommended, it is argued that before these can be effective, students' underlying language deficits should be addressed. Method Data from a longitudinal study are analyzed to determine the relationship between reading comprehension, word reading, and lexical and syntactic development in adolescents. Results The findings indicate that poor reading comprehension in adolescents is predicted by concurrent deficits in word reading ability, lexical development, and syntactic development. Conclusion When poor comprehension is accompanied by deficits in word reading ability and/or lexical and syntactic development, intervention should target the underlying areas of deficiency. Studies designed to improve reading comprehension in adolescents are needed. PMID:28384784

Controlling of water collection ability by an elasticity-regulated bioinspired fiber.

PubMed

Wang, Sijie; Feng, Shile; Hou, Yongping; Zheng, Yongmei

2015-03-01

A special artificial spider silk is presented which is fabricated by using both an elastic polymer and a fiber, and the water collection behavior is investigated. Through exerting tension in varying degree, the length of the three-phase contact line (TCL) and the area of spindle knot can be regulated readily, which makes a great contribution to the improvement of collecting efficiency and water-hanging ability. The water-hanging ability can be predicted at a given stretching ratio according to the given expression of the TCL. As a result, liquid capture or release of distinct measure can be achieved via exerting tension. This research is helpful to design smart materials for developing applications in fogwater collection, dehumidification, high-efficiency humidity control, and controllable adhesion. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Children’s Forgetting of Pain-Related Memories

PubMed Central

Briere, Jennifer L.; von Baeyer, Carl L.

2016-01-01

Objective Given that forgetting negative experiences can help children cope with these experiences, we examined their ability to forget negative aspects of painful events. Methods 86 children aged 7–15 years participated in a retrieval-induced forgetting task whereby they repeatedly retrieved positive details of a physically painful experience, and an experimental pain task (cold-pressor task). Results Repeatedly retrieving positive details of a prior pain experience produced forgetting of the negative aspects of that experience. Pain-related self-efficacy predicted retrieval-induced forgetting; children with a poorer belief in their ability to cope with pain experienced less forgetting. Children who had a more difficult time forgetting prior negative experiences were more anxious about the pain task and reported higher pain thresholds. Conclusions Understanding children’s memory for painful experiences may help improve their pain management and coping ability. PMID:26666267

The relative importance of two different mathematical abilities to mathematical achievement.

PubMed

Nunes, Terezinha; Bryant, Peter; Barros, Rossana; Sylva, Kathy

2012-03-01

Two distinct abilities, mathematical reasoning and arithmetic skill, might make separate and specific contributions to mathematical achievement. However, there is little evidence to inform theory and educational practice on this matter. The aims of this study were (1) to assess whether mathematical reasoning and arithmetic make independent contributions to the longitudinal prediction of mathematical achievement over 5 years and (2) to test the specificity of this prediction. Data from Avon Longitudinal Study of Parents and Children (ALSPAC) were available on 2,579 participants for analyses of KS2 achievement and on 1,680 for the analyses of KS3 achievement. Hierarchical regression analyses were used to assess the independence and specificity of the contribution of mathematical reasoning and arithmetic skill to the prediction of achievement in KS2 and KS3 mathematics, science, and English. Age, intelligence, and working memory (WM) were controls in these analyses. Mathematical reasoning and arithmetic did make independent contributions to the prediction of mathematical achievement; mathematical reasoning was by far the stronger predictor of the two. These predictions were specific in so far as these measures were more strongly related to mathematics than to science or English. Intelligence and WM were non-specific predictors; intelligence contributed more to the prediction of science than of maths, and WM predicted maths and English equally well. There is clear justification for making a distinction between mathematical reasoning and arithmetic skills. The implication is that schools must plan explicitly to improve mathematical reasoning as well as arithmetic skills. ©2011 The British Psychological Society.

The use of tools for learning science in small groups

NASA Astrophysics Data System (ADS)

Valdes, Rosa Maria

2000-10-01

"Hands-on" learning through the use of tools or manipulatives representative of science concepts has long been an important component of the middle school science curriculum. However, scarce research exists on the impact of tool use on learning of science concepts, particularly on the processes involved in such learning. This study investigated how the use of tools by students engaged in small group discussion about the concept of electrical resistance and the explanations that accompany such use leads to improved understandings of the concept. Specifically, the main hypothesis of the study was that students who observe explanations by their high-ability peers accompanied by accurate tool use and who are highly engaged in these explanations would show learning gains. Videotaped interactions of students working in small groups to solve tasks on electricity were coded using scales that measured the accuracy of the tool use, the accuracy of the explanations presented, and the level of engagement of target students. The data of 48 students whose knowledge of the concept of resistance was initially low and who also were determined to be low achievers as shown by their scores on a set of pretest, was analyzed. Quantitative and qualitative analyses showed that students who observed their peers give explanations using tools and who were engaged at least moderately made gains in their understandings of resistance. Specifically, the results of regression analyses showed that both the level of accuracy of a high-ability peer's explanation and the target student's level of engagement in the explanation significantly predicted target students' outcome scores. The number of presentations offered by a high-ability peer also significantly predicted outcome scores. Case study analyses of six students found that students who improved their scores the most from pretest to posttest had high-ability peers who tended to be verbal and who gave numerous explanations, whereas students who improved the least had high-ability peers who gave no explanations at all. Important implications of this study for teaching are that (1) teachers should group students heterogeneously and should monitor students' small groups to insure that students are producing content-oriented discussion, and (2) students should be allowed to manipulate tools that allow experimentation as students build understandings and promote communication of abstract ideas.

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

PubMed

Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

2017-01-01

Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p < 0.05). The patient factors model was compared to the traditional surgical scheduling system estimates, which uses historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2  = 0.18). The use of routinely available pre-operative patient factors improves the prediction of operative duration during cholecystectomy.

A comparative study on improved Arrhenius-type and artificial neural network models to predict high-temperature flow behaviors in 20MnNiMo alloy.

