Timely Antecedent CT or MRI Can Help Predict Hemorrhage Site of Posttreatment Head and Neck Cancer, With Digital Subtraction Angiography Used as the Reference Standard.

PubMed

Ku, Yi-Kang; Wong, Yon-Cheong; Fu, Chen-Ju; Tseng, Hsiao-Jung; Wang, Li-Jen; Wang, Chao-Jan; Chin, Shy-Chyi

2016-04-01

We investigated the timing of CT and MRI performed before digital subtraction angiography (DSA) in the prediction of hemorrhage sites in patients with head and neck cancers who present with acute oral or neck bleeding after receiving treatment. A total of 123 DSA examinations that evaluated 123 oral or neck bleeding events in 85 patients were analyzed. The last CT or MRI examinations performed within a time frame of 0-337 days before transarterial embolization were reviewed retrospectively, with three findings (pseudoaneurysm, air-containing necrotic tissue, and residual tumor) used to predict hemorrhage sites. DSA findings of pseudoaneurysm or active contrast extravasation were used as a reference standard. The sensitivity of CT and MRI for correctly predicting hemorrhage sites was used to determine the optimal timing of CT or MRI examinations performed before DSA. A total of 8.9% of the DSA examinations (11/123) had equivocal findings but were followed by another bleeding event for which DSA findings were positive. CT or MRI was statistically significantly better at predicting hemorrhage sites in patients with bleeding events associated with nonhypopharyngeal cancers (p = 0.019) than in those with bleeding events associated with hypopharyngeal cancers. The sensitivity of CT or MRI in the prediction of hemorrhage sites was statistically significantly higher for the common carotid artery and the internal carotid artery when CT or MRI was performed less than 30 days before bleeding events occurred. Prediction of hemorrhagic sites was better with the use of CT angiography than with the use of enhanced CT or MRI, although it was not statistically significant. DSA findings can temporarily be equivocal. CT or MRI examinations performed within 30 days of bleeding events can predict the site of hemorrhage. If no CT or MRI findings from the past 30 days are available, we suggest performing emergent CT angiography for the sake of obtaining better arterial detail.

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

PubMed Central

Hu, Danqing; Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei; Lu, Xudong; Duan, Huilong

2016-01-01

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. PMID:27649220

Gamma-glutamyl transpeptidase to cholinesterase and platelet ratio in predicting significant liver fibrosis and cirrhosis of chronic hepatitis B.

PubMed

Liu, Danping; Li, Jian; Lu, Wei; Wang, Yanbing; Zhou, Xinlan; Huang, Dan; Li, Xiufen; Ding, Rongrong; Zhang, Zhanqing

2018-06-12

To evaluate the performance of a new mathematical model gamma- glutamyl transpeptidase to cholinesterase and platelet ratio (GCPR) versus gamma- glutamyl transpeptidase to platelet ratio (GPR) in predicting significant fibrosis and cirrhosis of chronic hepatitis B (CHB). A complete cohort of 2343 patients was divided into early and late cohort depending on the time of liver biopsy. With reference to the Scheuer standard, liver pathological stage ≥S2 and ≥S4 were defined as significant fibrosis and cirrhosis, respectively. ROC curve was used to evaluate the performance of investigated models. In early cohort，the areas under ROC curves (AUROCs) of GCPR in predicting significant fibrosis of HBeAg-positive and HBeAg-negative patients (0.782 and 0.775) were both significantly greater than those of GPR (0.748 and 0.747) (Z＝8.198 and Z＝6.023, both P<0.0001); the AUROCs of GCPR in predicting cirrhosis of HBeAg-positive and HBeAg-negative patients (0.842 and 0.861) were both significantly greater than those of GPR (0.802 and 0.823) (Z＝6.686 and Z＝6.116, both P<0.0001). In early, late and complete cohort, using a single cutoff of GPCR>0.080, the specificities of GCPR in predicting significant fibrosis of HBeAg-positive patients were 83.3%, 88.2% and 85.0%, and of HBeAg-negative patients were 87.6%, 87.4% and 87.6%, respectively; and the sensitivities of GCPR in predicting cirrhosis of HBeAg-positive patients were 81.9%, 88.7% and 84.2%, and of HBeAg-negative patients were 83.1%,82.1% and 82.7%, respectively. GCPR has higher performance than GPR in predicting significant fibrosis and cirrhosis of CHB. Copyright © 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.

