Individual laboratory-measured discount rates predict field behavior.

PubMed

Chabris, Christopher F; Laibson, David; Morris, Carrie L; Schuldt, Jonathon P; Taubinsky, Dmitry

2008-12-01

We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.

A CFD Study on the Prediction of Cyclone Collection Efficiency

NASA Astrophysics Data System (ADS)

Gimbun, Jolius; Chuah, T. G.; Choong, Thomas S. Y.; Fakhru'L-Razi, A.

2005-09-01

This work presents a Computational Fluid Dynamics calculation to predict and to evaluate the effects of temperature, operating pressure and inlet velocity on the collection efficiency of gas cyclones. The numerical solutions were carried out using spreadsheet and commercial CFD code FLUENT 6.0. This paper also reviews four empirical models for the prediction of cyclone collection efficiency, namely Lapple [1], Koch and Licht [2], Li and Wang [3], and Iozia and Leith [4]. All the predictions proved to be satisfactory when compared with the presented experimental data. The CFD simulations predict the cyclone cut-off size for all operating conditions with a deviation of 3.7% from the experimental data. Specifically, results obtained from the computer modelling exercise have demonstrated that CFD model is the best method of modelling the cyclones collection efficiency.

The role of effort in moderating the anxiety-performance relationship: Testing the prediction of processing efficiency theory in simulated rally driving.

PubMed

Wilson, Mark; Smith, Nickolas C; Chattington, Mark; Ford, Mike; Marple-Horvat, Dilwyn E

2006-11-01

We tested some of the key predictions of processing efficiency theory using a simulated rally driving task. Two groups of participants were classified as either dispositionally high or low anxious based on trait anxiety scores and trained on a simulated driving task. Participants then raced individually on two similar courses under counterbalanced experimental conditions designed to manipulate the level of anxiety experienced. The effort exerted on the driving tasks was assessed though self-report (RSME), psychophysiological measures (pupil dilation) and visual gaze data. Efficiency was measured in terms of efficiency of visual processing (search rate) and driving control (variability of wheel and accelerator pedal) indices. Driving performance was measured as the time taken to complete the course. As predicted, increased anxiety had a negative effect on processing efficiency as indexed by the self-report, pupillary response and variability of gaze data. Predicted differences due to dispositional levels of anxiety were also found in the driving control and effort data. Although both groups of drivers performed worse under the threatening condition, the performance of the high trait anxious individuals was affected to a greater extent by the anxiety manipulation than the performance of the low trait anxious drivers. The findings suggest that processing efficiency theory holds promise as a theoretical framework for examining the relationship between anxiety and performance in sport.

Predicting future violence among individuals with psychopathy.

PubMed

Coid, Jeremy W; Ullrich, Simone; Kallis, Constantinos

2013-11-01

Structured risk assessment aims to help clinicians classify offenders according to likelihood of future violent and criminal behaviour. We investigated how confident clinicians can be using three commonly used instruments (HCR-20, VRAG, OGRS-II) in individuals with different diagnoses. Moderate to good predictive accuracy for future violence was achieved for released prisoners with no mental disorder, low to moderate for clinical syndromes and personality disorder, but accuracy was no better than chance for individuals with psychopathy. Comprehensive diagnostic assessment should precede an assessment of risk. Risk assessment instruments cannot be relied upon when managing public risk from individuals with psychopathy.

Individual differences in working memory capacity and search efficiency.

PubMed

Miller, Ashley L; Unsworth, Nash

2018-05-29

In two experiments, we examined how various learning conditions impact the relation between working memory capacity (WMC) and memory search abilities. Experiment 1 employed a delayed free recall task with semantically related words to induce the buildup of proactive interference (PI) and revealed that the buildup of PI differentially impacted recall accuracy and recall latency for low-WMC and high-WMC individuals. Namely, the buildup of PI impaired recall accuracy and slowed recall latency for low-WMC individuals to a greater extent than what was observed for high-WMC individuals. To provide a circumstance in which previously learned information remains relevant over the course of learning, Experiment 2 required participants to complete a multitrial delayed free recall task with unrelated words. Results revealed that with increased practice with the same word list, WMC-related differences were eventually eliminated in interresponse times (IRTs) and recall accuracy, but not recall latency. Thus, despite still accumulating larger search sets, low-WMC individuals searched LTM as efficiently as high-WMC individuals. Collectively, these results are consistent with the notion that under normal free recall conditions, low-WMC individuals search LTM less efficiently than do high-WMC individuals because of their reliance on noisy temporal-contextual cues at retrieval. However, it appears that under conditions in which previously learned items remain relevant at recall, this tendency to rely on vague self-generated retrieval cues can actually facilitate the ability to accurately and quickly recall information.

Prediction and design of efficient exciplex emitters for high-efficiency, thermally activated delayed-fluorescence organic light-emitting diodes.

PubMed

Liu, Xiao-Ke; Chen, Zhan; Zheng, Cai-Jun; Liu, Chuan-Lin; Lee, Chun-Sing; Li, Fan; Ou, Xue-Mei; Zhang, Xiao-Hong

2015-04-08

High-efficiency, thermally activated delayed-fluorescence organic light-emitting diodes based on exciplex emitters are demonstrated. The best device, based on a TAPC:DPTPCz emitter, shows a high external quantum efficiency of 15.4%. Strategies for predicting and designing efficient exciplex emitters are also provided. This approach allow prediction and design of efficient exciplex emitters for achieving high-efficiency organic light-emitting diodes, for future use in displays and lighting applications. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Developmental dyslexia: predicting individual risk

PubMed Central

Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

2015-01-01

Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as ‘dyslexic’ or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Results Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Conclusions Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. PMID:25832320

Individualized prediction of lung-function decline in chronic obstructive pulmonary disease

PubMed Central

Zafari, Zafar; Sin, Don D.; Postma, Dirkje S.; Löfdahl, Claes-Göran; Vonk, Judith; Bryan, Stirling; Lam, Stephen; Tammemagi, C. Martin; Khakban, Rahman; Man, S.F. Paul; Tashkin, Donald; Wise, Robert A.; Connett, John E.; McManus, Bruce; Ng, Raymond; Hollander, Zsuszanna; Sadatsafavi, Mohsen

2016-01-01

Background: The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD. Methods: Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations. Results: We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being −124 to −15 mL/yr for smokers and −83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets. Interpretation: A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD. Trial registration: Lung Health Study — ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study — ClinicalTrials.gov, no. NCT00751660 PMID:27486205

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

PubMed

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free

Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging.

PubMed

Hou, YanBing; Luo, ChunYan; Yang, Jing; Ou, RuWei; Song, Wei; Wei, QianQian; Cao, Bei; Zhao, Bi; Wu, Ying; Shang, Hui-Fang; Gong, QiYong

2016-07-15

Neuroimaging holds the promise that it may one day aid the clinical assessment. However, the vast majority of studies using resting-state functional magnetic resonance imaging (fMRI) have reported average differences between Parkinson's disease (PD) patients and healthy controls, which do not permit inferences at the level of individuals. This study was to develop a model for the prediction of PD illness severity ratings from individual fMRI brain scan. The resting-state fMRI scans were obtained from 84 patients with PD and the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) scores were obtained before scanning. The RVR method was used to predict clinical scores (UPDRS-III) from fMRI scans. The application of RVR to whole-brain resting-state fMRI data allowed prediction of UPDRS-III scores with statistically significant accuracy (correlation=0.35, P-value=0.001; mean sum of squares=222.17, P-value=0.002). This prediction was informed strongly by negative weight areas including prefrontal lobe and medial occipital lobe, and positive weight areas including medial parietal lobe. It was suggested that fMRI scans contained sufficient information about neurobiological change in patients with PD to permit accurate prediction about illness severity, on an individual subject basis. Our results provided preliminary evidence, as proof-of-concept, to support that fMRI might be possible to be a clinically useful quantitative assessment aid in PD at individual level. This may enable clinicians to target those uncooperative patients and machines to replace human for a more efficient use of health care resources. Copyright © 2016 Elsevier B.V. All rights reserved.

Can the theory of planned behaviour predict the physical activity behaviour of individuals?

PubMed

Hobbs, Nicola; Dixon, Diane; Johnston, Marie; Howie, Kate

2013-01-01

The theory of planned behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series of n-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Six n-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus, n-of-1 designs and time series analyses can be used to test theory in an individual.

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows.

PubMed

McParland, S; Berry, D P

2016-05-01

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

PubMed Central

Meng, Xing; Jiang, Rongtao; Lin, Dongdong; Bustillo, Juan; Jones, Thomas; Chen, Jiayu; Yu, Qingbao; Du, Yuhui; Zhang, Yu; Jiang, Tianzi; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. PMID:27177764

Optimal predictions in everyday cognition: the wisdom of individuals or crowds?

PubMed

Mozer, Michael C; Pashler, Harold; Homaei, Hadjar

2008-10-01

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect averages over many individuals. On the conjecture that the accuracy of the group response is chiefly a consequence of aggregating across individuals, we constructed simple, heuristic approximations to the Bayesian model premised on the hypothesis that individuals have access merely to a sample of k instances drawn from the relevant distribution. The accuracy of the group response reported by Griffiths and Tenenbaum could be accounted for by supposing that individuals each utilize only two instances. Moreover, the variability of the group data is more consistent with this small-sample hypothesis than with the hypothesis that people utilize veridical or nearly veridical representations of the underlying prior distributions. Our analyses lead to a qualitatively different view of how individuals reason from past experience than the view espoused by Griffiths and Tenenbaum. 2008 Cognitive Science Society, Inc.

Developmental dyslexia: predicting individual risk.

PubMed

Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

2015-09-01

Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

An individual risk prediction model for lung cancer based on a study in a Chinese population.

PubMed

Wang, Xu; Ma, Kewei; Cui, Jiuwei; Chen, Xiao; Jin, Lina; Li, Wei

2015-01-01

Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk. We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point. Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively. The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

Auditory working memory predicts individual differences in absolute pitch learning.

PubMed

Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

2015-07-01

Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

Comparison of measured efficiencies of nine turbine designs with efficiencies predicted by two empirical methods

NASA Technical Reports Server (NTRS)

English, Robert E; Cavicchi, Richard H

1951-01-01

Empirical methods of Ainley and Kochendorfer and Nettles were used to predict performances of nine turbine designs. Measured and predicted performances were compared. Appropriate values of blade-loss parameter were determined for the method of Kochendorfer and Nettles. The measured design-point efficiencies were lower than predicted by as much as 0.09 (Ainley and 0.07 (Kochendorfer and Nettles). For the method of Kochendorfer and Nettles, appropriate values of blade-loss parameter ranged from 0.63 to 0.87 and the off-design performance was accurately predicted.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches.

PubMed

Rawassizadeh, Reza; Tomitsch, Martin; Nourizadeh, Manouchehr; Momeni, Elaheh; Peery, Aaron; Ulanova, Liudmila; Pazzani, Michael

2015-09-08

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

PubMed Central

Rawassizadeh, Reza; Tomitsch, Martin; Nourizadeh, Manouchehr; Momeni, Elaheh; Peery, Aaron; Ulanova, Liudmila; Pazzani, Michael

2015-01-01

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras. PMID:26370997

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

Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates

PubMed Central

Chambert, Thierry; Rotella, Jay J; Higgs, Megan D

2014-01-01

The investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. Individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark–recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of Tuljapurkar et al. (Ecology Letters, 12, 93, 2009) on dynamic heterogeneity. Model-selection approaches based on information criteria or Bayes factors have been used to address this question. Here, we suggest that, in addition to model-selection, model-checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. Specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. Posterior predictive checking is a straightforward and flexible approach for performing model checks in a Bayesian framework that is based on comparisons of observed data to model-generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. If discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. We illustrate this approach using analyses of vital rates with long-term mark–recapture data for Weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest. We show how posterior predictive checks can be used to strengthen inferences in

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

ERIC Educational Resources Information Center

Mozer, Michael C.; Pashler, Harold; Homaei, Hadjar

2008-01-01

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…

Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

PubMed

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

2018-05-01

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

Relationship among performance, carcass, and feed efficiency characteristics, and their ability to predict economic value in the feedlot.

PubMed

Retallick, K M; Faulkner, D B; Rodriguez-Zas, S L; Nkrumah, J D; Shike, D W

2013-12-01

A 4-yr study was conducted using 736 steers of known Angus, Simmental, or Simmental × Angus genetics to determine performance, carcass, and feed efficiency factors that explained variation in economic performance. Steers were pen fed and individual DMI was recorded using a GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada). Steers consumed a similar diet and received similar management each year. The objectives of this study were to: 1) determine current economic value of feed efficiency and 2) identify performance, carcass, and feed efficiency characteristics that predict: carcass value, profit, cost of gain, and feed costs. Economic data used were from 2011 values. Feed efficiency values investigated were: feed conversion ratio (FCR; feed to gain), residual feed intake (RFI), residual BW gain (RG), and residual intake and BW gain (RIG). Dependent variables were carcass value ($/steer), profit ($/steer), feed costs ($/steer • d(-1)), and cost of gain ($/kg). Independent variables were year, DMI, ADG, HCW, LM area, marbling, yield grade, dam breed, and sire breed. A 10% improvement in RG (P < 0.05) yielded the lowest cost of gain at $0.09/kg and highest carcass value at $17.92/steer. Carcass value increased (P < 0.05) as feed efficiency improved for FCR, RG, and RIG. Profit increased with a 10% improvement in feed efficiency (P < 0.05) with FCR at $34.65/steer, RG at $31.21/steer, RIG at $21.66/steer, and RFI at $11.47/steer. The carcass value prediction model explained 96% of the variation among carcasses and included HCW, marbling score, and yield grade. Average daily gain, marbling score, yield grade, DMI, HCW, and year born constituted 81% of the variation for prediction of profit. Eighty-five percent of the variation in cost of gain was explained by ADG, DMI, HCW, and year. Prediction equations were developed that excluded ADG and DMI, and included feed efficiency values. Using these equations, cost of gain was explained

Individual Factors Predicting Mental Health Court Diversion Outcome

ERIC Educational Resources Information Center

Verhaaff, Ashley; Scott, Hannah

2015-01-01

Objective: This study examined which individual factors predict mental health court diversion outcome among a sample of persons with mental illness participating in a postcharge diversion program. Method: The study employed secondary analysis of existing program records for 419 persons with mental illness in a court diversion program. Results:…

Periodontitis in older Swedish individuals fails to predict mortality.

PubMed

Renvert, Stefan; Wallin-Bengtsson, Viveca; Berglund, Johan; Persson, Rutger G

2015-03-01

This study aims to assess mortality risk and its association to health aspects in dentate individuals 60 years of age and older. Medical and periodontal data from 870 dentate individuals (age range 60–96) participating in the Swedish National Study on Aging and Care in Blekinge (SNACBlekinge)with survival statistics over 6 years were studied. During 6 years of follow-up, 42/474 of the individuals(8.9 %), who at baseline were between age 60 and 75, and 134/396 individuals of the individuals (33.9 %), who at baseline were ≥75 years, died. Surviving dentate individuals had more teeth (mean 19.3, S.D.±7.9) than those who died (mean 15.9,S.D.±7.3; mean diff 3,3; S.E. mean diff 0.7; 95 % CI 2.0, 4.6;p=0.001). A self-reported history of high blood pressure (F=15.0, p<0.001), heart failure (F=24.5, p<0.001, observed power=0.99), older age (F=34.7, p<0.001), male gender(F=6.3, p<0.01), serum HbA1c with 6.5 % as cutoff level(F=9.3, p=0.002) were factors associated with mortality. A medical diagnosis of heart disease, diabetes, any form of cancer,or periodontitis failed to predict mortality. A self-reported history of angina pectoris, chronic heart failure, elevated serum HbA1c, and few remaining teeth were associated with mortality risk. A professional diagnosis of cardiovascular disease, diabetes, cancer, or periodontitis was not predictive of mortality. Self-health reports are important to observe in the assessment of disease and survival in older individual.

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

PubMed Central

Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

2016-01-01

Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

PubMed

Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

Predictive models of alcohol use based on attitudes and individual values.

PubMed

García del Castillo Rodríguez, José A; López-Sánchez, Carmen; Quiles Soler, M Carmen; García del Castillo-López, Alvaro; Gázquez Pertusa, Mónica; Marzo Campos, Juan Carlos; Inglés, Candido J

2013-01-01

Two predictive models are developed in this article: the first is designed to predict people's attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The questionnaire used obtained information on participants' alcohol use, attitudes and personal values. The results show that the attitudes model correctly classifies 76.3% of cases. Likewise, the model for level of alcohol use correctly classifies 82% of cases. According to our results, we can conclude that there are a series of individual values that influence drinking and attitudes to alcohol use, which therefore provides us with a potentially powerful instrument for developing preventive intervention programs.

Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets

NASA Astrophysics Data System (ADS)

Eom, Cheoljun; Choi, Sunghoon; Oh, Gabjin; Jung, Woo-Sung

2008-07-01

We empirically investigated the relationships between the degree of efficiency and the predictability in financial time-series data. The Hurst exponent was used as the measurement of the degree of efficiency, and the hit rate calculated from the nearest-neighbor prediction method was used for the prediction of the directions of future price changes. We used 60 market indexes of various countries. We empirically discovered that the relationship between the degree of efficiency (the Hurst exponent) and the predictability (the hit rate) is strongly positive. That is, a market index with a higher Hurst exponent tends to have a higher hit rate. These results suggested that the Hurst exponent is useful for predicting future price changes. Furthermore, we also discovered that the Hurst exponent and the hit rate are useful as standards that can distinguish emerging capital markets from mature capital markets.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

PubMed

Kong, Ru; Li, Jingwei; Orban, Csaba; Sabuncu, Mert R; Liu, Hesheng; Schaefer, Alexander; Sun, Nanbo; Zuo, Xi-Nian; Holmes, Avram J; Eickhoff, Simon B; Yeo, B T Thomas

2018-06-06

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Neural network configuration and efficiency underlies individual differences in spatial orientation ability.

PubMed

Arnold, Aiden E G F; Protzner, Andrea B; Bray, Signe; Levy, Richard M; Iaria, Giuseppe

2014-02-01

Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation--network configuration and efficiency--and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

Predicting the size of individual and group differences on speeded cognitive tasks.

PubMed

Chen, Jing; Hale, Sandra; Myerson, Joel

2007-06-01

An a priori test of the difference engine model (Myerson, Hale, Zheng, Jenkins, & Widaman, 2003) was conducted using a large, diverse sample of individuals who performed three speeded verbal tasks and three speeded visuospatial tasks. Results demonstrated that, as predicted by the model, the group standard deviation (SD) on any task was proportional to the amount of processing required by that task. Both individual performances as well as those of fast and slow subgroups could be accurately predicted by the model using no free parameters, just an individual or subgroup's mean z-score and the values of theoretical constructs estimated from fits to the group SDs. Taken together, these results are consistent with post hoc analyses reported by Myerson et al. and provide even stronger supporting evidence. In particular, the ability to make quantitative predictions without using any free parameters provides the clearest demonstration to date of the power of an analytic approach on the basis of the difference engine.

Individual differences in physiological flexibility predict spontaneous avoidance.

PubMed

Aldao, Amelia; Dixon-Gordon, Katherine L; De Los Reyes, Andres

2016-08-01

People often regulate their emotions by resorting to avoidance, a putatively maladaptive strategy. Prior work suggests that increased psychopathology symptoms predict greater spontaneous utilisation of this strategy. Extending this work, we examined whether heightened resting cardiac vagal tone (which reflects a general ability to regulate emotions in line with contextual demands) predicts decreased spontaneous avoidance. In Study 1, greater resting vagal tone was associated with reduced spontaneous avoidance in response to disgust-eliciting pictures, beyond anxiety and depression symptoms and emotional reactivity. In Study 2, resting vagal tone interacted with anxiety and depression symptoms to predict spontaneous avoidance in response to disgust-eliciting film clips. The positive association between symptoms and spontaneous avoidance was more pronounced among participants with reduced resting vagal tone. Thus, increased resting vagal tone might protect against the use of avoidance. Our findings highlight the importance of assessing both subjective and biological processes when studying individual differences in emotion regulation.

New method for probabilistic traffic demand predictions for en route sectors based on uncertain predictions of individual flight events.

DOT National Transportation Integrated Search

2011-06-14

This paper presents a novel analytical approach to and techniques for translating characteristics of uncertainty in predicting sector entry times and times in sector for individual flights into characteristics of uncertainty in predicting one-minute ...

ANNIT - An Efficient Inversion Algorithm based on Prediction Principles

NASA Astrophysics Data System (ADS)

Růžek, B.; Kolář, P.

2009-04-01

Solution of inverse problems represents meaningful job in geophysics. The amount of data is continuously increasing, methods of modeling are being improved and the computer facilities are also advancing great technical progress. Therefore the development of new and efficient algorithms and computer codes for both forward and inverse modeling is still up to date. ANNIT is contributing to this stream since it is a tool for efficient solution of a set of non-linear equations. Typical geophysical problems are based on parametric approach. The system is characterized by a vector of parameters p, the response of the system is characterized by a vector of data d. The forward problem is usually represented by unique mapping F(p)=d. The inverse problem is much more complex and the inverse mapping p=G(d) is available in an analytical or closed form only exceptionally and generally it may not exist at all. Technically, both forward and inverse mapping F and G are sets of non-linear equations. ANNIT solves such situation as follows: (i) joint subspaces {pD, pM} of original data and model spaces D, M, resp. are searched for, within which the forward mapping F is sufficiently smooth that the inverse mapping G does exist, (ii) numerical approximation of G in subspaces {pD, pM} is found, (iii) candidate solution is predicted by using this numerical approximation. ANNIT is working in an iterative way in cycles. The subspaces {pD, pM} are searched for by generating suitable populations of individuals (models) covering data and model spaces. The approximation of the inverse mapping is made by using three methods: (a) linear regression, (b) Radial Basis Function Network technique, (c) linear prediction (also known as "Kriging"). The ANNIT algorithm has built in also an archive of already evaluated models. Archive models are re-used in a suitable way and thus the number of forward evaluations is minimized. ANNIT is now implemented both in MATLAB and SCILAB. Numerical tests show good

A network of amygdala connections predict individual differences in trait anxiety.

PubMed

Greening, Steven G; Mitchell, Derek G V

2015-12-01

In this study we demonstrate that the pattern of an amygdala-centric network contributes to individual differences in trait anxiety. Individual differences in trait anxiety were predicted using maximum likelihood estimates of amygdala structural connectivity to multiple brain targets derived from diffusion-tensor imaging (DTI) and probabilistic tractography on 72 participants. The prediction was performed using a stratified sixfold cross validation procedure using a regularized least square regression model. The analysis revealed a reliable network of regions predicting individual differences in trait anxiety. Higher trait anxiety was associated with stronger connections between the amygdala and dorsal anterior cingulate cortex, an area implicated in the generation of emotional reactions, and inferior temporal gyrus and paracentral lobule, areas associated with perceptual and sensory processing. In contrast, higher trait anxiety was associated with weaker connections between amygdala and regions implicated in extinction learning such as medial orbitofrontal cortex, and memory encoding and environmental context recognition, including posterior cingulate cortex and parahippocampal gyrus. Thus, trait anxiety is not only associated with reduced amygdala connectivity with prefrontal areas associated with emotion modulation, but also enhanced connectivity with sensory areas. This work provides novel anatomical insight into potential mechanisms behind information processing biases observed in disorders of emotion. © 2015 Wiley Periodicals, Inc.

An analytical method to predict efficiency of aircraft gearboxes

NASA Technical Reports Server (NTRS)

Anderson, N. E.; Loewenthal, S. H.; Black, J. D.

1984-01-01

A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.

Realistic prediction of individual facial emotion expressions for craniofacial surgery simulations

NASA Astrophysics Data System (ADS)

Gladilin, Evgeny; Zachow, Stefan; Deuflhard, Peter; Hege, Hans-Christian

2003-05-01

In addition to the static soft tissue prediction, the estimation of individual facial emotion expressions is an important criterion for the evaluation of the carniofacial surgery planning. In this paper, we present an approach for the estimation of individual facial emotion expressions on the basis of geometrical models of human anatomy derived from tomographic data and the finite element modeling of facial tissue biomechanics.

An Efficient Scheme for Crystal Structure Prediction Based on Structural Motifs

DOE PAGES

Zhu, Zizhong; Wu, Ping; Wu, Shunqing; ...

2017-05-15

An efficient scheme based on structural motifs is proposed for the crystal structure prediction of materials. The key advantage of the present method comes in two fold: first, the degrees of freedom of the system are greatly reduced, since each structural motif, regardless of its size, can always be described by a set of parameters (R, θ, φ) with five degrees of freedom; second, the motifs could always appear in the predicted structures when the energies of the structures are relatively low. Both features make the present scheme a very efficient method for predicting desired materials. The method has beenmore » applied to the case of LiFePO 4, an important cathode material for lithium-ion batteries. Numerous new structures of LiFePO 4 have been found, compared to those currently available, available, demonstrating the reliability of the present methodology and illustrating the promise of the concept of structural motifs.« less

An Efficient Scheme for Crystal Structure Prediction Based on Structural Motifs

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

Zhu, Zizhong; Wu, Ping; Wu, Shunqing

An efficient scheme based on structural motifs is proposed for the crystal structure prediction of materials. The key advantage of the present method comes in two fold: first, the degrees of freedom of the system are greatly reduced, since each structural motif, regardless of its size, can always be described by a set of parameters (R, θ, φ) with five degrees of freedom; second, the motifs could always appear in the predicted structures when the energies of the structures are relatively low. Both features make the present scheme a very efficient method for predicting desired materials. The method has beenmore » applied to the case of LiFePO 4, an important cathode material for lithium-ion batteries. Numerous new structures of LiFePO 4 have been found, compared to those currently available, available, demonstrating the reliability of the present methodology and illustrating the promise of the concept of structural motifs.« less

Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

PubMed Central

Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

2007-01-01

increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings. Citation: Van Dongen HPA; Mott CG; Huang JK; Mollicone DJ; McKenzie FD; Dinges DF. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes. SLEEP 2007;30(9):1129-1143. PMID:17910385

The role of pre-morbid intelligence and cognitive reserve in predicting cognitive efficiency in a sample of Italian elderly.

PubMed

Caffò, Alessandro O; Lopez, Antonella; Spano, Giuseppina; Saracino, Giuseppe; Stasolla, Fabrizio; Ciriello, Giuseppe; Grattagliano, Ignazio; Lancioni, Giulio E; Bosco, Andrea

2016-12-01

Models of cognitive reserve in aging suggest that individual's life experience (education, working activity, and leisure) can exert a neuroprotective effect against cognitive decline and may represent an important contribution to successful aging. The objective of the present study is to investigate the role of cognitive reserve, pre-morbid intelligence, age, and education level, in predicting cognitive efficiency in a sample of healthy aged individuals and with probable mild cognitive impairment. Two hundred and eight aging participants recruited from the provincial region of Bari (Apulia, Italy) took part in the study. A battery of standardized tests was administered to them to measure cognitive reserve, pre-morbid intelligence, and cognitive efficiency. Protocols for 10 participants were excluded since they did not meet inclusion criteria, and statistical analyses were conducted on data from the remaining 198 participants. A path analysis was used to test the following model: age, education level, and intelligence directly influence cognitive reserve and cognitive efficiency; cognitive reserve mediates the influence of age, education level, and intelligence on cognitive efficiency. Cognitive reserve fully mediates the relationship between pre-morbid intelligence and education level and cognitive efficiency, while age maintains a direct effect on cognitive efficiency. Cognitive reserve appears to exert a protective effect regarding cognitive decline in normal and pathological populations, thus masking, at least in the early phases of neurodegeneration, the decline of memory, orientation, attention, language, and reasoning skills. The assessment of cognitive reserve may represent a useful evaluation supplement in neuropsychological screening protocols of cognitive decline.

Can traits predict individual growth performance? A test in a hyperdiverse tropical forest.

PubMed

Poorter, Lourens; Castilho, Carolina V; Schietti, Juliana; Oliveira, Rafael S; Costa, Flávia R C

2018-07-01

The functional trait approach has, as a central tenet, that plant traits are functional and shape individual performance, but this has rarely been tested in the field. Here, we tested the individual-based trait approach in a hyperdiverse Amazonian tropical rainforest and evaluated intraspecific variation in trait values, plant strategies at the individual level, and whether traits are functional and predict individual performance. We evaluated > 1300 tree saplings belonging to > 383 species, measured 25 traits related to growth and defense, and evaluated the effects of environmental conditions, plant size, and traits on stem growth. A total of 44% of the trait variation was observed within species, indicating a strong potential for acclimation. Individuals showed two strategy spectra, related to tissue toughness and organ size vs leaf display. In this nutrient- and light-limited forest, traits measured at the individual level were surprisingly poor predictors of individual growth performance because of convergence of traits and growth rates. Functional trait approaches based on individuals or species are conceptually fundamentally different: the species-based approach focuses on the potential and the individual-based approach on the realized traits and growth rates. Counterintuitively, the individual approach leads to a poor prediction of individual performance, although it provides a more realistic view on community dynamics. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

Impact of microbial efficiency to predict MP supply when estimating protein requirements of growing beef cattle from performance.

PubMed

Watson, A K; Klopfenstein, T J; Erickson, G E; MacDonald, J C; Wilkerson, V A

2017-07-01

Data from 16 trials were compiled to calculate microbial CP (MCP) production and MP requirements of growing cattle on high-forage diets. All cattle were individually fed diets with 28% to 72% corn cobs in addition to either alfalfa, corn silage, or sorghum silage at 18% to 60% of the diet (DM basis). The remainder of the diet consisted of protein supplement. Source of protein within the supplement varied and included urea, blood meal, corn gluten meal, dry distillers grains, feather meal, meat and bone meal, poultry by-product meal, soybean meal, and wet distillers grains. All trials included a urea-only treatment. Intake of all cattle within an experiment was held constant, as a percentage of BW, established by the urea-supplemented group. In each trial the base diet (forage and urea supplement) was MP deficient. Treatments consisted of increasing amounts of test protein replacing the urea supplement. As protein in the diet increased, ADG plateaued. Among experiments, ADG ranged from 0.11 to 0.73 kg. Three methods of calculating microbial efficiency were used to determine MP supply. Gain was then regressed against calculated MP supply to determine MP requirement for maintenance and gain. Method 1 (based on a constant 13% microbial efficiency as used by the beef NRC model) predicted an MP maintenance requirement of 3.8 g/kg BW and 385 g MP/kg gain. Method 2 calculated microbial efficiency using low-quality forage diets and predicted MP requirements of 3.2 g/kg BW for maintenance and 448 g/kg for gain. Method 3 (based on an equation predicting MCP yield from TDN intake, proposed by the Beef Cattle Nutrient Requirements Model [BCNRM]) predicted MP requirements of 3.1 g/kg BW for maintenance and 342 g/kg for gain. The factorial method of calculating MP maintenance requirements accounts for scurf, endogenous urinary, and metabolic fecal protein losses and averaged 4.2 g/kg BW. Cattle performance data demonstrate formulating diets to meet the beef NRC model recommended

Individual and population pharmacokinetic compartment analysis: a graphic procedure for quantification of predictive performance.

PubMed

Eksborg, Staffan

2013-01-01

Pharmacokinetic studies are important for optimizing of drug dosing, but requires proper validation of the used pharmacokinetic procedures. However, simple and reliable statistical methods suitable for evaluation of the predictive performance of pharmacokinetic analysis are essentially lacking. The aim of the present study was to construct and evaluate a graphic procedure for quantification of predictive performance of individual and population pharmacokinetic compartment analysis. Original data from previously published pharmacokinetic compartment analyses after intravenous, oral, and epidural administration, and digitized data, obtained from published scatter plots of observed vs predicted drug concentrations from population pharmacokinetic studies using the NPEM algorithm and NONMEM computer program and Bayesian forecasting procedures, were used for estimating the predictive performance according to the proposed graphical method and by the method of Sheiner and Beal. The graphical plot proposed in the present paper proved to be a useful tool for evaluation of predictive performance of both individual and population compartment pharmacokinetic analysis. The proposed method is simple to use and gives valuable information concerning time- and concentration-dependent inaccuracies that might occur in individual and population pharmacokinetic compartment analysis. Predictive performance can be quantified by the fraction of concentration ratios within arbitrarily specified ranges, e.g. within the range 0.8-1.2.

Individualized predictions of time to menopause using multiple measurements of antimüllerian hormone.

PubMed

Gohari, Mahmood Reza; Ramezani Tehrani, Fahime; Chenouri, Shojaeddin; Solaymani-Dodaran, Masoud; Azizi, Fereidoun

2016-08-01

The ability of antimüllerian hormone (AMH) to predict age at menopause has been reported in several studies, and a decrease in AMH level has been found to increase the probability of menopause. The rate of decline varies among women, and there is also a variability of decline between women's cycles. As a result, individualized evaluation is required to accurately predict time of menopause. To this end, we have used the AMH trajectories of individual women to predict each one's age at menopause. From a cohort study, 266 women (ages 20-50 y) who had regular and predictable menstrual cycles at the initiation of the study were randomly selected from among 1,265 women for multiple AMH measurements. Participants were visited at approximately 3-year intervals and followed for an average of 6.5 years. Individual likelihood of menopause was predicted by fitting the shared random-effects joint model to the baseline covariates and the specific AMH trajectory of each woman. In total, 23.7% of the women reached menopause during the follow-up period. The estimated mean (SD) AMH concentration at the time of menopause was 0.05 ng/mL (0.06 ng/mL), compared with 1.36 ng/mL (1.85 ng/mL) for those with a regular menstrual cycle at their last assessment. The decline rate in the AMH level varied among age groups, and age was a significant prognostic factor for AMH level (P < 0.001). Adjusting for age and body mass index, each woman had her own specific AMH trajectory. Lower AMH and older age had significant effects on the onset of menopause. Individualized prediction of time to menopause was obtained from the fitted model. Longitudinal measurements of AMH will enable physicians to individualize the prediction of menopause, thereby facilitating counseling on the timing of childbearing or medical management of health issues associated with menopause.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

PubMed

Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

2017-09-01

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of

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.

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

PubMed

Schwartz, H Andrew; Sap, Maarten; Kern, Margaret L; Eichstaedt, Johannes C; Kapelner, Adam; Agrawal, Megha; Blanco, Eduardo; Dziurzynski, Lukasz; Park, Gregory; Stillwell, David; Kosinski, Michal; Seligman, Martin E P; Ungar, Lyle H

2016-01-01

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

PubMed Central

Chavoya, Arturo; Lopez-Martin, Cuauhtemoc; Andalon-Garcia, Irma R.; Meda-Campaña, M. E.

2012-01-01

Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. PMID:23226305

Individual differences in the balance of GABA to glutamate in pFC predict the ability to select among competing options.

PubMed

de la Vega, Alejandro; Brown, Mark S; Snyder, Hannah R; Singel, Debra; Munakata, Yuko; Banich, Marie T

2014-11-01

Individuals vary greatly in their ability to select one item or response when presented with a multitude of options. Here we investigate the neural underpinnings of these individual differences. Using magnetic resonance spectroscopy, we found that the balance of inhibitory versus excitatory neurotransmitters in pFC predicts the ability to select among task-relevant options in two language production tasks. The greater an individual's concentration of GABA relative to glutamate in the lateral pFC, the more quickly he or she could select a relevant word from among competing options. This outcome is consistent with our computational modeling of this task [Snyder, H. R., Hutchison, N., Nyhus, E., Curran, T., Banich, M. T., O'Reilly, R. C., et al. Neural inhibition enables selection during language processing. Proceedings of the National Academy of Sciences, U.S.A., 107, 16483-16488, 2010], which predicts that greater net inhibition in pFC increases the efficiency of resolving competition among task-relevant options. Moreover, the association with the GABA/glutamate ratio was specific to selection and was not observed for executive function ability in general. These findings are the first to link the balance of excitatory and inhibitory neural transmission in pFC to specific aspects of executive function.

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

ERIC Educational Resources Information Center

Kidd, Evan; Arciuli, Joanne

2016-01-01

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68)…

Individual differences in working memory capacity predict visual attention allocation.

PubMed

Bleckley, M Kathryn; Durso, Francis T; Crutchfield, Jerry M; Engle, Randall W; Khanna, Maya M

2003-12-01

To the extent that individual differences in working memory capacity (WMC) reflect differences in attention (Baddeley, 1993; Engle, Kane, & Tuholski, 1999), differences in WMC should predict performance on visual attention tasks. Individuals who scored in the upper and lower quartiles on the OSPAN working memory test performed a modification of Egly and Homa's (1984) selective attention task. In this task, the participants identified a central letter and localized a displaced letter flashed somewhere on one of three concentric rings. When the displaced letter occurred closer to fixation than the cue implied, high-WMC, but not low-WMC, individuals showed a cost in the letter localization task. This suggests that low-WMC participants allocated attention as a spotlight, whereas those with high WMC showed flexible allocation.

Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Reid, Terry; Schifer, Nicholas; Briggs, Maxwell

2011-01-01

Past methods of predicting net heat input needed to be validated. Validation effort pursued with several paths including improving model inputs, using test hardware to provide validation data, and validating high fidelity models. Validation test hardware provided direct measurement of net heat input for comparison to predicted values. Predicted value of net heat input was 1.7 percent less than measured value and initial calculations of measurement uncertainty were 2.1 percent (under review). Lessons learned during validation effort were incorporated into convertor modeling approach which improved predictions of convertor efficiency.

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

NASA Astrophysics Data System (ADS)

Kandare, Kaja; Ørka, Hans Ole; Dalponte, Michele; Næsset, Erik; Gobakken, Terje

2017-08-01

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m2 plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI

An unsteady aerodynamic formulation for efficient rotor tonal noise prediction

NASA Astrophysics Data System (ADS)

Gennaretti, M.; Testa, C.; Bernardini, G.

2013-12-01

An aerodynamic/aeroacoustic solution methodology for predction of tonal noise emitted by helicopter rotors and propellers is presented. It is particularly suited for configurations dominated by localized, high-frequency inflow velocity fields as those generated by blade-vortex interactions. The unsteady pressure distributions are determined by the sectional, frequency-domain Küssner-Schwarz formulation, with downwash including the wake inflow velocity predicted by a three-dimensional, unsteady, panel-method formulation suited for the analysis of rotors operating in complex aerodynamic environments. The radiated noise is predicted through solution of the Ffowcs Williams-Hawkings equation. The proposed approach yields a computationally efficient solution procedure that may be particularly useful in preliminary design/multidisciplinary optimization applications. It is validated through comparisons with solutions that apply the airloads directly evaluated by the time-marching, panel-method formulation. The results are provided in terms of blade loads, noise signatures and sound pressure level contours. An estimation of the computational efficiency of the proposed solution process is also presented.

Preventing Heat Injuries by Predicting Individualized Human Core Temperature

DTIC Science & Technology

2015-10-14

hardware/software warning system of an impending rise in TC and generate alerts to potentially prevent heat injuries. PREVENTING HEAT INJURIES BY...TC estimates, provides ahead-of-time alerts about an impending rise in TC and 2) an individualized model that uses non-invasive measurements of AC...PREDICTION AND ALERT ALGORITHMS Here, we detail the development of an algorithm that uses a time series of recent-past TC measurements to provide

Who punishes? Personality traits predict individual variation in punitive sentiment.

PubMed

Roberts, S Craig; Vakirtzis, Antonios; Kristjánsdóttir, Lilja; Havlíček, Jan

2013-02-18

Cross-culturally, participants in public goods games reward participants and punish defectors to a degree beyond that warranted by rational, profit-maximizing considerations. Costly punishment, where individuals impose costs on defectors at a cost to themselves, is thought to promote the maintenance of cooperation. However, despite substantial variation in the extent to which people punish, little is known about why some individuals, and not others, choose to pay these costs. Here, we test whether personality traits might contribute to variation in helping and punishment behavior. We first replicate a previous study using public goods scenarios to investigate effects of sex, relatedness and likelihood of future interaction on willingness to help a group member or to punish a transgressor. As in the previous study, we find that individuals are more willing to help related than unrelated needy others and that women are more likely to express desire to help than men. Desire to help was higher if the probability of future interaction is high, at least among women. In contrast, among these variables, only participant sex predicted some measures of punitive sentiment. Extending the replication, we found that punitive sentiment, but not willingness to help, was predicted by personality traits. Most notably, participants scoring lower on Agreeableness expressed more anger towards and greater desire to punish a transgressor, and were more willing to engage in costly punishment, at least in our scenario. Our results suggest that some personality traits may contribute to underpinning individual variation in social enforcement of cooperation.