PubMed

Quan, Guo-zheng; Yu, Chun-tang; Liu, Ying-ying; Xia, Yu-feng

2014-01-01

The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173 ∼ 1473 K and strain rate range of 0.01 ∼ 10 s(-1). Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former, R and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of -39.99% ∼ 35.05% and -3.77% ∼ 16.74%. As for the former, only 16.3% of the test data set possesses η-values within ± 1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.

Improving Soldier Training: An Aptitude-Treatment Interaction Approach.

DTIC Science & Technology

1979-06-01

magazines. Eighteen percent of American adults lack basic literacy skills to the point where they cannot even fill out basic forms. Dr. Food emphasized...designed to upgrade the literacy and computational skills of Army personnel found deficient. The magnitude of the problem is such, however, that the services...knowledge, (WK); arithmetic reasoning, AR); etc.) predict the aiount learned or the rate of learning or both. Special abilities such as psychomotor skills

Toward a descriptive model of solar particles in the heliosphere

NASA Technical Reports Server (NTRS)

Shea, M. A.; Smart, D. F.; Adams, James H., Jr.; Chenette, D.; Feynman, Joan; Hamilton, Douglas C.; Heckman, G. R.; Konradi, A.; Lee, Martin A.; Nachtwey, D. S.

1988-01-01

During a workshop on the interplanetary charged particle environment held in 1987, a descriptive model of solar particles in the heliosphere was assembled. This model includes the fluence, composition, energy spectra, and spatial and temporal variations of solar particles both within and beyong 1 AU. The ability to predict solar particle fluences was also discussed. Suggestions for specific studies designed to improve the basic model were also made.

Improving College Performance and Retention the Easy Way: Unpacking the ACT Exam. NBER Working Paper No. 17119

ERIC Educational Resources Information Center

Bettinger, Eric P.; Evans, Brent J.; Pope, Devin G.

2011-01-01

Colleges rely on the ACT exam in their admission decisions to increase their ability to differentiate between students likely to succeed and those that have a high risk of under-performing and dropping out. We show that two of the four sub tests of the ACT, English and Mathematics, are highly predictive of positive college outcomes while the other…

Turning Towards or Turning Away: A Comparison of Mindfulness Meditation and Guided Imagery Relaxation in Patients with Acute Depression.

PubMed

Costa, Ana; Barnhofer, Thorsten

2016-07-01

Disengaging from maladaptive thinking is an important imperative in the treatment of depression. Mindfulness training is aimed at helping patients acquire relevant skills for this purpose. It remains unclear, however, whether this practice is helpful when patients are acutely depressed. In order to investigate effects of mindfulness on symptoms and self-regulatory capacities in this group, the current study compared a brief training in mindfulness (n = 19) to guided imagery relaxation (n = 18). Participants were introduced to the respective techniques in a single session, and practised daily over one week. Self-reported severity of symptoms, difficulties in emotion-regulation, attentional control, the ability to decentre, and mindfulness were assessed pre and postintervention, and at a one-week follow-up. Symptoms of depression significantly decreased and self-regulatory functioning significantly increased in both groups, with changes being maintained during follow-up. When controlling for change in depressive symptoms, results showed significantly higher improvements in emotion regulation at follow-up in the mindfulness group. The ability to decentre predicted changes in symptoms from pre to postintervention, while mindfulness skills predicted changes in symptoms during the maintenance phase. The findings suggest that both practices can help to instigate reductions in symptoms and enhance self-regulatory functioning in depression. However, in order to improve emotion regulation above levels explained by reductions in symptoms more intentional mental training seems necessary. Furthermore, while the ability to disengage from negative patterns of thinking seems crucial for initial reduction of symptoms, maintenance of gains might require broader skills in mindfulness.

Independent walking as a major skill for the development of anticipatory postural control: evidence from adjustments to predictable perturbations.

PubMed

Cignetti, Fabien; Zedka, Milan; Vaugoyeau, Marianne; Assaiante, Christine

2013-01-01

Although there is suggestive evidence that a link exists between independent walking and the ability to establish anticipatory strategy to stabilize posture, the extent to which this skill facilitates the development of anticipatory postural control remains largely unknown. Here, we examined the role of independent walking on the infants' ability to anticipate predictable external perturbations. Non-walking infants, walking infants and adults were sitting on a platform that produced continuous rotation in the frontal plane. Surface electromyography (EMG) of neck and lower back muscles and the positions of markers located on the platform, the upper body and the head were recorded. Results from cross-correlation analysis between rectified and filtered EMGs and platform movement indicated that although muscle activation already occurred before platform movement in non-walking infants, only walking infants demonstrated an adult-like ability for anticipation. Moreover, results from further cross-correlation analysis between segmental angular displacement and platform movement together with measures of balance control at the end-points of rotation of the platform evidenced two sorts of behaviour. The adults behaved as a non-rigid non-inverted pendulum, rather stabilizing head in space, while both the walking and non-walking infants followed the platform, behaving as a rigid inverted pendulum. These results suggest that the acquisition of independent walking plays a role in the development of anticipatory postural control, likely improving the internal model for the sensorimotor control of posture. However, despite such improvement, integrating the dynamics of an external object, here the platform, within the model to maintain balance still remains challenging in infants.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Putting mechanisms into crop production models.

PubMed

Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

2013-09-01

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

Exploring a Method for Improving Turbulent Separated-Flow Predictions with kappa-omega Models

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.

2009-01-01

A particular failing of Reynolds-averaged Navier-Stokes separated turbulent flow computations is addressed within the context of a kappa-omega two-equation turbulence model. The failing is the tendency for turbulence models to under-predict turbulent shear stress in the shear layers of some separation bubbles, yielding late boundary layer reattachment and recovery. Inspired by unpublished work of Volker, Langtry, and Menter, the author undertook an independent investigation in an attempt to improve the ability of the Menter shear stress transport (SST) model to predict flowfield characteristics in and downstream of separation bubbles. The fix is an ad hoc term that is a function of the local ratio of turbulent production to dissipation; it is used to multiply the omega-destruction term, increasing eddy viscosity in separated regions. With this fix, several flowfields are investigated. Results show that, although the "separation fix" can provide dramatic improvement in some cases, it is not consistently good for all flows. Thus, although it may prove helpful in many situations in its current form, this model may benefit from further refinements, including better sensitization to the energetics of turbulence in the separated region.