PubMed

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chun; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-03-02

The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score. ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P <0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance. The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer.

Predicting in-treatment performance and post-treatment outcomes in methamphetamine users.

PubMed

Hillhouse, Maureen P; Marinelli-Casey, Patricia; Gonzales, Rachel; Ang, Alfonso; Rawson, Richard A

2007-04-01

This study examines the utility of individual drug use and treatment characteristics for predicting in-treatment performance and post-treatment outcomes over a 1-year period. Data were collected from 420 adults who participated in the Methamphetamine Treatment Project (MTP), a multi-site study of randomly assigned treatment for methamphetamine dependence. Interviews were conducted at baseline, during treatment and during three follow-up time-points: treatment discharge and at 6 and 12 months following admission. The Addiction Severity Index (ASI); the Craving, Frequency, Intensity and Duration Estimate (CFIDE); and laboratory urinalysis results were used in the current study. Analyses addressed both in-treatment performance and post-treatment outcomes. The most consistent finding is that pre-treatment methamphetamine use predicts in-treatment performance and post-treatment outcomes. No one variable predicted all in-treatment performance measures; however, gender, route of administration and pre-treatment methamphetamine use were significant predictors. Similarly, post-treatment outcomes were predicted by a range of variables, although pre-treatment methamphetamine use was significantly associated with each post-treatment outcome. These findings provide useful empirical information about treatment outcomes for methamphetamine abusers, and highlight the utility of assessing individual and in-treatment characteristics in the development of appropriate treatment plans.

Can paediatric early warning scores (PEWS) be used to guide the need for hospital admission and predict significant illness in children presenting to the emergency department? An assessment of PEWS diagnostic accuracy using sensitivity and specificity.

PubMed

Lillitos, Peter J; Hadley, Graeme; Maconochie, Ian

2016-05-01

Designed to detect early deterioration of the hospitalised child, paediatric early warning scores (PEWS) validity in the emergency department (ED) is less validated. We aimed to evaluate sensitivity and specificity of two commonly used PEWS (Brighton and COAST) in predicting hospital admission and, for the first time, significant illness. Retrospective analysis of PEWS data for paediatric ED attendances at St Mary's Hospital, London, UK, in November 2012. Patients with missing data were excluded. Diagnoses were grouped: medical and surgical. To classify diagnoses as significant, established guidelines were used and, where not available, common agreement between three acute paediatricians. 1921 patients were analysed. There were 211 admissions (11%). 1630 attendances were medical (86%) and 273 (14%) surgical. Brighton and COAST PEWS performed similarly. hospital admission: PEWS of ≥3 was specific (93%) but poorly sensitive (32%). The area under the receiver operating curve (AUC) was low at 0.690. Significant illness: for medical illness, PEWS ≥3 was highly specific (96%) but poorly sensitive (44%). The AUC was 0.754 and 0.755 for Brighton and COAST PEWS, respectively. Both scores performed poorly for predicting significant surgical illness (AUC 0.642). PEWS ≥3 performed well in predicting significant respiratory illness: sensitivity 75%, specificity 91%. Both Brighton and COAST PEWS scores performed similarly. A score of ≥3 has good specificity but poor sensitivity for predicting hospital admission and significant illness. Therefore, a high PEWS should be taken seriously but a low score is poor at ruling out the requirement for admission or serious underlying illness. PEWS was better at detecting significant medical illness compared with detecting the need for admission. PEWS performed poorly in detecting significant surgical illness. PEWS may be particularly useful in evaluating respiratory illness in a paediatric ED. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Impact of External Cue Validity on Driving Performance in Parkinson's Disease

PubMed Central

Scally, Karen; Charlton, Judith L.; Iansek, Robert; Bradshaw, John L.; Moss, Simon; Georgiou-Karistianis, Nellie