Individual differences in intrinsic brain connectivity predict decision strategy.

PubMed

Barnes, Kelly Anne; Anderson, Kevin M; Plitt, Mark; Martin, Alex

2014-10-15

When humans are provided with ample time to make a decision, individual differences in strategy emerge. Using an adaptation of a well-studied decision making paradigm, motion direction discrimination, we probed the neural basis of individual differences in strategy. We tested whether strategies emerged from moment-to-moment reconfiguration of functional brain networks involved in decision making with task-evoked functional MRI (fMRI) and whether intrinsic properties of functional brain networks, measured at rest with functional connectivity MRI (fcMRI), were associated with strategy use. We found that human participants reliably selected one of two strategies across 2 days of task performance, either continuously accumulating evidence or waiting for task difficulty to decrease. Individual differences in decision strategy were predicted both by the degree of task-evoked activation of decision-related brain regions and by the strength of pretask correlated spontaneous brain activity. These results suggest that spontaneous brain activity constrains strategy selection on perceptual decisions.

[Analysis of 14 individuals who requested predictive genetic testing for hereditary neuromuscular diseases].

PubMed

Yoshida, Kunihiro; Tamai, Mariko; Kubota, Takeo; Kawame, Hiroshi; Amano, Naoji; Ikeda, Shu-ichi; Fukushima, Yoshimitsu

2002-02-01

Predictive genetic testing for hereditary neuromuscular diseases is a delicate issue for individuals at risk and their families, as well as for medical staff because these diseases are often late-onset and intractable. Therefore careful pre- and post-test genetic counseling and psychosocial support should be provided along with such genetic testing. The Division of Clinical and Molecular Genetics was established at our hospital in May 1996 to provide skilled professional genetic counseling. Since its establishment, 14 individuals have visited our clinic to request predictive genetic testing for hereditary neuromuscular diseases (4 for myotonic dystrophy, 6 for spinocerebellar ataxia, 3 for Huntington's disease, and 1 for Alzheimer's disease). The main reasons for considering testing were to remove uncertainty about the genetic status and to plan for the future. Nine of 14 individuals requested testing for making decisions about a forthcoming marriage or pregnancy (family planning). Other reasons raised by the individuals included career or financial planning, planning for their own health care, and knowing the risk for their children. At the first genetic counseling session, all of the individuals expressed hopes of not being a gene carrier and of escaping from fear of disease, and seemed not to be mentally well prepared for an increased-risk result. To date, 7 of the 14 individuals have received genetic testing and only one, who underwent predictive genetic testing for spinocerebellar ataxia, was given an increased-risk result. The seven individuals including the one with an increased-risk result, have coped well with their new knowledge about their genetic status after the testing results were disclosed. None of them has expressed regret. In pre-test genetic counseling sessions, we consider it quite important not only to determine the psychological status of the individual, but also to make the individual try to anticipate the changes in his/her life upon

The Efficiency of First-Trimester Uterine Artery Doppler, ADAM12, PAPP-A and Maternal Characteristics in the Prediction of Pre-Eclampsia

PubMed Central

GOETZINGER, Katherine R.; ZHONG, Yan; CAHILL, Alison G.; ODIBO, Linda; MACONES, George A.; ODIBO, Anthony O.

2014-01-01

Objective To estimate the efficiency of first-trimester uterine artery Doppler, A-disintegrin and metalloprotease 12 (ADAM12), pregnancy-associated plasma protein A (PAPP-A) and maternal characteristics in the prediction of pre-eclampsia. Methods This is a prospective cohort study of patients presenting for first-trimester aneuploidy screening between 11-14 weeks’ gestation. Maternal serum ADAM12 and PAPP-A levels were measured by immunoassay, and mean uterine artery Doppler pulsatility indices (PI) were calculated. Outcomes of interest included pre-eclampsia, early pre-eclampsia, defined as requiring delivery at <34 weeks’ gestation, and gestational hypertension. Logistic regression analysis was used to model the prediction of pre-eclampsia using ADAM12 multiples of the median (MoM), PAPP-A MoM, and uterine artery Doppler PI MoM, either individually or in combination. Sensitivity, specificity, and area under the receiver-operating characteristic curves (AUC) were used to compare the screening efficiency of the models using non-parametric U-statistics. Results Of 578 patients with complete outcome data, there were 54 (9.3%) cases of preeclampsia and 13 (2.2%) cases of early pre-eclampsia. Median ADAM12 levels were significantly lower in patients who developed pre-eclampsia compared to those who did not. (0.81 v. 1.01 MoMs; p<0.04) For a fixed false positive rate (FPR) of 10%, ADAM12, PAPP-A, and uterine artery Doppler in combination with maternal characteristics identified 50%, 48%, and 52% of patients who developed pre-eclampsia, respectively. Combining these first-trimester parameters did not improve the predictive efficiency of the models. Conclusion First-trimester ADAM12, PAPP-A, and uterine artery Doppler are not sufficiently predictive of pre-eclampsia. Combinations of these parameters do not further improve their screening efficiency. PMID:23980220

Personality traits and individual differences predict threat-induced changes in postural control.

PubMed

Zaback, Martin; Cleworth, Taylor W; Carpenter, Mark G; Adkin, Allan L

2015-04-01

This study explored whether specific personality traits and individual differences could predict changes in postural control when presented with a height-induced postural threat. Eighty-two healthy young adults completed questionnaires to assess trait anxiety, trait movement reinvestment (conscious motor processing, movement self-consciousness), physical risk-taking, and previous experience with height-related activities. Tests of static (quiet standing) and anticipatory (rise to toes) postural control were completed under low and high postural threat conditions. Personality traits and individual differences significantly predicted height-induced changes in static, but not anticipatory postural control. Individuals less prone to taking physical risks were more likely to lean further away from the platform edge and sway at higher frequencies and smaller amplitudes. Individuals more prone to conscious motor processing were more likely to lean further away from the platform edge and sway at larger amplitudes. Individuals more self-conscious about their movement appearance were more likely to sway at smaller amplitudes. Evidence is also provided that relationships between physical risk-taking and changes in static postural control are mediated through changes in fear of falling and physiological arousal. Results from this study may have indirect implications for balance assessment and treatment; however, further work exploring these factors in patient populations is necessary. Copyright © 2015 Elsevier B.V. All rights reserved.

Predicting Individual Characteristics from Digital Traces on Social Media: A Meta-Analysis.

PubMed

Settanni, Michele; Azucar, Danny; Marengo, Davide

2018-04-01

The increasing utilization of social media provides a vast and new source of user-generated ecological data (digital traces), which can be automatically collected for research purposes. The availability of these data sets, combined with the convergence between social and computer sciences, has led researchers to develop automated methods to extract digital traces from social media and use them to predict individual psychological characteristics and behaviors. In this article, we reviewed the literature on this topic and conducted a series of meta-analyses to determine the strength of associations between digital traces and specific individual characteristics; personality, psychological well-being, and intelligence. Potential moderator effects were analyzed with respect to type of social media platform, type of digital traces examined, and study quality. Our findings indicate that digital traces from social media can be studied to assess and predict theoretically distant psychosocial characteristics with remarkable accuracy. Analysis of moderators indicated that the collection of specific types of information (i.e., user demographics), and the inclusion of different types of digital traces, could help improve the accuracy of predictions.

Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid.

PubMed

Sneyd, Mary Jane; Cameron, Claire; Cox, Brian

2014-05-22

New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma. A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years. For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71. We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.

Individual prediction of heart failure among childhood cancer survivors.

PubMed

Chow, Eric J; Chen, Yan; Kremer, Leontien C; Breslow, Norman E; Hudson, Melissa M; Armstrong, Gregory T; Border, William L; Feijen, Elizabeth A M; Green, Daniel M; Meacham, Lillian R; Meeske, Kathleen A; Mulrooney, Daniel A; Ness, Kirsten K; Oeffinger, Kevin C; Sklar, Charles A; Stovall, Marilyn; van der Pal, Helena J; Weathers, Rita E; Robison, Leslie L; Yasui, Yutaka

2015-02-10

To create clinically useful models that incorporate readily available demographic and cancer treatment characteristics to predict individual risk of heart failure among 5-year survivors of childhood cancer. Survivors in the Childhood Cancer Survivor Study (CCSS) free of significant cardiovascular disease 5 years after cancer diagnosis (n = 13,060) were observed through age 40 years for the development of heart failure (ie, requiring medications or heart transplantation or leading to death). Siblings (n = 4,023) established the baseline population risk. An additional 3,421 survivors from Emma Children's Hospital (Amsterdam, the Netherlands), the National Wilms Tumor Study, and the St Jude Lifetime Cohort Study were used to validate the CCSS prediction models. Heart failure occurred in 285 CCSS participants. Risk scores based on selected exposures (sex, age at cancer diagnosis, and anthracycline and chest radiotherapy doses) achieved an area under the curve of 0.74 and concordance statistic of 0.76 at or through age 40 years. Validation cohort estimates ranged from 0.68 to 0.82. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups, corresponding to cumulative incidences of heart failure at age 40 years of 0.5% (95% CI, 0.2% to 0.8%), 2.4% (95% CI, 1.8% to 3.0%), and 11.7% (95% CI, 8.8% to 14.5%), respectively. In comparison, siblings had a cumulative incidence of 0.3% (95% CI, 0.1% to 0.5%). Using information available to clinicians soon after completion of childhood cancer therapy, individual risk for subsequent heart failure can be predicted with reasonable accuracy and discrimination. These validated models provide a framework on which to base future screening strategies and interventions. © 2014 by American Society of Clinical Oncology.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Working Memory Delay Activity Predicts Individual Differences in Cognitive Abilities

PubMed Central

Unsworth, Nash; Fukuda, Keisuke; Awh, Edward; Vogel, Edward K.

2015-01-01

A great deal of prior research has examined the relation between estimates of working memory and cognitive abilities. Yet, the neural mechanisms that account for these relations are still not very well understood. The current study explored whether individual differences in working memory delay activity would be a significant predictor of cognitive abilities. A large number of participants performed multiple measures of capacity, attention control, long-term memory, working memory span, and fluid intelligence, and latent variable analyses were used to examine the data. During two working memory change detection tasks, we acquired EEG data and examined the contra-lateral delay activity. The results demonstrated that the contralateral delay activity was significantly related to cognitive abilities, and importantly these relations were because of individual differences in both capacity and attention control. These results suggest that individual differences in working memory delay activity predict individual differences in a broad range of cognitive abilities, and this is because of both differences in the number of items that can be maintained and the ability to control access to working memory. PMID:25436671

Working memory delay activity predicts individual differences in cognitive abilities.

PubMed

Unsworth, Nash; Fukuda, Keisuke; Awh, Edward; Vogel, Edward K

2015-05-01

A great deal of prior research has examined the relation between estimates of working memory and cognitive abilities. Yet, the neural mechanisms that account for these relations are still not very well understood. The current study explored whether individual differences in working memory delay activity would be a significant predictor of cognitive abilities. A large number of participants performed multiple measures of capacity, attention control, long-term memory, working memory span, and fluid intelligence, and latent variable analyses were used to examine the data. During two working memory change detection tasks, we acquired EEG data and examined the contralateral delay activity. The results demonstrated that the contralateral delay activity was significantly related to cognitive abilities, and importantly these relations were because of individual differences in both capacity and attention control. These results suggest that individual differences in working memory delay activity predict individual differences in a broad range of cognitive abilities, and this is because of both differences in the number of items that can be maintained and the ability to control access to working memory.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

Inter-view prediction of intra mode decision for high-efficiency video coding-based multiview video coding

NASA Astrophysics Data System (ADS)

da Silva, Thaísa Leal; Agostini, Luciano Volcan; da Silva Cruz, Luis A.

2014-05-01

Intra prediction is a very important tool in current video coding standards. High-efficiency video coding (HEVC) intra prediction presents relevant gains in encoding efficiency when compared to previous standards, but with a very important increase in the computational complexity since 33 directional angular modes must be evaluated. Motivated by this high complexity, this article presents a complexity reduction algorithm developed to reduce the HEVC intra mode decision complexity targeting multiview videos. The proposed algorithm presents an efficient fast intra prediction compliant with singleview and multiview video encoding. This fast solution defines a reduced subset of intra directions according to the video texture and it exploits the relationship between prediction units (PUs) of neighbor depth levels of the coding tree. This fast intra coding procedure is used to develop an inter-view prediction method, which exploits the relationship between the intra mode directions of adjacent views to further accelerate the intra prediction process in multiview video encoding applications. When compared to HEVC simulcast, our method achieves a complexity reduction of up to 47.77%, at the cost of an average BD-PSNR loss of 0.08 dB.

Hormone levels predict individual differences in reproductive success in a passerine bird.

PubMed

Ouyang, Jenny Q; Sharp, Peter J; Dawson, Alistair; Quetting, Michael; Hau, Michaela

2011-08-22

Hormones mediate major physiological and behavioural components of the reproductive phenotype of individuals. To understand basic evolutionary processes in the hormonal regulation of reproductive traits, we need to know whether, and during which reproductive phases, individual variation in hormone concentrations relates to fitness in natural populations. We related circulating concentrations of prolactin and corticosterone to parental behaviour and reproductive success during both the pre-breeding and the chick-rearing stages in both individuals of pairs of free-living house sparrows, Passer domesticus. Prolactin and baseline corticosterone concentrations in pre-breeding females, and prolactin concentrations in pre-breeding males, predicted total number of fledglings. When the strong effect of lay date on total fledgling number was corrected for, only pre-breeding baseline corticosterone, but not prolactin, was negatively correlated with the reproductive success of females. During the breeding season, nestling provisioning rates of both sexes were negatively correlated with stress-induced corticosterone levels. Lastly, individuals of both sexes with low baseline corticosterone before and high baseline corticosterone during breeding raised the most offspring, suggesting that either the plasticity of this trait contributes to reproductive success or that high parental effort leads to increased hormone concentrations. Thus hormone concentrations both before and during breeding, as well as their seasonal dynamics, predict reproductive success, suggesting that individual variation in absolute concentrations and in plasticity is functionally significant, and, if heritable, may be a target of selection.

Within-person Changes in Individual Symptoms of Depression Predict Subsequent Depressive Episodes in Adolescents: A Prospective Study

PubMed Central

Kouros, Chrystyna D.; Morris, Matthew C.; Garber, Judy

2015-01-01

The current longitudinal study examined which individual symptoms of depression uniquely predicted a subsequent Major Depressive Episode (MDE) in adolescents, and whether these relations differed by sex. Adolescents (N=240) were first interviewed in grade 6 (M=11.86 years old; SD = 0.56; 54% female; 81.5% Caucasian) and then annually through grade 12 regarding their individual symptoms of depression as well as the occurrence of MDEs. Individual symptoms of depression were assessed with the Children’s Depression Rating Scale-Revised (CDRS-R) and depressive episodes were assessed with the Longitudinal Interval Follow-up Evaluation (LIFE). Results showed that within-person changes in sleep problems and low self-esteem/excessive guilt positively predicted an increased likelihood of an MDE for both boys and girls. Significant sex differences also were found. Within-person changes in anhedonia predicted an increased likelihood of a subsequent MDE among boys, whereas irritability predicted a decreased likelihood of a future MDE among boys, and concentration difficulties predicted a decreased likelihood of an MDE in girls. These results identified individual depressive symptoms that predicted subsequent depressive episodes in male and female adolescents, and may be used to guide the early detection, treatment, and prevention of depressive disorders in youth. PMID:26105209

Mathematical model for prediction of efficiency indicators of educational activity in high school

NASA Astrophysics Data System (ADS)

Tikhonova, O. M.; Kushnikov, V. A.; Fominykh, D. S.; Rezchikov, A. F.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.

2018-05-01

The quality of high school is a current problem all over the world. The paper presents the system dedicated to predicting the accreditation indicators of technical universities based on J. Forrester mechanism of system dynamics. The mathematical model is developed for prediction of efficiency indicators of the educational activity and is based on the apparatus of nonlinear differential equations.

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Prediction of individual season of birth using MRI.

PubMed

Pantazatos, Spiro P

2014-03-01

Previous research suggests statistical associations between season of birth (SOB) with prevalence of neurobehavioral disorders such as schizophrenia and bipolar disorder, personality traits such as novelty and sensation seeking, and suicidal behavior. These effects are thought to be mediated by seasonal differences in perinatal photoperiod, which was recently shown to imprint circadian clock neurons and behavior in rodents. However, it is unknown whether SOB is associated with any measurable differences in the normal human adult brain, and whether individual SOB can be deduced based on phenotype. Here I show that SOB predicts morphological differences in brain structure, and that MRI scans carry spatially distributed information allowing significantly above chance prediction of an individual's SOB. Using an open source database of over 550 structural brain scans, Voxel-Based Morphometry (VBM) analysis showed a significant SOB effect in the left superior temporal gyrus (STG) in males (p=0.009, FWE whole-brain corrected), with greater gray matter volumes in fall and winter births. A cosinor analysis revealed a significant annual periodicity in the left STG gray matter volume (Zero Amplitude Test: p<5×10(-7)), with a peak towards the end of December and a nadir towards the end of June, suggesting that perinatal photoperiod accounts for this SOB effect. Whole-brain VBM maps were used as input features to multivariate machine-learning based analyses to classify SOB. Significantly greater than chance prediction was achieved in females (overall accuracy 35%, p<0.001), but not in males (overall accuracy 26%, p=0.45). Pairwise binary classification in females revealed that the highest discrimination was obtained for winter vs. summer classification (peak area under the ROC curve=0.71, p<0.0005). Discriminating regions included fusiform and middle temporal gyrus, inferior and superior parietal lobe, cerebellum, and dorsolateral and dorsomedial prefrontal cortex. Results

Individual Differences in Childhood Sleep Problems Predict Later Cognitive Executive Control

PubMed Central

Friedman, Naomi P.; Corley, Robin P.; Hewitt, John K.; Wright, Kenneth P.

2009-01-01

Study Objective: To determine whether individual differences in developmental patterns of general sleep problems are associated with 3 executive function abilities—inhibiting, updating working memory, and task shifting—in late adolescence. Participants: 916 twins (465 female, 451 male) and parents from the Colorado Longitudinal Twin Study. Measurements and Results: Parents reported their children's sleep problems at ages 4 years, 5 y, 7 y, and 9–16 y based on a 7-item scale from the Child-Behavior Checklist; a subset of children (n = 568) completed laboratory assessments of executive functions at age 17. Latent variable growth curve analyses were used to model individual differences in longitudinal trajectories of childhood sleep problems. Sleep problems declined over time, with ~70% of children having ≥ 1 problem at age 4 and ~33% of children at age 16. However, significant individual differences in both the initial levels of problems (intercept) and changes across time (slope) were observed. When executive function latent variables were added to the model, the intercept did not significantly correlate with the later executive function latent variables; however, the slope variable significantly (P < 0.05) negatively correlated with inhibiting (r = −0.27) and updating (r = −0.21), but not shifting (r = −0.10) abilities. Further analyses suggested that the slope variable predicted the variance common to the 3 executive functions (r = −0.29). Conclusions: Early levels of sleep problems do not seem to have appreciable implications for later executive functioning. However, individuals whose sleep problems decrease more across time show better general executive control in late adolescence. Citation: Friedman NP; Corley RP; Hewitt JK; Wright KP. Individual differences in childhood sleep problems predict later cognitive executive control. SLEEP 2009;32(3):323-333. PMID:19294952

Efficient prediction of human protein-protein interactions at a global scale.

PubMed

Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

2014-12-10

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Limitations of diagnostic precision and predictive utility in the individual case: a challenge for forensic practice.

PubMed

Cooke, David J; Michie, Christine

2010-08-01

Knowledge of group tendencies may not assist accurate predictions in the individual case. This has importance for forensic decision making and for the assessment tools routinely applied in forensic evaluations. In this article, we applied Monte Carlo methods to examine diagnostic agreement with different levels of inter-rater agreement given the distributional characteristics of PCL-R scores. Diagnostic agreement and score agreement were substantially less than expected. In addition, we examined the confidence intervals associated with individual predictions of violent recidivism. On the basis of empirical findings, statistical theory, and logic, we conclude that predictions of future offending cannot be achieved in the individual case with any degree of confidence. We discuss the problems identified in relation to the PCL-R in terms of the broader relevance to all instruments used in forensic decision making.

Predicting Efficiency of Travel in Young, Visually Impaired Children from Their Other Spatial Skills.

ERIC Educational Resources Information Center

Hill, Anita; And Others

1985-01-01

To test ways of predicting how efficiently visually impaired children learn travel skills, a criteria checklist of spatial skills was developed for close-body space, local space, and geographical/travel space. Comparison was made between predictors of efficient learning including subjective ratings of teachers, personal qualities and factors of…

Numerical flow simulation and efficiency prediction for axial turbines by advanced turbulence models

NASA Astrophysics Data System (ADS)

Jošt, D.; Škerlavaj, A.; Lipej, A.

2012-11-01

Numerical prediction of an efficiency of a 6-blade Kaplan turbine is presented. At first, the results of steady state analysis performed by different turbulence models for different operating regimes are compared to the measurements. For small and optimal angles of runner blades the efficiency was quite accurately predicted, but for maximal blade angle the discrepancy between calculated and measured values was quite large. By transient analysis, especially when the Scale Adaptive Simulation Shear Stress Transport (SAS SST) model with zonal Large Eddy Simulation (ZLES) in the draft tube was used, the efficiency was significantly improved. The improvement was at all operating points, but it was the largest for maximal discharge. The reason was better flow simulation in the draft tube. Details about turbulent structure in the draft tube obtained by SST, SAS SST and SAS SST with ZLES are illustrated in order to explain the reasons for differences in flow energy losses obtained by different turbulence models.

Predicting individual contrast sensitivity functions from acuity and letter contrast sensitivity measurements

PubMed Central

Thurman, Steven M.; Davey, Pinakin Gunvant; McCray, Kaydee Lynn; Paronian, Violeta; Seitz, Aaron R.

2016-01-01

Contrast sensitivity (CS) is widely used as a measure of visual function in both basic research and clinical evaluation. There is conflicting evidence on the extent to which measuring the full contrast sensitivity function (CSF) offers more functionally relevant information than a single measurement from an optotype CS test, such as the Pelli–Robson chart. Here we examine the relationship between functional CSF parameters and other measures of visual function, and establish a framework for predicting individual CSFs with effectively a zero-parameter model that shifts a standard-shaped template CSF horizontally and vertically according to independent measurements of high contrast acuity and letter CS, respectively. This method was evaluated for three different CSF tests: a chart test (CSV-1000), a computerized sine-wave test (M&S Sine Test), and a recently developed adaptive test (quick CSF). Subjects were 43 individuals with healthy vision or impairment too mild to be considered low vision (acuity range of −0.3 to 0.34 logMAR). While each test demands a slightly different normative template, results show that individual subject CSFs can be predicted with roughly the same precision as test–retest repeatability, confirming that individuals predominantly differ in terms of peak CS and peak spatial frequency. In fact, these parameters were sufficiently related to empirical measurements of acuity and letter CS to permit accurate estimation of the entire CSF of any individual with a deterministic model (zero free parameters). These results demonstrate that in many cases, measuring the full CSF may provide little additional information beyond letter acuity and contrast sensitivity. PMID:28006065

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Discrete modes of social information processing predict individual behavior of fish in a group

PubMed Central

Harpaz, Roy; Tkačik, Gašper

2017-01-01

Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an “active” mode, in which they are sensitive to the swimming patterns of conspecifics, and a “passive” mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors’ behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multimodal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species. PMID:28874581

Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory.

PubMed

Martin, Benjamin T; Jager, Tjalling; Nisbet, Roger M; Preuss, Thomas G; Grimm, Volker

2013-04-01

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Efficient Prediction Structures for H.264 Multi View Coding Using Temporal Scalability

NASA Astrophysics Data System (ADS)

Guruvareddiar, Palanivel; Joseph, Biju K.

2014-03-01

Prediction structures with "disposable view components based" hierarchical coding have been proven to be efficient for H.264 multi view coding. Though these prediction structures along with the QP cascading schemes provide superior compression efficiency when compared to the traditional IBBP coding scheme, the temporal scalability requirements of the bit stream could not be met to the fullest. On the other hand, a fully scalable bit stream, obtained by "temporal identifier based" hierarchical coding, provides a number of advantages including bit rate adaptations and improved error resilience, but lacks in compression efficiency when compared to the former scheme. In this paper it is proposed to combine the two approaches such that a fully scalable bit stream could be realized with minimal reduction in compression efficiency when compared to state-of-the-art "disposable view components based" hierarchical coding. Simulation results shows that the proposed method enables full temporal scalability with maximum BDPSNR reduction of only 0.34 dB. A novel method also has been proposed for the identification of temporal identifier for the legacy H.264/AVC base layer packets. Simulation results also show that this enables the scenario where the enhancement views could be extracted at a lower frame rate (1/2nd or 1/4th of base view) with average extraction time for a view component of only 0.38 ms.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Individual predictions of eye-movements with dynamic scenes

NASA Astrophysics Data System (ADS)

Barth, Erhardt; Drewes, Jan; Martinetz, Thomas

2003-06-01

We present a model that predicts saccadic eye-movements and can be tuned to a particular human observer who is viewing a dynamic sequence of images. Our work is motivated by applications that involve gaze-contingent interactive displays on which information is displayed as a function of gaze direction. The approach therefore differs from standard approaches in two ways: (1) we deal with dynamic scenes, and (2) we provide means of adapting the model to a particular observer. As an indicator for the degree of saliency we evaluate the intrinsic dimension of the image sequence within a geometric approach implemented by using the structure tensor. Out of these candidate saliency-based locations, the currently attended location is selected according to a strategy found by supervised learning. The data are obtained with an eye-tracker and subjects who view video sequences. The selection algorithm receives candidate locations of current and past frames and a limited history of locations attended in the past. We use a linear mapping that is obtained by minimizing the quadratic difference between the predicted and the actually attended location by gradient descent. Being linear, the learned mapping can be quickly adapted to the individual observer.

Enhancing Arithmetic and Word-Problem Solving Skills Efficiently by Individualized Computer-Assisted Practice

ERIC Educational Resources Information Center

Schoppek, Wolfgang; Tulis, Maria

2010-01-01

The fluency of basic arithmetical operations is a precondition for mathematical problem solving. However, the training of skills plays a minor role in contemporary mathematics instruction. The authors proposed individualization of practice as a means to improve its efficiency, so that the time spent with the training of skills is minimized. As a…

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction.

PubMed

Cheng, Hao; Garrick, Dorian J; Fernando, Rohan L

2017-01-01

A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model. Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis. Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.

Individual differences in episodic memory abilities predict successful prospective memory output monitoring.

PubMed

Hunter Ball, B; Pitães, Margarida; Brewer, Gene A

2018-02-07

Output monitoring refers to memory for one's previously completed actions. In the context of prospective memory (PM) (e.g., remembering to take medication), failures of output monitoring can result in repetitions and omissions of planned actions (e.g., over- or under-medication). To be successful in output monitoring paradigms, participants must flexibly control attention to detect PM cues as well as engage controlled retrieval of previous actions whenever a particular cue is encountered. The current study examined individual differences in output monitoring abilities in a group of younger adults differing in attention control (AC) and episodic memory (EM) abilities. The results showed that AC ability uniquely predicted successful cue detection on the first presentation, whereas EM ability uniquely predicted successful output monitoring on the second presentation. The current study highlights the importance of examining external correlates of PM abilities and contributes to the growing body of research on individual differences in PM.

Predicting Smartphone Operating System from Personality and Individual Differences.

PubMed

Shaw, Heather; Ellis, David A; Kendrick, Libby-Rae; Ziegler, Fenja; Wiseman, Richard

2016-12-01

Android and iPhone devices account for over 90 percent of all smartphones sold worldwide. Despite being very similar in functionality, current discourse and marketing campaigns suggest that key individual differences exist between users of these two devices; however, this has never been investigated empirically. This is surprising, as smartphones continue to gain momentum across a variety of research disciplines. In this article, we consider if individual differences exist between these two distinct groups. In comparison to Android users, we found that iPhone owners are more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. Key differences in personality were also observed with iPhone users displaying lower levels of Honesty-Humility and higher levels of emotionality. Following this analysis, we were also able to build and test a model that predicted smartphone ownership at above chance level based on these individual differences. In line with extended self-theory, the type of smartphone owned provides some valuable information about its owner. These findings have implications for the increasing use of smartphones within research particularly for those working within Computational Social Science and PsychoInformatics, where data are typically collected from devices and applications running a single smartphone operating system.

Birth weight predicted baseline muscular efficiency, but not response of energy expenditure to calorie restriction: An empirical test of the predictive adaptive response hypothesis.

PubMed

Workman, Megan; Baker, Jack; Lancaster, Jane B; Mermier, Christine; Alcock, Joe

2016-07-01

Aiming to test the evolutionary significance of relationships linking prenatal growth conditions to adult phenotypes, this study examined whether birth size predicts energetic savings during fasting. We specifically tested a Predictive Adaptive Response (PAR) model that predicts greater energetic saving among adults who were born small. Data were collected from a convenience sample of young adults living in Albuquerque, NM (n = 34). Indirect calorimetry quantified changes in resting energy expenditure (REE) and active muscular efficiency that occurred in response to a 29-h fast. Multiple regression analyses linked birth weight to baseline and postfast metabolic values while controlling for appropriate confounders (e.g., sex, body mass). Birth weight did not moderate the relationship between body size and energy expenditure, nor did it predict the magnitude change in REE or muscular efficiency observed from baseline to after fasting. Alternative indicators of birth size were also examined (e.g., low v. normal birth weight, comparison of tertiles), with no effects found. However, baseline muscular efficiency improved by 1.1% per 725 g (S.D.) increase in birth weight (P = 0.037). Birth size did not influence the sensitivity of metabolic demands to fasting-neither at rest nor during activity. Moreover, small birth size predicted a reduction in the efficiency with which muscles convert energy expended into work accomplished. These results do not support the ascription of adaptive function to phenotypes associated with small birth size. © 2015 Wiley Periodicals, Inc. Am. J. Hum. Biol. 28:484-492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2013-12-01

Climate change may alter the spatial distribution, composition, structure, and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate solar radiation absorbed by individual plants for understanding and predicting their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the analytical solutions of random distributions of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and is suitable for ecological models to simulate long-term transient responses of plant communities to climate change.

Comparison of Predicted Thermoelectric Energy Conversion Efficiency by Cumulative Properties and Reduced Variables Approaches

NASA Astrophysics Data System (ADS)

Linker, Thomas M.; Lee, Glenn S.; Beekman, Matt

2018-06-01

The semi-analytical methods of thermoelectric energy conversion efficiency calculation based on the cumulative properties approach and reduced variables approach are compared for 21 high performance thermoelectric materials. Both approaches account for the temperature dependence of the material properties as well as the Thomson effect, thus the predicted conversion efficiencies are generally lower than that based on the conventional thermoelectric figure of merit ZT for nearly all of the materials evaluated. The two methods also predict material energy conversion efficiencies that are in very good agreement which each other, even for large temperature differences (average percent difference of 4% with maximum observed deviation of 11%). The tradeoff between obtaining a reliable assessment of a material's potential for thermoelectric applications and the complexity of implementation of the three models, as well as the advantages of using more accurate modeling approaches in evaluating new thermoelectric materials, are highlighted.

Labour-efficient in vitro lymphocyte population tracking and fate prediction using automation and manual review.

PubMed

Chakravorty, Rajib; Rawlinson, David; Zhang, Alan; Markham, John; Dowling, Mark R; Wellard, Cameron; Zhou, Jie H S; Hodgkin, Philip D

2014-01-01

Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows.

PubMed

McParland, S; Lewis, E; Kennedy, E; Moore, S G; McCarthy, B; O'Donovan, M; Butler, S T; Pryce, J E; Berry, D P

2014-09-01

Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX=0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX=0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

Individual differences in executive processing predict susceptibility to interference in verbal working memory.

PubMed

Hedden, Trey; Yoon, Carolyn

2006-09-01

Recent theories have suggested that resistance to interference is a unifying principle of executive function and that individual differences in interference may be explained by executive function (M. J. Kane & R. W. Engle, 2002). Measures of executive function, memory, and perceptual speed were obtained from 121 older adults (ages 63-82). We used structural equation modeling to investigate the relationships of these constructs with interference in a working memory task. Executive function was best described as two related subcomponent processes: shifting and updating goal-relevant representations and inhibition of proactive interference. These subcomponents were distinct from verbal and visual memory and speed. Individual differences in interference susceptibility and recollection were best predicted by shifting and updating and by resistance to proactive interference, and variability in familiarity was predicted by resistance to proactive interference and speed. ((c) 2006 APA, all rights reserved).

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows.

PubMed

Phuong, H N; Blavy, P; Martin, O; Schmidely, P; Friggens, N C

2016-01-01

Reproductive success is a key component of lifetime efficiency - which is the ratio of energy in milk (MJ) to energy intake (MJ) over the lifespan, of cows. At the animal level, breeding and feeding management can substantially impact milk yield, body condition and energy balance of cows, which are known as major contributors to reproductive failure in dairy cattle. This study extended an existing lifetime performance model to incorporate the impacts that performance changes due to changing breeding and feeding strategies have on the probability of reproducing and thereby on the productive lifespan, and thus allow the prediction of a cow's lifetime efficiency. The model is dynamic and stochastic, with an individual cow being the unit modelled and one day being the unit of time. To evaluate the model, data from a French study including Holstein and Normande cows fed high-concentrate diets and data from a Scottish study including Holstein cows selected for high and average genetic merit for fat plus protein that were fed high- v. low-concentrate diets were used. Generally, the model consistently simulated productive and reproductive performance of various genotypes of cows across feeding systems. In the French data, the model adequately simulated the reproductive performance of Holsteins but significantly under-predicted that of Normande cows. In the Scottish data, conception to first service was comparably simulated, whereas interval traits were slightly under-predicted. Selection for greater milk production impaired the reproductive performance and lifespan but not lifetime efficiency. The definition of lifetime efficiency used in this model did not include associated costs or herd-level effects. Further works should include such economic indicators to allow more accurate simulation of lifetime profitability in different production scenarios.

An Individual-Tree Growth and Yield Prediction System for Even-Aged Natural Shortleaf Pine Forests

Treesearch

Thomas B. Lynch; Kenneth L. Hitch; Michael M. Huebschmann; Paul A. Murphy

1999-01-01

The development of a system of equations that model the growth and development of even-aged natural shortleaf (Pinus echinata Mill.) pine forests is described. The growth prediction system is a distance-independent individual-tree simulator containing equations that predict basal-area growth, survival, total and merchantable heights, and total and...

Comparison of Prediction Models for Lynch Syndrome Among Individuals With Colorectal Cancer

PubMed Central

Ojha, Rohit P.; Leenen, Celine; Alvero, Carmelita; Mercado, Rowena C.; Balmaña, Judith; Valenzuela, Irene; Balaguer, Francesc; Green, Roger; Lindor, Noralane M.; Thibodeau, Stephen N.; Newcomb, Polly; Win, Aung Ko; Jenkins, Mark; Buchanan, Daniel D.; Bertario, Lucio; Sala, Paola; Hampel, Heather; Syngal, Sapna; Steyerberg, Ewout W.

2016-01-01

Background: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. Methods: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. Results: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. Conclusions: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family. PMID:26582061

Affective-cognitive meta-bases versus structural bases of attitudes predict processing interest versus efficiency.

PubMed

See, Ya Hui Michelle; Petty, Richard E; Fabrigar, Leandre R

2013-08-01

We proposed that (a) processing interest for affective over cognitive information is captured by meta-bases (i.e., the extent to which people subjectively perceive themselves to rely on affect or cognition in their attitudes) and (b) processing efficiency for affective over cognitive information is captured by structural bases (i.e., the extent to which attitudes are more evaluatively congruent with affect or cognition). Because processing speed can disentangle interest from efficiency by being manifest as longer or shorter reading times, we hypothesized and found that more affective meta-bases predicted longer affective than cognitive reading time when processing efficiency was held constant (Study 1). In contrast, more affective structural bases predicted shorter affective than cognitive reading time when participants were constrained in their ability to allocate resources deliberatively (Study 2). When deliberation was neither encouraged nor constrained, effects for meta-bases and structural bases emerged (Study 3). Implications for affective-cognitive processing and other attitudes-relevant constructs are discussed.

Food-Predicting Stimuli Differentially Influence Eye Movements and Goal-Directed Behavior in Normal-Weight, Overweight, and Obese Individuals

PubMed Central

Lehner, Rea; Balsters, Joshua H.; Bürgler, Alexandra; Hare, Todd A.; Wenderoth, Nicole

2017-01-01

Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues as for example food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behavior but it is currently unknown, whether this effect differs between normal-weight, overweight, and obese individuals. Here, we investigate this question by using a Pavlovian-to-instrumental transfer (PIT) task in normal-weight (N = 20), overweight (N = 17), and obese (N = 17) individuals. Furthermore, we applied eye tracking during Pavlovian conditioning to measure the participants’ conditioned response as a proxy of the incentive salience of the predicted reward. Our results show that the goal-directed behavior of overweight individuals was more strongly influenced by food-predicting cues (i.e., stronger PIT effect) than that of normal-weight and obese individuals (p < 0.001). The weight groups were matched for age, gender, education, and parental education. Eye movements during Pavlovian conditioning also differed between weight categories (p < 0.05) and were used to categorize individuals based on their fixation style into “high eye index” versus “low eye index” as well. Our main finding was that the fixation style exhibited a complex interaction with the weight category. Furthermore, we found that normal-weight individuals of the group “high eye index” had higher body mass index within the healthy range than individuals of the group “low eye index” (p < 0.001), but this relationship was not found within in the overweight or obese groups (p > 0.646). Our findings are largely consistent with the incentive sensitization theory predicting that overweight individuals are more susceptible to food-related cues than normal-weight controls. However, this hypersensitivity might be reduced in obese individuals, possibly due to habitual/compulsive overeating or differences in

Developing and validating risk prediction models in an individual participant data meta-analysis

PubMed Central

2014-01-01

Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

Predicting patriarchy: using individual and contextual factors to examine patriarchal endorsement in communities.

PubMed

Crittenden, Courtney A; Wright, Emily M

2013-04-01

In much feminist literature, patriarchy has often been studied as a predictive variable for attitudes toward or acts of violence against women. However, rarely has patriarchy been examined as an outcome across studies. The current study works toward filling this gap by examining several individual-and neighborhood-level factors that might influence patriarchy. Specifically, this research seeks to determine if neighborhood-level attributes related to socioeconomic status, family composition, and demographic information affect patriarchal views after individual-level correlates of patriarchy were controlled. Findings suggest that factors at both the individual- and neighborhood levels, particularly familial characteristics and dynamics, do influence the endorsement of patriarchal views.

'Bodily precision': a predictive coding account of individual differences in interoceptive accuracy.

PubMed

Ainley, Vivien; Apps, Matthew A J; Fotopoulou, Aikaterini; Tsakiris, Manos

2016-11-19

Individuals differ in their awareness of afferent information from within their bodies, which is typically assessed by a heartbeat perception measure of 'interoceptive accuracy' (IAcc). Neural and behavioural correlates of this trait have been investigated, but a theoretical explanation has yet to be presented. Building on recent models that describe interoception within the free energy/predictive coding framework, this paper applies similar principles to IAcc, proposing that individual differences in IAcc depend on 'precision' in interoceptive systems, i.e. the relative weight accorded to 'prior' representations and 'prediction errors' (that part of incoming interoceptive sensation not accounted for by priors), at various levels within the cortical hierarchy and between modalities. Attention has the effect of optimizing precision both within and between sensory modalities. Our central assumption is that people with high IAcc are able, with attention, to prioritize interoception over other sensory modalities and can thus adjust the relative precision of their interoceptive priors and prediction errors, where appropriate, given their personal history. This characterization explains key findings within the interoception literature; links results previously seen as unrelated or contradictory; and may have important implications for understanding cognitive, behavioural and psychopathological consequences of both high and low interoceptive awareness.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'. © 2016 The Author(s).

Individual differences in spatial configuration learning predict the occurrence of intrusive memories.