A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt.

PubMed

Li, Xiaochuan; Bai, Xuedong; Wu, Yaohong; Ruan, Dike

2016-03-15

To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD). From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different admission times to hospital. Outcome was assessed according to the Japanese Orthopedic Association (JOA) recovery rate as excellent, good, fair, and poor. The first two results were considered as effective clinical outcome (ECO). Baseline patient and clinical characteristics were considered as secondary variables. A multivariate logistic regression model was used to construct a model with the ECO as a dependent variable and other factors as explanatory variables. The odds ratios (ORs) of each risk factor were adjusted and transformed into a scoring system. Area under the curve (AUC) was calculated and validated in both internal and external samples. Moreover, calibration plot and predictive ability of this scoring system were also tested for further validation. Patients with DD with ECOs in both construction and validation models were around 76 % (76.4 and 75.5 % respectively). more preoperative visual analog pain scale (VAS) score (OR = 1.56, p < 0.01), stenosis levels of L4/5 or L5/S1 (OR = 1.44, p = 0.04), stenosis locations with neuroforamen (OR = 1.95, p = 0.01), neurological deficit (OR = 1.62, p = 0.01), and more VAS improvement of selective nerve route block (SNRB) (OR = 3.42, p = 0.02). the internal area under the curve (AUC) was 0.85, and the external AUC was 0.72, with a good calibration plot of prediction accuracy. Besides, the predictive ability of ECOs was not different from the actual results (p = 0.532). We have constructed and validated a predictive model for confirming responsible nerve roots in patients with DD. The associated risk factors were preoperative VAS score, stenosis levels of L4/5 or L5/S1, stenosis locations with neuroforamen, neurological deficit, and VAS improvement of SNRB. A tool such as this is beneficial in the preoperative counseling of patients, shared surgical decision making, and ultimately improving safety in spine surgery.

Monitoring Wind Turbine Loading Using Power Converter Signals

NASA Astrophysics Data System (ADS)

Rieg, C. A.; Smith, C. J.; Crabtree, C. J.

2016-09-01

The ability to detect faults and predict loads on a wind turbine drivetrain's mechanical components cost-effectively is critical to making the cost of wind energy competitive. In order to investigate whether this is possible using the readily available power converter current signals, an existing permanent magnet synchronous generator based wind energy conversion system computer model was modified to include a grid-side converter (GSC) for an improved converter model and a gearbox. The GSC maintains a constant DC link voltage via vector control. The gearbox was modelled as a 3-mass model to allow faults to be included. Gusts and gearbox faults were introduced to investigate the ability of the machine side converter (MSC) current (I q) to detect and quantify loads on the mechanical components. In this model, gearbox faults were not detectable in the I q signal due to shaft stiffness and damping interaction. However, a model that predicts the load change on mechanical wind turbine components using I q was developed and verified using synthetic and real wind data.

Predicting successful tactile mapping of virtual objects.

PubMed

Brayda, Luca; Campus, Claudio; Gori, Monica

2013-01-01

Improving spatial ability of blind and visually impaired people is the main target of orientation and mobility (O&M) programs. In this study, we use a minimalistic mouse-shaped haptic device to show a new approach aimed at evaluating devices providing tactile representations of virtual objects. We consider psychophysical, behavioral, and subjective parameters to clarify under which circumstances mental representations of spaces (cognitive maps) can be efficiently constructed with touch by blindfolded sighted subjects. We study two complementary processes that determine map construction: low-level perception (in a passive stimulation task) and high-level information integration (in an active exploration task). We show that jointly considering a behavioral measure of information acquisition and a subjective measure of cognitive load can give an accurate prediction and a practical interpretation of mapping performance. Our simple TActile MOuse (TAMO) uses haptics to assess spatial ability: this may help individuals who are blind or visually impaired to be better evaluated by O&M practitioners or to evaluate their own performance.

Prediction of new-onset atrial fibrillation after first-ever ischemic stroke: A comparison of CHADS2, CHA2DS2-VASc and HATCH scores and the added value of stroke severity.

PubMed

Hsieh, Cheng-Yang; Lee, Cheng-Han; Wu, Darren Philbert; Sung, Sheng-Feng

2018-05-01

Early detection of atrial fibrillation after stroke is important for secondary prevention in stroke patients without known atrial fibrillation (AF). We aimed to compare the performance of CHADS 2 , CHA 2 DS 2 -VASc and HATCH scores in predicting AF detected after stroke (AFDAS) and to test whether adding stroke severity to the risk scores improves predictive performance. Adult patients with first ischemic stroke event but without a prior history of AF were retrieved from a nationwide population-based database. We compared C-statistics of CHADS 2 , CHA 2 DS 2 -VASc and HATCH scores for predicting the occurrence of AFDAS during stroke admission (cohort I) and during follow-up after hospital discharge (cohort II). The added value of stroke severity to prediction models was evaluated using C-statistics, net reclassification improvement, and integrated discrimination improvement. Cohort I comprised 13,878 patients and cohort II comprised 12,567 patients. Among them, 806 (5.8%) and 657 (5.2%) were diagnosed with AF, respectively. The CHADS 2 score had the lowest C-statistics (0.558 in cohort I and 0.597 in cohort II), whereas the CHA 2 DS 2 -VASc score had comparable C-statistics (0.603 and 0.644) to the HATCH score (0.612 and 0.653) in predicting AFDAS. Adding stroke severity to each of the three risk scores significantly increased the model performance. In stroke patients without known AF, all three risk scores predicted AFDAS during admission and follow-up, but with suboptimal discrimination. Adding stroke severity improved their predictive abilities. These risk scores, when combined with stroke severity, may help prioritize patients for continuous cardiac monitoring in daily practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Axisymmetric inlet minimum weight design method

NASA Technical Reports Server (NTRS)

Nadell, Shari-Beth

1995-01-01

An analytical method for determining the minimum weight design of an axisymmetric supersonic inlet has been developed. The goal of this method development project was to improve the ability to predict the weight of high-speed inlets in conceptual and preliminary design. The initial model was developed using information that was available from inlet conceptual design tools (e.g., the inlet internal and external geometries and pressure distributions). Stiffened shell construction was assumed. Mass properties were computed by analyzing a parametric cubic curve representation of the inlet geometry. Design loads and stresses were developed at analysis stations along the length of the inlet. The equivalent minimum structural thicknesses for both shell and frame structures required to support the maximum loads produced by various load conditions were then determined. Preliminary results indicated that inlet hammershock pressures produced the critical design load condition for a significant portion of the inlet. By improving the accuracy of inlet weight predictions, the method will improve the fidelity of propulsion and vehicle design studies and increase the accuracy of weight versus cost studies.