2011-01-01

This study sought to investigate the impact of external cue validity on simulated driving performance in 19 Parkinson's disease (PD) patients and 19 healthy age-matched controls. Braking points and distance between deceleration point and braking point were analysed for red traffic signals preceded either by Valid Cues (correctly predicting signal), Invalid Cues (incorrectly predicting signal), and No Cues. Results showed that PD drivers braked significantly later and travelled significantly further between deceleration and braking points compared with controls for Invalid and No-Cue conditions. No significant group differences were observed for driving performance in response to Valid Cues. The benefit of Valid Cues relative to Invalid Cues and No Cues was significantly greater for PD drivers compared with controls. Trail Making Test (B-A) scores correlated with driving performance for PDs only. These results highlight the importance of external cues and higher cognitive functioning for driving performance in mild to moderate PD. PMID:21789275

Socioeconomic Status and Race Outperform Concussion History and Sport Participation in Predicting Collegiate Athlete Baseline Neurocognitive Scores.

PubMed

Houck, Zac; Asken, Breton; Clugston, James; Perlstein, William; Bauer, Russell

2018-01-01

The purpose of this study was to assess the contribution of socioeconomic status (SES) and other multivariate predictors to baseline neurocognitive functioning in collegiate athletes. Data were obtained from the Concussion Assessment, Research and Education (CARE) Consortium. Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) baseline assessments for 403 University of Florida student-athletes (202 males; age range: 18-23) from the 2014-2015 and 2015-2016 seasons were analyzed. ImPACT composite scores were consolidated into one memory and one speed composite score. Hierarchical linear regressions were used for analyses. In the overall sample, history of learning disability (β=-0.164; p=.001) and attention deficit-hyperactivity disorder (β=-0.102; p=.038) significantly predicted worse memory and speed performance, respectively. Older age predicted better speed performance (β=.176; p<.001). Black/African American race predicted worse memory (β=-0.113; p=.026) and speed performance (β=-.242; p<.001). In football players, higher maternal SES predicted better memory performance (β=0.308; p=.007); older age predicted better speed performance (β=0.346; p=.001); while Black/African American race predicted worse speed performance (β=-0.397; p<.001). Baseline memory and speed scores are significantly influenced by history of neurodevelopmental disorder, age, and race. In football players, specifically, maternal SES independently predicted baseline memory scores, but concussion history and years exposed to sport were not predictive. SES, race, and medical history beyond exposure to brain injury or subclinical brain trauma are important factors when interpreting variability in cognitive scores among collegiate athletes. Additionally, sport-specific differences in the proportional representation of various demographic variables (e.g., SES and race) may also be an important consideration within the broader biopsychosocial attributional model. (JINS, 2018, 24, 1-10).

Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other predictor variables: an analysis of 2,342 words.

PubMed

Cortese, Michael J; Khanna, Maya M

2007-08-01

Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses to predict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictors used by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above and beyond the standard predictor set. However, AoA was more strongly related to lexical-decision performance than to naming performance. Finally, the previously reported effect of imageability on naming latencies by Balota et al. was not significant with AoA included as a factor. These results are consistent with the idea either that AoA has a semantic/lexical locus or that AoA effects emerge primarily in situations in which the input-output mapping is arbitrary.

Performance of bed-load transport equations relative to geomorphic significance: Predicting effective discharge and its transport rate

Treesearch

Jeffrey J. Barry; John M. Buffington; Peter Goodwin; John .G. King; William W. Emmett

2008-01-01

Previous studies assessing the accuracy of bed-load transport equations have considered equation performance statistically based on paired observations of measured and predicted bed-load transport rates. However, transport measurements were typically taken during low flows, biasing the assessment of equation performance toward low discharges, and because equation...

What physical performance measures predict incident cognitive decline among intact older adults? A 4.4year follow up study.

PubMed

Veronese, Nicola; Stubbs, Brendon; Trevisan, Caterina; Bolzetta, Francesco; De Rui, Marina; Solmi, Marco; Sartori, Leonardo; Musacchio, Estella; Zambon, Sabina; Perissinotto, Egle; Crepaldi, Gaetano; Manzato, Enzo; Sergi, Giuseppe