PubMed

Meyer, Thomas; Smeets, Tom; Giesbrecht, Timo; Quaedflieg, Conny W E M; Girardelli, Marta M; Mackay, Georgina R N; Merckelbach, Harald

2013-03-01

The dual-representation model of posttraumatic stress disorder (PTSD; Brewin, Gregory, Lipton, & Burgess, Psychological Review, 117, 210-232 2010) argues that intrusions occur when people fail to construct context-based representations during adverse experiences. The present study tested a specific prediction flowing from this model. In particular, we investigated whether the efficiency of temporal-lobe-based spatial configuration learning would account for individual differences in intrusive experiences and physiological reactivity in the laboratory. Participants (N = 82) completed the contextual cuing paradigm, which assesses spatial configuration learning that is believed to depend on associative encoding in the parahippocampus. They were then shown a trauma film. Afterward, startle responses were quantified during presentation of trauma reminder pictures versus unrelated neutral and emotional pictures. PTSD symptoms were recorded in the week following participation. Better configuration learning performance was associated with fewer perceptual intrusions, r = -.33, p < .01, but was unrelated to physiological responses to trauma reminder images (ps > .46) and had no direct effect on intrusion-related distress and overall PTSD symptoms, rs > -.12, ps > .29. However, configuration learning performance tended to be associated with reduced physiological responses to unrelated negative images, r = -.20, p = .07. Thus, while spatial configuration learning appears to be unrelated to affective responding to trauma reminders, our overall findings support the idea that the context-based memory system helps to reduce intrusions.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

PubMed

Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

2018-06-01

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Acoustic Prediction Methodology and Test Validation for an Efficient Low-Noise Hybrid Wing Body Subsonic Transport

NASA Technical Reports Server (NTRS)

Kawai, Ronald T. (Compiler)

2011-01-01

This investigation was conducted to: (1) Develop a hybrid wing body subsonic transport configuration with noise prediction methods to meet the circa 2007 NASA Subsonic Fixed Wing (SFW) N+2 noise goal of -52 dB cum relative to FAR 36 Stage 3 (-42 dB cum re: Stage 4) while achieving a -25% fuel burned compared to current transports (re :B737/B767); (2) Develop improved noise prediction methods for ANOPP2 for use in predicting FAR 36 noise; (3) Design and fabricate a wind tunnel model for testing in the LaRC 14 x 22 ft low speed wind tunnel to validate noise predictions and determine low speed aero characteristics for an efficient low noise Hybrid Wing Body configuration. A medium wide body cargo freighter was selected to represent a logical need for an initial operational capability in the 2020 time frame. The Efficient Low Noise Hybrid Wing Body (ELNHWB) configuration N2A-EXTE was evolved meeting the circa 2007 NRA N+2 fuel burn and noise goals. The noise estimates were made using improvements in jet noise shielding and noise shielding prediction methods developed by UC Irvine and MIT. From this the Quiet Ultra Integrated Efficient Test Research Aircraft #1 (QUIET-R1) 5.8% wind tunnel model was designed and fabricated.

On the Efficiency of Individualized Theta/Beta Ratio Neurofeedback Combined with Forehead EMG Training in ADHD Children.

PubMed

Bazanova, Olga M; Auer, Tibor; Sapina, Elena A

2018-01-01

Background: Neurofeedback training (NFT) to decrease the theta/beta ratio (TBR) has been used for treating hyperactivity and impulsivity in attention deficit hyperactivity disorder (ADHD); however, often with low efficiency. Individual variance in EEG profile can confound NFT, because it may lead to influencing non-relevant activity, if ignored. More importantly, it may lead to influencing ADHD-related activities adversely, which may even result in worsening ADHD symptoms. Electromyogenic (EMG) signal resulted from forehead muscles can also explain the low efficiency of the NFT in ADHD from both practical and psychological point-of-view. The first aim of this study was to determine EEG and EMG biomarkers most related to the main ADHD characteristics, such as impulsivity and hyperactivity. The second aim was to confirm our hypothesis that the efficiency of the TBR NFT can be increased by individual adjustment of the frequency bands and simultaneous training on forehead muscle tension. Methods: We recruited 94 children diagnosed with ADHD (ADHD) and 23 healthy controls (HC). All participants were male and aged between six and nine. Impulsivity and attention were assessed with Go/no-Go task and delayed gratification task, respectively; and 19-channel EEG and forehead EMG were recorded. Then, the ADHD group was randomly subdivided into (1) standard, (2) individualized, (3) individualized+EMG, and (4) sham NFT (control) groups. The groups were compared based on TBR and EEG alpha activity, as well as hyperactivity and impulsivity three times: pre-NFT, post-NFT and 6 months after the NFT (follow-up). Results: ADHD children were characterized with decreased individual alpha peak frequency, alpha bandwidth and alpha amplitude suppression magnitude, as well as with increased alpha1/alpha2 (a1/a2) ratio and scalp muscle tension when c (η 2 ≥ 0.212). All contingent TBR NFT groups exhibited significant NFT-related decrease in TBR not evident in the control group. Moreover

[Predicting individual risk of high healthcare cost to identify complex chronic patients].

PubMed

Coderch, Jordi; Sánchez-Pérez, Inma; Ibern, Pere; Carreras, Marc; Pérez-Berruezo, Xavier; Inoriza, José M

2014-01-01

To develop a predictive model for the risk of high consumption of healthcare resources, and assess the ability of the model to identify complex chronic patients. A cross-sectional study was performed within a healthcare management organization by using individual data from 2 consecutive years (88,795 people). The dependent variable consisted of healthcare costs above the 95th percentile (P95), including all services provided by the organization and pharmaceutical consumption outside of the institution. The predictive variables were age, sex, morbidity-based on clinical risk groups (CRG)-and selected data from previous utilization (use of hospitalization, use of high-cost drugs in ambulatory care, pharmaceutical expenditure). A univariate descriptive analysis was performed. We constructed a logistic regression model with a 95% confidence level and analyzed sensitivity, specificity, positive predictive values (PPV), and the area under the ROC curve (AUC). Individuals incurring costs >P95 accumulated 44% of total healthcare costs and were concentrated in ACRG3 (aggregated CRG level 3) categories related to multiple chronic diseases. All variables were statistically significant except for sex. The model had a sensitivity of 48.4% (CI: 46.9%-49.8%), specificity of 97.2% (CI: 97.0%-97.3%), PPV of 46.5% (CI: 45.0%-47.9%), and an AUC of 0.897 (CI: 0.892 to 0.902). High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Individual reactions to stress predict performance during a critical aviation incident.

PubMed

Vine, Samuel J; Uiga, Liis; Lavric, Aureliu; Moore, Lee J; Tsaneva-Atanasova, Krasimira; Wilson, Mark R

2015-01-01

Understanding the influence of stress on human performance is of theoretical and practical importance. An individual's reaction to stress predicts their subsequent performance; with a "challenge" response to stress leading to better performance than a "threat" response. However, this contention has not been tested in truly stressful environments with highly skilled individuals. Furthermore, the effect of challenge and threat responses on attentional control during visuomotor tasks is poorly understood. Thus, this study aimed to examine individual reactions to stress and their influence on attentional control, among a cohort of commercial pilots performing a stressful flight assessment. Sixteen pilots performed an "engine failure on take-off" scenario, in a high-fidelity flight simulator. Reactions to stress were indexed via self-report; performance was assessed subjectively (flight instructor assessment) and objectively (simulator metrics); gaze behavior data were captured using a mobile eye tracker, and measures of attentional control were subsequently calculated (search rate, stimulus driven attention, and entropy). Hierarchical regression analyses revealed that a threat response was associated with poorer performance and disrupted attentional control. The findings add to previous research showing that individual reactions to stress influence performance and shed light on the processes through which stress influences performance.

A data envelope analysis to assess factors affecting technical and economic efficiency of individual broiler breeder hens.

PubMed

Romero, L F; Zuidhof, M J; Jeffrey, S R; Naeima, A; Renema, R A; Robinson, F E

2010-08-01

This study evaluated the effect of feed allocation and energetic efficiency on technical and economic efficiency of broiler breeder hens using the data envelope analysis methodology and quantified the effect of variables affecting technical efficiency. A total of 288 Ross 708 pullets were placed in individual cages at 16 wk of age and assigned to 1 of 4 feed allocation groups. Three of them had feed allocated on a group basis with divergent BW targets: standard, high (standard x 1.1), and low (standard x 0.9). The fourth group had feed allocated on an individual bird basis following the standard BW target. Birds were classified in 3 energetic efficiency categories: low, average, and high, based on estimated maintenance requirements. Technical efficiency considered saleable chicks as output and cumulative ME intake and time as inputs. Economic efficiency of feed allocation treatments was analyzed under different cost scenarios. Birds with low feed allocation exhibited a lower technical efficiency (69.4%) than standard (72.1%), which reflected a reduced egg production rate. Feed allocation of the high treatment could have been reduced by 10% with the same chick production as the standard treatment. The low treatment exhibited reduced economic efficiency at greater capital costs, whereas high had reduced economic efficiency at greater feed costs. The average energetic efficiency hens had a lower technical efficiency in the low compared with the standard feed allocation. A 1% increment in estimated maintenance requirement changed technical efficiency by -0.23%, whereas a 1% increment in ME intake had a -0.47% effect. The negative relationship between technical efficiency and ME intake was counterbalanced by a positive correlation of ME intake and egg production. The negative relationship of technical efficiency and maintenance requirements was synergized by a negative correlation of hen maintenance and egg production. Economic efficiency methodologies are effective

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, W.; Li, J.

2014-07-01

Climate change may alter the spatial distribution, composition, structure and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate the solar radiation absorbed by individual plants in order to understand and predict their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming that crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the results of random distribution of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and can be included in vegetation models to simulate long-term transient responses of plant communities to climate change. The code and a user's manual are provided as Supplement of the paper.

Genetic basis of between-individual and within-individual variance of docility.

PubMed

Martin, J G A; Pirotta, E; Petelle, M B; Blumstein, D T

2017-04-01

Between-individual variation in phenotypes within a population is the basis of evolution. However, evolutionary and behavioural ecologists have mainly focused on estimating between-individual variance in mean trait and neglected variation in within-individual variance, or predictability of a trait. In fact, an important assumption of mixed-effects models used to estimate between-individual variance in mean traits is that within-individual residual variance (predictability) is identical across individuals. Individual heterogeneity in the predictability of behaviours is a potentially important effect but rarely estimated and accounted for. We used 11 389 measures of docility behaviour from 1576 yellow-bellied marmots (Marmota flaviventris) to estimate between-individual variation in both mean docility and its predictability. We then implemented a double hierarchical animal model to decompose the variances of both mean trait and predictability into their environmental and genetic components. We found that individuals differed both in their docility and in their predictability of docility with a negative phenotypic covariance. We also found significant genetic variance for both mean docility and its predictability but no genetic covariance between the two. This analysis is one of the first to estimate the genetic basis of both mean trait and within-individual variance in a wild population. Our results indicate that equal within-individual variance should not be assumed. We demonstrate the evolutionary importance of the variation in the predictability of docility and illustrate potential bias in models ignoring variation in predictability. We conclude that the variability in the predictability of a trait should not be ignored, and present a coherent approach for its quantification. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Learning Efficiency: Identifying Individual Differences in Learning Rate and Retention in Healthy Adults.

PubMed

Zerr, Christopher L; Berg, Jeffrey J; Nelson, Steven M; Fishell, Andrew K; Savalia, Neil K; McDermott, Kathleen B

2018-06-01

People differ in how quickly they learn information and how long they remember it, yet individual differences in learning abilities within healthy adults have been relatively neglected. In two studies, we examined the relation between learning rate and subsequent retention using a new foreign-language paired-associates task (the learning-efficiency task), which was designed to eliminate ceiling effects that often accompany standardized tests of learning and memory in healthy adults. A key finding was that quicker learners were also more durable learners (i.e., exhibited better retention across a delay), despite studying the material for less time. Additionally, measures of learning and memory from this task were reliable in Study 1 ( N = 281) across 30 hr and Study 2 ( N = 92; follow-up n = 46) across 3 years. We conclude that people vary in how efficiently they learn, and we describe a reliable and valid method for assessing learning efficiency within healthy adults.

Anthelmintic efficiency of doramectin, fenbendazole, and nitroxynil, in combination or individually, in sheep worm control.

PubMed

Holsback, Luciane; Luppi, Pedro Alex Ramsey; Silva, Camile Sanches; Negrão, Gustavo Kremer; Conde, Gabriel; Gabriel, Hugo Vinícius; Balestrieri, João Vitor; Tomazella, Lucas

2016-01-01

The anthelmintic efficiency of doramectin, fenbendazole, and nitroxynil, used individually or in combination, was determined by the Fecal Egg Count Reduction (FECR) test and cultivation of larvae of anthelminthic-treated sheep grouped as follows: G1 (doramectin), G2 (fenbendazole), G3 (nitroxynil), G4 (doramectin + fenbendazole), G5 (doramectin + nitroxynil), G6 (fenbendazole + nitroxynil), G7 (doramectin + nitroxynil + fenbendazole), G8 (untreated). In addition to individually used doramectin and fenbendazole, the helminths were also resistant to the combination of doramectin + fenbendazole; nitroxynil + fenbendazole; and doramectin + nitroxynil + fenbendazole, with their FECR rates ranging from 62-83%. The helminths showed possible nitroxynil-resistance, but had low resistance when the drug was administered in combination with doramectin. The evaluation of individual helminth species revealed that fenbendazole was fully effective against Cooperia; doramectin (G1), moderately effective against Haemonchus and insufficiently active against Cooperia; nitroxynil, effective against Haemonchus and insufficiently active against Cooperia. It was concluded from the results that herd nematodes are resistant to doramectin, fenbendazole, and nitroxynil, and that the combined use of the drugs not only fails to significantly improve the anthelmintic efficiency against Haemonchus and Cooperia, but is also cost-ineffective.

Do individualism and collectivism on three levels (country, individual, and situation) influence theory-of-mind efficiency? A cross-country study.

PubMed

Vu, Tuong-Van; Finkenauer, Catrin; Huizinga, Mariette; Novin, Sheida; Krabbendam, Lydia

2017-01-01

This study investigated whether individualism and collectivism (IC) at country, individual, and situational level influence how quickly and accurately people can infer mental states (i.e. theory of mind, or ToM), indexed by accuracy and reaction time in a ToM task. We hypothesized that collectivism (having an interdependent self and valuing group concerns), compared to individualism (having an independent self and valuing personal concerns), is associated with greater accuracy and speed in recognizing and understanding the thoughts and feelings of others. Students (N = 207) from individualism-representative (the Netherlands) and collectivism-representative (Vietnam) countries (Country IC) answered an individualism-collectivism questionnaire (Individual IC) and were randomly assigned to an individualism-primed, collectivism-primed, or no-prime task (Situational IC) before performing a ToM task. The data showed vast differences between the Dutch and Vietnamese groups that might not be attributable to experimental manipulation. Therefore, we analyzed the data for the groups separately and found that Individual IC did not predict ToM accuracy or reaction time performance. Regarding Situational IC, when primed with individualism, the accuracy performance of Vietnamese participants in affective ToM trials decreased compared to when primed with collectivism and when no prime was used. However, an interesting pattern emerged: Dutch participants were least accurate in affective ToM trials, while Vietnamese participants were quickest in affective ToM trials. Our research also highlights a dilemma faced by cross-cultural researchers who use hard-to-reach populations but face the challenge of disentangling experimental effects from biases that might emerge due to an interaction between cultural differences and experimental settings. We propose suggestions for overcoming such challenges.

Do individualism and collectivism on three levels (country, individual, and situation) influence theory-of-mind efficiency? A cross-country study

PubMed Central

Finkenauer, Catrin; Huizinga, Mariette; Novin, Sheida; Krabbendam, Lydia

2017-01-01

This study investigated whether individualism and collectivism (IC) at country, individual, and situational level influence how quickly and accurately people can infer mental states (i.e. theory of mind, or ToM), indexed by accuracy and reaction time in a ToM task. We hypothesized that collectivism (having an interdependent self and valuing group concerns), compared to individualism (having an independent self and valuing personal concerns), is associated with greater accuracy and speed in recognizing and understanding the thoughts and feelings of others. Students (N = 207) from individualism-representative (the Netherlands) and collectivism-representative (Vietnam) countries (Country IC) answered an individualism-collectivism questionnaire (Individual IC) and were randomly assigned to an individualism-primed, collectivism-primed, or no-prime task (Situational IC) before performing a ToM task. The data showed vast differences between the Dutch and Vietnamese groups that might not be attributable to experimental manipulation. Therefore, we analyzed the data for the groups separately and found that Individual IC did not predict ToM accuracy or reaction time performance. Regarding Situational IC, when primed with individualism, the accuracy performance of Vietnamese participants in affective ToM trials decreased compared to when primed with collectivism and when no prime was used. However, an interesting pattern emerged: Dutch participants were least accurate in affective ToM trials, while Vietnamese participants were quickest in affective ToM trials. Our research also highlights a dilemma faced by cross-cultural researchers who use hard-to-reach populations but face the challenge of disentangling experimental effects from biases that might emerge due to an interaction between cultural differences and experimental settings. We propose suggestions for overcoming such challenges. PMID:28832602

Oxygen Uptake Efficiency Plateau Best Predicts Early Death in Heart Failure

PubMed Central

Hansen, James E.; Stringer, William W.

2012-01-01

Background: The responses of oxygen uptake efficiency (ie, oxygen uptake/ventilation = V˙o2/V˙e) and its highest plateau (OUEP) during incremental cardiopulmonary exercise testing (CPET) in patients with chronic left heart failure (HF) have not been previously reported. We planned to test the hypothesis that OUEP during CPET is the best single predictor of early death in HF. Methods: We evaluated OUEP, slope of V˙o2 to log(V˙e) (oxygen uptake efficiency slope), oscillatory breathing, and all usual resting and CPET measurements in 508 patients with low-ejection-fraction (< 35%) HF. Each had further evaluations at other sites, including cardiac catheterization. Outcomes were 6-month all-reason mortality and morbidity (death or > 24 h cardiac hospitalization). Statistical analyses included area under curve of receiver operating characteristics, ORs, univariate and multivariate Cox regression, and Kaplan-Meier plots. Results: OUEP, which requires only moderate exercise, was often reduced in patients with HF. A low % predicted OUEP was the single best predictor of mortality (P < .0001), with an OR of 13.0 (P < .001). When combined with oscillatory breathing, the OR increased to 56.3, superior to all other resting or exercise parameters or combinations of parameters. Other statistical analyses and morbidity analysis confirmed those findings. Conclusions: OUEP is often reduced in patients with HF. Low % predicted OUEP (< 65% predicted) is the single best predictor of early death, better than any other CPET or other cardiovascular measurement. Paired with oscillatory breathing, it is even more powerful. PMID:22030802

Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals.

PubMed

McLay-Cooke, Rebecca T; Gray, Andrew R; Jones, Lynnette M; Taylor, Rachael W; Skidmore, Paula M L; Brown, Rachel C

2017-09-13

Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d -1 , p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d -1 . For measured RMR ≤7000 kJ·d -1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

Experimental evaluation of a mathematical model for predicting transfer efficiency of a high volume-low pressure air spray gun.

PubMed

Tan, Y M; Flynn, M R

2000-10-01

The transfer efficiency of a spray-painting gun is defined as the amount of coating applied to the workpiece divided by the amount sprayed. Characterizing this transfer process allows for accurate estimation of the overspray generation rate, which is important for determining a spray painter's exposure to airborne contaminants. This study presents an experimental evaluation of a mathematical model for predicting the transfer efficiency of a high volume-low pressure spray gun. The effects of gun-to-surface distance and nozzle pressure on the agreement between the transfer efficiency measurement and prediction were examined. Wind tunnel studies and non-volatile vacuum pump oil in place of commercial paint were used to determine transfer efficiency at nine gun-to-surface distances and four nozzle pressure levels. The mathematical model successfully predicts transfer efficiency within the uncertainty limits. The least squares regression between measured and predicted transfer efficiency has a slope of 0.83 and an intercept of 0.12 (R2 = 0.98). Two correction factors were determined to improve the mathematical model. At higher nozzle pressure settings, 6.5 psig and 5.5 psig, the correction factor is a function of both gun-to-surface distance and nozzle pressure level. At lower nozzle pressures, 4 psig and 2.75 psig, gun-to-surface distance slightly influences the correction factor, while nozzle pressure has no discernible effect.

Food-Predicting Stimuli Differentially Influence Eye Movements and Goal-Directed Behavior in Normal-Weight, Overweight, and Obese Individuals.

PubMed

Lehner, Rea; Balsters, Joshua H; Bürgler, Alexandra; Hare, Todd A; Wenderoth, Nicole

2017-01-01

Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues as for example food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behavior but it is currently unknown, whether this effect differs between normal-weight, overweight, and obese individuals. Here, we investigate this question by using a Pavlovian-to-instrumental transfer (PIT) task in normal-weight ( N  = 20), overweight ( N  = 17), and obese ( N  = 17) individuals. Furthermore, we applied eye tracking during Pavlovian conditioning to measure the participants' conditioned response as a proxy of the incentive salience of the predicted reward. Our results show that the goal-directed behavior of overweight individuals was more strongly influenced by food-predicting cues (i.e., stronger PIT effect) than that of normal-weight and obese individuals ( p  < 0.001). The weight groups were matched for age, gender, education, and parental education. Eye movements during Pavlovian conditioning also differed between weight categories ( p  < 0.05) and were used to categorize individuals based on their fixation style into "high eye index" versus "low eye index" as well. Our main finding was that the fixation style exhibited a complex interaction with the weight category. Furthermore, we found that normal-weight individuals of the group "high eye index" had higher body mass index within the healthy range than individuals of the group "low eye index" ( p  < 0.001), but this relationship was not found within in the overweight or obese groups ( p  > 0.646). Our findings are largely consistent with the incentive sensitization theory predicting that overweight individuals are more susceptible to food-related cues than normal-weight controls. However, this hypersensitivity might be reduced in obese individuals, possibly due to habitual/compulsive overeating or differences in

Pre-trauma individual differences in extinction learning predict posttraumatic stress.

PubMed

Lommen, Miriam J J; Engelhard, Iris M; Sijbrandij, Marit; van den Hout, Marcel A; Hermans, Dirk

2013-02-01

In the aftermath of a traumatic event, many people suffer from psychological distress, but only a minority develops posttraumatic stress disorder (PTSD). Pre-trauma individual differences in fear conditioning, most notably reduced extinction learning, have been proposed as playing an important role in the etiology of PTSD. However, prospective data are lacking. In this study, we prospectively tested whether reduced extinction was a predictor for later posttraumatic stress. Dutch soldiers (N = 249) were administered a conditioning task before their four-month deployment to Afghanistan to asses individual differences in extinction learning. After returning home, posttraumatic stress was measured. Results showed that reduced extinction learning before deployment predicted subsequent PTSD symptom severity, over and beyond degree of pre-deployment stress symptoms, neuroticism, and exposure to stressors on deployment. The findings suggest that reduced extinction learning may play a role in the development of PTSD. Copyright © 2012 Elsevier Ltd. All rights reserved.

Assessing Soldier Individual Differences to Enable Tailored Training

DTIC Science & Technology

2010-04-01

upon effective and efficient training. However, there is ample evidence that learning-related individual differences exist ( Thorndike , 1985; Jensen...in both civilian and military settings (Schmidt, Hunter, & Outerbridge, 1986; Thorndike , 1985). Prior knowledge or knowledge of facts and...predictive power ( Thorndike , 1985; Jensen, 1998). Further, there is a good deal of evidence that general mental ability impacts performance largely

A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients.

PubMed

Oberije, Cary; De Ruysscher, Dirk; Houben, Ruud; van de Heuvel, Michel; Uyterlinde, Wilma; Deasy, Joseph O; Belderbos, Jose; Dingemans, Anne-Marie C; Rimner, Andreas; Din, Shaun; Lambin, Philippe

2015-07-15

Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048. The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system. Copyright © 2015 The Authors. Published by Elsevier Inc. All

Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

PubMed

Lamberink, Herm J; Boshuisen, Kim; Otte, Willem M; Geleijns, Karin; Braun, Kees P J

2018-03-01

The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Predicting the Individual Risk of Acute Severe Colitis at Diagnosis

PubMed Central

Cesarini, Monica; Collins, Gary S.; Rönnblom, Anders; Santos, Antonieta; Wang, Lai Mun; Sjöberg, Daniel; Parkes, Miles; Keshav, Satish

2017-01-01

Abstract Background and Aims: Acute severe colitis [ASC] is associated with major morbidity. We aimed to develop and externally validate an index that predicted ASC within 3 years of diagnosis. Methods: The development cohort included patients aged 16–89 years, diagnosed with ulcerative colitis [UC] in Oxford and followed for 3 years. Primary outcome was hospitalization for ASC, excluding patients admitted within 1 month of diagnosis. Multivariable logistic regression examined the adjusted association of seven risk factors with ASC. Backwards elimination produced a parsimonious model that was simplified to create an easy-to-use index. External validation occurred in separate cohorts from Cambridge, UK, and Uppsala, Sweden. Results: The development cohort [Oxford] included 34/111 patients who developed ASC within a median 14 months [range 1–29]. The final model applied the sum of 1 point each for extensive disease, C-reactive protein [CRP] > 10mg/l, or haemoglobin < 12g/dl F or < 14g/dl M at diagnosis, to give a score from 0/3 to 3/3. This predicted a 70% risk of developing ASC within 3 years [score 3/3]. Validation cohorts included different proportions with ASC [Cambridge = 25/96; Uppsala = 18/298]. Of those scoring 3/3 at diagnosis, 18/18 [Cambridge] and 12/13 [Uppsala] subsequently developed ASC. Discriminant ability [c-index, where 1.0 = perfect discrimination] was 0.81 [Oxford], 0.95 [Cambridge], 0.97 [Uppsala]. Internal validation using bootstrapping showed good calibration, with similar predicted risk across all cohorts. A nomogram predicted individual risk. Conclusions: An index applied at diagnosis reliably predicts the risk of ASC within 3 years in different populations. Patients with a score 3/3 at diagnosis may merit early immunomodulator therapy. PMID:27647858

Predicting the Individual Risk of Acute Severe Colitis at Diagnosis.

PubMed

Cesarini, Monica; Collins, Gary S; Rönnblom, Anders; Santos, Antonieta; Wang, Lai Mun; Sjöberg, Daniel; Parkes, Miles; Keshav, Satish; Travis, Simon P L

2017-03-01

Acute severe colitis [ASC] is associated with major morbidity. We aimed to develop and externally validate an index that predicted ASC within 3 years of diagnosis. The development cohort included patients aged 16-89 years, diagnosed with ulcerative colitis [UC] in Oxford and followed for 3 years. Primary outcome was hospitalization for ASC, excluding patients admitted within 1 month of diagnosis. Multivariable logistic regression examined the adjusted association of seven risk factors with ASC. Backwards elimination produced a parsimonious model that was simplified to create an easy-to-use index. External validation occurred in separate cohorts from Cambridge, UK, and Uppsala, Sweden. The development cohort [Oxford] included 34/111 patients who developed ASC within a median 14 months [range 1-29]. The final model applied the sum of 1 point each for extensive disease, C-reactive protein [CRP] > 10mg/l, or haemoglobin < 12g/dl F or < 14g/dl M at diagnosis, to give a score from 0/3 to 3/3. This predicted a 70% risk of developing ASC within 3 years [score 3/3]. Validation cohorts included different proportions with ASC [Cambridge = 25/96; Uppsala = 18/298]. Of those scoring 3/3 at diagnosis, 18/18 [Cambridge] and 12/13 [Uppsala] subsequently developed ASC. Discriminant ability [c-index, where 1.0 = perfect discrimination] was 0.81 [Oxford], 0.95 [Cambridge], 0.97 [Uppsala]. Internal validation using bootstrapping showed good calibration, with similar predicted risk across all cohorts. A nomogram predicted individual risk. An index applied at diagnosis reliably predicts the risk of ASC within 3 years in different populations. Patients with a score 3/3 at diagnosis may merit early immunomodulator therapy. Copyright © 2016 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Individual differences in bodily freezing predict emotional biases in decision making

PubMed Central

Ly, Verena; Huys, Quentin J. M.; Stins, John F.; Roelofs, Karin; Cools, Roshan

2014-01-01

Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this hypothesis directly, we investigated whether effects of emotional faces on instrumental action can be predicted by effects of emotional faces on bodily freezing, an innately specified response to aversive relative to appetitive cues. We tested 43 women using a novel emotional decision making task combined with posturography, which involves a force platform to detect small oscillations of the body to accurately quantify postural control in upright stance. On the platform, participants learned whole body approach-avoidance actions based on monetary feedback, while being primed by emotional faces (angry/happy). Our data evidence an emotional biasing of instrumental action. Thus, angry relative to happy faces slowed instrumental approach relative to avoidance responses. Critically, individual differences in this emotional biasing effect were predicted by individual differences in bodily freezing. This result suggests that emotional biasing of instrumental action involves interaction with a system that controls innately specified responses. Furthermore, our findings help bridge (animal and human) decision making and emotion research to advance our mechanistic understanding of decision making anomalies in daily encounters as well as in a wide range of psychopathology. PMID:25071491

Aberrant monocyte responses predict and characterize dengue virus infection in individuals with severe disease.

PubMed

Yong, Yean K; Tan, Hong Y; Jen, Soe Hui; Shankar, Esaki M; Natkunam, Santha K; Sathar, Jameela; Manikam, Rishya; Sekaran, Shamala D

2017-05-31

Currently, several assays can diagnose acute dengue infection. However, none of these assays can predict the severity of the disease. Biomarkers that predicts the likelihood that a dengue patient will develop a severe form of the disease could permit more efficient patient triage and allows better supportive care for the individual in need, especially during dengue outbreaks. We measured 20 plasma markers i.e. IFN-γ, IL-10, granzyme-B, CX3CL1, IP-10, RANTES, CXCL8, CXCL6, VCAM, ICAM, VEGF, HGF, sCD25, IL-18, LBP, sCD14, sCD163, MIF, MCP-1 and MIP-1β in 141 dengue patients in over 230 specimens and correlate the levels of these plasma markers with the development of dengue without warning signs (DWS-), dengue with warning signs (DWS+) and severe dengue (SD). Our results show that the elevation of plasma levels of IL-18 at both febrile and defervescence phase was significantly associated with DWS+ and SD; whilst increase of sCD14 and LBP at febrile phase were associated with severity of dengue disease. By using receiver operating characteristic (ROC) analysis, the IL-18, LBP and sCD14 were significantly predicted the development of more severe form of dengue disease (DWS+/SD) (AUC = 0.768, P < 0.0001; AUC = 0.819, P < 0.0001 and AUC = 0.647, P = 0.014 respectively). Furthermore, we also found that the levels of VEGF were directly correlated and sCD14 was inversely correlated with platelet count, suggesting that the endothelial activation and microbial translocation may played a role in pathogenesis of dengue disease. Given that the elevation IL-18, LBP and sCD14 among patients with severe form of dengue disease, our findings suggest a pathogenic role for an aberrant inflammasome and monocyte activation in the development of severe form of dengue disease.

Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing.

PubMed

McGregor, Heather R; Gribble, Paul L

2017-08-01

Action observation can facilitate the acquisition of novel motor skills; however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess preobservation gray matter volume and preobservation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not gray matter volume, predicted postobservation gains in motor learning. Preobservation resting-state FC between left primary somatosensory cortex and bilateral dorsal premotor cortex, primary motor cortex, and primary somatosensory cortex and left superior parietal lobule was positively correlated with behavioral measures of postobservation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning. NEW & NOTEWORTHY We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke. Copyright © 2017 the American Physiological Society.

Neural sensitivity to sex steroids predicts individual differences in aggression: implications for behavioural evolution.

PubMed

Rosvall, K A; Bergeon Burns, C M; Barske, J; Goodson, J L; Schlinger, B A; Sengelaub, D R; Ketterson, E D

2012-09-07

Testosterone (T) regulates many traits related to fitness, including aggression. However, individual variation in aggressiveness does not always relate to circulating T, suggesting that behavioural variation may be more closely related to neural sensitivity to steroids, though this issue remains unresolved. To assess the relative importance of circulating T and neural steroid sensitivity in predicting behaviour, we measured aggressiveness during staged intrusions in free-living male and female dark-eyed juncos (Junco hyemalis). We compared aggressiveness to plasma T levels and to the abundance of androgen receptor (AR), aromatase (AROM) and oestrogen receptor alpha (ORα) mRNA in behaviourally relevant brain areas (avian medial amygdala, hypothalamus and song control regions). We also asked whether patterns of covariation among behaviour and endocrine parameters differed in males and females, anticipating that circulating T may be a better predictor of behaviour in males than in females. We found that circulating T related to aggressiveness only in males, but that gene expression for ORα, AR and AROM covaried with individual differences in aggressiveness in both sexes. These findings are among the first to show that individual variation in neural gene expression for three major sex steroid-processing molecules predicts individual variation in aggressiveness in both sexes in nature. The results have broad implications for our understanding of the mechanisms by which aggressive behaviour may evolve.

Verbal working memory predicts co-speech gesture: evidence from individual differences.

PubMed

Gillespie, Maureen; James, Ariel N; Federmeier, Kara D; Watson, Duane G

2014-08-01

Gesture facilitates language production, but there is debate surrounding its exact role. It has been argued that gestures lighten the load on verbal working memory (VWM; Goldin-Meadow, Nusbaum, Kelly, & Wagner, 2001), but gestures have also been argued to aid in lexical retrieval (Krauss, 1998). In the current study, 50 speakers completed an individual differences battery that included measures of VWM and lexical retrieval. To elicit gesture, each speaker described short cartoon clips immediately after viewing. Measures of lexical retrieval did not predict spontaneous gesture rates, but lower VWM was associated with higher gesture rates, suggesting that gestures can facilitate language production by supporting VWM when resources are taxed. These data also suggest that individual variability in the propensity to gesture is partly linked to cognitive capacities. Copyright © 2014 Elsevier B.V. All rights reserved.

Efficient first-principles prediction of solid stability: Towards chemical accuracy

NASA Astrophysics Data System (ADS)

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; Chen, Tina; Dacek, Stephen T.; Sarmiento-Pérez, Rafael A.; Marques, Maguel A. L.; Peng, Haowei; Ceder, Gerbrand; Perdew, John P.; Sun, Jianwei

2018-03-01

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.

Efficient first-principles prediction of solid stability: Towards chemical accuracy

DOE PAGES

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; ...

2018-03-09

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less

Efficient first-principles prediction of solid stability: Towards chemical accuracy

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

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for mainmore » group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.« less

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite.

PubMed

Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus Bm; Schmitz, Christoph

2014-01-01

Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predicted by the patient's individual cellulite grade at baseline, individual patient age, body mass index (BMI), weight, and/or height. Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean) cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast(®) device (Electro Medical Systems, S.A., Nyon, Switzerland). Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right), totaling eight treatments on the selected side. Treatment was performed at 3.5-4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area). Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires. The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades) and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades). Compared with baseline, no patient's condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, P<0.05) correlation was found between individual values for δ-1 and δ-2 and cellulite grade at baseline, BMI, weight, height, or age. Radial shock wave therapy is a safe and effective treatment option for cellulite. The individual clinical outcome cannot be

The six-minute walk test predicts cardiorespiratory fitness in individuals with aneurysmal subarachnoid hemorrhage.

PubMed

Harmsen, Wouter J; Ribbers, Gerard M; Slaman, Jorrit; Heijenbrok-Kal, Majanka H; Khajeh, Ladbon; van Kooten, Fop; Neggers, Sebastiaan J C M M; van den Berg-Emons, Rita J

2017-05-01

Peak oxygen uptake (VO 2peak ) established during progressive cardiopulmonary exercise testing (CPET) is the "gold-standard" for cardiorespiratory fitness. However, CPET measurements may be limited in patients with aneurysmal subarachnoid hemorrhage (a-SAH) by disease-related complaints, such as cardiovascular health-risks or anxiety. Furthermore, CPET with gas-exchange analyses require specialized knowledge and infrastructure with limited availability in most rehabilitation facilities. To determine whether an easy-to-administer six-minute walk test (6MWT) is a valid clinical alternative to progressive CPET in order to predict VO 2peak in individuals with a-SAH. Twenty-seven patients performed the 6MWT and CPET with gas-exchange analyses on a cycle ergometer. Univariate and multivariate regression models were made to investigate the predictability of VO 2peak from the six-minute walk distance (6MWD). Univariate regression showed that the 6MWD was strongly related to VO 2peak (r = 0.75, p < 0.001), with an explained variance of 56% and a prediction error of 4.12 ml/kg/min, representing 18% of mean VO 2peak . Adding age and sex to an extended multivariate regression model improved this relationship (r = 0.82, p < 0.001), with an explained variance of 67% and a prediction error of 3.67 ml/kg/min corresponding to 16% of mean VO 2peak . The 6MWT is an easy-to-administer submaximal exercise test that can be selected to estimate cardiorespiratory fitness at an aggregated level, in groups of patients with a-SAH, which may help to evaluate interventions in a clinical or research setting. However, the relatively large prediction error does not allow for an accurate prediction in individual patients.

Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

PubMed Central

Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

2014-01-01

Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of

Predictive value and efficiency of laboratory testing.

PubMed

Galen, R S

1980-11-01

Literature on determining reference values and reference intervals on "normal" or "healthy" individuals is abundant. It is impossible, however, to evaluate a data set of reference values and select a suitable reference interval that will be meaningful for the practice of medicine. The reference interval, no matter how derived statistically, tells us nothing about disease. This is the main reason the concepts of "normal values" have failed us and why "reference values" will prove similarly disappointing. By studying these same constituents in a variety of disease states as well, it will be possible to select "referent values" that will make the test procedure meaningful for diagnostic purposes. In order to obtain meaningful referent values for predicting disease, it is necessary to study not only the "healthy" reference population, but patients with the disease in question, and patients who are free of the disease in question but who have other diseases. Studies of this type are not frequently found for laboratory tests that are in common use today.

Predicting Achievement in Mathematics in Adolescent Students: The Role of Individual and Social Factors

ERIC Educational Resources Information Center

Levpuscek, Melita Puklek; Zupancic, Maja; Socan, Gregor

2013-01-01

The study examined individual factors and social factors that influence adolescent students' achievement in mathematics. The predictive model suggested direct positive effects of student intelligence, self-rated openness and parental education on achievement in mathematics, whereas direct effects of extraversion on measures of achievement were…

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Individual Differences in Typical Reappraisal Use Predict Amygdala and Prefrontal Responses

PubMed Central

Drabant, Emily M.; McRae, Kateri; Manuck, Stephen B.; Hariri, Ahmad R.; Gross, James J.

2010-01-01

Background Participants who are instructed to use reappraisal to downregulate negative emotion show decreased amygdala responses and increased prefrontal responses. However, it is not known whether individual differences in the tendency to use reappraisal manifests in similar neural responses when individuals are spontaneously confronted with negative situations. Such spontaneous emotion regulation might play an important role in normal and pathological responses to the emotional challenges of everyday life. Methods Fifty-six healthy women completed a blood oxygenation-level dependent functional magnetic resonance imaging challenge paradigm involving the perceptual processing of emotionally negative facial expressions. Participants also completed measures of typical emotion regulation use, trait anxiety, and neuroticism. Results Greater use of reappraisal in everyday life was related to decreased amygdala activity and increased prefrontal and parietal activity during the processing of negative emotional facial expressions. These associations were not attributable to variation in trait anxiety, neuroticism, or the use of another common form of emotion regulation, namely suppression. Conclusions These findings suggest that, like instructed reappraisal, individual differences in reappraisal use are associated with decreased activation in ventral emotion generative regions and increased activation in prefrontal control regions in response to negative stimuli. Such individual differences in emotion regulation might predict successful coping with emotional challenges as well as the onset of affective disorders. PMID:18930182

Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System.

PubMed

Eser, Alexander; Primas, Christian; Reinisch, Sieglinde; Vogelsang, Harald; Novacek, Gottfried; Mould, Diane R; Reinisch, Walter

2018-01-30

Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P < .0001). The Bayesian dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab. © 2018, The American College of Clinical Pharmacology.

Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs.