Improving the Accuracy of Estimation of Climate Extremes

NASA Astrophysics Data System (ADS)

Zolina, Olga; Detemmerman, Valery; Trenberth, Kevin E.

2010-12-01

Workshop on Metrics and Methodologies of Estimation of Extreme Climate Events; Paris, France, 27-29 September 2010; Climate projections point toward more frequent and intense weather and climate extremes such as heat waves, droughts, and floods, in a warmer climate. These projections, together with recent extreme climate events, including flooding in Pakistan and the heat wave and wildfires in Russia, highlight the need for improved risk assessments to help decision makers and the public. But accurate analysis and prediction of risk of extreme climate events require new methodologies and information from diverse disciplines. A recent workshop sponsored by the World Climate Research Programme (WCRP) and hosted at United Nations Educational, Scientific and Cultural Organization (UNESCO) headquarters in France brought together, for the first time, a unique mix of climatologists, statisticians, meteorologists, oceanographers, social scientists, and risk managers (such as those from insurance companies) who sought ways to improve scientists' ability to characterize and predict climate extremes in a changing climate.

Children's and Parents' Ability to Tolerate Child Distress: Impact on Cognitive Behavioral Therapy for Pediatric Obsessive Compulsive Disorder.

PubMed

Selles, Robert R; Franklin, Martin; Sapyta, Jeffrey; Compton, Scott N; Tommet, Doug; Jones, Richard N; Garcia, Abbe; Freeman, Jennifer

2018-04-01

The present study explored the concept of tolerance for child distress in 46 children (ages 5-8), along with their mothers and fathers, who received family-based CBT for OCD. The study sought to describe baseline tolerance, changes in tolerance with treatment, and the predictive impact of tolerance on symptom improvement. Tolerance was rated by clinicians on a single item and the CY-BOCS was used to measure OCD severity. Descriptive results suggested that all participants had some difficulty tolerating the child's distress at baseline while paired t tests indicated large improvements were made over treatment (d = 1.2-2.0). Fathers' initial tolerance was significantly related to symptom improvement in a multivariate regression as were fathers' and children's changes in distress tolerance over the course of treatment. Overall, results provide support for examining tolerance of child distress including its predictive impact and potential as a supplemental intervention target.

WaterNet:The NASA Water Cycle Solutions Network

NASA Astrophysics Data System (ADS)

Belvedere, D. R.; Houser, P. R.; Pozzi, W.; Imam, B.; Schiffer, R.; Schlosser, C. A.; Gupta, H.; Martinez, G.; Lopez, V.; Vorosmarty, C.; Fekete, B.; Matthews, D.; Lawford, R.; Welty, C.; Seck, A.

2008-12-01

Water is essential to life and directly impacts and constrains society's welfare, progress, and sustainable growth, and is continuously being transformed by climate change, erosion, pollution, and engineering. Projections of the effects of such factors will remain speculative until more effective global prediction systems and applications are implemented. NASA's unique role is to use its view from space to improve water and energy cycle monitoring and prediction, and has taken steps to collaborate and improve interoperability with existing networks and nodes of research organizations, operational agencies, science communities, and private industry. WaterNet is a Solutions Network, devoted to the identification and recommendation of candidate solutions that propose ways in which water-cycle related NASA research results can be skillfully applied by partner agencies, international organizations, state, and local governments. It is designed to improve and optimize the sustained ability of water cycle researchers, stakeholders, organizations and networks to interact, identify, harness, and extend NASA research results to augment Decision Support Tools that address national needs.

Long-term adherence and effects on grip strength and upper leg performance of prescribed supplemental vitamin D in pregnant and recently pregnant women of Somali and Swedish birth with 25-hydroxyvitamin D deficiency: a before-and-after treatment study.

PubMed

Kalliokoski, Paul; Rodhe, Nils; Bergqvist, Yngve; Löfvander, Monica

2016-11-15

Muscular weakness and severe vitamin D deficiency is prevalent in Somali (veiled) pregnant women, Sweden. The study aims here were to explore adherence to prescribed supplemental vitamin D in new mothers with vitamin D deficiency and its effects on grip strength and upper leg performance in Somali (target group TG) and Swedish women (reference group RG) from spring through winter. A before- and after study was designed. A cross-sectional sample of women in antenatal care with serum 25-OHD ≤50 nmol/L were prescribed one or two tablets daily (800 or 1600 IU vitamin D3 with calcium) for 10 months. Reminders were made by Somali nurses (TG) or Swedish doctors (RG). Baseline and 10 month measurements of plasma nmol/L 25-OHD, maximal grip strength held for 10 s (Newton, N) and ability to squat (yes;no) were done. Total tablet intake (n) was calculated. Outcome variables were changes from baseline in grip strength and ability to squat. Predicting variables for change in grip strength and ability to squat were calculated using linear and binary regression in final models. Undetectable 25-OHD values (<10 nmol/L) were replaced with '9' in statistic calculations. Seventy-one women (46 TG, 1/3 with undetectable baseline 25-OHD; 25 RG) participated. At the 10-month follow up, 17% TG and 8% RG women reported having refrained from supplement. Mean 25-OHD increased 16 to 49 nmol/L (TG) and 39 nmol/L to 67 nmol/L (RG), (both p < 0.001). Grip strength had improved from 153 to 188 N (TG) (p < 0.001) and from 257 to 297 N (RG) (p = 0.003) and inability to squat had decreased in TG (35 to 9, p < 0.001). Intake of number of tablets predicted increased grip strength (B 0.067, 95%CI 0.008-0.127, p = 0.027). One tablet daily (>300 in total) predicted improved ability to squat (OR 16; 95% CI 1.8-144.6). Adherence to supplemental vitamin D and calcium should be encouraged as an even moderate intake was associated to improved grip strength and upper leg performance, which was particularly useful for the women with severe 25-OHD deficiency and poor physical performance at baseline. ClinicalTrials.gov Identifier: NCT02922803 . Date of registration: September 28, 2016.