2016-08-01

Reductions in physical performance, cognitive impairment (CI) and decline (CD), are common in older age, but few prospective cohort studies have considered the relationship between these domains. In this study we investigated whether reduced physical performance and low handgrip/lower limbs strength, could predict a higher incidence of CI/CD during a 4-year follow-up among a cohort of elderly individuals. From 3099 older community-dwelling individuals initially enrolled in the Progetto Veneto Anziani (PRO.V.A.) study, 1249 participants without CI at the baseline were included (mean age 72.2years, 59.5% females). Physical performance measures included the Short Physical Performance Battery (SPPB), 4m gait speed, chair stands time, leg extension and flexion, handgrip strength, and 6-Minute Walking Test (6MWT), categorized in gender-specific tertiles. CI was defined as a Mini-Mental State Examination (MMSE) score below 24; CD a decline of 3 or more points in the MMSE without CI. At baseline, participants developing CI during follow-up scored significantly worse across all physical performance measures compared to those that retained normal cognitive status. After adjusting for potential confounders, a significant trend for MMSE changes was noted for all physical performance tests, except for the SPPB and chair stands time. Multinomial logistic regression revealed that slow gait speed at baseline significantly predicted CD at follow up. Poor SPPB performance and slower gait speed predicted the onset of CI at the follow-up. In conclusion, slow walking speed appears to be the best independent predictor of poor cognitive status over a 4.4-year follow-up, while other items of SPPB were also significantly associated with CI. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of the Performance of Texas Pavements Made with Different Coarse Aggregates

DOT National Transportation Integrated Search

2000-10-01

This report summarizes 23 years of work undertaken in Texas to understand the reasons for significant performance differences found in pavements placed around the state. To a significant degree, pavement performance can be predicted based on the conc...

NASA Lewis Stirling engine computer code evaluation

NASA Technical Reports Server (NTRS)

Sullivan, Timothy J.

1989-01-01

In support of the U.S. Department of Energy's Stirling Engine Highway Vehicle Systems program, the NASA Lewis Stirling engine performance code was evaluated by comparing code predictions without engine-specific calibration factors to GPU-3, P-40, and RE-1000 Stirling engine test data. The error in predicting power output was -11 percent for the P-40 and 12 percent for the Re-1000 at design conditions and 16 percent for the GPU-3 at near-design conditions (2000 rpm engine speed versus 3000 rpm at design). The efficiency and heat input predictions showed better agreement with engine test data than did the power predictions. Concerning all data points, the error in predicting the GPU-3 brake power was significantly larger than for the other engines and was mainly a result of inaccuracy in predicting the pressure phase angle. Analysis into this pressure phase angle prediction error suggested that improvements to the cylinder hysteresis loss model could have a significant effect on overall Stirling engine performance predictions.

Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

PubMed Central

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Chang, Shu-Shya; Rau, Cheng-Shyuan; Tai, Hsueh-Ling; Peng, Shu-Hui; Lin, Yi-Chun; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-01-01

Background The aim of this study was to develop an effective surgical site infection (SSI) prediction model in patients receiving free-flap reconstruction after surgery for head and neck cancer using artificial neural network (ANN), and to compare its predictive power with that of conventional logistic regression (LR). Materials and methods There were 1,836 patients with 1,854 free-flap reconstructions and 438 postoperative SSIs in the dataset for analysis. They were randomly assigned tin ratio of 7:3 into a training set and a test set. Based on comprehensive characteristics of patients and diseases in the absence or presence of operative data, prediction of SSI was performed at two time points (pre-operatively and post-operatively) with a feed-forward ANN and the LR models. In addition to the calculated accuracy, sensitivity, and specificity, the predictive performance of ANN and LR were assessed based on area under the curve (AUC) measures of receiver operator characteristic curves and Brier score. Results ANN had a significantly higher AUC (0.892) of post-operative prediction and AUC (0.808) of pre-operative prediction than LR (both P<0.0001). In addition, there was significant higher AUC of post-operative prediction than pre-operative prediction by ANN (p<0.0001). With the highest AUC and the lowest Brier score (0.090), the post-operative prediction by ANN had the highest overall predictive performance. Conclusion The post-operative prediction by ANN had the highest overall performance in predicting SSI after free-flap reconstruction in patients receiving surgery for head and neck cancer. PMID:29568393

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

PubMed

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Relationships between episodic memory performance prediction and sociodemographic variables among healthy older adults.