PubMed

Conger, Scott A; Scott, Stacy N; Bassett, David R

2014-07-01

To examine the relationship between hand rim propulsion power and energy expenditure (EE) during wheelchair wheeling and to investigate whether adding other variables to the model could improve on the prediction of EE. Individuals who use manual wheelchairs (n=14) performed five different wheeling activities in a wheelchair with a PowerTap power meter hub built into the right rear wheel. Activities included wheeling on a smooth, level surface at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised track at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. EE was measured using a portable indirect calorimetry system. Stepwise linear regression was performed to predict EE from power output variables. A repeated-measures analysis of variance was used to compare the measured EE to the estimates from the power models. Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values. EE and power were significantly correlated (r=0.694, p<0.001). Regression analysis yielded three significant prediction models utilising measured power; measured power and speed; and measured power, speed and heart rate. No significant differences were found between measured EE and any of the prediction models. EE can be accurately and precisely estimated based on hand rim propulsion power. These results indicate that power could be used as a method to assess EE in individuals who use wheelchairs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

NASA Astrophysics Data System (ADS)

Iliev, Iliycho; Gocheva-Ilieva, Snezhana; Kulin, Chavdar

2018-01-01

Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others) and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART) technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Lexical Familiarity and Processing Efficiency: Individual Differences in Naming, Lexical Decision, and Semantic Categorization

PubMed Central

Lewellen, Mary Jo; Goldinger, Stephen D.; Pisoni, David B.; Greene, Beth G.

2012-01-01

College students were separated into 2 groups (high and low) on the basis of 3 measures: subjective familiarity ratings of words, self-reported language experiences, and a test of vocabulary knowledge. Three experiments were conducted to determine if the groups also differed in visual word naming, lexical decision, and semantic categorization. High Ss were consistently faster than low Ss in naming visually presented words. They were also faster and more accurate in making difficult lexical decisions and in rejecting homophone foils in semantic categorization. Taken together, the results demonstrate that Ss who differ in lexical familiarity also differ in processing efficiency. The relationship between processing efficiency and working memory accounts of individual differences in language processing is also discussed. PMID:8371087

An Individual-Tree Growth and Yield Prediction System for Uneven-Aged Shortleaf Pine Stands

Treesearch

Michael M. Huebschmann; Lawrence R. Gering; Thomas B. Lynch; Onesphore Bitoki; Paul A. Murphy

2000-01-01

A system of equations modeling the growth and development of uneven-aged shortleaf pine (Pinus echinata Mill.) stands is described. The prediction system consists of two main components: (1) a distance-independent, individual-tree simulator containing equations that forecast ingrowth, basal-area growth, probability of survival, total and...

Deriving the species richness distribution of Geotrupinae (Coleoptera: Scarabaeoidea) in Mexico from the overlap of individual model predictions.

PubMed

Trotta-Moreu, Nuria; Lobo, Jorge M

2010-02-01

Predictions from individual distribution models for Mexican Geotrupinae species were overlaid to obtain a total species richness map for this group. A database (GEOMEX) that compiles available information from the literature and from several entomological collections was used. A Maximum Entropy method (MaxEnt) was applied to estimate the distribution of each species, taking into account 19 climatic variables as predictors. For each species, suitability values ranging from 0 to 100 were calculated for each grid cell on the map, and 21 different thresholds were used to convert these continuous suitability values into binary ones (presence-absence). By summing all of the individual binary maps, we generated a species richness prediction for each of the considered thresholds. The number of species and faunal composition thus predicted for each Mexican state were subsequently compared with those observed in a preselected set of well-surveyed states. Our results indicate that the sum of individual predictions tends to overestimate species richness but that the selection of an appropriate threshold can reduce this bias. Even under the most optimistic prediction threshold, the mean species richness error is 61% of the observed species richness, with commission errors being significantly more common than omission errors (71 +/- 29 versus 18 +/- 10%). The estimated distribution of Geotrupinae species richness in Mexico in discussed, although our conclusions are preliminary and contingent on the scarce and probably biased available data.

Changes in Brain Network Efficiency and Working Memory Performance in Aging

PubMed Central

Stanley, Matthew L.; Simpson, Sean L.; Dagenbach, Dale; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory. PMID:25875001

Changes in brain network efficiency and working memory performance in aging.

PubMed

Stanley, Matthew L; Simpson, Sean L; Dagenbach, Dale; Lyday, Robert G; Burdette, Jonathan H; Laurienti, Paul J

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

Prediction Error Representation in Individuals with Generalized Anxiety Disorder During Passive Avoidance

PubMed Central

White, Stuart F.; Geraci, Marilla; Lewis, Elizabeth; Leshin, Joseph; Teng, Cindy; Averbeck, Bruno; Meffert, Harma; Ernst, Monique; Blair, James R.; Grillon, Christian; Blair, Karina S.

2017-01-01

Objective Deficits in reinforcement-based decision-making have been reported in Generalized Anxiety Disorder. However, the pathophysiology of these deficits is largely unknown, extant studies have mainly examined youth and the integrity of core functional processes underpinning decision-making remain undetermined. In particular, it is unclear whether the representation of reinforcement prediction error (PE: the difference between received and expected reinforcement) is disrupted in Generalized Anxiety Disorder. The current study addresses these issues in adults with the disorder. Methods Forty-six un-medicated individuals with Generalized Anxiety Disorder and 32 healthy controls group-matched on IQ, gender and age, completed a passive avoidance task while undergoing functional MRI. Results Behaviorally, individuals with Generalized Anxiety Disorder showed impaired reinforcement-based decision-making. Imaging results revealed that during feedback, individuals with Generalized Anxiety Disorder relative to healthy controls showed a reduced correlation between PE and activity within ventromedial prefrontal cortex, ventral striatum and other structures implicated in decision-making. In addition, individuals with Generalized Anxiety Disorder relative to healthy participants showed a reduced correlation between punishment, but not reward, PEs and activity within bilateral lentiform nucleus/putamen. Conclusions This is the first study to identify computational impairments during decision-making in Generalized Anxiety Disorder. PE signaling is significantly disrupted in individuals with the disorder and may underpin the decision-making deficits observed in patients with GAD. PMID:27631963

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of

The impact of receiving predictive genetic information about Lynch syndrome on individual colonoscopy and smoking behaviors

PubMed Central

Kim, Joanne Soo-Min; Coyte, Peter C.; Cotterchio, Michelle; Keogh, Louise A.; Flander, Louisa B.; Gaff, Clara; Laporte, Audrey

2016-01-01

Background This study investigated whether receiving the results of predictive genetic testing for Lynch syndrome—indicating the presence or absence of an inherited predisposition to various cancers, including colorectal cancer—was associated with change in individual colonoscopy and smoking behaviours, which could prevent colorectal cancer. Methods The study population included individuals with no previous diagnosis of colorectal cancer, whose families had already-identified deleterious mutations in the mismatch repair or EPCAM genes. Hypotheses were generated from a simple health economics model and tested against individual-level panel data from the Australasian Colorectal Cancer Family Registry. Results The empirical analysis revealed evidence consistent with some of the hypotheses, with a higher likelihood of undergoing colonoscopy in those who discovered their genetic predisposition to colorectal cancer and a lower likelihood of quitting smoking in those who discovered their lack thereof. Conclusion Predictive genetic information about Lynch syndrome was associated with change in individual colonoscopy and smoking behaviours but not necessarily in ways to improve population health. Impact The study findings suggest that the impact of personalized medicine on disease prevention is intricate, warranting further analyses to determine the net benefits and costs. PMID:27528600

A New Method for Predicting Patient Survivorship Using Efficient Bayesian Network Learning

PubMed Central

Jiang, Xia; Xue, Diyang; Brufsky, Adam; Khan, Seema; Neapolitan, Richard

2014-01-01

The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individually. EBMC_S is based on the EBMC BN algorithm, which has been shown to handle high-dimensional data. BNs have excellent architecture for decision support systems. In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis. PMID:24558297

A new method for predicting patient survivorship using efficient bayesian network learning.

PubMed

Jiang, Xia; Xue, Diyang; Brufsky, Adam; Khan, Seema; Neapolitan, Richard

2014-01-01

The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individually. EBMC_S is based on the EBMC BN algorithm, which has been shown to handle high-dimensional data. BNs have excellent architecture for decision support systems. In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis.

Modeling recombination processes and predicting energy conversion efficiency of dye sensitized solar cells from first principles

NASA Astrophysics Data System (ADS)

Ma, Wei; Meng, Sheng

2014-03-01

We present a set of algorithms based on solo first principles calculations, to accurately calculate key properties of a DSC device including sunlight harvest, electron injection, electron-hole recombination, and open circuit voltages. Two series of D- π-A dyes are adopted as sample dyes. The short circuit current can be predicted by calculating the dyes' photo absorption, and the electron injection and recombination lifetime using real-time time-dependent density functional theory (TDDFT) simulations. Open circuit voltage can be reproduced by calculating energy difference between the quasi-Fermi level of electrons in the semiconductor and the electrolyte redox potential, considering the influence of electron recombination. Based on timescales obtained from real time TDDFT dynamics for excited states, the estimated power conversion efficiency of DSC fits nicely with the experiment, with deviation below 1-2%. Light harvesting efficiency, incident photon-to-electron conversion efficiency and the current-voltage characteristics can also be well reproduced. The predicted efficiency can serve as either an ideal limit for optimizing photovoltaic performance of a given dye, or a virtual device that closely mimicking the performance of a real device under different experimental settings.

Efficient Method of Achieving Agreements between Individuals and Organizations about RFID Privacy

NASA Astrophysics Data System (ADS)

Cha, Shi-Cho

This work presents novel technical and legal approaches that address privacy concerns for personal data in RFID systems. In recent years, to minimize the conflict between convenience and the privacy risk of RFID systems, organizations have been requested to disclose their policies regarding RFID activities, obtain customer consent, and adopt appropriate mechanisms to enforce these policies. However, current research on RFID typically focuses on enforcement mechanisms to protect personal data stored in RFID tags and prevent organizations from tracking user activity through information emitted by specific RFID tags. A missing piece is how organizations can obtain customers' consent efficiently and flexibly. This study recommends that organizations obtain licenses automatically or semi-automatically before collecting personal data via RFID technologies rather than deal with written consents. Such digitalized and standard licenses can be checked automatically to ensure that collection and use of personal data is based on user consent. While individuals can easily control who has licenses and license content, the proposed framework provides an efficient and flexible way to overcome the deficiencies in current privacy protection technologies for RFID systems.

Algorithm for measuring the internal quantum efficiency of individual injection lasers

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

Sommers, H.S. Jr.

1978-05-01

A new algorithm permits determination of the internal quantum efficiency eta/sub i/ of individual lasers. Above threshold, the current is partitioned into a ''coherent'' component driving the lasing modes and the ''noncoherent'' remainder. Below threshold the current is known to grow as exp(qV/n/sub 0/KT); the algorithm proposes that extrapolation of this equation into the lasing region measures the noncoherent remainder, enabling deduction of the coherent component and of its current derivative eta/sub i/. Measurements on five (AlGa)As double-heterojunction lasers cut from one wafer demonstrate the power of the new method. Comparison with band calculations of Stern shows that n/sub 0/more » originates in carrier degeneracy.« less

Individual differences in fluid intelligence predicts inattentional blindness in a sample of older adults: a preliminary study.

PubMed

O'Shea, Deirdre M; Fieo, Robert A

2015-07-01

Previous research has shown that aging increases susceptibility to inattentional blindness (Graham and Burke, Psychol Aging 26:162, 2011) as well as individual differences in cognitive ability related to working memory and executive functions in separate studies. Therefore, the present study was conducted in an attempt to bridge a gap that involved investigating 'age-sensitive' cognitive abilities that may predict inattentional blindness in a sample of older adults. We investigated whether individual differences in general fluid intelligence and speed of processing would predict inattentional blindness in our sample of older adults. Thirty-six healthy older adults took part in the study. Using the inattentional blindness paradigm developed by Most et al. (Psychol Rev 112:217, 2005), we investigated whether rates of inattentional blindness could be predicted by participant's performance on the Raven's Advanced Progressive Matrices and a choice-reaction time task. A Mann-Whitney U test revealed that a higher score on the Raven's Advanced Progressive Matrices was significantly associated with lower incidences of inattentional blindness. However, a t test revealed that choice-reaction times were not significantly associated with inattentional blindness. Preliminary results from the present study suggest that individual differences in general fluid intelligence are predictive of inattentional blindness in older adults but not speed of processing. Moreover, our findings are consistent with previous studies that have suggested executive attention control may be the source of these individual differences. These findings also highlight the association between attention and general fluid intelligence and how it may impact environmental awareness. Future research would benefit from repeating these analyses in a larger sample and also including a younger comparison group.

Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions

PubMed Central

2016-01-01

Recent studies of children's tool innovation have revealed that there is variation in children's success in middle-childhood. In two individual differences studies, we sought to identify personal characteristics that might predict success on an innovation task. In Study 1, we found that although measures of divergent thinking were related to each other they did not predict innovation success. In Study 2, we measured executive functioning including: inhibition, working memory, attentional flexibility and ill-structured problem-solving. None of these measures predicted innovation, but, innovation was predicted by children's performance on a receptive vocabulary scale that may function as a proxy for general intelligence. We did not find evidence that children's innovation was predicted by specific personal characteristics. PMID:26926280

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations

CFD-RANS prediction of individual exposure from continuous release of hazardous airborne materials in complex urban environments

NASA Astrophysics Data System (ADS)

Efthimiou, G. C.; Andronopoulos, S.; Bartzis, J. G.; Berbekar, E.; Harms, F.; Leitl, B.

2017-02-01

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict individual exposure (maximum dosages) of an airborne material which is released continuously from a point source. The present work addresses the question whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict individual exposure for various exposure times. This is feasible by providing the two RANS concentration moments (mean and variance) and a turbulent time scale to a deterministic model. The whole effort is focused on the prediction of individual exposure inside a complex real urban area. The capabilities of the proposed methodology are validated against wind-tunnel data (CUTE experiment). The present simulations were performed 'blindly', i.e. the modeller had limited information for the inlet boundary conditions and the results were kept unknown until the end of the COST Action ES1006. Thus, a high uncertainty of the results was expected. The general performance of the methodology due to this 'blind' strategy is good. The validation metrics fulfil the acceptance criteria. The effect of the grid and the turbulence model on the model performance is examined.

Predictability of the individual clinical outcome of extracorporeal shock wave therapy for cellulite

PubMed Central

Schlaudraff, Kai-Uwe; Kiessling, Maren C; Császár, Nikolaus BM; Schmitz, Christoph

2014-01-01

Background Extracorporeal shock wave therapy has been successfully introduced for the treatment of cellulite in recent years. However, it is still unknown whether the individual clinical outcome of cellulite treatment with extracorporeal shock wave therapy can be predicted by the patient’s individual cellulite grade at baseline, individual patient age, body mass index (BMI), weight, and/or height. Methods Fourteen Caucasian females with cellulite were enrolled in a prospective, single-center, randomized, open-label Phase II study. The mean (± standard error of the mean) cellulite grade at baseline was 2.5±0.09 and mean BMI was 22.8±1.17. All patients were treated with radial extracorporeal shock waves using the Swiss DolorClast® device (Electro Medical Systems, S.A., Nyon, Switzerland). Patients were treated unilaterally with 2 weekly treatments for 4 weeks on a randomly selected side (left or right), totaling eight treatments on the selected side. Treatment was performed at 3.5–4.0 bar, with 15,000 impulses per session applied at 15 Hz. Impulses were homogeneously distributed over the posterior thigh and buttock area (resulting in 7,500 impulses per area). Treatment success was evaluated after the last treatment and 4 weeks later by clinical examination, photographic documentation, contact thermography, and patient satisfaction questionnaires. Results The mean cellulite grade improved from 2.5±0.09 at baseline to 1.57±0.18 after the last treatment (ie, mean δ-1 was 0.93 cellulite grades) and 1.68±0.16 at follow-up (ie, mean δ-2 was 0.82 cellulite grades). Compared with baseline, no patient’s condition worsened, the treatment was well tolerated, and no unwanted side effects were observed. No statistically significant (ie, P<0.05) correlation was found between individual values for δ-1 and δ-2 and cellulite grade at baseline, BMI, weight, height, or age. Conclusion Radial shock wave therapy is a safe and effective treatment option for cellulite. The

Explaining growth of individual trees: Light interception and efficiency of light use by Eucalyptus at four sites in Brazil

Treesearch

Dan Binkley; Jose Luiz Stape; William L. Bauerle; Michael G. Ryan

2010-01-01

The growth of wood in trees and forests depends on the acquisition of resources (light, water, and nutrients), the efficiency of using resources for photosynthesis, and subsequent partitioning to woody tissues. Patterns of efficiency over time for individual trees, or between trees at one time, result from changes in rates photosynthesis and shifts in...

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

PubMed

Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

2018-02-01

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

Individual differences and their impact on the safety and the efficiency of human-wheelchair systems.

PubMed

Jipp, Meike

2012-12-01

The extent to which individual differences in fine motor abilities affect indoor safety and efficiency of human-wheelchair systems was examined. To reduce the currently large number of indoor wheelchair accidents, assistance systems with a high level of automation were developed. It was proposed to adapt the wheelchair's level of automation to the user's ability to steer the device to avoid drawbacks of highly automated wheelchairs. The state of the art, however, lacks an empirical identification of those abilities. A study with 23 participants is described. The participants drove through various sections of a course with a powered wheelchair. Repeatedly measured criteria were safety (numbers of collisions) and efficiency (times required for reaching goals). As covariates, the participants' fine motor abilities were assessed. A random coefficient modeling approach was conducted to analyze the data,which were available on two levels as course sections were nested within participants.The participants' aiming, precision, and armhand speed contributed significantly to both criteria: Participants with lower fine motor abilities had more collisions and required more time for reaching goals. Adapting the wheelchair's level of automation to these fine motor abilities can improve indoor safety and efficiency. In addition, the results highlight the need to further examine the impact of individual differences on the design of automation features for powered wheelchairs as well as other applications of automation. The results facilitate the improvement of current wheelchair technology.

Rare Copy Number Deletions Predict Individual Variation in Intelligence

PubMed Central

Yeo, Ronald A.; Gangestad, Steven W.; Liu, Jingyu; Calhoun, Vince D.; Hutchison, Kent E.

2011-01-01

Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in “mutation load” emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = −.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed. PMID:21298096

Deconstructing Pretest Risk Enrichment to Optimize Prediction of Psychosis in Individuals at Clinical High Risk.

PubMed

Fusar-Poli, Paolo; Rutigliano, Grazia; Stahl, Daniel; Schmidt, André; Ramella-Cravaro, Valentina; Hitesh, Shetty; McGuire, Philip

2016-12-01

Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model. Clinical register-based cohort study. Individuals were drawn from electronic, real-world, real-time clinical records relating to routine mental health care of CHR services in South London and the Maudsley National Health Service Trust in London, United Kingdom. The study included nonpsychotic individuals referred on suspicion of psychosis risk and assessed by the Outreach and Support in South London CHR service from 2002 to 2015. Model development and validation was performed with machine-learning methods based on Least Absolute Shrinkage and Selection Operator for Cox proportional hazards model. Pretest risk of psychosis onset in individuals undergoing CHR assessment. Predictors included age, sex, age × sex interaction, race/ethnicity, socioeconomic status, marital status, referral source, and referral year. A total of 710 nonpsychotic individuals undergoing CHR assessment were included. The mean age was 23 years. Three hundred ninety-nine individuals were men (56%), their race/ethnicity was heterogenous, and they were referred from a variety of sources. The cumulative 6-year pretest risk of psychosis was 14.55% (95% CI, 11.71% to 17.99%), confirming substantial pretest risk enrichment during the recruitment of individuals undergoing CHR assessment. Race/ethnicity and source of referral were associated with pretest risk enrichment. The predictive model based on these factors was externally validated, showing moderately good discrimination and

Predicting lymph node output efficiency using systems biology

PubMed Central

Gong, Chang; Mattila, Joshua T.; Miller, Mark; Flynn, JoAnne L.; Linderman, Jennifer J.; Kirschner, D.

2013-01-01

Dendritic cells (DCs) capture pathogens and foreign antigen (Ag) in peripheral tissues and migrate to secondary lymphoid tissues, such as lymph nodes (LNs), where they present processed Ag as MHC-bound peptide (pMHC) to naïve T cells. Interactions between DCs and T cells result, over periods of hours, in activation, clonal expansion and differentiation of antigen-specific T cells, leading to primed cells that can now participate in immune responses. Two-photon microscopy (2PM) has been widely adopted to analyze lymphocyte dynamics and can serve as a powerful in vivo assay for cell trafficking and activation over short length and time scales. Linking biological phenomena between vastly different spatiotemporal scales can be achieved using a systems biology approach. We developed a 3D agent-based cellular model of a LN that allows for the simultaneous in silico simulation of T cell trafficking, activation and production of effector cells under different antigen (Ag) conditions. The model anatomy is based on in situ analysis of LN sections (from primates and mice) and cell dynamics based on quantitative measurements from 2PM imaging of mice. Our simulations make three important predictions. First, T cell encounters by DCs and T cell receptor (TCR) repertoire scanning are more efficient in a 3D model compared with 2D, suggesting that a 3D model is needed to analyze LN function. Second, LNs are able to produce primed CD4+T cells at the same efficiency over broad ranges of cognate frequencies (from 10−5 to 10−2). Third, reducing the time that naïve T cells are required to bind DCs before becoming activated will increase the rate at which effector cells are produced. This 3D model provides a robust platform to study how T cell trafficking and activation dynamics relate to the efficiency of T cell priming and clonal expansion. We envision that this systems biology approach will provide novel insights for guiding vaccine development and understanding immune responses

Model-based evaluation of subsurface monitoring networks for improved efficiency and predictive certainty of regional groundwater models

NASA Astrophysics Data System (ADS)

Gosses, M. J.; Wöhling, Th.; Moore, C. R.; Dann, R.; Scott, D. M.; Close, M.

2012-04-01

-specific prediction target under consideration. Therefore, the worth of individual observation locations may differ for different prediction targets. Our evaluation is based on predictions of lowland stream discharge resulting from changes in land use and irrigation in the upper Central Plains catchment. In our analysis, we adopt the model predictive uncertainty analysis method by Moore and Doherty (2005) which accounts for contributions from both measurement errors and uncertain structural heterogeneity. The method is robust and efficient due to a linearity assumption in the governing equations and readily implemented for application in the model-independent parameter estimation and uncertainty analysis toolkit PEST (Doherty, 2010). The proposed methods can be applied not only for the evaluation of monitoring networks, but also for the optimization of networks, to compare alternative monitoring strategies, as well as to identify best cost-benefit monitoring design even prior to any data acquisition.

Schmallenberg virus: Predicting within-herd seroprevalence using bulk-tank milk antibody titres and exploring individual animal antibody titres using empirical distribution functions (EDF).

PubMed

Collins, Á B; Grant, J; Barrett, D; Doherty, M L; Hallinan, A; Mee, J F

2017-08-01

Schmallenberg virus (SBV) is transmitted by Culicoides spp. biting midges and can cause abortions and congenital malformations in ruminants and milk drop in dairy cattle. Estimating true within-herd seroprevalence is an essential component of efficient and cost-effective SBV surveillance programs. The objectives of this study were: (1) determine the correlation between bulk-tank milk (BTM)-ELISA results and within-herd seroprevalence, (2) evaluate the ability of BTM-ELISA results to predict within-herd seroprevalence and (3) explore the distributions of individual animal serology results using novel statistical methodology. BTM samples (n=24) and blood samples (n=4019) collected from all lactating cows contributing to the BTM in 26 Irish dairy herds (58-444 cows/herd) in 2014 located in a region exposed to SBV in 2012/2013, were analysed for SBV-specific antibodies using IDVet ® ELISA kits. The correlation between BTM-ELISA results and within-herd seroprevalence was determined by calculating Pearson's correlation coefficient. Linear regression models were used to assess the ability of BTM-ELISA results to predict within-herd seroprevalence. The distributions of individual animal serology results were explored by determining the empirical distribution functions (EDF) of the individual animal serum ELISA results in each herd. EDFs were compared pairwise across herds, using the Kolmogorov-Smirnov statistical test. Herds with similar BTM-ELISA results, herds with similar within-herd seroprevalence and herds with similar mean-herd serology ELISA results were stratified in order to explore their respective paired-herd EDF comparisons. Statistical significance was set at p<0.05. Twenty-two herds were BTM-ELISA-positive (within-herd seroprevalence 30.6-100%) and two herds were BTM-ELISA-negative (within-herd seroprevalence 10.7 and 16.2%) indicating BTM-ELISA-negative herds can have seropositive animals present. BTM-ELISA results were highly correlated (r=0.807, p<0

An approximate solution to improve computational efficiency of impedance-type payload load prediction

NASA Technical Reports Server (NTRS)

White, C. W.

1981-01-01

The computational efficiency of the impedance type loads prediction method was studied. Three goals were addressed: devise a method to make the impedance method operate more efficiently in the computer; assess the accuracy and convenience of the method for determining the effect of design changes; and investigate the use of the method to identify design changes for reduction of payload loads. The method is suitable for calculation of dynamic response in either the frequency or time domain. It is concluded that: the choice of an orthogonal coordinate system will allow the impedance method to operate more efficiently in the computer; the approximate mode impedance technique is adequate for determining the effect of design changes, and is applicable for both statically determinate and statically indeterminate payload attachments; and beneficial design changes to reduce payload loads can be identified by the combined application of impedance techniques and energy distribution review techniques.

Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.

PubMed

van Giessen, A; Moons, K G M; de Wit, G A; Verschuren, W M M; Boer, J M A; Koffijberg, H

2015-01-01

The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it. In a large Dutch population cohort (n = 21,992) we classified individuals to low (< 5%) and high (≥ 5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE. Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate. In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added

Robust prediction of individual creative ability from brain functional connectivity.

PubMed

Beaty, Roger E; Kenett, Yoed N; Christensen, Alexander P; Rosenberg, Monica D; Benedek, Mathias; Chen, Qunlin; Fink, Andreas; Qiu, Jiang; Kwapil, Thomas R; Kane, Michael J; Silvia, Paul J

2018-01-30

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Induced optimism as mental rehearsal to decrease depressive predictive certainty.

PubMed

Miranda, Regina; Weierich, Mariann; Khait, Valerie; Jurska, Justyna; Andersen, Susan M

2017-03-01

The present study examined whether practice in making optimistic future-event predictions would result in change in the hopelessness-related cognitions that characterize depression. Individuals (N = 170) with low, mild, and moderate-to-severe depressive symptoms were randomly assigned to a condition in which they practiced making optimistic future-event predictions or to a control condition in which they viewed the same stimuli but practiced determining whether a given phrase contained an adjective. Overall, individuals in the induced optimism condition showed increases in optimistic predictions, relative to the control condition, as a result of practice, but only individuals with moderate-to-severe symptoms of depression who practiced making optimistic future-event predictions showed decreases in depressive predictive certainty, relative to the control condition. In addition, they showed gains in efficiency in making optimistic predictions over the practice blocks, as assessed by response time. There was no difference in depressed mood by practice condition. Mental rehearsal might be one way of changing the hopelessness-related cognitions that characterize depression. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)

PubMed Central

Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G.M.

2015-01-01

Background— Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. Methods— The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. Results— The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. Conclusions— To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25561516

A Multifactorial Approach to Predicting Death Anxiety: Assessing the Role of Religiosity, Susceptibility to Mortality Cues, and Individual Differences.

PubMed

French, Carrie; Greenauer, Nathan; Mello, Catherine

2017-01-01

Death anxiety is not only experienced by individuals receiving end-of-life care, but also by family members, social workers, and other service providers who support these individuals. Thus, identifying predictors of individual differences in experienced death anxiety levels may have both theoretical and clinical ramifications. The present study assessed the relative influence of religiosity, susceptibility to mortality cues, state and trait anxiety, and demographic factors in the experience of death anxiety through an online survey distributed to members of two online communities related to end-of-life care. Results indicated that cognitive and emotional susceptibility to mortality cues, as well as gender, predicted differences in death anxiety. Conversely, religiosity and age did not increase the predictive power of the model. Thus, death anxiety may be a function of emotional, cognitive, and sociocultural factors that interact in complex, but predictable, ways to modulate the response to mortality cues that occur in one's life.

Auditory processing efficiency deficits in children with developmental language impairments

NASA Astrophysics Data System (ADS)

Hartley, Douglas E. H.; Moore, David R.

2002-12-01

The ``temporal processing hypothesis'' suggests that individuals with specific language impairments (SLIs) and dyslexia have severe deficits in processing rapidly presented or brief sensory information, both within the auditory and visual domains. This hypothesis has been supported through evidence that language-impaired individuals have excess auditory backward masking. This paper presents an analysis of masking results from several studies in terms of a model of temporal resolution. Results from this modeling suggest that the masking results can be better explained by an ``auditory efficiency'' hypothesis. If impaired or immature listeners have a normal temporal window, but require a higher signal-to-noise level (poor processing efficiency), this hypothesis predicts the observed small deficits in the simultaneous masking task, and the much larger deficits in backward and forward masking tasks amongst those listeners. The difference in performance on these masking tasks is predictable from the compressive nonlinearity of the basilar membrane. The model also correctly predicts that backward masking (i) is more prone to training effects, (ii) has greater inter- and intrasubject variability, and (iii) increases less with masker level than do other masking tasks. These findings provide a new perspective on the mechanisms underlying communication disorders and auditory masking.

Validity, accuracy, and predictive value of urinary tract infection signs and symptoms in individuals with spinal cord injury on intermittent catheterization.

PubMed

Massa, Luiz M; Hoffman, Jeanne M; Cardenas, Diana D

2009-01-01

To determine the validity, accuracy, and predictive value of the signs and symptoms of urinary tract infection (UTI) for individuals with spinal cord injury (SCI) using intermittent catheterization (IC) and the accuracy of individuals with SCI on IC at predicting their own UTI. Prospective cohort based on data from the first 3 months of a 1-year randomized controlled trial to evaluate UTI prevention effectiveness of hydrophilic and standard catheters. Fifty-six community-based individuals on IC. Presence of UTI as defined as bacteriuria with a colony count of at least 10(5) colony-forming units/mL and at least 1 sign or symptom of UTI. Analysis of monthly urine culture and urinalysis data combined with analysis of monthly data collected using a questionnaire that asked subjects to self-report on UTI signs and symptoms and whether or not they felt they had a UTI. Overall, "cloudy urine" had the highest accuracy (83.1%), and "leukocytes in the urine" had the highest sensitivity (82.8%). The highest specificity was for "fever" (99.0%); however, it had a very low sensitivity (6.9%). Subjects were able to predict their own UTI with an accuracy of 66.2%, and the negative predictive value (82.8%) was substantially higher than the positive predictive value (32.6%). The UTI signs and symptoms can predict a UTI more accurately than individual subjects can by using subjective impressions of their own signs and symptoms. Subjects were better at predicting when they did not have a UTI than when they did have a UTI.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is

Attentional and motor impulsivity interactively predict 'food addiction' in obese individuals.

PubMed

Meule, Adrian; de Zwaan, Martina; Müller, Astrid

2017-01-01

Impulsivity is a multifaceted construct and constitutes a common risk factor for a range of behaviors associated with poor self-control (e.g., substance use or binge eating). The short form of the Barratt Impulsiveness Scale (BIS-15) measures impulsive behaviors related to attentional (inability to focus attention or concentrate), motor (acting without thinking), and non-planning (lack of future orientation or forethought) impulsivity. Eating-related measures appear to be particularly related to attentional and motor impulsivity and recent findings suggest that interactive effects between these two facets may play a role in eating- and weight-regulation. One-hundred thirty-three obese individuals presenting for bariatric surgery (77.4% female) completed the BIS-15 and the Yale Food Addiction Scale (YFAS) 2.0, which measures addiction-like eating based on the eleven symptoms of substance use disorder outlined in the fifth version of the Diagnostic and Statistical Manual of Mental Disorders. Sixty-three participants (47.4%) were classified as being 'food addicted'. Scores on attentional and motor impulsivity interactively predicted 'food addiction' status: higher attentional impulsivity was associated with a higher likelihood of receiving a YFAS 2.0 diagnosis only at high (+1 SD), but not at low (-1 SD) levels of motor impulsivity. Results support previous findings showing that non-planning impulsivity does not appear to play a role in eating-related self-regulation. Furthermore, this is the first study that shows interactive effects between different impulsivity facets when predicting 'food addiction' in obese individuals. Self-regulatory failure in eating-regulation (e.g., addiction-like overeating) may particularly emerge when both attentional and motor impulsivity levels are elevated. Copyright © 2016 Elsevier Inc. All rights reserved.

Trait aspects of auditory mismatch negativity predict response to auditory training in individuals with early illness schizophrenia.

PubMed

Biagianti, Bruno; Roach, Brian J; Fisher, Melissa; Loewy, Rachel; Ford, Judith M; Vinogradov, Sophia; Mathalon, Daniel H

2017-01-01

Individuals with schizophrenia have heterogeneous impairments of the auditory processing system that likely mediate differences in the cognitive gains induced by auditory training (AT). Mismatch negativity (MMN) is an event-related potential component reflecting auditory echoic memory, and its amplitude reduction in schizophrenia has been linked to cognitive deficits. Therefore, MMN may predict response to AT and identify individuals with schizophrenia who have the most to gain from AT. Furthermore, to the extent that AT strengthens auditory deviance processing, MMN may also serve as a readout of the underlying changes in the auditory system induced by AT. Fifty-six individuals early in the course of a schizophrenia-spectrum illness (ESZ) were randomly assigned to 40 h of AT or Computer Games (CG). Cognitive assessments and EEG recordings during a multi-deviant MMN paradigm were obtained before and after AT and CG. Changes in these measures were compared between the treatment groups. Baseline and trait-like MMN data were evaluated as predictors of treatment response. MMN data collected with the same paradigm from a sample of Healthy Controls (HC; n = 105) were compared to baseline MMN data from the ESZ group. Compared to HC, ESZ individuals showed significant MMN reductions at baseline ( p = .003). Reduced Double-Deviant MMN was associated with greater general cognitive impairment in ESZ individuals ( p = .020). Neither ESZ intervention group showed significant change in MMN. We found high correlations in all MMN deviant types (rs = .59-.68, all ps < .001) between baseline and post-intervention amplitudes irrespective of treatment group, suggesting trait-like stability of the MMN signal. Greater deficits in trait-like Double-Deviant MMN predicted greater cognitive improvements in the AT group ( p = .02), but not in the CG group. In this sample of ESZ individuals, AT had no effect on auditory deviance processing as assessed by MMN. In ESZ individuals, baseline MMN

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

PubMed Central

Hou, Jiateng; Sun, Yingfei; Sun, Lixin; Pan, Bingyu; Huang, Zhipei; Wu, Jiankang; Zhang, Zhiqiang

2016-01-01

This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF) and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation. PMID:27916853

Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

PubMed

Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta; Watzl, Bernhard; Bub, Achim; Miller, Edgar R; Castenmiller, Jacqueline J M; Pasman, Wilrike J; van Het Hof, Karin; Chopra, Mridula; Karlsen, Anette; Dragsted, Lars O; Winkels, Renate; Itsiopoulos, Catherine; Brazionis, Laima; O'Dea, Kerin; van Loo-Bouwman, Carolien A; Naber, Ton H J; van der Voet, Hilko; Boshuizen, Hendriek C

2015-05-14

Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.

Energy-efficient container handling using hybrid model predictive control

NASA Astrophysics Data System (ADS)

Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

2015-11-01

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

A Study of Individual and Institutional Demographic Factors That Predict Candidate Performance on the Board of Certification Examination

ERIC Educational Resources Information Center

Parham, Candace S.

2017-01-01

The purpose of this study was to determine if individual and institutional factors predict candidates' performance on the Board of Certification for the Athletic Trainer (BOC) examination and to determine if institutions' demographic profiles of athletic training predict pass rates on the BOC examination for people of color (POC). A BOC candidate…

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

PubMed

Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

2015-01-01

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

Identification of the Predictive Power of Five Factor Personality Traits for Individual Instrument Performance Anxiety

ERIC Educational Resources Information Center

Özdemir, Gökhan; Dalkiran, Esra

2017-01-01

This study, with the aim of identifying the predictive power of the five-factor personality traits of music teacher candidates on individual instrument performance anxiety, was designed according to the relational screening model. The study population was students attending the Music Education branch of Fine Arts Education Departments in…

Prediction Error Representation in Individuals With Generalized Anxiety Disorder During Passive Avoidance.

PubMed

White, Stuart F; Geraci, Marilla; Lewis, Elizabeth; Leshin, Joseph; Teng, Cindy; Averbeck, Bruno; Meffert, Harma; Ernst, Monique; Blair, James R; Grillon, Christian; Blair, Karina S

2017-02-01

Deficits in reinforcement-based decision making have been reported in generalized anxiety disorder. However, the pathophysiology of these deficits is largely unknown; published studies have mainly examined adolescents, and the integrity of core functional processes underpinning decision making remains undetermined. In particular, it is unclear whether the representation of reinforcement prediction error (PE) (the difference between received and expected reinforcement) is disrupted in generalized anxiety disorder. This study addresses these issues in adults with the disorder. Forty-six unmedicated individuals with generalized anxiety disorder and 32 healthy comparison subjects group-matched on IQ, gender, and age performed a passive avoidance task while undergoing functional MRI. Data analyses were performed using a computational modeling approach. Behaviorally, individuals with generalized anxiety disorder showed impaired reinforcement-based decision making. Imaging results revealed that during feedback, individuals with generalized anxiety disorder relative to healthy subjects showed a reduced correlation between PE and activity within the ventromedial prefrontal cortex, ventral striatum, and other structures implicated in decision making. In addition, individuals with generalized anxiety disorder relative to healthy participants showed a reduced correlation between punishment PEs, but not reward PEs, and activity within the left and right lentiform nucleus/putamen. This is the first study to identify computational impairments during decision making in generalized anxiety disorder. PE signaling is significantly disrupted in individuals with the disorder and may lead to their decision-making deficits and excessive worry about everyday problems by disrupting the online updating ("reality check") of the current relationship between the expected values of current response options and the actual received rewards and punishments.

Human brain structure predicts individual differences in preconscious evaluation of facial dominance and trustworthiness

PubMed Central

Kanai, Ryota; Bahrami, Bahador; Rees, Geraint

2015-01-01

Social cues conveyed by the human face, such as eye gaze direction, are evaluated even before they are consciously perceived. While there is substantial individual variability in such evaluation, its neural basis is unknown. Here we asked whether individual differences in preconscious evaluation of social face traits were associated with local variability in brain structure. Adult human participants (n = 36) monocularly viewed faces varying in dominance and trustworthiness, which were suppressed from awareness by a dynamic noise pattern shown to the other eye. The time taken for faces to emerge from suppression and become visible (t2e) was used as a measure of potency in competing for visual awareness. Both dominant and untrustworthy faces resulted in slower t2e than neutral faces, with substantial individual variability in these effects. Individual differences in t2e were correlated with gray matter volume in right insula for dominant faces, and with gray matter volume in medial prefrontal cortex, right temporoparietal junction and bilateral fusiform face area for untrustworthy faces. Thus, individual differences in preconscious social processing can be predicted from local brain structure, and separable correlates for facial dominance and untrustworthiness suggest distinct mechanisms of preconscious processing. PMID:25193945

More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions

PubMed Central

Vinck, Martin; Bosman, Conrado A.

2016-01-01

During visual stimulation, neurons in visual cortex often exhibit rhythmic and synchronous firing in the gamma-frequency (30–90 Hz) band. Whether this phenomenon plays a functional role during visual processing is not fully clear and remains heavily debated. In this article, we explore the function of gamma-synchronization in the context of predictive and efficient coding theories. These theories hold that sensory neurons utilize the statistical regularities in the natural world in order to improve the efficiency of the neural code, and to optimize the inference of the stimulus causes of the sensory data. In visual cortex, this relies on the integration of classical receptive field (CRF) data with predictions from the surround. Here we outline two main hypotheses about gamma-synchronization in visual cortex. First, we hypothesize that the precision of gamma-synchronization reflects the extent to which CRF data can be accurately predicted by the surround. Second, we hypothesize that different cortical columns synchronize to the extent that they accurately predict each other’s CRF visual input. We argue that these two hypotheses can account for a large number of empirical observations made on the stimulus dependencies of gamma-synchronization. Furthermore, we show that they are consistent with the known laminar dependencies of gamma-synchronization and the spatial profile of intercolumnar gamma-synchronization, as well as the dependence of gamma-synchronization on experience and development. Based on our two main hypotheses, we outline two additional hypotheses. First, we hypothesize that the precision of gamma-synchronization shows, in general, a negative dependence on RF size. In support, we review evidence showing that gamma-synchronization decreases in strength along the visual hierarchy, and tends to be more prominent in species with small V1 RFs. Second, we hypothesize that gamma-synchronized network dynamics facilitate the emergence of spiking output that

Individually assessed boldness predicts Perca fluviatilis behaviour in shoals, but is not associated with the capture order or angling method.