Do Teachers' Perceptions of Children's Math and Reading Related Ability and Effort Predict Children's Self-Concept of Ability in Math and Reading?

ERIC Educational Resources Information Center

Upadyaya, Katja; Eccles, Jacquelynne

2015-01-01

This study investigated to what extent primary school teachers' perceptions of their students' ability and effort predict developmental changes in children's self-concepts of ability in math and reading after controlling for students' academic performance and general intelligence. Three cohorts (N?=?849) of elementary school children and their…

Interpreting incremental value of markers added to risk prediction models.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

2012-09-15

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

Associations Between the Serum Metabolome and All-Cause Mortality Among African Americans in the Atherosclerosis Risk in Communities (ARIC) Study

PubMed Central

Yu, Bing; Heiss, Gerardo; Alexander, Danny; Grams, Morgan E.; Boerwinkle, Eric

2016-01-01

Early and accurate identification of people at high risk of premature death may assist in the targeting of preventive therapies in order to improve overall health. To identify novel biomarkers for all-cause mortality, we performed untargeted metabolomics in the Atherosclerosis Risk in Communities (ARIC) Study. We included 1,887 eligible ARIC African Americans, and 671 deaths occurred during a median follow-up period of 22.5 years (1987–2011). Chromatography and mass spectroscopy identified and quantitated 204 serum metabolites, and Cox proportional hazards models were used to analyze the longitudinal associations with all-cause and cardiovascular mortality. Nine metabolites, including cotinine, mannose, glycocholate, pregnendiol disulfate, α-hydroxyisovalerate, N-acetylalanine, andro-steroid monosulfate 2, uridine, and γ-glutamyl-leucine, showed independent associations with all-cause mortality, with an average risk change of 18% per standard-deviation increase in metabolite level (P < 1.23 × 10−4). A metabolite risk score, created on the basis of the weighted levels of the identified metabolites, improved the predictive ability of all-cause mortality over traditional risk factors (bias-corrected Harrell's C statistic 0.752 vs. 0.730). Mannose and glycocholate were associated with cardiovascular mortality (P < 1.23 × 10−4), but predictive ability was not improved beyond the traditional risk factors. This metabolomic analysis revealed potential novel biomarkers for all-cause mortality beyond the traditional risk factors. PMID:26956554

Improving the detection of tectonic transients in Japan by accounting for Earth's deformation response to surface mass loading

NASA Astrophysics Data System (ADS)

Martens, H. R.; Simons, M.; Moore, A. W.; Owen, S. E.; Rivera, L. A.

2016-12-01

We explore the contributions of oceanic, atmospheric, and hydrologic mass loading to Global Navigation Satellite System (GNSS)-inferred observations of surface displacements in Japan. Surface mass loading (SML) generates mm- to cm-level deformation of the solid Earth on time scales of hours to years, which exceeds the measurement uncertainties of most GNSS position estimates. By improving the efficiency and accuracy of the prediction and empirical estimation of SML response, we aim to reduce the variance of GNSS time series and therefore enhance the ability to resolve subtle tectonic signals, such as aseismic transients associated with subduction zone processes. Using the GIPSY software in precise point positioning mode, we estimate time series of sub-daily receiver positions for the GNSS Earth Observation Network System (GEONET) in Japan. We also model the Earth's elastic deformation response to a variety of surface mass loads, including loads of atmospheric (e.g., ECMWF) and oceanic (e.g., TPXO8-Atlas, ECCO2) origin. We extract periodic signals, such as the ocean tides and seasonal variations in hydrological loading, using harmonic analysis. Deformation caused by non-periodic loads, such as non-tidal oceanic and atmospheric loads, can be predicted and removed to further reduce the variance. We seek to streamline the workflow for estimating SML-induced surface displacements from a variety of sources in order to account for loading signals in routine GNSS data processing, thereby improving the ability to assess the mechanics of plate boundaries.

Collective action and the collaborative brain

PubMed Central

Gavrilets, Sergey

2015-01-01

Humans are unique both in their cognitive abilities and in the extent of cooperation in large groups of unrelated individuals. How our species evolved high intelligence in spite of various costs of having a large brain is perplexing. Equally puzzling is how our ancestors managed to overcome the collective action problem and evolve strong innate preferences for cooperative behaviour. Here, I theoretically study the evolution of social-cognitive competencies as driven by selection emerging from the need to produce public goods in games against nature or in direct competition with other groups. I use collaborative ability in collective actions as a proxy for social-cognitive competencies. My results suggest that collaborative ability is more likely to evolve first by between-group conflicts and then later be utilized and improved in games against nature. If collaborative abilities remain low, the species is predicted to become genetically dimorphic with a small proportion of individuals contributing to public goods and the rest free-riding. Evolution of collaborative ability creates conditions for the subsequent evolution of collaborative communication and cultural learning. PMID:25551149

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

PubMed Central

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T.; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J.

2017-01-01

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival. PMID:28401902

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

PubMed

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

2017-04-12

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Fan Noise Prediction with Applications to Aircraft System Noise Assessment

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

2009-01-01

This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

Benthic Light Availability Improves Predictions of Riverine Primary Production

NASA Astrophysics Data System (ADS)

Kirk, L.; Cohen, M. J.