PubMed

Kekäläinen, J; Podgorniak, T; Puolakka, T; Hyvärinen, P; Vainikka, A

2014-11-01

Selectivity of recreational angling on fish behaviour was studied by examining whether capture order or lure type (natural v. artificial bait) in ice-fishing could explain behavioural variation among perch Perca fluviatilis individuals. It was also tested if individually assessed personality predicts fish behaviour in groups, in the presence of natural predators. Perca fluviatilis showed individually repeatable behaviour both in individual and in group tests. Capture order, capture method, condition factor or past growth rate did not explain variation in individual behaviour. Individually determined boldness as well as fish size, however, were positively associated with first entrance to the predator zone (i.e. initial risk taking) in group behaviour tests. Individually determined boldness also explained long-term activity and total time spent in the vicinity of predators in the group. These findings suggest that individual and laboratory-based boldness tests predict boldness of P. fluviatilis in also ecologically relevant conditions, i.e. in shoals and in the presence of natural predators. The present results, however, also indicate that the above-mentioned two angling methods may not be selective for certain behavioural types in comparison to each other. © 2014 The Fisheries Society of the British Isles.

GAPIT: genome association and prediction integrated tool.

PubMed

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

PubMed Central

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

An experiment on individual 'parochial altruism' revealing no connection between individual 'altruism' and individual 'parochialism'.

PubMed

Corr, Philip J; Hargreaves Heap, Shaun P; Seger, Charles R; Tsutsui, Kei

2015-01-01

Is parochial altruism an attribute of individual behavior? This is the question we address with an experiment. We examine whether the individual pro-sociality that is revealed in the public goods and trust games when interacting with fellow group members helps predict individual parochialism, as measured by the in-group bias (i.e., the difference in these games in pro-sociality when interacting with own group members as compared with members of another group). We find that it is not. An examination of the Big-5 personality predictors of each behavior reinforces this result: they are different. In short, knowing how pro-social individuals are with respect to fellow group members does not help predict their parochialism.

Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

PubMed Central

Vaphiades, Michael S.; Kline, Lanning B.; McGwin, Gerald; Owsley, Cynthia; Shah, Ritu; Wood, Joanne M.

2014-01-01

Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault) for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from −0.29 to 0.04). The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28). Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions. PMID:24683493

Prediction of driving safety in individuals with homonymous hemianopia and quadrantanopia from clinical neuroimaging.

PubMed

Vaphiades, Michael S; Kline, Lanning B; McGwin, Gerald; Owsley, Cynthia; Shah, Ritu; Wood, Joanne M

2014-01-01

Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault) for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from -0.29 to 0.04). The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28). Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

Individual gregariousness predicts behavioural synchronization in a foraging herbivore, the sheep (Ovis aries).

PubMed

Hauschildt, Verena; Gerken, Martina

2015-04-01

Diversity of animal personalities has been proposed to arise from differences in social attraction, and to enhance behavioural flexibility of a population. The present study evaluated gregariousness as a personality trait in 14 ewes kept on pasture. Gregariousness was defined based on the frequency of having a close neighbour (<3m). Highly gregarious (HG) and less gregarious (LG) animals were separated into two groups (n=7) which were reintegrated after 18 days. During direct field observations, behaviour was recorded individually every 15 min. Each session lasted 2.5h (08.30-11.00 h or 14.30-17.00 h, respectively). Behavioural synchronization was highest when the group consisted only of HG individuals (κHG=0.69, κLG=0.31; t=5.29; p<0.001), indicating that gregariousness predicted behavioural synchronization in sheep. Though sheep are generally recognized as a highly gregarious species, HG and LG individuals could be differentiated clearly and consistently. Research on animal personality might help explain social influences on behavioural synchronization. Copyright © 2015 Elsevier B.V. All rights reserved.

Me After You: Partner Influence and Individual Effort Predict Rejection of Self-Aspects and Self-Concept Clarity After Relationship Dissolution.

PubMed

Slotter, Erica B; Emery, Lydia F; Luchies, Laura B

2014-07-01

Individuals in ongoing romantic relationships incorporate attributes from their partner into their own self-concepts. However, little research has investigated what happens to these attributes should the relationship end. Across three studies, the present research sought to examine factors that predicted whether individuals retain or reject attributes from their self-concept that they initially gained during a relationship. We predicted that individuals would be more likely to reject attributes from their self post-dissolution if their ex-partner was influential in them adding those attributes to the self in the first place. However, we expected this effect to be moderated such that individuals who exerted greater, versus lesser, effort in maintaining relevant attributes would retain them as part of the self, regardless of whether the attribute originated from the partner. In addition, in two of our three studies, we explored the roles of partner influence, effort, and attribute rejection on individuals' post-dissolution self-concept clarity. © 2014 by the Society for Personality and Social Psychology, Inc.

Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction

PubMed Central

Wessel, Jennifer; Gupta, Jyoti; de Groot, Mary

2016-01-01

The purpose of this study was to identify attitudes and perceptions of willingness to participate in genetic testing for type 2 diabetes (T2D) risk prediction in the general population. Adults (n = 598) were surveyed on attitudes about utilizing genetic testing to predict future risk of T2D. Participants were recruited from public libraries (53%), online registry (37%) and a safety net hospital emergency department (10%). Respondents were 37±11 years old, primarily White (54%), female (69%), college educated (46%), with an annual income ≥$25,000 (56%). Half of participants were interested in genetic testing for T2D (52%) and 81% agreed/strongly agreed genetic testing should be available to the public. Only 57% of individuals knew T2D is preventable. A multivariate model to predict interest in genetic testing was adjusted for age, gender, recruitment location and BMI; significant predictors were motivation (high perceived personal risk of T2D [OR = 4.38 (1.76, 10.9)]; family history [OR = 2.56 (1.46, 4.48)]; desire to know risk prior to disease onset [OR = 3.25 (1.94, 5.42)]; and knowing T2D is preventable [OR = 2.11 (1.24, 3.60)], intention (if the cost is free [OR = 10.2 (4.27, 24.6)]; and learning T2D is preventable [OR = 5.18 (1.95, 13.7)]) and trust of genetic testing results [OR = 0.03 (0.003, 0.30)]. Individuals are interested in genetic testing for T2D risk which offers unique information that is personalized. Financial accessibility, validity of the test and availability of diabetes prevention programs were identified as predictors of interest in T2D testing. PMID:26789839

Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction.

PubMed

Wessel, Jennifer; Gupta, Jyoti; de Groot, Mary

2016-01-01

The purpose of this study was to identify attitudes and perceptions of willingness to participate in genetic testing for type 2 diabetes (T2D) risk prediction in the general population. Adults (n = 598) were surveyed on attitudes about utilizing genetic testing to predict future risk of T2D. Participants were recruited from public libraries (53%), online registry (37%) and a safety net hospital emergency department (10%). Respondents were 37 ± 11 years old, primarily White (54%), female (69%), college educated (46%), with an annual income ≥$25,000 (56%). Half of participants were interested in genetic testing for T2D (52%) and 81% agreed/strongly agreed genetic testing should be available to the public. Only 57% of individuals knew T2D is preventable. A multivariate model to predict interest in genetic testing was adjusted for age, gender, recruitment location and BMI; significant predictors were motivation (high perceived personal risk of T2D [OR = 4.38 (1.76, 10.9)]; family history [OR = 2.56 (1.46, 4.48)]; desire to know risk prior to disease onset [OR = 3.25 (1.94, 5.42)]; and knowing T2D is preventable [OR = 2.11 (1.24, 3.60)], intention (if the cost is free [OR = 10.2 (4.27, 24.6)]; and learning T2D is preventable [OR = 5.18 (1.95, 13.7)]) and trust of genetic testing results [OR = 0.03 (0.003, 0.30)]. Individuals are interested in genetic testing for T2D risk which offers unique information that is personalized. Financial accessibility, validity of the test and availability of diabetes prevention programs were identified as predictors of interest in T2D testing.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

Trait aspects of auditory mismatch negativity predict response to auditory training in individuals with early illness schizophrenia

PubMed Central

Biagianti, Bruno; Roach, Brian J.; Fisher, Melissa; Loewy, Rachel; Ford, Judith M.; Vinogradov, Sophia; Mathalon, Daniel H.

2017-01-01

Background Individuals with schizophrenia have heterogeneous impairments of the auditory processing system that likely mediate differences in the cognitive gains induced by auditory training (AT). Mismatch negativity (MMN) is an event-related potential component reflecting auditory echoic memory, and its amplitude reduction in schizophrenia has been linked to cognitive deficits. Therefore, MMN may predict response to AT and identify individuals with schizophrenia who have the most to gain from AT. Furthermore, to the extent that AT strengthens auditory deviance processing, MMN may also serve as a readout of the underlying changes in the auditory system induced by AT. Methods Fifty-six individuals early in the course of a schizophrenia-spectrum illness (ESZ) were randomly assigned to 40 h of AT or Computer Games (CG). Cognitive assessments and EEG recordings during a multi-deviant MMN paradigm were obtained before and after AT and CG. Changes in these measures were compared between the treatment groups. Baseline and trait-like MMN data were evaluated as predictors of treatment response. MMN data collected with the same paradigm from a sample of Healthy Controls (HC; n = 105) were compared to baseline MMN data from the ESZ group. Results Compared to HC, ESZ individuals showed significant MMN reductions at baseline (p = .003). Reduced Double-Deviant MMN was associated with greater general cognitive impairment in ESZ individuals (p = .020). Neither ESZ intervention group showed significant change in MMN. We found high correlations in all MMN deviant types (rs = .59–.68, all ps < .001) between baseline and post-intervention amplitudes irrespective of treatment group, suggesting trait-like stability of the MMN signal. Greater deficits in trait-like Double-Deviant MMN predicted greater cognitive improvements in the AT group (p = .02), but not in the CG group. Conclusions In this sample of ESZ individuals, AT had no effect on auditory deviance processing as assessed by

Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

PubMed

Zago, Laure; Hervé, Pierre-Yves; Genuer, Robin; Laurent, Alexandre; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Joliot, Marc

2017-12-01

We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus word-list production fMRI-BOLD contrast map. The SVM classifier was trained at discriminating between Dominant and Non-Dominant hemispheric language production activation pattern on a group of 250 participants previously identified as Typicals (HFLI strongly leftward). Then, SVM was applied to each hemispheric language activation pattern of 47 non-typical individuals. The results showed that at least one hemisphere (left or right) was found to be Dominant in every, except 3 individuals, indicating that the "dominant" type of functional organization is the most frequent in non-typicals. Specifically, left hemisphere dominance was predicted in all non-typical right-handers (RH) and in 57.4% of non-typical left-handers (LH). When both hemisphere classifications were jointly considered, four types of brain patterns were observed. The most often predicted pattern (51%) was left-dominant (Dominant left-hemisphere and Non-Dominant right-hemisphere), followed by right-dominant (23%, Dominant right-hemisphere and Non-Dominant left-hemisphere) and co-dominant (19%, 2 Dominant hemispheres) patterns. Co-non-dominant was rare (6%, 2 Non-Dominant hemispheres), but was normal variants of hemispheric specialization. In RH, only left-dominant (72%) and co-dominant patterns were detected, while for LH, all types were found, although with different occurrences. Among the 10 LH with a strong rightward HFLI, 8 had a right-dominant brain pattern. Whole-brain analysis of the right-dominant pattern group confirmed that it exhibited a functional organization strictly mirroring that of left-dominant pattern group. Hum Brain Mapp 38:5871-5889, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Predicting maximal strength of quadriceps from submaximal performance in individuals with knee joint osteoarthritis.

PubMed

McNair, Peter J; Colvin, Matt; Reid, Duncan

2011-02-01

To compare the accuracy of 12 maximal strength (1-repetition maximum [1-RM]) equations for predicting quadriceps strength in people with osteoarthritis (OA) of the knee joint. Eighteen subjects with OA of the knee joint attended a rehabilitation gymnasium on 3 occasions: 1) a familiarization session, 2) a session where the 1-RM of the quadriceps was established using a weights machine for an open-chain knee extension exercise and a leg press exercise, and 3) a session where the subjects performed with a load at which they could lift for approximately 10 repetitions only. The data were used in 12 prediction equations to calculate 1-RM strength and compared to the actual 1-RM data. Data were examined using Bland and Altman graphs and statistics, intraclass correlation coefficients (ICCs), and typical error values between the actual 1-RM and the respective 1-RM prediction equation data. Difference scores (predicted 1-RM--actual 1-RM) across the injured and control legs were also compared. For the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al, Poliquin, and Wathen prediction equations demonstrated the greatest levels of predictive accuracy. All of the ICCs were high (range 0.96–0.99), and typical errors were between 3% and 4%. For the knee press exercise, the Adams, Berger, Kemmler et al, and O'Conner et al equations demonstrated the greatest levels of predictive accuracy. All of the ICCs were high (range 0.95-0.98), and the typical errors ranged from 5.9-6.3%. This study provided evidence supporting the use of prediction equations to assess maximal strength in individuals with a knee joint with OA.

Efficiency at rest: magnetoencephalographic resting-state connectivity and individual differences in verbal working memory.

PubMed

del Río, David; Cuesta, Pablo; Bajo, Ricardo; García-Pacios, Javier; López-Higes, Ramón; del-Pozo, Francisco; Maestú, Fernando

2012-11-01

Inter-individual differences in cognitive performance are based on an efficient use of task-related brain resources. However, little is known yet on how these differences might be reflected on resting-state brain networks. Here we used Magnetoencephalography resting-state recordings to assess the relationship between a behavioral measurement of verbal working memory and functional connectivity as measured through Mutual Information. We studied theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-13 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) frequency bands. A higher verbal working memory capacity was associated with a lower mutual information in the low alpha band, prominently among right-anterior and left-lateral sensors. The results suggest that an efficient brain organization in the domain of verbal working memory might be related to a lower resting-state functional connectivity across large-scale brain networks possibly involving right prefrontal and left perisylvian areas. Copyright © 2012 Elsevier B.V. All rights reserved.

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

PubMed

Zou, Tengyue; Lin, Shouying; Feng, Qijie; Chen, Yanlian

2016-01-04

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks' activities in an uninterrupted and efficient manner.

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

PubMed Central

Zou, Tengyue; Lin, Shouying; Feng, Qijie; Chen, Yanlian

2016-01-01

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner. PMID:26742042

Efficient prediction of terahertz quantum cascade laser dynamics from steady-state simulations

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

Agnew, G.; Lim, Y. L.; Nikolić, M.

2015-04-20

Terahertz-frequency quantum cascade lasers (THz QCLs) based on bound-to-continuum active regions are difficult to model owing to their large number of quantum states. We present a computationally efficient reduced rate equation (RE) model that reproduces the experimentally observed variation of THz power with respect to drive current and heat-sink temperature. We also present dynamic (time-domain) simulations under a range of drive currents and predict an increase in modulation bandwidth as the current approaches the peak of the light–current curve, as observed experimentally in mid-infrared QCLs. We account for temperature and bias dependence of the carrier lifetimes, gain, and injection efficiency,more » calculated from a full rate equation model. The temperature dependence of the simulated threshold current, emitted power, and cut-off current are thus all reproduced accurately with only one fitting parameter, the interface roughness, in the full REs. We propose that the model could therefore be used for rapid dynamical simulation of QCL designs.« less

FDG-PET Response Prediction in Pediatric Hodgkin's Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value.

PubMed

Hussien, Amr Elsayed M; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

2015-01-28

In pediatric Hodgkin's lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

2015-11-01

This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

Consistent differences in individual reactions to drugs and dummies

PubMed Central

Joyce, C. R. B.

1959-01-01

The tendency of some individuals to report changes of physical and mental state after taking pharmacologically inert substances has been investigated experimentally. In a class of healthy medical students, those individuals who reported symptoms and those who did not made significantly different scores on a number of behavioural tests. The likely reactions of the members of a second class (containing none of the previous participants) to dummies were then predicted from their scores on the same tests, some of which were found to be much more efficient predictors than would have been expected by chance. Some implications for further research and for clinical medicine are discussed. PMID:14408028

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

PubMed

Taheri, Mahboobeh; Mohebbi, Ali

2008-08-30

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

PubMed

Mourao-Miranda, J; Reinders, A A T S; Rocha-Rego, V; Lappin, J; Rondina, J; Morgan, C; Morgan, K D; Fearon, P; Jones, P B; Doody, G A; Murray, R M; Kapur, S; Dazzan, P

2012-05-01

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

PubMed Central

Mourao-Miranda, J.; Reinders, A. A. T. S.; Rocha-Rego, V.; Lappin, J.; Rondina, J.; Morgan, C.; Morgan, K. D.; Fearon, P.; Jones, P. B.; Doody, G. A.; Murray, R. M.; Kapur, S.; Dazzan, P.

2012-01-01

Background To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. Method One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. Results At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). Conclusions We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data. PMID:22059690

An efficient approach for site-specific scenery prediction in surveillance imaging near Earth's surface

NASA Astrophysics Data System (ADS)

Jylhä, Juha; Marjanen, Kalle; Rantala, Mikko; Metsäpuro, Petri; Visa, Ari

2006-09-01

Surveillance camera automation and camera network development are growing areas of interest. This paper proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS) when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation. Image processing is aided by predicting certain advance features of visible terrain. The features include distance from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The performance of the approach is studied by comparing a photograph of Finnish forested landscape with the prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes apparent.

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex

PubMed Central

Tobler, Philippe N.

2015-01-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others’ rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. PMID:25326037

Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

PubMed

Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

2015-11-01

To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Individual stress vulnerability is predicted by short-term memory and AMPA receptor subunit ratio in the hippocampus.

PubMed

Schmidt, Mathias V; Trümbach, Dietrich; Weber, Peter; Wagner, Klaus; Scharf, Sebastian H; Liebl, Claudia; Datson, Nicole; Namendorf, Christian; Gerlach, Tamara; Kühne, Claudia; Uhr, Manfred; Deussing, Jan M; Wurst, Wolfgang; Binder, Elisabeth B; Holsboer, Florian; Müller, Marianne B

2010-12-15

Increased vulnerability to aversive experiences is one of the main risk factors for stress-related psychiatric disorders as major depression. However, the molecular bases of vulnerability, on the one hand, and stress resilience, on the other hand, are still not understood. Increasing clinical and preclinical evidence suggests a central involvement of the glutamatergic system in the pathogenesis of major depression. Using a mouse paradigm, modeling increased stress vulnerability and depression-like symptoms in a genetically diverse outbred strain, and we tested the hypothesis that differences in AMPA receptor function may be linked to individual variations in stress vulnerability. Vulnerable and resilient animals differed significantly in their dorsal hippocampal AMPA receptor expression and AMPA receptor binding. Treatment with an AMPA receptor potentiator during the stress exposure prevented the lasting effects of chronic social stress exposure on physiological, neuroendocrine, and behavioral parameters. In addition, spatial short-term memory, an AMPA receptor-dependent behavior, was found to be predictive of individual stress vulnerability and response to AMPA potentiator treatment. Finally, we provide evidence that genetic variations in the AMPA receptor subunit GluR1 are linked to the vulnerable phenotype. Therefore, we propose genetic variations in the AMPA receptor system to shape individual stress vulnerability. Those individual differences can be predicted by the assessment of short-term memory, thereby opening up the possibility for a specific treatment by enhancing AMPA receptor function.

Validation of spot urine in predicting 24-h sodium excretion at the individual level.

PubMed

Zhou, Long; Tian, Yu; Fu, Jun-Jie; Jiang, Ying-Ying; Bai, Ya-Min; Zhang, Zi-Hua; Hu, Xiao-He; Lian, Hong-Wu; Guo, Min; Yang, Zheng-Xiong; Zhao, Lian-Cheng

2017-06-01

Background: Evidence for the effect of dietary sodium intake on the risk of cardiovascular disease has been controversial. One of the main explanations for the conflicting results lies in the great variability associated with measurement methods for sodium intake. Spot urine collection is a convenient method commonly used for sodium estimation, but its validity for predicting 24-h urinary sodium excretion at the individual level has not been well evaluated among the general population. Objective: The aim of this study was to evaluate the validity of the Kawasaki, the International Cooperative Study on Salt, Other Factors, and Blood Pressure (INTERSALT), and the Tanaka formulas in predicting 24-h urinary sodium excretion by using spot urine samples in Chinese adults. Design: We analyzed the relative and absolute differences and misclassification at the individual level from 3 commonly used methods for estimating sodium intake among 141 Chinese community residents. Results: The mean measured 24-h sodium excretion was 220.8 mmol/d. The median (95% CIs) differences between measured sodium and those estimated from the Kawasaki, INTERSALT, and Tanaka methods were 6.4 mmol/d (-17.5, 36.8 mmol/d), -67.3 mmol/d (-96.5, -46.9 mmol/d), and -42.9 mmol/d (-59.1, -24.8 mmol/d), respectively. The proportions of relative differences >40% with the Kawasaki, INTERSALT, and Tanaka methods were 31.2%, 41.1%, and 22.0%, respectively; and the absolute difference for the 3 methods was >51.3 mmol/d (3 g salt) in approximately half of the participants. The misclassification rate was 63.1% for the Kawasaki method, 78.7% for the INTERSALT method, and 66.0% for the Tanaka method at the individual level. Conclusion: The results from our study do not support the use of spot urine to estimate 24-h urinary sodium excretion at the individual level because of its poor performance with respect to misclassification. This trial was registered at www.chictr.org.cn as ChiCTR-IOR-16010278. © 2017

Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences

PubMed Central

Gibbs, Mark J; Armstrong, John S; Gibbs, Adrian J

2005-01-01

Background Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridise with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by 'dichotomous keys' that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way. Results We have developed a practical method for finding minimal sets of sub-sequences that identify individual sequences, and could be targeted by combinations of probes, so that the efficient strategy of traditional taxonomic identification could be used in DNA diagnosis. The sizes of minimal sub-sequence sets depended mostly on sequence diversity and sub-sequence length and interactions between these parameters. We found that 201 distinct cytochrome oxidase subunit-1 (CO1) genes from moths (Lepidoptera) were distinguished using only 15 sub-sequences 20 nucleotides long, whereas only 8–10 sub-sequences 6–10 nucleotides long were required to distinguish the CO1 genes of 92 species from the 9 largest orders of insects. Conclusion The presence/absence of sub-sequences in a set of gene sequences can be used like the questions in a traditional dichotomous taxonomic key; hybridisation probes complementary to such sub-sequences should provide a very efficient means for identifying individual species, subtypes or genotypes. Sequence diversity and sub-sequence length are the major factors that determine the numbers of distinguishing sub-sequences in any set of sequences. PMID:15817134

Ten problems and solutions when predicting individual outcome from lesion site after stroke.

PubMed

Price, Cathy J; Hope, Thomas M; Seghier, Mohamed L

2017-01-15

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Spectroscopic ellipsometry for analysis of polycrystalline thin-film photovoltaic devices and prediction of external quantum efficiency

NASA Astrophysics Data System (ADS)

Ibdah, Abdel-Rahman; Koirala, Prakash; Aryal, Puruswottam; Pradhan, Puja; Marsillac, Sylvain; Rockett, Angus A.; Podraza, Nikolas J.; Collins, Robert W.

2017-11-01

Complete polycrystalline thin-film photovoltaic (PV) devices employing CuIn1-xGaxSe2/CdS and CdS/CdTe heterojunctions have been studied by ex situ spectroscopic ellipsometry (SE). In this study, layer thicknesses have been extracted along with photon energy independent parameters such as compositions that describe the dielectric function spectra ε(E) of the individual layers. For accurate ex situ SE analysis of these PV devices, a database of ε(E) spectra is required for all thin film component materials used in each of the two absorber technologies. When possible, database measurements are performed by applying SE in situ immediately after deposition of the thin film materials and after cooling to room temperature in order to avoid oxidation and surface contamination. Determination of ε(E) from the resulting in situ SE data requires structural information that can be obtained from analysis of SE data acquired in real time during the deposition process. From the results of ex situ analysis of the complete CuIn1-xGaxSe2 (CIGS) and CdTe PV devices, the deduced layer thicknesses in combination with the parameters describing ε(E) can be employed in further studies that simulate the external quantum efficiency (EQE) spectra of the devices. These simulations have been performed here by assuming that all electron-hole pairs generated within the active layers, i.e. layers incorporating a dominant absorber component (either CIGS or CdTe), are separated and collected. The active layers may include not only the bulk absorber but also window and back contact interface layers, and individual current contributions from these layers have been determined in the simulations. In addition, the ex situ SE analysis results enable calculation of the absorbance spectra for the inactive layers and the overall reflectance spectra, which lead to quantification of all optical losses in terms of a current density deficit. Mapping SE can be performed given the high speed of multichannel

Nomogram for individualized prediction of hepatocellular carcinoma occurrence in hepatitis C virus cirrhosis (ANRS CO12 CirVir).

PubMed

Ganne-Carrié, Nathalie; Layese, Richard; Bourcier, Valérie; Cagnot, Carole; Marcellin, Patrick; Guyader, Dominique; Pol, Stanislas; Larrey, Dominique; de Lédinghen, Victor; Ouzan, Denis; Zoulim, Fabien; Roulot, Dominique; Tran, Albert; Bronowicki, Jean-Pierre; Zarski, Jean-Pierre; Riachi, Ghassan; Calès, Paul; Péron, Jean-Marie; Alric, Laurent; Bourlière, Marc; Mathurin, Philippe; Blanc, Jean-Frédéric; Abergel, Armand; Serfaty, Lawrence; Mallat, Ariane; Grangé, Jean-Didier; Attali, Pierre; Bacq, Yannick; Wartelle, Claire; Dao, Thông; Benhamou, Yves; Pilette, Christophe; Silvain, Christine; Christidis, Christos; Capron, Dominique; Bernard-Chabert, Brigitte; Zucman, David; Di Martino, Vincent; Trinchet, Jean-Claude; Nahon, Pierre; Roudot-Thoraval, Françoise

2016-10-01

The aim of this work was to develop an individualized score for predicting hepatocellular carcinoma (HCC) in patients with hepatitis C (HCV)-compensated cirrhosis. Among 1,323 patients with HCV cirrhosis enrolled in the French prospective ANRS CO12 CirVir cohort, 720 and 360 were randomly assigned to training and validation sets, respectively. Cox's multivariate model was used to predict HCC, after which a nomogram was computed to assess individualized risk. During follow-up (median, 51.0 months), 103 and 39 patients developed HCC in the training and validation sets, respectively. Five variables were independently associated with occurrence of HCC: age > 50 years (hazard ratio [HR], 1.94; 95% confidence interval [CI], 1.16; 3.25; P = 0.012); past excessive alcohol intake (HR, 1.55; 95% CI, 1.02; 2.36; P = 0.041); low platelet count (<100 Giga/mm(3) : HR, 2.70; 95% CI, 1.62; 4.51; P < 0.001; [100; 150] Giga/mm(3) : HR, 1.87; 95% CI, 1.10; 3.18; P = 0.021); gamma-glutamyl transpeptidase above the upper limit of normal (HR, 1.96; 95% CI, 1.11; 3.47; P = 0.021); and absence of a sustained virological response during follow-up (HR, 3.02; 95% CI, 1.67; 5.48; P < 0.001). An 11-point risk score was derived from the training cohort and validated in the validation set. Based on this score, the population was stratified into three groups, in which HCC development gradually increased, from 0% to 30.1% at 5 years for patients with the lowest (≤3) and highest (≥8) scores (P < 0.001). Using this score, a nomogram was built enabling individualized prediction of HCC occurrence at 1, 3, and 5 years. This HCC score can accurately predict HCC at an individual level in French patients with HCV cirrhosis. (Hepatology 2016;64:1136-1147). © 2016 by the American Association for the Study of Liver Diseases.

PLASMA OXYTOCIN LEVELS PREDICT SOCIAL CUE RECOGNITION IN INDIVIDUALS WITH SCHIZOPHRENIA

PubMed Central

Strauss, Gregory P.; Keller, William R.; Koenig, James I.; Gold, James M.; Frost, Katherine H.; Buchanan, Robert W.

2015-01-01

Lower endogenous levels of the neuropeptide oxytocin may be an important biological predictor of social cognition impairments in schizophrenia (SZ). Prior studies have demonstrated that lower-level social cognitive processes (e.g., facial affect perception) are significantly associated with reduced plasma oxytocin levels in SZ; however, it is unclear whether higher-level social cognition, which requires inferential processes and knowledge not directly presented in the stimulus, is associated with endogenous oxytocin. The current study explored the association between endogenous oxytocin levels and lower- and higher-level social cognition in 40 individuals diagnosed with SZ and 22 demographically matched healthy controls (CN). All participants received the Social Cue Recognition Test (SCRT), which presents participants with videotaped interpersonal vignettes and subsequent true/false questions related to concrete or abstract aspects of social interactions in the vignettes. Results indicated that SZ had significantly higher plasma oxytocin concentrations than CN. SZ and CN did not differ on SCRT hits, but SZ had more false positives and lower sensitivity scores than CN. Higher plasma oxytocin levels were associated with better sensitivity scores for abstract items in CN and fewer false positives for concrete items in individuals with SZ. Findings indicate that endogenous oxytocin levels predict accurate encoding of lower-level socially relevant information in SZ. PMID:25673435

Using the Theory of Planned Behavior to Predict College Students' Intention to Intervene With a Suicidal Individual.

PubMed

Aldrich, Rosalie S

2015-01-01

Suicide among college students is an issue of serious concern. College peers may effectively intervene with at-risk persons due to their regular contact and close personal relationships with others in this population of significantly enhanced risk. The current study was designed to investigate whether the theory of planned behavior constructs predicted intention to intervene when a college peer is suicidal. Undergraduate students (n = 367) completed an on-line questionnaire; they answered questions about their attitudes, subjective norms, perceived behavioral control regarding suicide and suicide intervention, as well as their intention to intervene when someone is suicidal. The data were analyzed using multiple regression. The statistical significance of this cross-sectional study indicates that the theory of planned behavior constructs predicts self-reported intention to intervene with a suicidal individual. Theory of planned behavior is an effective framework for understanding peers' intention to intervene with a suicidal individual.

A Simple and Efficient Computational Approach to Chafed Cable Time-Domain Reflectometry Signature Prediction

NASA Technical Reports Server (NTRS)

Kowalski, Marc Edward

2009-01-01

A method for the prediction of time-domain signatures of chafed coaxial cables is presented. The method is quasi-static in nature, and is thus efficient enough to be included in inference and inversion routines. Unlike previous models proposed, no restriction on the geometry or size of the chafe is required in the present approach. The model is validated and its speed is illustrated via comparison to simulations from a commercial, three-dimensional electromagnetic simulator.

Paired D10A Cas9 nickases are sometimes more efficient than individual nucleases for gene disruption.

PubMed

Gopalappa, Ramu; Suresh, Bharathi; Ramakrishna, Suresh; Kim, Hyongbum Henry

2018-03-23

The use of paired Cas9 nickases instead of Cas9 nuclease drastically reduces off-target effects. Because both nickases must function for a nickase pair to make a double-strand break, the efficiency of paired nickases can intuitively be expected to be lower than that of either corresponding nuclease alone. Here, we carefully compared the gene-disrupting efficiency of Cas9 paired nickases with that of nucleases. Interestingly, the T7E1 assay and deep sequencing showed that on-target efficiency of paired D10A Cas9 nickases was frequently comparable, but sometimes higher than that of either corresponding nucleases in mammalian cells. As the underlying mechanism, we found that the HNH domain, which is preserved in the D10A Cas9 nickase, has higher activity than the RuvC domain in mammalian cells. In this study, we showed: (i) the in vivo cleavage efficiency of the HNH domain of Cas9 in mammalian cells is higher than that of the RuvC domain, (ii) paired Cas9 nickases are sometimes more efficient than individual nucleases for gene disruption. We envision that our findings which were overlooked in previous reports will serve as a new potential guideline for tool selection for CRISPR-Cas9-mediated gene disruption, facilitating efficient and precise genome editing.

Real-time individualization of the unified model of performance.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Balkin, Thomas J; Reifman, Jaques

2017-12-01

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform. © 2017 European Sleep Research Society.

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

PubMed

Kundu, Kousik; Backofen, Rolf

2017-01-01

Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

Ultralow mode-volume photonic crystal nanobeam cavities for high-efficiency coupling to individual carbon nanotube emitters

PubMed Central

Miura, R.; Imamura, S.; Ohta, R.; Ishii, A.; Liu, X.; Shimada, T.; Iwamoto, S.; Arakawa, Y.; Kato, Y. K.

2014-01-01

The unique emission properties of single-walled carbon nanotubes are attractive for achieving increased functionality in integrated photonics. In addition to being room-temperature telecom-band emitters that can be directly grown on silicon, they are ideal for coupling to nanoscale photonic structures. Here we report on high-efficiency coupling of individual air-suspended carbon nanotubes to silicon photonic crystal nanobeam cavities. Photoluminescence images of dielectric- and air-mode cavities reflect their distinctly different mode profiles and show that fields in the air are important for coupling. We find that the air-mode cavities couple more efficiently, and estimated spontaneous emission coupling factors reach a value as high as 0.85. Our results demonstrate advantages of ultralow mode-volumes in air-mode cavities for coupling to low-dimensional nanoscale emitters. PMID:25420679

Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

NASA Astrophysics Data System (ADS)

Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

2018-06-01

Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

NASA Astrophysics Data System (ADS)

Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

2017-09-01

Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

Prediction of the period of psychotic episode in individual schizophrenics by simulation-data construction approach.

PubMed

Huang, Chun-Jung; Wang, Hsiao-Fan; Chiu, Hsien-Jane; Lan, Tsuo-Hung; Hu, Tsung-Ming; Loh, El-Wui

2010-10-01

Although schizophrenia can be treated, most patients still experience inevitable psychotic episodes from time to time. Precautious actions can be taken if the next onset can be predicted. However, sufficient information is always lacking in the clinical scenario. A possible solution is to use the virtual data generated from limited of original data. Data construction method (DCM) has been shown to generate the virtual felt earthquake data effectively and used in the prediction of further events. Here we investigated the performance of DCM in deriving the membership functions and discrete-event simulations (DES) in predicting the period embracing the initiation and termination time-points of the next psychotic episode of 35 individual schizophrenic patients. The results showed that 21 subjects had a success of simulations (RSS) ≥70%. Further analysis demonstrated that the co-morbidity of coronary heart diseases (CHD), risks of CHD, and the frequency of previous psychotic episodes increased the RSS.

Predicting individual differences in autonomy-connectedness: the role of body awareness, alexithymia, and assertiveness.

PubMed

Bekker, Marrie H J; Croon, Marcel A; van Balkom, Esther G A; Vermee, Jennifer B G

2008-06-01

Autonomy-connectedness is the capacity for being on one's own as well as for satisfactorily engaging in interpersonal relationships. Associations have been shown between autonomy-connectedness components (self-awareness, sensitivity to others, and the capacity for managing new situations) and various indices of psychopathology. Both in a theoretical sense as well as for enhancing treatment and prevention, it is relevant to identify which factors most powerfully predict individual differences in autonomy-connectedness: body awareness, alexithymia, or assertiveness. The present study examined this question in a clinical sample of women who were diagnosed as having autonomy problems (N=52) and in a female nonclinical community sample (N=59). In line with expectations, assertiveness was a strong predictor of (all three components of) autonomy-connectedness, as was emotionalizing, one of the alexithymia-components, but the latter in an opposite direction than we had expected: the higher an individual's ability to emotionalize was, the less self-aware and capable to manage new situations that person was, and the more sensitive to others. Cognitive alexithymia contributed to self-awareness as well as to the capacity for managing new situations, and one of the components of body awareness appeared to predict capacity for managing new situations. Our results indicate that assertiveness training and the enhancement of emotion regulation are important elements of autonomy-connectedness targeted interventions. (c) 2008 Wiley Periodicals, Inc.

A resolved two-way coupled CFD/6-DOF approach for predicting embolus transport and the embolus-trapping efficiency of IVC filters.

PubMed

Aycock, Kenneth I; Campbell, Robert L; Manning, Keefe B; Craven, Brent A

2017-06-01

Inferior vena cava (IVC) filters are medical devices designed to provide a mechanical barrier to the passage of emboli from the deep veins of the legs to the heart and lungs. Despite decades of development and clinical use, IVC filters still fail to prevent the passage of all hazardous emboli. The objective of this study is to (1) develop a resolved two-way computational model of embolus transport, (2) provide verification and validation evidence for the model, and (3) demonstrate the ability of the model to predict the embolus-trapping efficiency of an IVC filter. Our model couples computational fluid dynamics simulations of blood flow to six-degree-of-freedom simulations of embolus transport and resolves the interactions between rigid, spherical emboli and the blood flow using an immersed boundary method. Following model development and numerical verification and validation of the computational approach against benchmark data from the literature, embolus transport simulations are performed in an idealized IVC geometry. Centered and tilted filter orientations are considered using a nonlinear finite element-based virtual filter placement procedure. A total of 2048 coupled CFD/6-DOF simulations are performed to predict the embolus-trapping statistics of the filter. The simulations predict that the embolus-trapping efficiency of the IVC filter increases with increasing embolus diameter and increasing embolus-to-blood density ratio. Tilted filter placement is found to decrease the embolus-trapping efficiency compared with centered filter placement. Multiple embolus-trapping locations are predicted for the IVC filter, and the trapping locations are predicted to shift upstream and toward the vessel wall with increasing embolus diameter. Simulations of the injection of successive emboli into the IVC are also performed and reveal that the embolus-trapping efficiency decreases with increasing thrombus load in the IVC filter. In future work, the computational tool could be

Individual Differences in Initial Sensitivity and Acute Tolerance Predict Patterns of Chronic Drug Tolerance to Nitrous-Oxide-Induced Hypothermia in Rats

PubMed Central

Ramsay, Douglas S.; Kaiyala, Karl J.; Leroux, Brian G.; Woods, Stephen C.

2006-01-01

Rationale: A preventive strategy for drug addiction would benefit from being able to identify vulnerable individuals. Understanding how an individual responds during an initial drug exposure may be useful for predicting how that individual will respond to repeated drug administrations. Objectives: This study investigated whether individual differences in initial drug sensitivity and acute tolerance can predict how chronic tolerance develops. Methods: During an initial 3-h administration of 60% nitrous oxide (N2O), male Long-Evans rats were screened for N2O’s hypothermic effect into subsets based on being initially insensitive (II), sensitive with acute tolerance (AT), or sensitive with no intrasessional recovery (NR). Animals in each individual difference category were randomly assigned to receive six 90-min exposures of either 60% N2O or placebo gas. Core temperature was measured telemetrically. Results: Rats that exhibited a comparable degree of hypothermia during an initial N2O exposure, but differed in acute tolerance development, developed different patterns of chronic tolerance. Specifically, the NR group did not become fully tolerant over repeated N2O exposures while the AT group developed an initial hyperthermia followed by a return of core temperature to control levels indicative of full tolerance development. By the second N2O exposure, the II group breathing N2O became hyperthermic relative to the placebo control group and this hyperthermia persisted throughout the multiple N2O exposures. Conclusions: Individual differences in initial drug sensitivity and acute tolerance development predict different patterns of chronic tolerance. The hypothesis is suggested that individual differences in opponent adaptive responses may mediate this relationship. PMID:15778887

Culture and self in South Africa: individualism-collectivism predictions.

PubMed

Eaton, L; Louw, J

2000-04-01

People from collectivist cultures may have more concrete and interdependent self-concepts than do people from individualist cultures (G. Hofstede, 1980). African cultures are considered collectivist (H. C. Triandis, 1989), but research on self-concept and culture has neglected this continent. The authors attempted a partial replication in an African context of cross-cultural findings on the abstract-concrete and independent-interdependent dimensions of self-construal (referred to as the abstract-specific and the autonomous-social dimensions, respectively, by E. Rhee, J. S. Uleman, H. K. Lee, & R. J. Roman, 1995). University students in South Africa took the 20 Statements Test (M. Kuhn & T. S. McPartland, 1954; Rhee et al.); home languages were rough indicators of cultural identity. The authors used 3 coding schemes to analyze the content of 78 protocols from African-language speakers and 77 protocols from English speakers. In accord with predictions from individualism-collectivism theory, the African-language speakers produced more interdependent and concrete self-descriptions than did the English speakers. Additional findings concerned the orthogonality of the 2 dimensions and the nature and assessment of the social self-concept.

Individual differences in white matter microstructure predict semantic control.