2017-12-01

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

Westerly wind bursts simulated in CAM4 and CCSM4

NASA Astrophysics Data System (ADS)

Lian, Tao; Tang, Youmin; Zhou, Lei; Islam, Siraj Ul; Zhang, Chan; Li, Xiaojing; Ling, Zheng

2018-02-01

The equatorial westerly wind bursts (WWBs) play an important role in modulating and predicting the El Niño-Southern Oscillation (ENSO). In this study, the ability of the Community Atmospheric Model version 4 (CAM4) and the Community Climate System Model version 4 (CCSM4) in simulating WWBs is systematically evaluated. Many characteristics of WWBs, including their longitude distributions, durations, zonal extensions, variabilities at seasonal, intraseasonal, and interannual timescales, as well as their relations with the Madden-Julian Oscillation (MJO) and ENSO, are discussed. Generally speaking, these characteristics of WWBs can be successfully reproduced by CAM4, owning to the improvement of the deep convection in the model. In CCSM4, significant bias such as the lack of the equatorial Pacific WWBs in boreal spring season and the weak modulation by a strong MJO are found. Our findings confirm the fact that the WWBs are greatly modulated by the surface temperature. It's also suggested that improving the air-sea coupling in CCSM4 may improve model performance in simulating WWBs, and may further improve the predictability of ENSO in the coupled model.

White matter integrity as a marker for cognitive plasticity in aging.

PubMed

de Lange, Ann-Marie Glasø; Bråthen, Anne Cecilie Sjøli; Grydeland, Håkon; Sexton, Claire; Johansen-Berg, Heidi; Andersson, Jesper L R; Rohani, Darius A; Nyberg, Lars; Fjell, Anders M; Walhovd, Kristine B

2016-11-01

Age-related differences in white matter (WM) integrity are substantial, but it is unknown whether between-subject variability in WM integrity influences the capacity for cognitive improvement. We investigated the effects of memory training related to active and passive control conditions in older adults and tested whether WM integrity at baseline was predictive of training benefits. We hypothesized that (1) memory improvement would be restricted to the training group, (2) widespread areas would show greater mean diffusivity (MD) and lower fractional anisotropy in older adults relative to young adults, and (3) within these areas, variability in WM microstructure in the older group would be predictive of training gains. The results showed that only the group receiving training improved their memory. Significant age differences in MD and fractional anisotropy were found in widespread areas. Within these areas, voxelwise analyses showed a negative relationship between MD and memory improvement in 3 clusters, indicating that WM integrity could serve as a marker for the ability to adapt in response to cognitive challenges in aging. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Terrestrial biogeochemical cycles: global interactions with the atmosphere and hydrology

NASA Astrophysics Data System (ADS)

Schimel, David S.; Kittel, Timothy G. F.; Parton, William J.

1991-08-01

Ecosystem scientists have developed a body of theory to predict the behaviour of biogeochemical cycles when exchanges with other ecosystems are small or prescribed. Recent environmental changes make it clear that linkages between ecosystems via atmospheric and hydrological transport have large effects on ecosystem dynamics when considered over time periods of a decade to a century, time scales relevant to contemporary humankind. Our ability to predict behaviour of ecosystems coupled by transport is limited by our ability (1) to extrapolate biotic function to large spatial scales and (2) to measure and model transport. We review developments in ecosystem theory, remote sensing, and geographical information systems (GIS) that support new efforts in spatial modeling. A paradigm has emerged to predict behaviour of ecosystems based on understanding responses to multiple resources (e.g., water, nutrients, light). Several ecosystem models couple primary production to decomposition and nutrient availability using the above paradigm. These models require a fairly small set of environmental variables to simulate spatial and temporal variation in rates of biogeochemical cycling. Simultaneously, techniques for inferring ecosystem behaviour from remotely measured canopy light interception are improving our ability to infer plant activity from satellite observations. Efforts have begun to couple models of transport in air and water to models of ecosystem function. Preliminary work indicates that coupling of transport and ecosystem processes alters the behaviour of earth system components (hydrology, terrestrial ecosystems, and the atmosphere) from that of an uncoupled mode.

Constructional ability in two- versus three-dimensions: relationship to spatial vision and locus of cerebrovascular lesion.

PubMed

Capruso, Daniel X; Hamsher, Kerry deS

2011-06-01

Clinical evaluation and research on constructional ability have come to rely almost exclusively on two-dimensional tasks such as graphomotor copying or mosaic Block Design (BD). A return to the inclusion of a third dimension in constructional tests may increase the spatial demands of the task, and improve understanding of the relationship between visual perception and constructional ability in patients with cerebral disease. Subjects were patients (n=43) with focal or multifocal cerebrovascular lesions as determined by CT or MRI. Tests of temporal orientation, verbal intelligence, language, object vision and spatial vision were used to determine which factors were predictive of performance on two-dimensional BD and Three-Dimensional Block Construction (3-DBC) tasks. Stepwise linear regression indicated that spatial vision predicted BD performance, and was even more strongly predictive of 3-DBC. Other cognitive domains did not account for significant additional variance in performance of either BD or 3-DBC. Bilateral cerebral lesions produced more severe deficits on BD than did unilateral cerebral lesions. The presence of a posterior cerebral lesion was the significant factor in producing deficits in 3-DBC. The spatial aspect of a constructional task is enhanced when the patient is required to assemble an object in all three dimensions of space. In the typical patient with cerebrovascular disease, constructional deficits typically occur in the context of a wider syndrome of deficits in spatial vision. Copyright © 2010 Elsevier Srl. All rights reserved.

The Reliability and Predictive Ability of a Biomarker of Oxidative DNA Damage on Functional Outcomes after Stroke Rehabilitation

PubMed Central

Hsieh, Yu-Wei; Lin, Keh-Chung; Korivi, Mallikarjuna; Lee, Tsong-Hai; Wu, Ching-Yi; Wu, Kuen-Yuh

2014-01-01

We evaluated the reliability of 8-hydroxy-2′-deoxyguanosine (8-OHdG), and determined its ability to predict functional outcomes in stroke survivors. The rehabilitation effect on 8-OHdG and functional outcomes were also assessed. Sixty-one stroke patients received a 4-week rehabilitation. Urinary 8-OHdG levels were determined by liquid chromatography–tandem mass spectrometry. The test-retest reliability of 8-OHdG was good (interclass correlation coefficient = 0.76). Upper-limb motor function and muscle power determined by the Fugl-Meyer Assessment (FMA) and Medical Research Council (MRC) scales before rehabilitation showed significant negative correlation with 8-OHdG (r = −0.38, r = −0.30; p < 0.05). After rehabilitation, we found a fair and significant correlation between 8-OHdG and FMA (r = −0.34) and 8-OHdG and pain (r = 0.26, p < 0.05). Baseline 8-OHdG was significantly correlated with post-treatment FMA, MRC, and pain scores (r = −0.34, −0.31, and 0.25; p < 0.05), indicating its ability to predict functional outcomes. 8-OHdG levels were significantly decreased, and functional outcomes were improved after rehabilitation. The exploratory study findings conclude that 8-OHdG is a reliable and promising biomarker of oxidative stress and could be a valid predictor of functional outcomes in patients. Monitoring of behavioral indicators along with biomarkers may have crucial benefits in translational stroke research. PMID:24743892

Seizure Forecasting and the Preictal State in Canine Epilepsy.

PubMed

Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M; Worrell, Gregory A; Brinkmann, Benjamin H

2017-02-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

SEIZURE FORECASTING AND THE PREICTAL STATE IN CANINE EPILEPSY

PubMed Central

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent M.; Worrell, Gregory A.; Brinkmann, Benjamin H.

2017-01-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state. PMID:27464854

Predicting heavy episodic drinking using an extended temporal self-regulation theory.

PubMed

Black, Nicola; Mullan, Barbara; Sharpe, Louise

2017-10-01

Alcohol consumption contributes significantly to the global burden from disease and injury, and specific patterns of heavy episodic drinking contribute uniquely to this burden. Temporal self-regulation theory and the dual-process model describe similar theoretical constructs that might predict heavy episodic drinking. The aims of this study were to test the utility of temporal self-regulation theory in predicting heavy episodic drinking, and examine whether the theoretical relationships suggested by the dual-process model significantly extend temporal self-regulation theory. This was a predictive study with 149 Australian adults. Measures were questionnaires (self-report habit index, cues to action scale, purpose-made intention questionnaire, timeline follow-back questionnaire) and executive function tasks (Stroop, Tower of London, operation span). Participants completed measures of theoretical constructs at baseline and reported their alcohol consumption two weeks later. Data were analysed using hierarchical multiple linear regression. Temporal self-regulation theory significantly predicted heavy episodic drinking (R 2 =48.0-54.8%, p<0.001) and the hypothesised extension significantly improved the prediction of heavy episodic drinking frequency (ΔR 2 =4.5%, p=0.001) but not peak consumption (ΔR 2 =1.4%, p=0.181). Intention and behavioural prepotency directly predicted heavy episodic drinking (p<0.01). Planning ability moderated the intention-behaviour relationship and inhibitory control moderated the behavioural prepotency-behaviour relationship (p<0.05). Both temporal self-regulation theory and the extended temporal self-regulation theory provide good prediction of heavy episodic drinking. Intention, behavioural prepotency, planning ability and inhibitory control may be good targets for interventions designed to decrease heavy episodic drinking. Copyright © 2017 Elsevier Ltd. All rights reserved.

Risk Adjustment for a Children's Capitation Rate

PubMed Central

Newhouse, Joseph P.; Sloss, Elizabeth M.; Manning, Willard G.; Keeler, Emmett B.

1993-01-01

Few capitation arrangements vary premiums by a child's health characteristics, yielding an incentive to discriminate against children with predictably high expenditures from chronic diseases. In this article, we explore risk adjusters for the 35 percent of the variance in annual outpatient expenditure we find to be potentially predictable. Demographic factors such as age and gender only explain 5 percent of such variance; health status measures explain 25 percent, prior use and health status measures together explain 65 to 70 percent. The profit from risk selection falls less than proportionately with improved ability to adjust for risk. Partial capitation rates may be necessary to mitigate skimming and dumping. PMID:10133708

Risk adjustment for a children's capitation rate.

PubMed

Newhouse, J P; Sloss, E M; Manning, W G; Keeler, E B

1993-01-01

Few capitation arrangements vary premiums by a child's health characteristics, yielding an incentive to discriminate against children with predictably high expenditures from chronic diseases. In this article, we explore risk adjusters for the 35 percent of the variance in annual out-patient expenditure we find to be potentially predictable. Demographic factors such as age and gender only explain 5 percent of such variance; health status measures explain 25 percent, prior use and health status measures together explain 65 to 70 percent. The profit from risk selection falls less than proportionately with improved ability to adjust for risk. Partial capitation rates may be necessary to mitigate skimming and dumping.

Improvement of analytical dynamic models using modal test data

NASA Technical Reports Server (NTRS)

Berman, A.; Wei, F. S.; Rao, K. V.

1980-01-01

A method developed to determine maximum changes in analytical mass and stiffness matrices to make them consistent with a set of measured normal modes and natural frequencies is presented. The corrected model will be an improved base for studies of physical changes, boundary condition changes, and for prediction of forced responses. The method features efficient procedures not requiring solutions of the eigenvalue problem, and the ability to have more degrees of freedom than the test data. In addition, modal displacements are obtained for all analytical degrees of freedom, and the frequency dependence of the coordinate transformations is properly treated.

Fracture predictive ability of physical performance tests and history of falls in elderly women: a 10-year prospective study.