PubMed

Nugiel, Tehila; Alm, Kylie H; Olson, Ingrid R

2016-12-01

In everyday conversation, we make many rapid choices between competing concepts and words in order to convey our intent. This process is termed semantic control, and it is thought to rely on information transmission between a distributed semantic store in the temporal lobes and a more discrete region, optimized for retrieval and selection, in the left inferior frontal gyrus. Here, we used diffusion tensor imaging in a group of neurologically normal young adults to investigate the relationship between semantic control and white matter tracts that have been implicated in semantic memory retrieval. Participants completed a verb generation task that taps semantic control (Snyder & Munakata, 2008; Snyder et al., 2010) and underwent a diffusion imaging scan. Deterministic tractography was performed to compute indices representing the microstructural properties of the inferior fronto-occipital fasciculus (IFOF), the uncinate fasciculus (UF), and the inferior longitudinal fasciculus (ILF). Microstructural measures of the UF failed to predict semantic control performance. However, there was a significant relationship between microstructure of the left IFOF and ILF and individual differences in semantic control. Our findings support the view put forth by Duffau (2013) that the IFOF is a key structural pathway in semantic retrieval.

An experiment on individual ‘parochial altruism’ revealing no connection between individual ‘altruism’ and individual ‘parochialism’

PubMed Central

Corr, Philip J.; Hargreaves Heap, Shaun P.; Seger, Charles R.; Tsutsui, Kei

2015-01-01

Is parochial altruism an attribute of individual behavior? This is the question we address with an experiment. We examine whether the individual pro-sociality that is revealed in the public goods and trust games when interacting with fellow group members helps predict individual parochialism, as measured by the in-group bias (i.e., the difference in these games in pro-sociality when interacting with own group members as compared with members of another group). We find that it is not. An examination of the Big-5 personality predictors of each behavior reinforces this result: they are different. In short, knowing how pro-social individuals are with respect to fellow group members does not help predict their parochialism. PMID:26347703

Prediction of diabetes based on baseline metabolic characteristics in individuals at high risk.

PubMed

Defronzo, Ralph A; Tripathy, Devjit; Schwenke, Dawn C; Banerji, Maryann; Bray, George A; Buchanan, Thomas A; Clement, Stephen C; Henry, Robert R; Kitabchi, Abbas E; Mudaliar, Sunder; Ratner, Robert E; Stentz, Frankie B; Musi, Nicolas; Reaven, Peter D; Gastaldelli, Amalia

2013-11-01

Individuals with impaired glucose tolerance (IGT) are at high risk for developing type 2 diabetes mellitus (T2DM). We examined which characteristics at baseline predicted the development of T2DM versus maintenance of IGT or conversion to normal glucose tolerance. We studied 228 subjects at high risk with IGT who received treatment with placebo in ACT NOW and who underwent baseline anthropometric measures and oral glucose tolerance test (OGTT) at baseline and after a mean follow-up of 2.4 years. In a univariate analysis, 45 of 228 (19.7%) IGT individuals developed diabetes. After adjusting for age, sex, and center, increased fasting plasma glucose, 2-h plasma glucose, G0-120 during OGTT, HbA1c, adipocyte insulin resistance index, ln fasting plasma insulin, and ln I0-120, as well as family history of diabetes and presence of metabolic syndrome, were associated with increased risk of diabetes. At baseline, higher insulin secretion (ln [I0-120/G0-120]) during the OGTT was associated with decreased risk of diabetes. Higher β-cell function (insulin secretion/insulin resistance or disposition index; ln [I0-120/G0-120 × Matsuda index of insulin sensitivity]; odds ratio 0.11; P < 0.0001) was the variable most closely associated with reduced risk of diabetes. In a stepwise multiple-variable analysis, only HbA1c and β-cell function (ln insulin secretion/insulin resistance index) predicted the development of diabetes (r = 0.49; P < 0.0001).

Sibling configuration predicts individual and descendant socioeconomic success in a modern post-industrial society.

PubMed

Lawson, David W; Makoli, Arijeta; Goodman, Anna

2013-01-01

Growing up with many siblings, at least in the context of modern post-industrial low fertility, low mortality societies, is predictive of relatively poor performance on school tests in childhood, lower levels of educational attainment, and lower income throughout adulthood. Recent studies further indicate these relationships hold across generations, so that the descendants of those who grow up with many siblings are also at an apparent socioeconomic disadvantage. In this paper we add to this literature by considering whether such relationships interact with the sex and relative age of siblings. To do this we utilise a unique Swedish multigenerational birth cohort study that provides sibling configuration data on over 10,000 individuals born in 1915-1929, plus all their direct genetic descendants to the present day. Adjusting for parental and birth characteristics, we find that the 'socioeconomic cost' of growing up in a large family is independent of both the sex of siblings and the sex of the individual. However, growing up with several older as opposed to several younger siblings is predictive of relatively poor performance on school tests and a lower likelihood of progression to tertiary education. This later-born disadvantage also holds across generations, with the children of those with many older siblings achieving lower levels of educational attainment. Despite these differences, we find that while individual and descendant income is negatively related to the number of siblings, it is not influenced by the relative age of siblings. Thus, our findings imply that the educational disadvantage of later-born children, demonstrated here and in numerous other studies, does not necessarily translate into reduced earnings in adulthood. We discuss potential explanations for this pattern of results, and consider some important directions for future research into sibling configuration and wellbeing in modern societies.

Sibling Configuration Predicts Individual and Descendant Socioeconomic Success in a Modern Post-Industrial Society

PubMed Central

Lawson, David W.; Makoli, Arijeta; Goodman, Anna

2013-01-01

Growing up with many siblings, at least in the context of modern post-industrial low fertility, low mortality societies, is predictive of relatively poor performance on school tests in childhood, lower levels of educational attainment, and lower income throughout adulthood. Recent studies further indicate these relationships hold across generations, so that the descendants of those who grow up with many siblings are also at an apparent socioeconomic disadvantage. In this paper we add to this literature by considering whether such relationships interact with the sex and relative age of siblings. To do this we utilise a unique Swedish multigenerational birth cohort study that provides sibling configuration data on over 10,000 individuals born in 1915–1929, plus all their direct genetic descendants to the present day. Adjusting for parental and birth characteristics, we find that the ‘socioeconomic cost’ of growing up in a large family is independent of both the sex of siblings and the sex of the individual. However, growing up with several older as opposed to several younger siblings is predictive of relatively poor performance on school tests and a lower likelihood of progression to tertiary education. This later-born disadvantage also holds across generations, with the children of those with many older siblings achieving lower levels of educational attainment. Despite these differences, we find that while individual and descendant income is negatively related to the number of siblings, it is not influenced by the relative age of siblings. Thus, our findings imply that the educational disadvantage of later-born children, demonstrated here and in numerous other studies, does not necessarily translate into reduced earnings in adulthood. We discuss potential explanations for this pattern of results, and consider some important directions for future research into sibling configuration and wellbeing in modern societies. PMID:24040031

Prediction of Giant Thermoelectric Efficiency in Crystals with Interlaced Nanostructure.

PubMed

Puzyrev, Y S; Shen, X; Pantelides, S T

2016-01-13

We present a theoretical study of the thermoelectric efficiency of "interlaced crystals", recently discovered in hexagonal-CuInS2 nanoparticles. Interlaced crystals are I-III-VI2 or II-IV-V2 tetrahedrally bonded compounds. They have a perfect Bravais lattice in which the two cations have an infinite set of possible ordering patterns within the cation sublattice. The material comprises nanoscale interlaced domains and phases with corresponding boundaries. Here we employ density functional theory and large-scale molecular dynamics calculations based on model classical potentials to demonstrate that the phase and domain boundaries are effective phonon scatterers and greatly suppress thermal conductivity. However, the absence of both structural defects and strain in the interlaced material results in a minimal effect on electronic properties. We predict an increase of thermal resistivity of up to 2 orders of magnitude, which makes interlaced crystals an exceptional candidate for thermoelectric applications.

Balance and ankle muscle strength predict spatiotemporal gait parameters in individuals with diabetic peripheral neuropathy.

PubMed

Camargo, Marcela R; Barela, José A; Nozabieli, Andréa J L; Mantovani, Alessandra M; Martinelli, Alessandra R; Fregonesi, Cristina E P T

2015-01-01

The aims of this study were to evaluate aspects of balance, ankle muscle strength and spatiotemporal gait parameters in individuals with diabetic peripheral neuropathy (DPN) and verify whether deficits in spatiotemporal gait parameters were associated with ankle muscle strength and balance performance. Thirty individuals with DPN and 30 control individuals have participated. Spatiotemporal gait parameters were evaluated by measuring the time to walk a set distance during self-selected and maximal walking speeds. Functional mobility and balance performance were assessed using the Functional Reach and the Time Up and Go tests. Ankle isometric muscle strength was assessed with a handheld digital dynamometer. Analyses of variance were employed to verify possible differences between groups and conditions. Multiple linear regression analysis was employed to uncover possible predictors of gait deficits. Gait spatiotemporal, functional mobility, balance performance and ankle muscle strength were affected in individuals with DPN. The Time Up and Go test performance and ankle muscle isometric strength were associated to spatiotemporal gait changes, especially during maximal walking speed condition. Functional mobility and balance performance are damaged in DPN and balance performance and ankle muscle strength can be used to predict spatiotemporal gait parameters in individuals with DPN. Copyright © 2015 Diabetes India. Published by Elsevier Ltd. All rights reserved.

On the predictability of extreme events in records with linear and nonlinear long-range memory: Efficiency and noise robustness

NASA Astrophysics Data System (ADS)

Bogachev, Mikhail I.; Bunde, Armin

2011-06-01

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors, and (ii) the return interval approach (RIA) that exploits long-range memory incorporated in the elapsed time after the last extreme event. We find that the PRT always performs better when only linear memory is present. In the presence of nonlinear memory, both methods demonstrate comparable efficiency in the absence of white noise. When additional white noise is present in the record (which is the case in most observational records), the efficiency of the PRT decreases monotonously with increasing noise level. In contrast, the RIA shows an abrupt transition between a phase of low level noise where the prediction is as good as in the absence of noise, and a phase of high level noise where the prediction becomes poor. In the phase of low and intermediate noise the RIA predicts considerably better than the PRT, which explains our recent findings in physiological and financial records.

Quantitative property-property relationship (QPPR) approach in predicting flotation efficiency of chelating agents as mineral collectors.

PubMed

Natarajan, R; Nirdosh, I; Venuvanalingam, P; Ramalingam, M

2002-07-01

The QPPR approach has been used to model cupferrons as mineral collectors. Separation efficiencies (Es) of these chelating agents have been correlated with property parameters namely, log P, log Koc, substituent-constant sigma, Mullikan and ESP derived charges using multiple regression analysis. Es of substituted-cupferrons in the flotation of a uranium ore could be predicted within experimental error either by log P or log Koc and an electronic parameter. However, when a halo, methoxy or phenyl substituent was in para to the chelating group, experimental Es was greater than the predicted values. Inclusion of a Boolean type indicative parameter improved significantly the predictability power. This approach has been extended to 2-aminothiophenols that were used to float a zinc ore and the correlations were found to be reasonably good.

The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

PubMed

Okely, Judith A; Weiss, Alexander; Gale, Catharine R

2018-02-01

The link between greater wellbeing and longevity is well documented. The aim of the current study was to test whether this association is consistent across individualistic and collectivistic cultures. The sample consisted of 13,596 participants from 11 European countries, each of which was assigned an individualism score according to Hofstede et al.'s (Cultures and organizations: software of the mind, McGraw Hill, New York, 2010) cultural dimension of individualism. We tested whether individualism moderated the cross-sectional association between wellbeing and self-rated health or the longitudinal association between wellbeing and mortality risk. Our analysis revealed a significant interaction between individualism and wellbeing such that the association between wellbeing and self-rated health or risk of mortality from cardiovascular disease was stronger in more individualistic countries. However, the interaction between wellbeing and individualism was not significant in analysis predicting all-cause mortality. Further prospective studies are needed to confirm our finding and to explore the factors responsible for this culturally dependent effect.

Boldness predicts an individual's position along an exploration-exploitation foraging trade-off.

PubMed

Patrick, Samantha C; Pinaud, David; Weimerskirch, Henri

2017-09-01

Individuals do not have complete information about the environment and therefore they face a trade-off between gathering information (exploration) and gathering resources (exploitation). Studies have shown individual differences in components of this trade-off but how stable these strategies are in a population and the intrinsic drivers of these differences is not well understood. Top marine predators are expected to experience a particularly strong trade-off as many species have large foraging ranges and their prey often have a patchy distribution. This environment leads these species to exhibit pronounced exploration and exploitation phases but differences between individuals are poorly resolved. Personality differences are known to be important in foraging behaviour but also in the trade-off between exploration and exploitation. Here we test whether personality predicts an individual exploration-exploitation strategy using wide ranging wandering albatrosses (Diomedea exulans) as a model system. Using GPS tracking data from 276 wandering albatrosses, we extract foraging parameters indicative of exploration (searching) and exploitation (foraging) and show that foraging effort, time in patch and size of patch are strongly correlated, demonstrating these are indicative of an exploration-exploitation (EE) strategy. Furthermore, we show these are consistent within individuals and appear stable in the population, with no reproductive advantage. The searching and foraging behaviour of bolder birds placed them towards the exploration end of the trade-off, whereas shy birds showed greater exploitation. This result provides a mechanism through which individual foraging strategies may emerge. Age and sex affected components of the trade-off, but not the trade-off itself, suggesting these factors may drive behavioural compensation to maintain resource acquisition and this was supported by the evidence that there were no fitness consequence of any EE trait nor the trade

A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

NASA Astrophysics Data System (ADS)

Rubel, Aleksey S.; Lukin, Vladimir V.; Egiazarian, Karen O.

2015-03-01

Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Multi-agent cooperation rescue algorithm based on influence degree and state prediction

NASA Astrophysics Data System (ADS)

Zheng, Yanbin; Ma, Guangfu; Wang, Linlin; Xi, Pengxue

2018-04-01

Aiming at the multi-agent cooperative rescue in disaster, a multi-agent cooperative rescue algorithm based on impact degree and state prediction is proposed. Firstly, based on the influence of the information in the scene on the collaborative task, the influence degree function is used to filter the information. Secondly, using the selected information to predict the state of the system and Agent behavior. Finally, according to the result of the forecast, the cooperative behavior of Agent is guided and improved the efficiency of individual collaboration. The simulation results show that this algorithm can effectively solve the cooperative rescue problem of multi-agent and ensure the efficient completion of the task.

Ant groups optimally amplify the effect of transiently informed individuals

NASA Astrophysics Data System (ADS)

Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer

2015-07-01

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

Ant groups optimally amplify the effect of transiently informed individuals

PubMed Central

Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer

2015-01-01

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge. PMID:26218613

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

PubMed

Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Investigating the importance of various individual, interpersonal, organisational and demographic variables when predicting job burnout in disability support workers.

PubMed

Vassos, Maria V; Nankervis, Karen L

2012-01-01

Previous research has highlighted that factors such as large workload, role ambiguity, lack of support from colleagues, and challenging behaviour are associated with higher levels of burnout within the disability support worker (DSW) population. The aim of this research was to investigate which factors contribute the most to the prediction of the three facets of burnout--feeling exhausted and overextended by one's work (emotional exhaustion), detached and callous responses towards work (depersonalisation) and a lack of achievement and productivity within one's role (personal accomplishment). The factors chosen for analysis within this research were analysed within four categories linked to theories of burnout development (individual, interpersonal, organisational and demographic). A sample of 108 DSWs completed a questionnaire booklet that contained standardised measures of burnout and job stressors related to disability work. Results highlighted the importance of predictors such as challenging behaviour (interpersonal), workload (individual), supervisor support (individual), work-home conflict (individual), job feedback (individual), role ambiguity (organisational), low job status (organisational), role conflict (organisational), gender (demographic) and work hours (demographic) when predicting one or more of the facets of burnout. In conclusion, disability services and organisations may benefit from focusing on remodelling their staff-related organisational practices in order to prevent the development of burnout in their DSWs (e.g., increase supervision and support practices). Copyright © 2012 Elsevier Ltd. All rights reserved.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

Individual prediction of change in delayed recall of prose passages after left-sided anterior temporal lobectomy.

PubMed

Jokeit, H; Ebner, A; Holthausen, H; Markowitsch, H J; Moch, A; Pannek, H; Schulz, R; Tuxhorn, I

1997-08-01

Prognostic variables for individual memory outcome after left anterior temporal lobectomy (ATL) were studied in 27 patients with refractory temporal lobe epilepsy. The difference between pre- and postoperative performance in the delayed recall of two prose passages (Story A and B) from the Wechsler Memory Scale served as measure of postoperative memory change. Fifteen independent clinical, neuropsychological, and electrophysiological variables were submitted to a multiple linear regression analysis. Preoperative immediate and delayed recall of story content and right hemisphere Wada memory performance for pictorial and verbal items explained very well postoperative memory changes in recall of Story B. Delayed recall of Story B, but not of Story A, had high concurrent validity to other measures of memory. Patients who became seizure-free did not differ in memory change from patients who continued to have seizures after ATL. The variables age at epilepsy onset and probable age at temporal lobe damage provided complementary information for individual prediction but with less effectiveness than Wada test data. Our model confirmed that good preoperative memory functioning and impaired right hemispheric Wada memory performance for pictorial items predict a high risk of memory loss after left ATL. The analyses demonstrate that the combination of independent measures delivers more information than Wada test performance or any other variable alone. The suggested function can be used routinely to estimate the individual severity of verbal episodic memory impairment that might occur after left-sided ATL and offers a rational basis for the counseling of patients.

Can the prognosis of individual patients with glioblastoma be predicted using an online calculator?

PubMed Central

Parks, Christopher; Heald, James; Hall, Gregory; Kamaly-Asl, Ian

2013-01-01

Background In an exploratory subanalysis of the European Organisation for Research and Treatment of Cancer and National Cancer Institute of Canada (EORTC/NCIC) trial data, Gorlia et al. identified a variety of factors that were predictive of overall survival, including therapy administered, age, extent of surgery, mini-mental score, administration of corticosteroids, World Health Organization (WHO) performance status, and O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Gorlia et al. developed 3 nomograms, each intended to predict the survival times of patients with newly diagnosed glioblastoma on the basis of individual-specific combinations of prognostic factors. These are available online as a “GBM Calculator” and are intended for use in patient counseling. This study is an external validation of this calculator. Method One hundred eighty-seven patients from 2 UK neurosurgical units who had histologically confirmed glioblastoma (WHO grade IV) had their information at diagnosis entered into the GBM calculator. A record was made of the actual and predicted median survival time for each patient. Statistical analysis was performed to assess the accuracy, precision, correlation, and discrimination of the calculator. Results The calculator gives both inaccurate and imprecise predictions. Only 23% of predictions were within 25% of the actual survival, and the percentage bias is 140% in our series. The coefficient of variance is 76%, where a smaller percentage would indicate greater precision. There is only a weak positive correlation between the predicted and actual survival among patients (R2 of 0.07). Discrimination is inadequate as measured by a C-index of 0.62. Conclusions The authors would not recommend the use of this tool in patient counseling. If departments were considering its use, we would advise that a similar validating exercise be undertaken. PMID:23543729

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

PubMed

Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

2016-06-01

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial

Evaluating the Efficiency and Effectiveness of Online Individual Education Plans: A Case Study from a South Texas Elementary School

ERIC Educational Resources Information Center

Bonner, William Bryant, III

2017-01-01

The purpose of this qualitative case study based upon the theories of technology adoption and technology integration planning (TIP) with the focus on design and development was to understand design features that encourage effectiveness and efficiency for using online individual education plans (IEP) along with the online IEP lived experiences of…

Efficient removal of chromate and arsenate from individual and mixed system by malachite nanoparticles.

PubMed

Saikia, Jiban; Saha, Bedabrata; Das, Gopal

2011-02-15

Malachite nanoparticles of 100-150 nm have been efficiently and for the first time used as an adsorbent for the removal of toxic arsenate and chromate. We report a high adsorption capacity for chromate and arsenate on malachite nanoparticle from both individual and mixed solution in pH ∼4-5. However, the adsorption efficiency decreases with the increase of solution pH. Batch studies revealed that initial pH, temperature, malachite nanoparticles dose and initial concentration of chromate and arsenate were important parameters for the adsorption process. Thermodynamic analysis showed that adsorption of chromate and arsenate on malachite nanoparticles is endothermic and spontaneous. The adsorption of these anions has also been investigated quantitatively with the help of adsorption kinetics, isotherm, and selectivity coefficient (K) analysis. The adsorption data for both chromate and arsenate were fitted well in Langmuir isotherm and preferentially followed the second order kinetics. The binding affinity of chromate is found to be slightly higher than arsenate in a competitive adsorption process which leads to the comparatively higher adsorption of chromate on malachite nanoparticles surface. Copyright © 2010 Elsevier B.V. All rights reserved.

FKBP5 and emotional neglect interact to predict individual differences in amygdala reactivity.

PubMed

White, M G; Bogdan, R; Fisher, P M; Muñoz, K E; Williamson, D E; Hariri, A R

2012-10-01

Individual variation in physiological responsiveness to stress mediates risk for mental illness and is influenced by both experiential and genetic factors. Common polymorphisms in the human gene for FK506 binding protein 5 (FKBP5), which is involved in transcriptional regulation of the hypothalamic-pituitary-adrenal (HPA) axis, have been shown to interact with childhood abuse and trauma to predict stress-related psychopathology. In the current study, we examined if such gene-environment interaction effects may be related to variability in the threat-related reactivity of the amygdala, which plays a critical role in mediating physiological and behavioral adaptations to stress including modulation of the HPA axis. To this end, 139 healthy Caucasian youth completed a blood oxygen level-dependent functional magnetic resonance imaging probe of amygdala reactivity and self-report assessments of emotional neglect (EN) and other forms of maltreatment. These individuals were genotyped for 6 FKBP5 polymorphisms (rs7748266, rs1360780, rs9296158, rs3800373, rs9470080 and rs9394309) previously associated with psychopathology and/or HPA axis function. Interactions between each SNP and EN emerged such that risk alleles predicted relatively increased dorsal amygdala reactivity in the context of higher EN, even after correcting for multiple testing. Two different haplotype analyses confirmed this relationship as haplotypes with risk alleles also exhibited increased amygdala reactivity in the context of higher EN. Our results suggest that increased threat-related amygdala reactivity may represent a mechanism linking psychopathology to interactions between common genetic variants affecting HPA axis function and childhood trauma. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Reid, Terry V.; Schifer, Nicholas A.; Briggs, Maxwell H.

2012-01-01

The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two high-efficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. Microporous bulk insulation is used in the ground support test hardware to minimize the loss of thermal energy from the electric heat source to the environment. The insulation package is characterized before operation to predict how much heat will be absorbed by the convertor and how much will be lost to the environment during operation. In an effort to validate these predictions, numerous tasks have been performed, which provided a more accurate value for net heat input into the ASCs. This test and modeling effort included: (a) making thermophysical property measurements of test setup materials to provide inputs to the numerical models, (b) acquiring additional test data that was collected during convertor tests to provide numerical models with temperature profiles of the test setup via thermocouple and infrared measurements, (c) using multidimensional numerical models (computational fluid dynamics code) to predict net heat input of an operating convertor, and (d) using validation test hardware to provide direct comparison of numerical results and validate the multidimensional numerical models used to predict convertor net heat input. This effort produced high fidelity ASC net heat input predictions, which were successfully validated using

Application of empirical Bayes methods to predict the rate of decline in ERG at the individual level among patients with retinitis pigmentosa.

PubMed

Qiu, Weiliang; Sandberg, Michael A; Rosner, Bernard

2018-05-31

Retinitis pigmentosa is one of the most common forms of inherited retinal degeneration. The electroretinogram (ERG) can be used to determine the severity of retinitis pigmentosa-the lower the ERG amplitude, the more severe the disease is. In practice for career, lifestyle, and treatment counseling, it is of interest to predict the ERG amplitude of a patient at a future time. One approach is prediction based on the average rate of decline for individual patients. However, there is considerable variation both in initial amplitude and in rate of decline. In this article, we propose an empirical Bayes (EB) approach to incorporate the variations in initial amplitude and rate of decline for the prediction of ERG amplitude at the individual level. We applied the EB method to a collection of ERGs from 898 patients with 3 or more visits over 5 or more years of follow-up tested in the Berman-Gund Laboratory and observed that the predicted values at the last (kth) visit obtained by using the proposed method based on data for the first k-1 visits are highly correlated with the observed values at the kth visit (Spearman correlation =0.93) and have a higher correlation with the observed values than those obtained based on either the population average decline rate or those obtained based on the individual decline rate. The mean square errors for predicted values obtained by the EB method are also smaller than those predicted by the other methods. Copyright © 2018 John Wiley & Sons, Ltd.

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

PubMed Central

Przybyszewski, Andrzej W.; Kon, Mark; Szlufik, Stanislaw; Szymanski, Artur; Habela, Piotr; Koziorowski, Dariusz M.

2016-01-01

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD. PMID:27649187

Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case

PubMed Central

Li, Guowei; Thabane, Lehana; Delate, Thomas; Witt, Daniel M.; Levine, Mitchell A. H.; Cheng, Ji; Holbrook, Anne

2016-01-01

Objectives To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both) in patients with atrial fibrillation (AF) with and without warfarin therapy. Methods Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR) modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling. Results We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632), there were 136 strokes (2.94%), 280 major bleedings (6.04%) and 1194 deaths (25.78%) occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances. Conclusions In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making PMID:27513986

Predictable quantum efficient detector based on n-type silicon photodiodes

NASA Astrophysics Data System (ADS)

Dönsberg, Timo; Manoocheri, Farshid; Sildoja, Meelis; Juntunen, Mikko; Savin, Hele; Tuovinen, Esa; Ronkainen, Hannu; Prunnila, Mika; Merimaa, Mikko; Tang, Chi Kwong; Gran, Jarle; Müller, Ingmar; Werner, Lutz; Rougié, Bernard; Pons, Alicia; Smîd, Marek; Gál, Péter; Lolli, Lapo; Brida, Giorgio; Rastello, Maria Luisa; Ikonen, Erkki

2017-12-01

The predictable quantum efficient detector (PQED) consists of two custom-made induced junction photodiodes that are mounted in a wedged trap configuration for the reduction of reflectance losses. Until now, all manufactured PQED photodiodes have been based on a structure where a SiO2 layer is thermally grown on top of p-type silicon substrate. In this paper, we present the design, manufacturing, modelling and characterization of a new type of PQED, where the photodiodes have an Al2O3 layer on top of n-type silicon substrate. Atomic layer deposition is used to deposit the layer to the desired thickness. Two sets of photodiodes with varying oxide thicknesses and substrate doping concentrations were fabricated. In order to predict recombination losses of charge carriers, a 3D model of the photodiode was built into Cogenda Genius semiconductor simulation software. It is important to note that a novel experimental method was developed to obtain values for the 3D model parameters. This makes the prediction of the PQED responsivity a completely autonomous process. Detectors were characterized for temperature dependence of dark current, spatial uniformity of responsivity, reflectance, linearity and absolute responsivity at the wavelengths of 488 nm and 532 nm. For both sets of photodiodes, the modelled and measured responsivities were generally in agreement within the measurement and modelling uncertainties of around 100 parts per million (ppm). There is, however, an indication that the modelled internal quantum deficiency may be underestimated by a similar amount. Moreover, the responsivities of the detectors were spatially uniform within 30 ppm peak-to-peak variation. The results obtained in this research indicate that the n-type induced junction photodiode is a very promising alternative to the existing p-type detectors, and thus give additional credibility to the concept of modelled quantum detector serving as a primary standard. Furthermore, the manufacturing of

Efficient CRISPR/Cas9-Mediated Versatile, Predictable, and Donor-Free Gene Knockout in Human Pluripotent Stem Cells.

PubMed

Liu, Zhongliang; Hui, Yi; Shi, Lei; Chen, Zhenyu; Xu, Xiangjie; Chi, Liankai; Fan, Beibei; Fang, Yujiang; Liu, Yang; Ma, Lin; Wang, Yiran; Xiao, Lei; Zhang, Quanbin; Jin, Guohua; Liu, Ling; Zhang, Xiaoqing

2016-09-13

Loss-of-function studies in human pluripotent stem cells (hPSCs) require efficient methodologies for lesion of genes of interest. Here, we introduce a donor-free paired gRNA-guided CRISPR/Cas9 knockout strategy (paired-KO) for efficient and rapid gene ablation in hPSCs. Through paired-KO, we succeeded in targeting all genes of interest with high biallelic targeting efficiencies. More importantly, during paired-KO, the cleaved DNA was repaired mostly through direct end joining without insertions/deletions (precise ligation), and thus makes the lesion product predictable. The paired-KO remained highly efficient for one-step targeting of multiple genes and was also efficient for targeting of microRNA, while for long non-coding RNA over 8 kb, cleavage of a short fragment of the core promoter region was sufficient to eradicate downstream gene transcription. This work suggests that the paired-KO strategy is a simple and robust system for loss-of-function studies for both coding and non-coding genes in hPSCs. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Prediction of individual genetic risk to prostate cancer using a polygenic score.

PubMed

Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind A; Easton, Douglas; Kote-Jarai, Z Sofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Southey, Melissa C; Fitzgerald, Liesel M; Henderson, Brian E; Schumacher, Fredrick; Haiman, Christopher A; Schleutker, Johanna; Wahlfors, Tiina; Tammela, Teuvo L J; Nordestgaard, Børge G; Key, Tim J; Travis, Ruth C; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Pharoah, Paul; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon-Albright, Lisa; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, Jong Y; Sellers, Thomas; Lin, Hui-Yi; Lim, Hui-Yi; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A; Spurdle, Amanda; Teixeira, Manuel R; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Gronberg, Henrik; Wiklund, Fredrik

2015-09-01

Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. © 2015 Wiley Periodicals, Inc.

Individual differences in working memory capacity predict learned control over attentional capture.

PubMed

Robison, Matthew K; Unsworth, Nash

2017-11-01

Although individual differences in working memory capacity (WMC) typically predict susceptibility to attentional capture in various paradigms (e.g., Stroop, antisaccade, flankers), it sometimes fails to correlate with the magnitude of attentional capture effects in visual search (e.g., Stokes, 2016), which is 1 of the most frequently studied tasks to study capture (Theeuwes, 2010). But some studies have shown that search modes can mitigate the effects of attentional capture (Leber & Egeth, 2006). Therefore, the present study examined whether or not the relationship between WMC and attentional capture changes as a function of the search modes available. In Experiment 1, WMC was unrelated to attentional capture, but only 1 search mode (singleton-detection) could be employed. In Experiment 2, greater WMC predicted smaller attentional capture effects, but only when multiple search modes (feature-search and singleton-detection) could be employed. Importantly this relationship was entirely independent of variation in attention control, which suggests that this effect is driven by WMC-related long-term memory differences (Cosman & Vecera, 2013a, 2013b). The present set of findings help to further our understanding of the nuanced ways in which memory and attention interact. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Automation to improve efficiency of field expedient injury prediction screening.

PubMed

Teyhen, Deydre S; Shaffer, Scott W; Umlauf, Jon A; Akerman, Raymond J; Canada, John B; Butler, Robert J; Goffar, Stephen L; Walker, Michael J; Kiesel, Kyle B; Plisky, Phillip J

2012-07-01

Musculoskeletal injuries are a primary source of disability in the U.S. Military. Physical training and sports-related activities account for up to 90% of all injuries, and 80% of these injuries are considered overuse in nature. As a result, there is a need to develop an evidence-based musculoskeletal screen that can assist with injury prevention. The purpose of this study was to assess the capability of an automated system to improve the efficiency of field expedient tests that may help predict injury risk and provide corrective strategies for deficits identified. The field expedient tests include survey questions and measures of movement quality, balance, trunk stability, power, mobility, and foot structure and mobility. Data entry for these tests was automated using handheld computers, barcode scanning, and netbook computers. An automated algorithm for injury risk stratification and mitigation techniques was run on a server computer. Without automation support, subjects were assessed in 84.5 ± 9.1 minutes per subject compared with 66.8 ± 6.1 minutes per subject with automation and 47.1 ± 5.2 minutes per subject with automation and process improvement measures (p < 0.001). The average time to manually enter the data was 22.2 ± 7.4 minutes per subject. An additional 11.5 ± 2.5 minutes per subject was required to manually assign an intervention strategy. Automation of this injury prevention screening protocol using handheld devices and netbook computers allowed for real-time data entry and enhanced the efficiency of injury screening, risk stratification, and prescription of a risk mitigation strategy.

Quantitative Analysis of Defects in Silicon. [to predict energy conversion efficiency of silicon samples for solar cells

NASA Technical Reports Server (NTRS)

Natesh, R.; Smith, J. M.; Qidwai, H. A.; Bruce, T.

1979-01-01

The evaluation and prediction of the conversion efficiency for a variety of silicon samples with differences in structural defects, such as grain boundaries, twin boundaries, precipitate particles, dislocations, etc. are discussed. Quantitative characterization of these structural defects, which were revealed by etching the surface of silicon samples, is performed by using an image analyzer. Due to different crystal growth and fabrication techniques the various types of silicon contain a variety of trace impurity elements and structural defects. The two most important criteria in evaluating the various silicon types for solar cell applications are cost and conversion efficiency.

Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).

PubMed

Kowall, Bernd; Rathmann, Wolfgang; Giani, Guido; Schipf, Sabine; Baumeister, Sebastian; Wallaschofski, Henri; Nauck, Matthias; Völzke, Henry

2013-04-01

Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction. The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared. A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c). Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Do the Big Five personality traits predict individual differences in the left cheek bias for emotion perception?

PubMed

Galea, Samantha; Lindell, Annukka K

2016-01-01

Like language, emotion is a lateralized function. Because the right hemisphere typically dominates emotion processing, people express stronger emotion on the left side of their face. This prompts a left cheek bias: we offer the left cheek to express emotion and rate left cheek portraits more emotionally expressive than right cheek portraits. Though the majority of the population show this left cheek bias (60-70%), individual differences exist but remain largely unexplained. Given that people with higher self-rated emotional expressivity show a stronger left cheek bias, personality variables associated with increased emotional expressivity and emotional intelligence, such as extraversion and openness, may help account for individual differences. The present study thus examined whether the Big Five traits predict left cheek preferences. Participants (M = 58, F = 116) completed the NEO-Five Factor Personality Inventory (NEO-FFI) [Costa, P. T. J., & McCrae, R. R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources] and viewed pairs of left and right cheek images (half mirror-reversed); participants made forced-choice decisions, indicating which image in each pair looked happier. Hierarchical regression indicated that neither trait extraversion nor openness predicted left cheek selections, with NEO-FFI personality subscales accounting for negligible variance in preferences. As the Big Five traits have been discounted, exploration of other potential contributors to individual differences in the left cheek bias is clearly needed.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

PubMed

de Oliveira, Saulo H P; Law, Eleanor C; Shi, Jiye; Deane, Charlotte M

2018-04-01

Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. saulo.deoliveira@dtc.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Swimming efficiency of bacterium Escherichia coli

PubMed Central

Chattopadhyay, Suddhashil; Moldovan, Radu; Yeung, Chuck; Wu, X. L.

2006-01-01

We use measurements of swimming bacteria in an optical trap to determine fundamental properties of bacterial propulsion. In particular, we directly measure the force required to hold the bacterium in the optical trap and determine the propulsion matrix, which relates the translational and angular velocity of the flagellum to the torques and forces propelling the bacterium. From the propulsion matrix, dynamical properties such as torques, swimming speed, and power can be obtained by measuring the angular velocity of the motor. We find significant heterogeneities among different individuals even though all bacteria started from a single colony. The propulsive efficiency, defined as the ratio of the propulsive power output to the rotary power input provided by the motors, is found to be ≈2%, which is consistent with the efficiency predicted theoretically for a rigid helical coil. PMID:16954194

Nonuniversal star formation efficiency in turbulent ISM

DOE PAGES

Semenov, Vadim A.; Kravtsov, Andrey V.; Gnedin, Nickolay Y.

2016-07-29

Here, we present a study of a star formation prescription in which star formation efficiency depends on local gas density and turbulent velocity dispersion, as suggested by direct simulations of SF in turbulent giant molecular clouds (GMCs). We test the model using a simulation of an isolated Milky Way-sized galaxy with a self-consistent treatment of turbulence on unresolved scales. We show that this prescription predicts a wide variation of local star formation efficiency per free-fall time,more » $$\\epsilon_{\\rm ff} \\sim 0.1 - 10\\%$$, and gas depletion time, $$t_{\\rm dep} \\sim 0.1 - 10$$ Gyr. In addition, it predicts an effective density threshold for star formation due to suppression of $$\\epsilon_{\\rm ff}$$ in warm diffuse gas stabilized by thermal pressure. We show that the model predicts star formation rates in agreement with observations from the scales of individual star-forming regions to the kiloparsec scales. This agreement is non-trivial, as the model was not tuned in any way and the predicted star formation rates on all scales are determined by the distribution of the GMC-scale densities and turbulent velocities $$\\sigma$$ in the cold gas within the galaxy, which is shaped by galactic dynamics. The broad agreement of the star formation prescription calibrated in the GMC-scale simulations with observations, both gives credence to such simulations and promises to put star formation modeling in galaxy formation simulations on a much firmer theoretical footing.« less

Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk.

PubMed

Yarnitsky, David; Crispel, Yonathan; Eisenberg, Elon; Granovsky, Yelena; Ben-Nun, Alon; Sprecher, Elliot; Best, Lael-Anson; Granot, Michal

2008-08-15

Surgical and medical procedures, mainly those associated with nerve injuries, may lead to chronic persistent pain. Currently, one cannot predict which patients undergoing such procedures are 'at risk' to develop chronic pain. We hypothesized that the endogenous analgesia system is key to determining the pattern of handling noxious events, and therefore testing diffuse noxious inhibitory control (DNIC) will predict susceptibility to develop chronic post-thoracotomy pain (CPTP). Pre-operative psychophysical tests, including DNIC assessment (pain reduction during exposure to another noxious stimulus at remote body area), were conducted in 62 patients, who were followed 29.0+/-16.9 weeks after thoracotomy. Logistic regression revealed that pre-operatively assessed DNIC efficiency and acute post-operative pain intensity were two independent predictors for CPTP. Efficient DNIC predicted lower risk of CPTP, with OR 0.52 (0.33-0.77 95% CI, p=0.0024), i.e., a 10-point numerical pain scale (NPS) reduction halves the chance to develop chronic pain. Higher acute pain intensity indicated OR of 1.80 (1.28-2.77, p=0.0024) predicting nearly a double chance to develop chronic pain for each 10-point increase. The other psychophysical measures, pain thresholds and supra-threshold pain magnitudes, did not predict CPTP. For prediction of acute post-operative pain intensity, DNIC efficiency was not found significant. Effectiveness of the endogenous analgesia system obtained at a pain-free state, therefore, seems to reflect the individual's ability to tackle noxious events, identifying patients 'at risk' to develop post-intervention chronic pain. Applying this diagnostic approach before procedures that might generate pain may allow individually tailored pain prevention and management, which may substantially reduce suffering.

Efficient Reduction and Analysis of Model Predictive Error

NASA Astrophysics Data System (ADS)

Doherty, J.

2006-12-01

Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often

Individual differences in maternal response to immune challenge predict offspring behavior: Contribution of environmental factors

PubMed Central

Bronson, Stefanie L.; Ahlbrand, Rebecca; Horn, Paul S.; Kern, Joseph R.; Richtand, Neil M.

2011-01-01

Maternal infection during pregnancy elevates risk for schizophrenia and related disorders in offspring. Converging evidence suggests the maternal inflammatory response mediates the interaction between maternal infection, altered brain development, and behavioral outcome. The extent to which individual differences in the maternal response to immune challenge influence the development of these abnormalities is unknown. The present study investigated the impact of individual differences in maternal response to the viral mimic polyinosinic:polycytidylic acid (poly I:C) on offspring behavior. We observed significant variability in body weight alterations of pregnant rats induced by administration of poly I:C on gestational day 14. Furthermore, the presence or absence of maternal weight loss predicted MK-801 and amphetamine stimulated locomotor abnormalities in offspring. MK-801 stimulated locomotion was altered in offspring of all poly I:C treated dams; however, the presence or absence of maternal weight loss resulted in decreased and modestly increased locomotion, respectively. Adult offspring of poly I:C treated dams that lost weight exhibited significantly decreased amphetamine stimulated locomotion, while offspring of poly I:C treated dams without weight loss performed similarly to vehicle controls. Social isolation and increased maternal age predicted weight loss in response to poly I:C but not vehicle injection. In combination, these data identify environmental factors associated with the maternal response to immune challenge and functional outcome of offspring exposed to maternal immune activation. PMID:21255612

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod-statement.org. © 2015 American College of Physicians.