PubMed

Wihlborg, A; Englund, M; Åkesson, K; Gerdhem, P

2015-08-01

In a large cohort of elderly women followed for 10 years, we found that balance, gait speed, and self-reported history of fall independently predicted fracture. These clinical risk factors are easily evaluated and therefore advantageous in a clinical setting. They would improve fracture risk assessment and thereby also fracture prevention. The aim of this study was to identify additional risk factors for osteoporosis-related fracture by investigating the fracture predictive ability of physical performance tests and self-reported history of falls. In the population-based Osteoporosis Prospective Risk Assessment study (OPRA), 1044 women were recruited at the age of 75 and followed for 10 years. At inclusion, knee extension force, standing balance, gait speed, and bone mineral density (BMD) were examined. Falls the year before investigation was assessed by questionnaire. Cox proportional hazards regression analysis was used to determine fracture hazard ratios (HR) with BMD, history of fracture, BMI, smoking habits, bisphosphonate, vitamin D, glucocorticoid, and alcohol use as covariates. Continuous variables were standardized and HR shown for each standard deviation change. Of all women, 427 (41%) sustained at least one fracture during the 10-year follow-up. Failing the balance test had an HR of 1.98 (1.18-3.32) for hip fracture. Each standard deviation decrease in gait speed was associated with an HR of 1.37 (1.14-1.64) for hip fracture. Previous fall had an HR of 1.30 (1.03-1.65) for any fracture; 1.39 (1.08-1.79) for any osteoporosis-related fracture; and 1.60 (1.03-2.48) for distal forearm fracture. Knee extension force did not show fracture predictability. The balance test, gait speed test, and self-reported history of fall all hold independent fracture predictability. Consideration of these clinical risk factors for fracture would improve the fracture risk assessment and subsequently also fracture prevention.

The prognostic value of stromal FK506-binding protein 1 and androgen receptor in prostate cancer outcome.

PubMed

Leach, Damien A; Trotta, Andrew P; Need, Eleanor F; Risbridger, Gail P; Taylor, Renea A; Buchanan, Grant

2017-02-01

Improving our ability to predict cancer progression and response to conservative or radical intent therapy is critical if we are to prevent under or over treatment of individual patients. Whereas the majority of solid tumors now have a range of molecular and/or immunological markers to help define prognosis and treatment options, prostate cancer still relies mainly on histological grading and clinical parameters. We have recently reported that androgen receptor (AR) expression in stroma inversely associates with prostate cancer-specific survival, and that stromal AR reduces metastasis. For this paper, we tested the hypothesis that the AR-regulated gene FKBP51 could be used as a marker of AR activity to better predict outcome. Using immunohistochemistry on a cohort of 64 patient-matched benign and malignant prostate tissues, we assessed patient outcome by FKBP51 and AR levels. Immunoblot and RT-qPCR were used to demonstrate androgen regulation of FKBP51 in primary and primary human prostatic fibroblasts and fibroblast cell-lines. As predicted by FKBP51 level, high AR activity in cancer stroma was associated with longer median survival (1,306 days) compared with high AR alone (699 days), whereas those with low AR and/or low FKBP51 did poorly (384 and 338 days, respectively). Survival could not be predicted on the basis cancer epithelial AR levels or activity, and was not associated with immunoreactivity in patient matched benign tissues. FKBP51 improves the ability of stromal AR to predict prostate cancer-specific mortality. By adding additional immunological assessment, similar to what is already in place in a number of other cancers, we could better serve patients with prostate cancer in prognosis and informed treatment choices. Prostate 77:185-195, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

PubMed Central

Yoo, Tae Keun; Kim, Deok Won; Choi, Soo Beom; Oh, Ein; Park, Jee Soo

2016-01-01

Background Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. Methods The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. Results The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). Conclusions The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk. PMID:26859664

Neuroprediction, Violence, and the Law: Setting the Stage

PubMed Central

Bibas, Stephanos; Grafton, Scott; Kiehl, Kent A.; Mansfield, Andrew; Sinnott-Armstrong, Walter; Gazzaniga, Michael

2014-01-01

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In “Violence Risk Assessment and the Law”, we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing (“Violence Risk Assessment and Capital Sentencing”), civil commitment hearings (“Violence Risk Assessment and Civil Commitment”), and “sexual predator” statutes (“Violence Risk Assessment and Sexual Predator Statutes”). In “Clinical vs. Actuarial Violence Risk Assessment”, we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In “The Neural Correlates of Psychopathy”, we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection (“Cutting-Edge Data Collection: Genetically Informed Neuroimaging”) and data analysis (“Cutting-Edge Data Analysis: Pattern Classification”) that we believe will play an important role when it comes to future neuroscientific research on violence. In “The Potential Promise of Neuroprediction”, we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in “The Potential Perils of Neuroprediction”, we explore some potential evidentiary (“Evidentiary Issues”), constitutional (“Constitutional Issues”), and moral (“Moral Issues”) issues that may arise in the context of the neuroprediction of violence. PMID:25083168

Academic performance, career potential, creativity, and job performance: can one construct predict them all?

PubMed

Kuncel, Nathan R; Hezlett, Sarah A; Ones, Deniz S

2004-01-01

This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).

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

PubMed

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

2015-05-01

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. Copyright © 2015 the American Physiological Society.

Temporal prediction abilities are mediated by motor effector and rhythmic expertise.

PubMed

Manning, Fiona C; Harris, Jennifer; Schutz, Michael

2017-03-01

Motor synchronization is a critical part of musical performance and listening. Recently, motor control research has described how movements that contain more available degrees of freedom are more accurately timed. Previously, we demonstrated that stick tapping improves perception in a timing detection task, where percussionists greatly outperformed non-percussionists only when tapping along. Since most synchronization studies implement finger tapping to examine simple motor synchronization, here we completed a similar task where percussionists and non-percussionists synchronized using finger tapping; movement with fewer degrees of freedom than stick tapping. Percussionists and non-percussionists listened to an isochronous beat sequence and identified the timing of a probe tone. On half of the trials, they tapped along with their index finger, and on the other half of the trials, they listened without moving prior to making timing judgments. We found that both groups benefited from tapping overall. Interestingly, percussionists performed only marginally better than did non-percussionists when finger tapping and no different when listening alone, differing from past studies reporting highly superior timing abilities in percussionists. Additionally, we found that percussionist finger tapping was less variable and less asynchronous than was non-percussionist tapping. Moreover, in both groups finger tapping was more variable and more asynchronous than stick tapping in our previous study. This study demonstrates that the motor effector implemented in tapping studies affects not only synchronization abilities, but also subsequent prediction abilities. We discuss these findings in light of effector-specific training and degrees of freedom in motor timing, both of which impact timing abilities to different extents.