Changes of glucose utilization by erythrocytes, lactic acid concentration in the serum and blood cells, and haematocrit value during one hour rest after maximal effort in individuals differing in physical efficiency.

PubMed

Tomasik, M

1982-01-01

Glucose utilization by the erythrocytes, lactic acid concentration in the blood and erythrocytes, and haematocrit value were determined before exercise and during one hour rest following maximal exercise in 97 individuals of either sex differing in physical efficiency. In the investigations reported by the author individuals with strikingly high physical fitness performed maximal work one-third greater than that performed by individuals with medium fitness. The serum concentration of lactic acid was in all individuals above the resting value still after 60 minutes of rest. On the other hand, this concentration returned to the normal level in the erythrocytes but only in individuals with strikingly high efficiency. Glucose utilization by the erythrocytes during the restitution period was highest immediately after the exercise in all studied individuals and showed a tendency for more rapid return to resting values again in individuals with highest efficiency. The investigation of very efficient individuals repeated twice demonstrated greater utilization of glucose by the erythrocytes at the time of greater maximal exercise. This was associated with greater lactic acid concentration in the serum and erythrocytes throughout the whole one-hour rest period. The observed facts suggest an active participation of erythrocytes in the process of adaptation of the organism to exercise.

Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins.

PubMed

Mallika, V; Sivakumar, K C; Jaichand, S; Soniya, E V

2010-07-13

Type III Polyketide synthases (PKS) are family of proteins considered to have significant roles in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins shows positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machines (SVMs). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. The PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-20

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement

PubMed Central

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-01-01

Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). PMID:25562432

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Royal College of Obstetricians and Gynaecologists.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-06

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, G S; Reitsma, J B; Altman, D G; Moons, K G M

2015-02-01

Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study, regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). © 2015 Joint copyright. The Authors and Annals of Internal Medicine. Diabetic Medicine published by John Wiley Ltd. on behalf of Diabetes UK.

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-02-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Copyright © 2015 Elsevier Inc. All rights reserved.

Development of an Itemwise Efficiency Scoring Method: Concurrent, Convergent, Discriminant, and Neuroimaging-Based Predictive Validity Assessed in a Large Community Sample

PubMed Central

Moore, Tyler M.; Reise, Steven P.; Roalf, David R.; Satterthwaite, Theodore D.; Davatzikos, Christos; Bilker, Warren B.; Port, Allison M.; Jackson, Chad T.; Ruparel, Kosha; Savitt, Adam P.; Baron, Robert B.; Gur, Raquel E.; Gur, Ruben C.

2016-01-01

Traditional “paper-and-pencil” testing is imprecise in measuring speed and hence limited in assessing performance efficiency, but computerized testing permits precision in measuring itemwise response time. We present a method of scoring performance efficiency (combining information from accuracy and speed) at the item level. Using a community sample of 9,498 youths age 8-21, we calculated item-level efficiency scores on four neurocognitive tests, and compared the concurrent, convergent, discriminant, and predictive validity of these scores to simple averaging of standardized speed and accuracy-summed scores. Concurrent validity was measured by the scores' abilities to distinguish men from women and their correlations with age; convergent and discriminant validity were measured by correlations with other scores inside and outside of their neurocognitive domains; predictive validity was measured by correlations with brain volume in regions associated with the specific neurocognitive abilities. Results provide support for the ability of itemwise efficiency scoring to detect signals as strong as those detected by standard efficiency scoring methods. We find no evidence of superior validity of the itemwise scores over traditional scores, but point out several advantages of the former. The itemwise efficiency scoring method shows promise as an alternative to standard efficiency scoring methods, with overall moderate support from tests of four different types of validity. This method allows the use of existing item analysis methods and provides the convenient ability to adjust the overall emphasis of accuracy versus speed in the efficiency score, thus adjusting the scoring to the real-world demands the test is aiming to fulfill. PMID:26866796

Predicting body composition using foot-to-foot bioelectrical impedance analysis in healthy Asian individuals.

PubMed

Wu, Chun-Shien; Chen, Yu-Yawn; Chuang, Chih-Lin; Chiang, Li-Ming; Dwyer, Gregory B; Hsu, Ying-Lin; Huang, Ai-Chun; Lai, Chung-Liang; Hsieh, Kuen-Chang

2015-05-19

The objectives of this study were to develop a regression model for predicting fat-free mass (FFM) in a population of healthy Taiwanese individuals using standing foot-to-foot bioelectrical impedance analysis (BIA) and to test the model's performance in predicting FFM with different body fat percentages (BF%). We used dual-energy X-ray absorptiometry (DXA) to measure the FFM of 554 healthy Asian subjects (age, 16-75 y; body mass index, 15.8-43.1 kg/m(2)). We also evaluated the validity of the developed multivariate model using a double cross-validation technique and assessed the accuracy of the model in an all-subjects sample and subgroup samples with different body fat levels. Predictors in the all-subjects multivariate model included height(2)/impedance, weight, year, and sex (FFM = 13.055 + 0.204 weight + 0.394 height(2)/Impedance - 0.136 age + 8.125 sex (sex: Female = 0, Male = 1), r(2) = 0.92, standard error of the estimate = 3.17 kg). The correlation coefficients between predictive FFM by BIA (FFMBIA) and DXA-measured FFM (FFMDXA) in female subjects with a total-subjects BF%DXA of <20 %, 20 %-30 %, 30 %-40 % and >40 % were r = 0.87, 0.90, 0.91, 0.89, and 0.94, respectively, with bias ± 2SD of 0.0 ± 3.0 kg, -2.6 ± 1.7 kg, -1.5 ± 2.8 kg, 0.5 ± 2.7 kg, and 2.0 ± 2.9 kg, respectively. The correlation coefficients between FFMBIA and FFMDXA in male subjects with a total-subjects BF%DXA of <10 %, 10 %-20 %, 20 %-30 %, and >30 % were r = 0.89, 0.89, 0.90, 0.93, and 0.91, respectively, with bias ± 2SD of 0.0 ± 3.2 kg, -2.3 ± 2.5 kg, -0.5 ± 3.2 kg, 0.4 ± 3.1 kg, and 2.1 ± 3.2 kg, respectively. The standing foot-to-foot BIA method developed in this study can accurately predict FFM in healthy Asian individuals with different levels of body fat.

[Studies of marker screening efficiency and corresponding influencing factors in QTL composite interval mapping].

PubMed

Gao, Yong-Ming; Wan, Ping

2002-06-01

Screening markers efficiently is the foundation of mapping QTLs by composite interval mapping. Main and interaction markers distinguished, besides using background control for genetic variation, could also be used to construct intervals of two-way searching for mapping QTLs with epistasis, which can save a lot of calculation time. Therefore, the efficiency of marker screening would affect power and precision of QTL mapping. A doubled haploid population with 200 individuals and 5 chromosomes was constructed, with 50 markers evenly distributed at 10 cM space. Among a total of 6 QTLs, one was placed on chromosome I, two linked on chromosome II, and the other three linked on chromosome IV. QTL setting included additive effects and epistatic effects of additive x additive, the corresponding QTL interaction effects were set if data were collected under multiple environments. The heritability was assumed to be 0.5 if no special declaration. The power of marker screening by stepwise regression, forward regression, and three methods for random effect prediction, e.g. best linear unbiased prediction (BLUP), linear unbiased prediction (LUP) and adjusted unbiased prediction (AUP), was studied and compared through 100 Monte Carlo simulations. The results indicated that the marker screening power by stepwise regression at 0.1, 0.05 and 0.01 significant level changed from 2% to 68%, the power changed from 2% to 72% by forward regression. The larger the QTL effects, the higher the marker screening power. While the power of marker screening by three random effect prediction was very low, the maximum was only 13%. That suggested that regression methods were much better than those by using the approaches of random effect prediction to identify efficient markers flanking QTLs, and forward selection method was more simple and efficient. The results of simulation study on heritability showed that heightening of both general heritability and interaction heritability of genotype x

Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks

NASA Astrophysics Data System (ADS)

Maddah, Heydar; Ghasemi, Nahid

2017-12-01

In this study, heat transfer efficiency of water and iron oxide nanofluid in a double pipe heat exchanger equipped with a typical twisted tape is experimentally investigated and impacts of the concentration of nanofluid and twisted tape on the heat transfer efficiency are also studied. Experiments were conducted under the laminar and turbulent flow for Reynolds numbers in the range of 1000 to 6000 and the concentration of nanofluid was 0.01, 0.02 and 0.03 wt%. In order to model and predict the heat transfer efficiency, an artificial neural network was used. The temperature of the hot fluid (nanofluid), the temperature of the cold fluid (water), mass flow rate of hot fluid (nanofluid), mass flow rate of cold fluid (water), the concentration of nanofluid and twist ratio are input data in artificial neural network and heat transfer is output or target. Heat transfer efficiency in the presence of 0.03 wt% nanofluid increases by 30% while using both the 0.03 wt% nanofluid and twisted tape with twist ratio 2 increases the heat transfer efficiency by 60%. Implementation of various structures of neural network with different number of neurons in the middle layer showed that 1-10-6 arrangement with the correlation coefficient 0.99181 and normal root mean square error 0.001621 is suggested as a desirable arrangement. The above structure has been successful in predicting 72% to 97%of variation in heat transfer efficiency characteristics based on the independent variables changes. In total, comparing the predicted results in this study with other studies and also the statistical measures shows the efficiency of artificial neural network.

Role of Microalbuminuria in Predicting Cardiovascular Mortality in Individuals With Subclinical Hypothyroidism.

PubMed

Tuliani, Tushar A; Shenoy, Maithili; Belgrave, Kevin; Deshmukh, Abhishek; Pant, Sadip; Hilliard, Anthony; Afonso, Luis

2017-09-01

Studies suggest that subclinical hypothyroidism (SCH) is related to cardiovascular mortality (CVM). We explored the role of microalbuminuria (MIA) as a predictor of long-term CVM in population with and without SCH with normal kidney function. We examined the National Health and Nutrition Education Survey - III database (n = 6,812). Individuals younger than 40 years, thyroid-stimulating hormone levels ≥20 and ≤0.35mIU/L, estimated glomerular filtration rate <60mL/minute/1.73m 2 and urine albumin-to-creatinine ratio of >250mg/g in men and >355mg/g in women were excluded. SCH was defined as thyroid-stimulating hormone levels between 5 and 19.99mIU/L and serum T4 levels between 5 and 12µg/dL. MIA was defined as urine albumin-to-creatinine ratio of 17-250mg/g in men and 25-355mg/g in women. Patients were categorized into the following 4 groups: (1) no SCH or MIA, (2) MIA, but no SCH, (3) SCH, but no MIA and (4) both SCH and MIA. Prevalence of MIA in the subclinical hypothyroid cohort was 21% compared to 16.4% in those without SCH (P = 0.03). SCH was a significant independent predictor of MIA (n = 6,812), after adjusting for traditional risk factors (unadjusted odds ratio = 1.75; 95% CI: 1.24-2.48; P = 0.002 and adjusted odds ratio = 1.83; 95% CI: 1.2-2.79; P = 0.006). MIA was a significant independent predictor of long-term all-cause (adjusted hazard ratio = 1.7, 95% CI: 1.24-2.33) and CVM (adjusted hazard ratio = 1.72, 95% CI: 1.07-2.76) in subclinical hypothyroid individuals. In a cohort of subclinical hypothyroid individuals, the presence of MIA predicts increased risk of CVM as compared to nonmicroalbuminurics with SCH. Further randomized trials are needed to assess the benefits of treating microalbuminuric subclinical hypothyroid individuals and impact on CVM. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Using impairment and cognitions to predict walking in osteoarthritis: A series of n-of-1 studies with an individually tailored, data-driven intervention.

PubMed

O'Brien, Nicola; Philpott-Morgan, Siôn; Dixon, Diane

2016-02-01

First, this study compares the ability of an integrated model of activity and activity limitations, the International Classification of Functioning, Disability and Health (ICF), and the Theory of Planned Behaviour (TPB) to predict walking within individuals with osteoarthritis. Second, the effectiveness of a walking intervention in these individuals is determined. A series of n-of-1 studies with an AB intervention design was used. Diary methods were used to study four community-dwelling individuals with lower-limb osteoarthritis. Data on impairment symptoms (pain, pain on movement, and joint stiffness), cognitions (intention, self-efficacy, and perceived controllability), and walking (step count) were collected twice daily for 12 weeks. At 6 weeks, an individually tailored, data-driven walking intervention using action planning or a control cognition manipulation was delivered. Simulation modelling analysis examined cross-correlations and differences in baseline and intervention phase means. Post-hoc mediation analyses examined theoretical relationships and multiple regression analyses compared theoretical models. Cognitions, intention in particular, were better and more consistent within individual predictors of walking than impairment. The walking intervention did not increase walking in any of the three participants receiving it. The integrated model and the TPB, which recognize a predictive role for cognitions, were significant predictors of walking variance in all participants, whilst the biomedical ICF model was only predictive for one participant. Despite the lack of evidence for an individually tailored walking intervention, predictive data suggest that interventions for people with osteoarthritis that address cognitions are likely to be more effective than those that address impairment only. Further within-individual investigation, including testing mediational relationships, is warranted. What is already known on this subject? N-of-1 methods have been

Intrinsic spontaneous brain activity predicts individual variability in associative memory in older adults.

PubMed

Zheng, Zhiwei; Li, Rui; Xiao, Fengqiu; He, Rongqiao; Zhang, Shouzi; Li, Juan

2018-06-01

Older adults demonstrate notable individual differences in associative memory. Here, resting-state functional magnetic resonance imaging (rsfMRI) was used to investigate whether intrinsic brain activity at rest could predict individual differences in associative memory among cognitively healthy older adults. Regional amplitude of low-frequency fluctuations (ALFF) analysis and a correlation-based resting-state functional connectivity (RSFC) approach were used to analyze data acquired from 102 cognitively normal elderly who completed the paired-associative learning test (PALT) and underwent fMRI scans. Participants were divided into two groups based on the retrospective self-reports on whether or not they utilized encoding strategies during the PALT. The behavioral results revealed better associative memory performance in the participants who reported utilizing memory strategies compared with participants who reported not doing so. The fMRI results showed that higher associative memory performance was associated with greater functional connectivity between the right superior frontal gyrus and the right posterior cerebellum lobe in the strategy group. The regional ALFF values in the right superior frontal gyrus were linked to associative memory performance in the no-strategy group. These findings suggest that the regional spontaneous fluctuations and functional connectivity during rest may subserve the individual differences in the associative memory in older adults, and that this is modulated by self-initiated memory strategy use. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

The Importance of Encoding-Related Neural Dynamics in the Prediction of Inter-Individual Differences in Verbal Working Memory Performance

PubMed Central

Majerus, Steve; Salmon, Eric; Attout, Lucie

2013-01-01

Studies of brain-behaviour interactions in the field of working memory (WM) have associated WM success with activation of a fronto-parietal network during the maintenance stage, and this mainly for visuo-spatial WM. Using an inter-individual differences approach, we demonstrate here the equal importance of neural dynamics during the encoding stage, and this in the context of verbal WM tasks which are characterized by encoding phases of long duration and sustained attentional demands. Participants encoded and maintained 5-word lists, half of them containing an unexpected word intended to disturb WM encoding and associated task-related attention processes. We observed that inter-individual differences in WM performance for lists containing disturbing stimuli were related to activation levels in a region previously associated with task-related attentional processing, the left intraparietal sulcus (IPS), and this during stimulus encoding but not maintenance; functional connectivity strength between the left IPS and lateral prefrontal cortex (PFC) further predicted WM performance. This study highlights the critical role, during WM encoding, of neural substrates involved in task-related attentional processes for predicting inter-individual differences in verbal WM performance, and, more generally, provides support for attention-based models of WM. PMID:23874935

Inhibition and Updating, but Not Switching, Predict Developmental Dyslexia and Individual Variation in Reading Ability

PubMed Central

Doyle, Caoilainn; Smeaton, Alan F.; Roche, Richard A. P.; Boran, Lorraine

2018-01-01

To elucidate the core executive function profile (strengths and weaknesses in inhibition, updating, and switching) associated with dyslexia, this study explored executive function in 27 children with dyslexia and 29 age matched controls using sensitive z-mean measures of each ability and controlled for individual differences in processing speed. This study found that developmental dyslexia is associated with inhibition and updating, but not switching impairments, at the error z-mean composite level, whilst controlling for processing speed. Inhibition and updating (but not switching) error composites predicted both dyslexia likelihood and reading ability across the full range of variation from typical to atypical. The predictive relationships were such that those with poorer performance on inhibition and updating measures were significantly more likely to have a diagnosis of developmental dyslexia and also demonstrate poorer reading ability. These findings suggest that inhibition and updating abilities are associated with developmental dyslexia and predict reading ability. Future studies should explore executive function training as an intervention for children with dyslexia as core executive functions appear to be modifiable with training and may transfer to improved reading ability. PMID:29892245

Interrelationships between trait anxiety, situational stress and mental effort predict phonological processing efficiency, but not effectiveness.

PubMed

Edwards, Elizabeth J; Edwards, Mark S; Lyvers, Michael

2016-08-01

Attentional control theory (ACT) describes the mechanisms associated with the relationship between anxiety and cognitive performance. We investigated the relationship between cognitive trait anxiety, situational stress and mental effort on phonological performance using a simple (forward-) and complex (backward-) word span task. Ninety undergraduate students participated in the study. Predictor variables were cognitive trait anxiety, indexed using questionnaire scores; situational stress, manipulated using ego threat instructions; and perceived level of mental effort, measured using a visual analogue scale. Criterion variables (a) performance effectiveness (accuracy) and (b) processing efficiency (accuracy divided by response time) were analyzed in separate multiple moderated-regression analyses. The results revealed (a) no relationship between the predictors and performance effectiveness, and (b) a significant 3-way interaction on processing efficiency for both the simple and complex tasks, such that at higher effort, trait anxiety and situational stress did not predict processing efficiency, whereas at lower effort, higher trait anxiety was associated with lower efficiency at high situational stress, but not at low situational stress. Our results were in full support of the assumptions of ACT and implications for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

NASA Astrophysics Data System (ADS)

Magnuson, Brian

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The

Efficient biprediction decision scheme for fast high efficiency video coding encoding

NASA Astrophysics Data System (ADS)

Park, Sang-hyo; Lee, Seung-ho; Jang, Euee S.; Jun, Dongsan; Kang, Jung-Won

2016-11-01

An efficient biprediction decision scheme of high efficiency video coding (HEVC) is proposed for fast-encoding applications. For low-delay video applications, bidirectional prediction can be used to increase compression performance efficiently with previous reference frames. However, at the same time, the computational complexity of the HEVC encoder is significantly increased due to the additional biprediction search. Although a some research has attempted to reduce this complexity, whether the prediction is strongly related to both motion complexity and prediction modes in a coding unit has not yet been investigated. A method that avoids most compression-inefficient search points is proposed so that the computational complexity of the motion estimation process can be dramatically decreased. To determine if biprediction is critical, the proposed method exploits the stochastic correlation of the context of prediction units (PUs): the direction of a PU and the accuracy of a motion vector. Through experimental results, the proposed method showed that the time complexity of biprediction can be reduced to 30% on average, outperforming existing methods in view of encoding time, number of function calls, and memory access.

Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment.

PubMed

James, Katherine A; Meliker, Jaymie R; Buttenfield, Barbara E; Byers, Tim; Zerbe, Gary O; Hokanson, John E; Marshall, Julie A

2014-08-01

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83-0.92 for samples collected from the same well 15-25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.

Emotion, working memory task demands and individual differences predict behavior, cognitive effort and negative affect.

PubMed

Storbeck, Justin; Davidson, Nicole A; Dahl, Chelsea F; Blass, Sara; Yung, Edwin

2015-01-01

We examined whether positive and negative affect motivates verbal and spatial working memory processes, respectively, which have implications for the expenditure of mental effort. We argue that when emotion promotes cognitive tendencies that are goal incompatible with task demands, greater cognitive effort is required to perform well. We sought to investigate whether this increase in cognitive effort impairs behavioural control over a broad domain of self-control tasks. Moreover, we predicted that individuals with higher behavioural inhibition system (BIS) sensitivities would report more negative affect within the goal incompatible conditions because such individuals report higher negative affect during cognitive challenge. Positive or negative affective states were induced followed by completing a verbal or spatial 2-back working memory task. All participants then completed one of three self-control tasks. Overall, we observed that conditions of emotion and working memory incompatibility (positive/spatial and negative/verbal) performed worse on the self-control tasks, and within the incompatible conditions individuals with higher BIS sensitivities reported more negative affect at the end of the study. The combination of findings suggests that emotion and working memory compatibility reduces cognitive effort and impairs behavioural control.

Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

PubMed

Chen, Yu-Jen; Liu, Chih-Min; Hsu, Yung-Chin; Lo, Yu-Chun; Hwang, Tzung-Jeng; Hwu, Hai-Gwo; Lin, Yi-Tin; Tseng, Wen-Yih Isaac

2018-01-01

A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Individual, Family, and School Factors Indicative of Caring: Predicting High School Graduation for NLTS2 Students with ED

ERIC Educational Resources Information Center

Morris, Thomas O.

2017-01-01

This study examined data from the National Longitudinal Transition Study 2 (NLTS2) to determine if factors indicative of caring were predictive of high school graduation for students with ED. Selection of NLTS2 variables was guided by resiliency theory, which implies that an individual can successfully adapt to factors that threaten his or her…

Oxygen uptake efficiency slope and peak oxygen consumption predict prognosis in children with tetralogy of Fallot.

PubMed

Tsai, Yun-Jeng; Li, Min-Hui; Tsai, Wan-Jung; Tuan, Sheng-Hui; Liao, Tin-Yun; Lin, Ko-Long

2016-07-01

Oxygen uptake efficiency slope (OUES) and peak oxygen consumption (VO2peak) are exercise parameters that can predict cardiac morbidity in patients with numerous heart diseases. But the predictive value in patients with tetralogy of Fallot is still undetermined, especially in children. We evaluated the prognostic value of OUES and VO2peak in children with total repair of tetralogy of Fallot. Retrospective cohort study. Forty tetralogy of Fallot patients younger than 12 years old were recruited. They underwent a cardiopulmonary exercise test during the follow-up period after total repair surgery. The results of the cardiopulmonary exercise test were used to predict the cardiac related hospitalization in the following two years after the test. OUES normalized by body surface area (OUES/BSA) and the percentage of predicted VO2peak appeared to be predictive for two-year cardiac related hospitalization. Receiver operating characteristic curve analysis demonstrated that the best threshold value for OUES/BSA was 1.029 (area under the curve = 0.70, p = 0.03), and for VO2peak was 74% of age prediction (area under the curve = 0.72, p = 0.02). The aforementioned findings were confirmed by Kaplan-Meier plots and log-rank test. OUES/BSA and VO2peak are useful predictors of cardiac-related hospitalization in children with total repair of tetralogy of Fallot. © The European Society of Cardiology 2015.

Statistical procedures for evaluating daily and monthly hydrologic model predictions

USGS Publications Warehouse

Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

2004-01-01

The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

Cognitive control predicted by color vision, and vice versa.

PubMed

Colzato, Lorenza S; Sellaro, Roberta; Hulka, Lea M; Quednow, Boris B; Hommel, Bernhard

2014-09-01

One of the most important functions of cognitive control is to continuously adapt cognitive processes to changing and often conflicting demands of the environment. Dopamine (DA) has been suggested to play a key role in the signaling and resolution of such response conflict. Given that DA is found in high concentration in the retina, color vision discrimination has been suggested as an index of DA functioning and in particular blue-yellow color vision impairment (CVI) has been used to indicate a central hypodopaminergic state. We used color discrimination (indexed by the total color distance score; TCDS) to predict individual differences in the cognitive control of response conflict, as reflected by conflict-resolution efficiency in an auditory Simon task. As expected, participants showing better color discrimination were more efficient in resolving response conflict. Interestingly, participants showing a blue-yellow CVI were associated with less efficiency in handling response conflict. Our findings indicate that color vision discrimination might represent a promising predictor of cognitive controlability in healthy individuals. Copyright © 2014 Elsevier Ltd. All rights reserved.

Individual differences in learning predict the return of fear.

PubMed

Gershman, Samuel J; Hartley, Catherine A

2015-09-01

Using a laboratory analogue of learned fear (Pavlovian fear conditioning), we show that there is substantial heterogeneity across individuals in spontaneous recovery of fear following extinction training. We propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning. Whereas some individuals tend to form a new memory during extinction, leaving their fear memory intact, others update the original threat association with new safety information, effectively unlearning the fear memory. We formalize this account in a computational model of fear learning and show that individuals who, according to the model, are more likely to form new extinction memories tend to show greater spontaneous recovery compared to individuals who appear to only update a single memory. This qualitative variation in fear and extinction learning may have important implications for understanding vulnerability and resilience to fear-related psychiatric disorders.

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

NASA Astrophysics Data System (ADS)

He, Ling-Yun; Wen, Xing-Chun

2015-12-01

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

Numerical prediction of Pelton turbine efficiency

NASA Astrophysics Data System (ADS)

Jošt, D.; Mežnar, P.; Lipej, A.

2010-08-01

This paper presents a numerical analysis of flow in a 2 jet Pelton turbine with horizontal axis. The analysis was done for the model at several operating points in different operating regimes. The results were compared to the results of a test of the model. Analysis was performed using ANSYS CFX-12.1 computer code. A k-ω SST turbulent model was used. Free surface flow was modelled by two-phase homogeneous model. At first, a steady state analysis of flow in the distributor with two injectors was performed for several needle strokes. This provided us with data on flow energy losses in the distributor and the shape and velocity of jets. The second step was an unsteady analysis of the runner with jets. Torque on the shaft was then calculated from pressure distribution data. Averaged torque values are smaller than measured ones. Consequently, calculated turbine efficiency is also smaller than the measured values, the difference is about 4 %. The shape of the efficiency diagram conforms well to the measurements.

Forecasting Individual Headache Attacks Using Perceived Stress: Development of a Multivariable Prediction Model for Persons With Episodic Migraine.

PubMed

Houle, Timothy T; Turner, Dana P; Golding, Adrienne N; Porter, John A H; Martin, Vincent T; Penzien, Donald B; Tegeler, Charles H

2017-07-01

To develop and validate a prediction model that forecasts future migraine attacks for an individual headache sufferer. Many headache patients and physicians believe that precipitants of headache can be identified and avoided or managed to reduce the frequency of headache attacks. Of the numerous candidate triggers, perceived stress has received considerable attention for its association with the onset of headache in episodic and chronic headache sufferers. However, no evidence is available to support forecasting headache attacks within individuals using any of the candidate headache triggers. This longitudinal cohort with forecasting model development study enrolled 100 participants with episodic migraine with or without aura, and N = 95 contributed 4626 days of electronic diary data and were included in the analysis. Individual headache forecasts were derived from current headache state and current levels of stress using several aspects of the Daily Stress Inventory, a measure of daily hassles that is completed at the end of each day. The primary outcome measure was the presence/absence of any headache attack (head pain > 0 on a numerical rating scale of 0-10) over the next 24 h period. After removing missing data (n = 431 days), participants in the study experienced a headache attack on 1613/4195 (38.5%) days. A generalized linear mixed-effects forecast model using either the frequency of stressful events or the perceived intensity of these events fit the data well. This simple forecasting model possessed promising predictive utility with an AUC of 0.73 (95% CI 0.71-0.75) in the training sample and an AUC of 0.65 (95% CI 0.6-0.67) in a leave-one-out validation sample. This forecasting model had a Brier score of 0.202 and possessed good calibration between forecasted probabilities and observed frequencies but had only low levels of resolution (ie, sharpness). This study demonstrates that future headache attacks can be forecasted for a diverse group of

Using individual patient anatomy to predict protocol compliance for prostate intensity-modulated radiotherapy

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

Caine, Hannah; Whalley, Deborah; Kneebone, Andrew

If a prostate intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) plan has protocol violations, it is often a challenge knowing whether this is due to unfavorable anatomy or suboptimal planning. This study aimed to create a model to predict protocol violations based on patient anatomical variables and their potential relationship to target and organ at risk (OAR) end points in the setting of definitive, dose-escalated IMRT/VMAT prostate planning. Radiotherapy plans from 200 consecutive patients treated with definitive radiation for prostate cancer using IMRT or VMAT were analyzed. The first 100 patient plans (hypothesis-generating cohort) were examined to identifymore » anatomical variables that predict for dosimetric outcome, in particular OAR end points. Variables that scored significance were further assessed for their ability to predict protocol violations using a Classification and Regression Tree (CART) analysis. These results were then validated in a second group of 100 patients (validation cohort). In the initial analysis of the hypothesis-generating cohort, percentage of rectum overlap in the planning target volume (PTV) (%OR) and percentage of bladder overlap in the PTV (%OB) were highlighted as significant predictors of rectal and bladder dosimetry. Lymph node treatment was also significant for bladder outcomes. For the validation cohort, CART analysis showed that %OR of < 6%, 6% to 9% and > 9% predicted a 13%, 63%, and 100% rate of rectal protocol violations respectively. For the bladder, %OB of < 9% vs > 9% is associated with 13% vs 88% rate of bladder constraint violations when lymph nodes were not treated. If nodal irradiation was delivered, plans with a %OB of < 9% had a 59% risk of violations. Percentage of rectum and bladder within the PTV can be used to identify individual plan potential to achieve dose-volume histogram (DVH) constraints. A model based on these factors could be used to reduce planning time

[Prediction of the efficiency of endoscopic lung volume reduction by valves in severe emphysema].

PubMed

Bocquillon, V; Briault, A; Reymond, E; Arbib, F; Jankowski, A; Ferretti, G; Pison, C

2016-11-01

In severe emphysema, endoscopic lung volume reduction with valves is an alternative to surgery with less morbidity and mortality. In 2015, selection of patients who will respond to this technique is based on emphysema heterogeneity, a complete fissure visible on the CT-scan and absence of collateral ventilation between lobes. Our case report highlights that individualized prediction is possible. A 58-year-old woman had severe, disabling pulmonary emphysema. A high resolution thoracic computed tomography scan showed that the emphysema was heterogeneous, predominantly in the upper lobes, integrity of the left greater fissure and no collateral ventilation with the left lower lobe. A valve was inserted in the left upper lobe bronchus. At one year, clinical and functional benefits were significant with complete atelectasis of the treated lobe. The success of endoscopic lung volume reduction with a valve can be predicted, an example of personalized medicine. Copyright © 2016 SPLF. Published by Elsevier Masson SAS. All rights reserved.

Prediction of residual feed intake for first-lactation dairy cows using orthogonal polynomial random regression.

PubMed

Manafiazar, G; McFadden, T; Goonewardene, L; Okine, E; Basarab, J; Li, P; Wang, Z

2013-01-01

Residual Feed Intake (RFI) is a measure of energy efficiency. Developing an appropriate model to predict expected energy intake while accounting for multifunctional energy requirements of metabolic body weight (MBW), empty body weight (EBW), milk production energy requirements (MPER), and their nonlinear lactation profiles, is the key to successful prediction of RFI in dairy cattle. Individual daily actual energy intake and monthly body weight of 281 first-lactation dairy cows from 1 to 305 d in milk were recorded at the Dairy Research and Technology Centre of the University of Alberta (Edmonton, AB, Canada); individual monthly milk yield and compositions were obtained from the Dairy Herd Improvement Program. Combinations of different orders (1-5) of fixed (F) and random (R) factors were fitted using Legendre polynomial regression to model the nonlinear lactation profiles of MBW, EBW, and MPER over 301 d. The F5R3, F5R3, and F5R2 (subscripts indicate the order fitted) models were selected, based on the combination of the log-likelihood ratio test and the Bayesian information criterion, as the best prediction equations for MBW, EBW, and MPER, respectively. The selected models were used to predict daily individual values for these traits. To consider the body reserve changes, the differences of predicted EBW between 2 consecutive days were considered as the EBW change between these days. The smoothed total 301-d actual energy intake was then linearly regressed on the total 301-d predicted traits of MBW, EBW change, and MPER to obtain the first-lactation RFI (coefficient of determination=0.68). The mean of predicted daily average lactation RFI was 0 and ranged from -6.58 to 8.64 Mcal of NE(L)/d. Fifty-one percent of the animals had an RFI value below the mean (efficient) and 49% of them had an RFI value above the mean (inefficient). These results indicate that the first-lactation RFI can be predicted from its component traits with a reasonable coefficient of

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

PubMed

Benson, M

2016-03-01

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide

The role of individualism and the Five-Factor Model in the prediction of performance in a leaderless group discussion.

PubMed

Waldman, David A; Atwater, Leanne E; Davidson, Ronald A

2004-02-01

Personality has seen a resurgence in the work performance literature. The Five-Factor Model (FFM) represents a set of personality factors that has received the most attention in recent years. Despite its popularity, the FFM may not be sufficiently comprehensive to account for relevant variation across performance dimensions or tasks. Accordingly, the present study also considers how individualism may predict additional variance in performance beyond the FFM. The study involved 152 undergraduate students who experienced a leaderless group discussion (LGD) exercise. Results showed that while the FFM accounted for variance in students' LGD performance, individualism (independence) accounted for additional, unique variance. Furthermore, analyses of the group compositions revealed curvilinear relationships between the relative amount of extraversion, conscientiousness, and individualism in relation to group-level performance.

Chromium-picolinate therapy in diabetes care: individual outcomes require new guidelines and navigation by predictive diagnostics

PubMed Central

Yeghiazaryan, Kristina; Schild, Hans H.; Golubnitschaja, Olga

2013-01-01

Aims Nephropathy is the leading secondary complication of metabolic syndrome. Nutritional supplement by chromium-picolinate is assumed to have renoprotective effects. However, potential toxic effects reported increase concerns about safety of chromium-picolinate. The experimental design aimed at determining, whether the treatment with clinically relevant doses of chromium-picolinate can harm individual oucomes through DNA damage and extensive alterations in central detoxification / cell-cycle regulating pathways in treatment of diabetes. Methods The study was performed in a double-blind manner. Well-acknowledged animal model of db/db-mice and clinically relevant doses of chromium-picolinate were used. As an index of DNA-damage, measurement of DNA-breaks was performed using “Comet Assay”-analysis. Individual and group-specific expression patterns of SOD-1 and P53 were evaluated to get insights into central detoxification and cell-cycle regulating pathways under treatment conditions. Results Experimental data revealed highly individual reaction under treatment conditions. Highest variability of DNA-damage was monitored under prolonged treatment with high dosage of CrPic. Expression patterns demonstrated a correlation with subcellular imaging and dosage-dependent suppression under chromium-picolinate treatment. Interpretation and recommendations Population at-risk for diabetes is huge and increasing in pandemic scale. One of the reasons might be the failed attempt to prevent the disease by application of artificial supplements and drugs with hardly recognised individual risks. Consequently, a multimodal approach of integrative medicine by predictive diagnostics, targeted prevention and individually created treatment algorithms is highly desirable. PMID:23017160

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

PubMed

Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

2014-03-01

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

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.

Modeling of venturi scrubber efficiency

NASA Astrophysics Data System (ADS)

Crowder, Jerry W.; Noll, Kenneth E.; Davis, Wayne T.

The parameters affecting venturi scrubber performance have been rationally examined and modifications to the current modeling theory have been developed. The modified model has been validated with available experimental data for a range of throat gas velocities, liquid-to-gas ratios and particle diameters and is used to study the effect of some design parameters on collection efficiency. Most striking among the observations is the prediction of a new design parameter termed the minimum contactor length. Also noted is the prediction of little effect on collection efficiency with increasing liquid-to-gas ratio above about 2ℓ m-3. Indeed, for some cases a decrease in collection efficiency is predicted for liquid rates above this value.

Probabilistic prediction of aggregate traffic demand using uncertainty in individual flight predictions.

DOT National Transportation Integrated Search

2009-08-01

Federal Aviation Administration (FAA) air traffic flow management (TFM) : decision-making is based primarily on a comparison of deterministic predictions of demand : and capacity at National Airspace System (NAS) elements such as airports, fixes and ...

Individual differences in individualism and collectivism predict ratings of virtual cities' liveability and environmental quality.

PubMed

Rubin, Mark; Morrison, Tessa

2014-01-01

The present research investigated individual differences in individualism and collectivism as predictors of people's reactions to cities. Psychology undergraduate students (N = 148) took virtual guided tours around historical cities. They then evaluated the cities' liveability and environmental quality and completed measures of individualism and collectivism. Mediation analyses showed that people who scored high in self-responsibility (individualism) rated the cities as more liveable because they perceived them to be richer and better resourced. In contrast, people who scored high in collectivism rated the cities as having a better environmental quality because they perceived them to (1) provide a greater potential for community and social life and (2) allow people to express themselves. These results indicate that people's evaluations of virtual cities are based on the degree to which certain aspects of the cities are perceived to be consistent with individualist and collectivist values.

Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy.

PubMed

Yarnitsky, David; Granot, Michal; Nahman-Averbuch, Hadas; Khamaisi, Mogher; Granovsky, Yelena

2012-06-01

This study aims to individualize the selection of drugs for neuropathic pain by examining the potential coupling of a given drug's mechanism of action with the patient's pain modulation pattern. The latter is assessed by the conditioned pain modulation (CPM) and temporal summation (TS) protocols. We hypothesized that patients with a malfunctioning pain modulation pattern, such as less efficient CPM, would benefit more from drugs augmenting descending inhibitory pain control than would patients with a normal modulation pattern of efficient CPM. Thirty patients with painful diabetic neuropathy received 1 week of placebo, 1 week of 30 mg/d duloxetine, and 4 weeks of 60 mg/d duloxetine. Pain modulation was assessed psychophysically, both before and at the end of treatment. Patient assessment of drug efficacy, assessed weekly, was the study's primary outcome. Baseline CPM was found to be correlated with duloxetine efficacy (r=0.628, P<.001, efficient CPM is marked negative), such that less efficient CPM predicted efficacious use of duloxetine. Regression analysis (R(2)=0.673; P=.012) showed that drug efficacy was predicted only by CPM (P=.001) and not by pretreatment pain levels, neuropathy severity, depression level, or patient assessment of improvement by placebo. Furthermore, beyond its predictive value, the treatment-induced improvement in CPM was correlated with drug efficacy (r=-0.411, P=.033). However, this improvement occurred only in patients with less efficient CPM (16.8±16.0 to -1.1±15.5, P<.050). No predictive role was found for TS. In conclusion, the coupling of CPM and duloxetine efficacy highlights the importance of pain pathophysiology in the clinical decision-making process. This evaluative approach promotes personalized pain therapy. Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Ferritin levels predict severe dengue.

PubMed

Soundravally, R; Agieshkumar, B; Daisy, M; Sherin, J; Cleetus, C C

2015-02-01

Currently, no tests are available to monitor and predict severity and outcome of dengue. To find potential markers that predict dengue severity, the present study validated the serum level of three acute-phase proteins α-1 antitrypsin, ceruloplasmin and ferritin in a pool of severe dengue cases compared to non-severe forms and other febrile illness controls. A total of 96 patients were divided into two equal groups with group 'A' comprising dengue-infected cases and group 'B' with other febrile illness cases negative for dengue. Out of 48 dengue-infected cases, 13 had severe dengue and the remaining 35 were classified as non-severe dengue. Immunoassays were performed to evaluate the serum levels of acute-phase proteins both on the day of admission and on the day of defervescence. The efficiency of individual proteins in predicting the disease severity was assessed using receiver operating characteristic curve. The study did not find any significant difference in the levels of α-1 antitrypsin between the clinical groups. A significant increase in the levels of ceruloplasmin around defervescence in severe cases compared to non-severe and other febrile controls was observed and this is the first report describing the potential association of ceruloplasmin and dengue severity. Interestingly, a steady increase in the level of serum ferritin was recorded throughout the course of illness. Among all the three proteins, the elevated ferritin level could predict the disease severity with highest sensitivity and specificity of 76.9 and 83.3 %, respectively, on the day of admission and the same was found to be 90 and 91.6 % around defervescence. On the basis of this diagnostic efficiency, we propose that ferritin may serve as a potential biomarker for an early prediction of disease severity.

Olfactory Performance Is Predicted by Individual Sex-Atypicality, but Not Sexual Orientation

PubMed Central

Nováková, Lenka; Varella Valentová, Jaroslava; Havlíček, Jan

2013-01-01

Many previous studies have reported robust sex differences in olfactory perception. However, both men and women can be expected to vary in the degree to which they exhibit olfactory performance considered typical of their own or the opposite sex. Sex-atypicality is often described in terms of childhood gender nonconformity, which, however, is not a perfect correlate of non-heterosexual orientation. Here we explored intrasexual variability in psychophysical olfactory performance in a sample of 156 individuals (83 non-heterosexual) and found the lowest odor identification scores in heterosexual men. However, when childhood gender nonconformity was entered in the model along with sexual orientation, better odor identification scores were exhibited by gender-nonconforming men, and greater olfactory sensitivity by gender-conforming women, irrespective of their sexual orientation. Thus, sex-atypicality, but not sexual orientation predicts olfactory performance, and we propose that this might not be limited to olfaction, but represent a more general phenomenon. PMID:24244657

Olfactory performance is predicted by individual sex-atypicality, but not sexual orientation.

PubMed

Nováková, Lenka; Varella Valentová, Jaroslava; Havlíček, Jan

2013-01-01

Many previous studies have reported robust sex differences in olfactory perception. However, both men and women can be expected to vary in the degree to which they exhibit olfactory performance considered typical of their own or the opposite sex. Sex-atypicality is often described in terms of childhood gender nonconformity, which, however, is not a perfect correlate of non-heterosexual orientation. Here we explored intrasexual variability in psychophysical olfactory performance in a sample of 156 individuals (83 non-heterosexual) and found the lowest odor identification scores in heterosexual men. However, when childhood gender nonconformity was entered in the model along with sexual orientation, better odor identification scores were exhibited by gender-nonconforming men, and greater olfactory sensitivity by gender-conforming women, irrespective of their sexual orientation. Thus, sex-atypicality, but not sexual orientation predicts olfactory performance, and we propose that this might not be limited to olfaction, but represent a more general phenomenon.

Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

PubMed

Ette, E I; Howie, C A; Kelman, A W; Whiting, B

1995-05-01

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

The combination of work organizational climate and individual work commitment predicts return to work in women but not in men.

PubMed

Holmgren, Kristina; Ekbladh, Elin; Hensing, Gunnel; Dellve, Lotta

2013-02-01

To analyze if the combination of organizational climate and work commitment can predict return to work (RTW). This prospective Swedish study was based on 2285 participants, 19 to 64 years old, consecutively selected from the employed population, newly sick-listed for more than 14 days. Data were collected in 2008 through postal questionnaire and from register data. Among women, the combination of good organizational climate and fair work commitment predicted an early RTW with an adjusted relative risk of 2.05 (1.32 to 3.18). Among men, none of the adjusted variables or combinations of variables was found significantly to predict RTW. This study demonstrated the importance of integrative effects of organizational climate and individual work commitment on RTW among women. These factors did not predict RTW in men. More research is needed to understand the RTW process among men.

Preference assessments in the zoo: Keeper and staff predictions of enrichment preferences across species.

PubMed

Mehrkam, Lindsay R; Dorey, Nicole R

2015-01-01

Environmental enrichment is widely used in the management of zoo animals, and is an essential strategy for increasing the behavioral welfare of these populations. It may be difficult, however, to identify potentially effective enrichment strategies that are also cost-effective and readily available. An animal's preference for a potential enrichment item may be a reliable predictor of whether that individual will reliably interact with that item, and subsequently enable staff to evaluate the effects of that enrichment strategy. The aim of the present study was to assess the utility of preference assessments for identifying potential enrichment items across six different species--each representing a different taxonomic group. In addition, we evaluated the agreement between zoo personnel's predictions of animals' enrichment preferences and stimuli selected via a preference assessment. Five out of six species (nine out of 11 individuals) exhibited clear, systematic preferences for specific stimuli. Similarities in enrichment preferences were observed among all individuals of primates, whereas individuals within ungulate and avian species displayed individual differences in enrichment preferences. Overall, zoo personnel, regardless of experience level, were significantly more accurate at predicting least-preferred stimuli than most-preferred stimuli across species, and tended to make the same predictions for all individuals within a species. Preference assessments may therefore be a useful, efficient husbandry strategy for identifying viable enrichment items at both the individual and species levels. © 2015 Wiley Periodicals, Inc.

Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Moore, Catherine

2018-04-01

The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.

Trabecular Bone Strength Predictions of HR-pQCT and Individual Trabeculae Segmentation (ITS)-Based Plate and Rod Finite Element Model Discriminate Postmenopausal Vertebral Fractures

PubMed Central

Liu, X. Sherry; Wang, Ji; Zhou, Bin; Stein, Emily; Shi, Xiutao; Adams, Mark; Shane, Elizabeth; Guo, X. Edward

2013-01-01

While high-resolution peripheral quantitative computed tomography (HR-pQCT) has advanced clinical assessment of trabecular bone microstructure, nonlinear microstructural finite element (μFE) prediction of yield strength by HR-pQCT voxel model is impractical for clinical use due to its prohibitively high computational costs. The goal of this study was to develop an efficient HR-pQCT-based plate and rod (PR) modeling technique to fill the unmet clinical need for fast bone strength estimation. By using individual trabecula segmentation (ITS) technique to segment the trabecular structure into individual plates and rods, a patient-specific PR model was implemented by modeling each trabecular plate with multiple shell elements and each rod with a beam element. To validate this modeling technique, predictions by HR-pQCT PR model were compared with those of the registered high resolution μCT voxel model of 19 trabecular sub-volumes from human cadaveric tibiae samples. Both Young’s modulus and yield strength of HR-pQCT PR models strongly correlated with those of μCT voxel models (r2=0.91 and 0.86). Notably, the HR-pQCT PR models achieved major reductions in element number (>40-fold) and CPU time (>1,200-fold). Then, we applied PR model μFE analysis to HR-pQCT images of 60 postmenopausal women with (n=30) and without (n=30) a history of vertebral fracture. HR-pQCT PR model revealed significantly lower Young’s modulus and yield strength at the radius and tibia in fracture subjects compared to controls. Moreover, these mechanical measurements remained significantly lower in fracture subjects at both sites after adjustment for aBMD T-score at the ultradistal radius or total hip. In conclusion, we validated a novel HR-pQCT PR model of human trabecular bone against μCT voxel models and demonstrated its ability to discriminate vertebral fracture status in postmenopausal women. This accurate nonlinear μFE prediction of HR-pQCT PR model, which requires only seconds of

Sirolimus and everolimus clearance in maintenance kidney and liver transplant recipients: Diagnostic efficiency of the concentration/dose ratio for the prediction of trough steady-state concentrations

PubMed Central

Bouzas, Lorena; Hermida, Jesús

2010-01-01

Objectives Therapeutic monitoring of sirolimus and everolimus is necessary in order to minimize adverse side-effects and to ensure effective immunosuppression. A sirolimus-dosing model using the concentration/dose ratio has been previously proposed for kidney transplant patients, and the aim of our study was the evaluation of this single model for the prediction of trough sirolimus and everolimus concentrations. Methods Trough steady-state sirolimus concentrations were determined in several blood samples from each of 7 kidney and 9 liver maintenance transplant recipients, and everolimus concentrations from 20 kidney, 17 liver, and 3 kidney/liver maintenance transplant recipients. Predicted sirolimus and everolimus concentrations (Css), corresponding to the doses (D), were calculated using the measured concentrations (Css0) and corresponding doses (D0) on starting the study: Css = (Css0)(D)/D0. Results The diagnostic efficiency of the predicting model for the correct classification as subtherapeutic, therapeutic, and supratherapeutic values with respect to the experimentally obtained concentrations was 91.3% for sirolimus and 81.4% for everolimus in the kidney transplant patients. In the liver transplant patients the efficiency was 69.2% for sirolimus and 72.6% for everolimus, and in the kidney/liver transplant recipients the efficiency for everolimus was 67.9%. Conclusions The model has an acceptable diagnostic efficiency (>80%) for the prediction of sirolimus and everolimus concentrations in kidney transplant recipients, but not in liver transplant recipients. However, considering the wide ranges found for the prediction error of sirolimus and everolimus concentrations, the clinical relevance of this dosing model is weak. PMID:19943816

An efficient hybrid technique in RCS predictions of complex targets at high frequencies

NASA Astrophysics Data System (ADS)

Algar, María-Jesús; Lozano, Lorena; Moreno, Javier; González, Iván; Cátedra, Felipe

2017-09-01

Most computer codes in Radar Cross Section (RCS) prediction use Physical Optics (PO) and Physical theory of Diffraction (PTD) combined with Geometrical Optics (GO) and Geometrical Theory of Diffraction (GTD). The latter approaches are computationally cheaper and much more accurate for curved surfaces, but not applicable for the computation of the RCS of all surfaces of a complex object due to the presence of caustic problems in the analysis of concave surfaces or flat surfaces in the far field. The main contribution of this paper is the development of a hybrid method based on a new combination of two asymptotic techniques: GTD and PO, considering the advantages and avoiding the disadvantages of each of them. A very efficient and accurate method to analyze the RCS of complex structures at high frequencies is obtained with the new combination. The proposed new method has been validated comparing RCS results obtained for some simple cases using the proposed approach and RCS using the rigorous technique of Method of Moments (MoM). Some complex cases have been examined at high frequencies contrasting the results with PO. This study shows the accuracy and the efficiency of the hybrid method and its suitability for the computation of the RCS at really large and complex targets at high frequencies.

Active optimal control strategies for increasing the efficiency of photovoltaic cells

NASA Astrophysics Data System (ADS)

Aljoaba, Sharif Zidan Ahmad

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module edsigns toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module

Developmental Trajectories in Toddlers' Self-Restraint Predict Individual Differences in Executive Functions 14 Years Later: A Behavioral Genetic Analysis

ERIC Educational Resources Information Center

Friedman, Naomi P.; Miyake, Akira; Robinson, JoAnn L.; Hewitt, John K.

2011-01-01

We examined whether self-restraint in early childhood predicted individual differences in 3 executive functions (EFs; inhibiting prepotent responses, updating working memory, and shifting task sets) in late adolescence in a sample of approximately 950 twins. At ages 14, 20, 24, and 36 months, the children were shown an attractive toy and told not…

Individual differences in executive function and central coherence predict developmental changes in theory of mind in autism.

PubMed

Pellicano, Elizabeth

2010-03-01

There is strong evidence to suggest that individuals with autism show atypicalities in multiple cognitive domains, including theory of mind (ToM), executive function (EF), and central coherence (CC). In this study, the longitudinal relationships among these 3 aspects of cognition in autism were investigated. Thirty-seven cognitively able children with an autism spectrum condition were assessed on tests targeting ToM (false-belief prediction), EF (planning ability, cognitive flexibility, and inhibitory control), and CC (local processing) at intake and again 3 years later. Time 1 EF and CC skills were longitudinally predictive of change in children's ToM test performance, independent of age, language, nonverbal intelligence, and early ToM skills. Predictive relations in the opposite direction were not significant, and there were no developmental links between EF and CC. Rather than showing problems in ToM, EF and CC as co-occurring and independent atypicalities in autism, these findings suggest that early domain-general skills play a critical role in shaping the developmental trajectory of children's ToM.

Formation of the predicted training parameters in the form of a discrete information stream

NASA Astrophysics Data System (ADS)

Smolentseva, T. E.; Sumin, V. I.; Zolnikov, V. K.; Lavlinsky, V. V.

2018-03-01

In work process of training in the form of a discrete information stream is considered. On each of stages of the considered process portions of the training information and quality of their assimilation are analysed. Individual characteristics and reaction trained for every portion of information on appropriate sections are defined. The control algorithm of training with the predicted number of control checks of the trainee who allows to define what operating influence is considered it is necessary to create for the trainee. On the basis of this algorithm the vector of probabilities of ignorance of elements of the training information is received. As a result of the conducted researches the algorithm on formation of the predicted training parameters is developed. In work the task of comparison of duration of training received experimentally with predicted on the basis of it is solved the conclusion is drawn on efficiency of formation of the predicted training parameters. The program complex on the basis of the values of individual parameters received as a result of experiments on each trainee who allows to calculate individual characteristics is developed, to form rating and to monitor process of change of parameters of training.

Reproductive success is energetically linked to foraging efficiency in Antarctic fur seals

PubMed Central

2017-01-01

The efficiency with which individuals extract energy from their environment defines their survival and reproductive success, and thus their selective contribution to the population. Individuals that forage more efficiently (i.e., when energy gained exceeds energy expended) are likely to be more successful at raising viable offspring than individuals that forage less efficiently. Our goal was to test this prediction in large long-lived mammals under free-ranging conditions. To do so, we equipped 20 lactating Antarctic fur seals (Arctocephalus gazella) breeding on Kerguelen Island in the Southern Ocean with tags that recorded GPS locations, depth and tri-axial acceleration to determine at-sea behaviours and detailed time-activity budgets during their foraging trips. We also simultaneously measured energy spent at sea using the doubly-labeled water (DLW) method, and estimated the energy acquired while foraging from 1) type and energy content of prey species present in scat remains, and 2) numbers of prey capture attempts determined from head acceleration. Finally, we followed the growth of 36 pups from birth until weaning (of which 20 were the offspring of our 20 tracked mothers), and used the relative differences in body mass of pups at weaning as an index of first year survival and thus the reproductive success of their mothers. Our results show that females with greater foraging efficiencies produced relatively bigger pups at weaning. These mothers achieved greater foraging efficiency by extracting more energy per minute of diving rather than by reducing energy expenditure. This strategy also resulted in the females spending less time diving and less time overall at sea, which allowed them to deliver higher quality milk to their pups, or allowed their pups to suckle more frequently, or both. The linkage we demonstrate between reproductive success and the quality of individuals as foragers provides an individual-based quantitative framework to investigate how

Chromium-picolinate therapy in diabetes care: individual outcomes require new guidelines and navigation by predictive diagnostics.

PubMed

Yeghiazaryan, Kristina; Schild, Hans H; Golubnitschaja, Olga

2012-10-01

Nephropathy is the leading secondary complication of metabolic syndrome. Nutritional supplement by chromium-picolinate is assumed to have renoprotective effects. However, potential toxic effects reported increase the concerns about the safety of chromium-picolinate. The experimental design aimed at determining, whether the treatment with clinically relevant doses of chromium-picolinate can harm individual oucomes through DNA damage and extensive alterations in central detoxification / cell-cycle regulating pathways in treatment of diabetes. The study was performed in a double-blind manner. Well-acknowledged animal model of db/db-mice and clinically relevant doses of chromium- picolinate were used. As an index of DNA-damage, measurement of DNA-breaks was performed using "Comet Assay"-analysis. Individual and group-specific expression patterns of SOD-1 and P53 were evaluated to get insights into central detoxification and cell-cycle regulating pathways under the treatment conditions. Experimental data revealed highly individual reaction towards the treatment conditions. The highest variability of DNA-damage was monitored under the prolonged treatment with high dosage of CrPic. Expression patterns demonstrated a correlation with the subcellular imaging and dosage-dependent suppression under the chromium-picolinate treatment. INTERPRETATION AND RECOMMENDATIONS: Population at-risk for diabetes is huge and increasing in pandemic scale. One of the reasons might be the failed attempt to prevent the disease by application of artificial supplements and drugs with hardly recognised individual risks. Consequently, a multimodal approach of integrative medicine by predictive diagnostics, targeted prevention and individually created treatment algorithms is highly desirable.

Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders.

PubMed

Connors, Brenda L; Rende, Richard; Colton, Timothy J

2013-01-01

There has been a surge of interest in examining the utility of methods for capturing individual differences in decision-making style. We illustrate the potential offered by Movement Pattern Analysis (MPA), an observational methodology that has been used in business and by the US Department of Defense to record body movements that provide predictive insight into individual differences in decision-making motivations and actions. Twelve military officers participated in an intensive 2-h interview that permitted detailed and fine-grained observation and coding of signature movements by trained practitioners using MPA. Three months later, these subjects completed four hypothetical decision-making tasks in which the amount of information sought out before coming to a decision, as well as the time spent on the tasks, were under the partial control of the subject. A composite MPA indicator of how a person allocates decision-making actions and motivations to balance both Assertion (exertion of tangible movement effort on the environment to make something occur) and Perspective (through movements that support shaping in the body to perceive and create a suitable viewpoint for action) was highly correlated with the total number of information draws and total response time-individuals high on Assertion reached for less information and had faster response times than those high on Perspective. Discussion focuses on the utility of using movement-based observational measures to capture individual differences in decision-making style and the implications for application in applied settings geared toward investigations of experienced leaders and world statesmen where individuality rules the day.

Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders

PubMed Central

Connors, Brenda L.; Rende, Richard; Colton, Timothy J.

2013-01-01

There has been a surge of interest in examining the utility of methods for capturing individual differences in decision-making style. We illustrate the potential offered by Movement Pattern Analysis (MPA), an observational methodology that has been used in business and by the US Department of Defense to record body movements that provide predictive insight into individual differences in decision-making motivations and actions. Twelve military officers participated in an intensive 2-h interview that permitted detailed and fine-grained observation and coding of signature movements by trained practitioners using MPA. Three months later, these subjects completed four hypothetical decision-making tasks in which the amount of information sought out before coming to a decision, as well as the time spent on the tasks, were under the partial control of the subject. A composite MPA indicator of how a person allocates decision-making actions and motivations to balance both Assertion (exertion of tangible movement effort on the environment to make something occur) and Perspective (through movements that support shaping in the body to perceive and create a suitable viewpoint for action) was highly correlated with the total number of information draws and total response time—individuals high on Assertion reached for less information and had faster response times than those high on Perspective. Discussion focuses on the utility of using movement-based observational measures to capture individual differences in decision-making style and the implications for application in applied settings geared toward investigations of experienced leaders and world statesmen where individuality rules the day. PMID:24069012

Predictive Validity of a Cigarette Purchase Task in a Randomized Controlled Trial of Contingent Vouchers for Smoking in Individuals With Substance Use Disorders

PubMed Central

Mackillop, James; Murphy, Cara M.; Martin, Rosemarie A.; Stojek, Monika; Tidey, Jennifer W.; Colby, Suzanne M.

2016-01-01

Abstract Introduction: A cigarette purchase task (CPT) is a behavioral economic measure of the reinforcing value of smoking in monetary terms (ie, cigarette demand). This study investigated whether cigarette demand predicted response to contingent monetary rewards for abstinence among individuals with substance use disorders. It also sought to replicate evidence for greater price sensitivity at whole-dollar pack price transitions (ie, left-digit effects). Methods: Participants ( N = 338) were individuals in residential substance use disorder treatment who participated in a randomized controlled trial that compared contingent vouchers to noncontingent vouchers for smoking abstinence. Baseline demand indices were used to predict number of abstinent days during the 14-day voucher period (after the reduction lead-in) and at 1 and 3 months afterward. Results: Demand indices correlated with measures of smoking and nicotine dependence. As measured by elasticity, intensity and Omax , higher demand significantly predicted fewer abstinent exhaled carbon monoxide readings during voucher period for individuals in the noncontingent vouchers condition. Breakpoint exhibited a trend-level association with abstinent exhaled carbon monoxide readings. Demand indices did not predict abstinence in the contingent vouchers group, and did not predict abstinence at 1- and 3-month follow-ups. Left-digit price transitions were associated with significantly greater reductions in consumption. Conclusions: The association of cigarette demand with smoking behavior only in the group for whom abstinence was not incentivized indicates that CPT assesses the value of smoking more than the value of money per se and that vouchers counteract the effects of the intrinsic reinforcing value of cigarettes. Results provide initial short-term evidence of predictive validity for the CPT indices. Implications: This study provides the first evidence of the validity of the CPT for predicting early response to

Urinary composition predicts diuretic efficiency of hypertonic saline solution with furosemide therapy and heart failure prognosis.

PubMed

Ando, Tomotaka; Okuhara, Yoshitaka; Orihara, Yoshiyuki; Nishimura, Koichi; Yamamoto, Kyoko; Masuyama, Tohru; Hirotani, Shinichi

2018-03-19

Recently, we and other group have reported that furosemide administration along with hypertonic saline solution enhanced diuretic efficiency of furosemide. However, little is known about factors which associated with high diuretic efficiency by hypertonic saline solution with furosemide therapy. To identify predictors of diuretic efficiency in the hypertonic saline solution with furosemide therapy, we recruited 30 consecutive hospitalized heart failure (HF) patients with volume overload (77 ± 10 years, systolic blood pressure > 90 mmHg, and estimated glomerular filtration rate > 15 ml/min/1.73 m 2 ). Hypertonic saline with furosemide solution, consisting of 500 ml of 1.7% hypertonic saline solution with 40 mg of furosemide, was administered continuously over 24 h. The patients were divided into two groups on the basis of 24-h urine volume (UV) after initiation of diuretic treatment ≥ 2000 ml (high urine volume: HUV) and < 2000 ml (low urine volume: LUV). The basal clinical characteristics of both groups were analyzed and the predictors of HUV after receiving the treatment were identified. There were not significant differences between two groups in baseline clinical characteristics and medication. Univariate logistic analysis revealed that blood urea nitrogen/creatinine ratio, urine urea nitrogen/creatinine ratio (UUN/UCre), fractional excretion of sodium, and tricuspid annular plane systolic excursion positively associated with HUV. Multivariate logistic regression analysis revealed that UUN/UCre at baseline was independently associated with HUV, and UUN/UCre best predicts HUV by the therapy with a cut-off value of 6.16 g/dl/g Cre (AUC 0.910, 95% CI 0.696-0.999, sensitivity 80%, specificity 87%). The Kaplan-Meier curves revealed significant difference for HF rehospitalization and death rate at 180 days between patients with UUN/UCre ≥ 6.16 g/dl/g Cre and those with UUN/UCre < 6.16 g/dl/g Cre (log-rank P = 0

Can personality predict individual differences in brook trout spatial learning ability?

USGS Publications Warehouse

White, S.L.; Wagner, Tyler; Gowan, C.; Braithwaite, V.A.

2017-01-01

While differences in individual personality are common in animal populations, understanding the ecological significance of variation has not yet been resolved. Evidence suggests that personality may influence learning and memory; a finding that could improve our understanding of the evolutionary processes that produce and maintain intraspecific behavioural heterogeneity. Here, we tested whether boldness, the most studied personality trait in fish, could predict learning ability in brook trout. After quantifying boldness, fish were trained to find a hidden food patch in a maze environment. Stable landmark cues were provided to indicate the location of food and, at the conclusion of training, cues were rearranged to test for learning. There was a negative relationship between boldness and learning as shy fish were increasingly more successful at navigating the maze and locating food during training trials compared to bold fish. In the altered testing environment, only shy fish continued using cues to search for food. Overall, the learning rate of bold fish was found to be lower than that of shy fish for several metrics suggesting that personality could have widespread effects on behaviour. Because learning can increase plasticity to environmental change, these results have significant implications for fish conservation.

Individual Alpha Peak Frequency Predicts 10 Hz Flicker Effects on Selective Attention.

PubMed

Gulbinaite, Rasa; van Viegen, Tara; Wieling, Martijn; Cohen, Michael X; VanRullen, Rufin

2017-10-18

Rhythmic visual stimulation ("flicker") is primarily used to "tag" processing of low-level visual and high-level cognitive phenomena. However, preliminary evidence suggests that flicker may also entrain endogenous brain oscillations, thereby modulating cognitive processes supported by those brain rhythms. Here we tested the interaction between 10 Hz flicker and endogenous alpha-band (∼10 Hz) oscillations during a selective visuospatial attention task. We recorded EEG from human participants (both genders) while they performed a modified Eriksen flanker task in which distractors and targets flickered within (10 Hz) or outside (7.5 or 15 Hz) the alpha band. By using a combination of EEG source separation, time-frequency, and single-trial linear mixed-effects modeling, we demonstrate that 10 Hz flicker interfered with stimulus processing more on incongruent than congruent trials (high vs low selective attention demands). Crucially, the effect of 10 Hz flicker on task performance was predicted by the distance between 10 Hz and individual alpha peak frequency (estimated during the task). Finally, the flicker effect on task performance was more strongly predicted by EEG flicker responses during stimulus processing than during preparation for the upcoming stimulus, suggesting that 10 Hz flicker interfered more with reactive than proactive selective attention. These findings are consistent with our hypothesis that visual flicker entrained endogenous alpha-band networks, which in turn impaired task performance. Our findings also provide novel evidence for frequency-dependent exogenous modulation of cognition that is determined by the correspondence between the exogenous flicker frequency and the endogenous brain rhythms. SIGNIFICANCE STATEMENT Here we provide novel evidence that the interaction between exogenous rhythmic visual stimulation and endogenous brain rhythms can have frequency-specific behavioral effects. We show that alpha-band (10 Hz) flicker impairs stimulus

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one

Individual variation in baseline and stress-induced corticosterone and prolactin levels predicts parental effort by nesting mourning doves

USGS Publications Warehouse

Miller, David A.; Vleck, Carol M.; Otis, David L.

2009-01-01

Endocrine systems have an important mechanistic role in structuring life-history trade-offs. During breeding, individual variation in prolactin (PRL) and corticosterone (CORT) levels affects behavioral and physiological processes that drive trade-offs between reproduction and self-maintenance. We examined patterns in baseline (BL) and stress induced (SI; level following a standard capture-restraint protocol) levels of PRL and CORT for breeding mourning doves (Zenaida macroura). We determined whether the relationship of adult condition and parental effort to hormone levels in wild birds was consistent with life-history predictions. Both BL PRL and BL CORT level in adults were positively related to nestling weight at early nestling ages, consistent with the prediction of a positive relationship of hormone levels to current parental effort of adults and associated increased energy demand. Results are consistent with the two hormones acting together at baseline levels to limit negative effects of CORT on reproduction while maintaining beneficial effects such as increased foraging for nestling feeding. Our data did not support predictions that SI responses would vary in response to nestling or adult condition. The magnitude of CORT response in the parents to our capture-restraint protocol was negatively correlated with subsequent parental effort. Average nestling weights for adults with the highest SI CORT response were on average 10–15% lighter than expected for their age in follow-up visits after the stress event. Our results demonstrated a relationship between individual hormone levels and within population variation in parental effort and suggested that hormonal control plays an important role in structuring reproductive decisions for mourning doves.

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

PubMed

Jang, Jin-Young; Park, Taesung; Lee, Selyeong; Kim, Yongkang; Lee, Seung Yeoun; Kim, Sun-Whe; Kim, Song-Cheol; Song, Ki-Byung; Yamamoto, Masakazu; Hatori, Takashi; Hirono, Seiko; Satoi, Sohei; Fujii, Tsutomu; Hirano, Satoshi; Hashimoto, Yasushi; Shimizu, Yashuhiro; Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok; Motoi, Fuyuhiko; Matsumoto, Ippei; Lee, Woo Jung; Kang, Chang Moo; Han, Ho-Seong; Yoon, Yoo-Seok; Sho, Masayuki; Nagano, Hiroaki; Honda, Goro; Kim, Sang Geol; Yu, Hee Chul; Chung, Jun Chul; Nagakawa, Yuichi; Seo, Hyung Il; Yamaue, Hiroki

2017-12-01

This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Simulation based efficiency prediction of a Brushless DC drive applied in ventricular assist devices.

PubMed

Pohlmann, André; Hameyer, Kay

2012-01-01

Ventricular Assist Devices (VADs) are mechanical blood pumps that support the human heart in order to maintain a sufficient perfusion of the human body and its organs. During VAD operation blood damage caused by hemolysis, thrombogenecity and denaturation has to be avoided. One key parameter causing the blood's denaturation is its temperature which must not exceed 42 °C. As a temperature rise can be directly linked to the losses occuring in the drive system, this paper introduces an efficiency prediction chain for Brushless DC (BLDC) drives which are applied in various VAD systems. The presented chain is applied to various core materials and operation ranges, providing a general overview on the loss dependencies.

Superior Disembedding Performance in Childhood Predicts Adolescent Severity of Repetitive Behaviors: A Seven Years Follow-Up of Individuals With Autism Spectrum Disorder.

PubMed

Eussen, Mart L J M; Van Gool, Arthur R; Louwerse, Anneke; Verhulst, Frank C; Greaves-Lord, Kirstin

2016-02-01

Previous research suggests that individuals with autism spectrum disorder (ASD) show a detail-focused cognitive style. The aim of the current longitudinal study was to investigate whether this detail-focused cognitive style in childhood predicted a higher symptom severity of repetitive and restrictive behaviors and interests (RRBI) in adolescence. The Childhood Embedded Figures Test (CEFT) and the Autism Diagnostic Observation Schedule (ADOS) were administered in 87 children with ASD at the age of 6-12 years old (T1), and the ADOS was readministered 7 years later when the participants were 12-19 years old (T2). Linear regression analyses were performed to investigate whether accuracy and reaction time in the complex versus simple CEFT condition and performance in the complex condition predicted T2 ADOS RRBI calibrated severity scores (CSS), while taking into consideration relevant covariates and ADOS RRBI CSS at T1. The CEFT performance (accuracy in the complex condition divided by the time needed) significantly predicted higher ADOS RRBI CSS at T2 (ΔR(2)  = 15%). This finding further supports the detail-focused cognitive style in individuals with ASD, and shows that it is also predictive of future RRBI symptoms over time. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Rapid race perception despite individuation and accuracy goals.

PubMed

Kubota, Jennifer T; Ito, Tiffany

2017-08-01

Perceivers rapidly process social category information and form stereotypic impressions of unfamiliar others. However, a goal to individuate a target or to accurately predict their behavior can result in individuated impressions. It is unknown how the combination of both accuracy and individuation goals affects perceptual category processing. To explore this, participants were given both the goal to individuate targets and accurately predict behavior. We then recorded event-related brain potentials while participants viewed photos of black and white males along with four pieces of individuating information in the form of descriptions of past behavior. Even with explicit individuation and accuracy task goals, participants rapidly differentiated targets by race within 200 ms. Importantly, this rapid categorical processing did not influence behavioral outcomes as participants made individuated predictions. These findings indicate that individuals engage in category processing even when provided with individuation and accuracy goals, but that this processing does not necessarily result in category-based judgments.

Predicting Change in Postpartum Depression: An Individual Growth Curve Approach.

ERIC Educational Resources Information Center

Buchanan, Trey

Recently, methodologists interested in examining problems associated with measuring change have suggested that developmental researchers should focus upon assessing change at both intra-individual and inter-individual levels. This study used an application of individual growth curve analysis to the problem of maternal postpartum depression.…

Individual differences in symptom severity and behavior predict neural activation during face processing in adolescents with autism

PubMed Central

Scherf, K. Suzanne; Elbich, Daniel; Minshew, Nancy; Behrmann, Marlene

2014-01-01

Despite the impressive literature describing atypical neural activation in visuoperceptual face processing regions in autism, almost nothing is known about whether these perturbations extend to more affective regions in the circuitry and whether they bear any relationship to symptom severity or atypical behavior. Using fMRI, we compared face-, object-, and house-related activation in adolescent males with high-functioning autism (HFA) and typically developing (TD) matched controls. HFA adolescents exhibited hypo-activation throughout the core visuoperceptual regions, particularly in the right hemisphere, as well as in some of the affective/motivational face-processing regions, including the posterior cingulate cortex and right anterior temporal lobe. Conclusions about the relative hyper- or hypo-activation of the amygdala depended on the nature of the contrast that was used to define the activation. Individual differences in symptom severity predicted the magnitude of face activation, particularly in the right fusiform gyrus. Also, among the HFA adolescents, face recognition performance predicted the magnitude of face activation in the right anterior temporal lobe, a region that supports face individuation in TD adults. Our findings reveal a systematic relation between the magnitude of neural dysfunction, severity of autism symptoms, and variation in face recognition behavior in adolescents with autism. In so doing, we uncover brain–behavior relations that underlie one of the most prominent social deficits in autism and help resolve discrepancies in the literature. PMID:25610767

Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial.

PubMed

Gunn, Jane; Wachtler, Caroline; Fletcher, Susan; Davidson, Sandra; Mihalopoulos, Cathrine; Palmer, Victoria; Hegarty, Kelsey; Coe, Amy; Murray, Elizabeth; Dowrick, Christopher; Andrews, Gavin; Chondros, Patty

2017-07-20

Depression is a highly prevalent and costly disorder. Effective treatments are available but are not always delivered to the right person at the right time, with both under- and over-treatment a problem. Up to half the patients presenting to general practice report symptoms of depression, but general practitioners have no systematic way of efficiently identifying level of need and allocating treatment accordingly. Therefore, our team developed a new clinical prediction tool (CPT) to assist with this task. The CPT predicts depressive symptom severity in three months' time and based on these scores classifies individuals into three groups (minimal/mild, moderate, severe), then provides a matched treatment recommendation. This study aims to test whether using the CPT reduces depressive symptoms at three months compared with usual care. The Target-D study is an individually randomized controlled trial. Participants will be 1320 general practice patients with depressive symptoms who will be approached in the practice waiting room by a research assistant and invited to complete eligibility screening on an iPad. Eligible patients will provide informed consent and complete the CPT on a purpose-built website. A computer-generated allocation sequence stratified by practice and depressive symptom severity group, will randomly assign participants to intervention (treatment recommendation matched to predicted depressive symptom severity group) or comparison (usual care plus Target-D attention control) arms. Follow-up assessments will be completed online at three and 12 months. The primary outcome is depressive symptom severity at three months. Secondary outcomes include anxiety, mental health self-efficacy, quality of life, and cost-effectiveness. Intention-to-treat analyses will test for differences in outcome means between study arms overall and by depressive symptom severity group. To our knowledge, this is the first depressive symptom stratification tool designed for

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

PubMed

Olayan, Rawan S; Ashoor, Haitham; Bajic, Vladimir B

2018-04-01

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. The data and code are provided at https://bitbucket.org/RSO24/ddr/. vladimir.bajic@kaust.edu.sa. Supplementary data are available at Bioinformatics online.

Prediction of human adaptation and performance in underwater environments.

PubMed

Colodro Plaza, Joaquín; Garcés de los Fayos Ruiz, Enrique J; López García, Juan J; Colodro Conde, Lucía

2014-01-01

Environmental stressors require the professional diver to undergo a complex process of psychophysiological adaptation in order to overcome the demands of an extreme environment and carry out effective and efficient work under water. The influence of cognitive and personality traits in predicting underwater performance and adaptation has been a common concern for diving psychology, and definitive conclusions have not been reached. In this ex post facto study, psychological and academic data were analyzed from a large sample of personnel participating in scuba diving courses carried out in the Spanish Navy Diving Center. In order to verify the relevance of individual differences in adaptation to a hostile environment, we evaluated the predictive validity of general mental ability and personality traits with regression techniques. The data indicated the existence of psychological variables that can predict the performance ( R² = .30, p <.001) and adaptation ( R²(N) = .51, p <.001) of divers in underwater environment. These findings support the hypothesis that individual differences are related to the probability of successful adaptation and effective performance in professional diving. These results also verify that dispositional traits play a decisive role in diving training and are significant factors in divers' psychological fitness.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

PubMed Central

Xavier, Alencar; Muir, William M.; Rainey, Katy Martin

2016-01-01

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. PMID:27317786

Individual differences in the dominance of interhemispheric connections predict cognitive ability beyond sex and brain size.

PubMed

Martínez, Kenia; Janssen, Joost; Pineda-Pardo, José Ángel; Carmona, Susanna; Román, Francisco Javier; Alemán-Gómez, Yasser; Garcia-Garcia, David; Escorial, Sergio; Quiroga, María Ángeles; Santarnecchi, Emiliano; Navas-Sánchez, Francisco Javier; Desco, Manuel; Arango, Celso; Colom, Roberto

2017-07-15

Global structural brain connectivity has been reported to be sex-dependent with women having increased interhemispheric connectivity (InterHc) and men having greater intrahemispheric connectivity (IntraHc). However, (a) smaller brains show greater InterHc, (b) larger brains show greater IntraHc, and (c) women have, on average, smaller brains than men. Therefore, sex differences in brain size may modulate sex differences in global brain connectivity. At the behavioural level, sex-dependent differences in connectivity are thought to contribute to men-women differences in spatial and verbal abilities. But this has never been tested at the individual level. The current study assessed whether individual differences in global structural connectome measures (InterHc, IntraHc and the ratio of InterHc relative to IntraHc) predict spatial and verbal ability while accounting for the effect of sex and brain size. The sample included forty men and forty women, who did neither differ in age nor in verbal and spatial latent components defined by a broad battery of tests and tasks. High-resolution T 1 -weighted and diffusion-weighted images were obtained for computing brain size and reconstructing the structural connectome. Results showed that men had higher IntraHc than women, while women had an increased ratio InterHc/IntraHc. However, these sex differences were modulated by brain size. Increased InterHc relative to IntraHc predicted higher spatial and verbal ability irrespective of sex and brain size. The positive correlations between the ratio InterHc/IntraHc and the spatial and verbal abilities were confirmed in 1000 random samples generated by bootstrapping. Therefore, sex differences in global structural connectome connectivity were modulated by brain size and did not underlie sex differences in verbal and spatial abilities. Rather, the level of dominance of InterHc over IntraHc may be associated with individual differences in verbal and spatial abilities in both men and

Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient

USGS Publications Warehouse

Schoolmaster, Donald; Stagg, Camille L.

2018-01-01

A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-01-07

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and

Hidden Efficiencies: Making Completion of the Pediatric Vaccine Schedule More Efficient for Physicians

PubMed Central

Ciarametaro, Mike; Bradshaw, Steven E.; Guiglotto, Jillian; Hahn, Beth; Meier, Genevieve

2015-01-01

Abstract The objective of this work is to demonstrate the potential time and labor savings that may result from increased use of combination vaccinations. The study (GSK study identifier: HO-12-4735) was a model developed to evaluate the efficiency of the pediatric vaccine schedule, using time and motion studies. The model considered vaccination time and the associated labor costs, but vaccination acquisition costs were not considered. We also did not consider any efficacy or safety differences between formulations. The model inputs were supported by a targeted literature review. The reference year for the model was 2012. The most efficient vaccination program using currently available vaccines was predicted to reduce costs through a combination of fewer injections (62%) and less time per vaccination (38%). The most versus the least efficient vaccine program was predicted to result in a 47% reduction in vaccination time and a 42% reduction in labor and supply costs. The estimated administration cost saving with the most versus the least efficient program was estimated to be nearly US $45 million. If hypothetical 6- or 7-valent vaccines are developed using the already most efficient schedule by adding additional antigens (pneumococcal conjugate vaccine and Haemophilus influenzae type b) to the most efficient 5-valent vaccine, the savings are predicted to be even greater. Combination vaccinations reduce the time burden of the childhood immunization schedule and could create the potential to improve vaccination uptake and compliance as a result of fewer required injections. PMID:25634165

Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-09-08

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-08-25

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Income Redistribution Predicts Greater Life Satisfaction Across Individual, National, and Cultural Characteristics.

PubMed

Cheung, Felix

2017-08-03

The widening income gap between the rich and the poor has important social implications. Governmental-level income redistribution through tax and welfare policies presents an opportunity to reduce income inequality and its negative consequences. The current longitudinal studies examined whether within-region changes in income redistribution over time relate to life satisfaction. Moreover, I examined potential moderators of this relationship to test the strong versus weak hypotheses of income redistribution. The strong hypothesis posits that income redistribution is beneficial to most. The weak hypothesis posits that income redistribution is beneficial to some and damaging to others. Using a nationally representative sample of 57,932 German respondents from 16 German states across 30 years (Study 1) and a sample of 112,876 respondents from 33 countries across 24 years (Study 2), I found that within-state and within-nation changes in income redistribution over time were associated with life satisfaction. The models predicted that a 10% reduction in Gini through income redistribution in Germany increased life satisfaction to the same extent as an 37% increase in annual income (Study 1), and a 5% reduction in Gini through income redistribution increased life satisfaction to the same extent as a 11% increase in GDP (Study 2). These associations were positive across individual, national, and cultural characteristics. Increases in income redistribution predicted greater satisfaction for tax-payers and welfare-receivers, for liberals and conservatives, and for the poor and the rich. These findings support the strong hypothesis of income redistribution and suggest that redistribution policies may play an important role in societal well-being. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predicting Radiation Pneumonitis After Chemoradiation Therapy for Lung Cancer: An International Individual Patient Data Meta-analysis