PubMed

de Oliveira, Glaucia Martins; Cachioni, Meire; Falcão, Deusivania; Batistoni, Samila; Lopes, Andrea; Guimarães, Vanessa; Lima-Silva, Thais Bento; Neri, Anita Liberalesso; Yassuda, Mônica Sanches

2015-01-01

Previous studies have suggested that performance prediction, an aspect of metamemory, may be associated with objective performance on memory tasks. The objective of the study was to describe memory prediction before performing an episodic memory task, in community-dwelling older adults, stratified by sex, age group and educational level. Additionally, the association between predicted and objective performance on a memory task was investigated. The study was based on data from 359 participants in the FIBRA study carried out at Ermelino Matarazzo, São Paulo. Memory prediction was assessed by posing the question: "If someone showed you a sheet with drawings of 10 pictures to observe for 30 seconds, how many pictures do you think you could remember without seeing the sheet?". Memory performance was assessed by the memorization of 10 black and white pictures from the Brief Cognitive Screening Battery (BCSB). No differences were found between men and women, nor for age group and educational level, in memory performance prediction before carrying out the memory task. There was a modest association (rho=0.11, p=0.041) between memory prediction and performance in immediate memory. On multivariate linear regression analyses, memory performance prediction was moderately significantly associated with immediate memory (p=0.061). In this study, sociodemographic variables did not influence memory prediction, which was only modestly associated with immediate memory on the Brief Cognitive Screening Battery (BCSB).

Children's construction task performance and spatial ability: controlling task complexity and predicting mathematics performance.

PubMed

Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra

2014-12-01

This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.

Evaluation of the Performance of Texas Pavements Made with Different Coarse Aggregates: Project Summary Report

DOT National Transportation Integrated Search

1998-09-01

This report summarizes 23 years of work undertaken in Texas to understand the reasons for significant performance differences found in pavements placed around the state. To a significant degree, pavement performance can be predicted based on the conc...

CT volumetric measurement of colorectal cancer helps predict tumor staging and prognosis

PubMed Central

Park, Jin Young; Lee, Sang Min; Lee, Jeong Sub; Han, Joon Koo

2017-01-01

Purpose To evaluate feasibility of CT colonography (CTC) volumetry of colorectal cancer (CRC) and its correlation with disease stage and patients’ survival. Materials and methods CTC volumetry was performed for 126 patients who underwent preoperative CTC. Reproducibility of tumor volume (Tvol) between two readers was assessed. One-way ANOVA and ROC analysis evaluated correlation between Tvol and pTNM staging. ROC analysis compared diagnostic performance to predict pTNM staging between Tvol and radiologist. Kaplan-Meier test compared overall survival. Results Reproducibility among readers was excellent (interclass correlation = 0.9829). Mean Tvol showed an incremental trend with T stage and Tvol of pT4b stage was significantly larger than other stages (P<0.0001). Az value (0.780) of Tvol to predict pT4b stage was significantly larger than that (0.591) of radiologist (P = 0.004). However, Tvol was not significantly different according to pN stage. Az values (0.723~0.857) of Tvol to predict M1 or M1b were comparable to those (0.772~0.690) of radiologist (P>0.05). Smaller tumor burden (≤12.85cm3), ≤T3, N0, M0 stages, and curative surgery were significantly associated with patients’ longer survival (P<0.05). Conclusion CT volumetry has a limited value to predict N stage; however, it may outperform the radiologist’s performance when predicting pT4b and M1b stage and can be a useful prognostic marker. PMID:28570580

Longitudinal analysis of receptive vocabulary growth in young Spanish English-speaking children from migrant families.

PubMed

Jackson, Carla Wood; Schatschneider, Christopher; Leacox, Lindsey

2014-01-01

The authors of this study described developmental trajectories and predicted kindergarten performance of Spanish and English receptive vocabulary acquisition of young Latino/a English language learners (ELLs) from socioeconomically disadvantaged migrant families. In addition, the authors examined the extent to which gender and individual initial performance in Spanish predict receptive vocabulary performance and growth rate. The authors used hierarchical linear modeling of 64 children's receptive vocabulary performance to generate growth trajectories, predict performance at school entry, and examine potential predictors of rate of growth. The timing of testing varied across children. The ELLs (prekindergarten to 2nd grade) participated in 2-5 testing sessions, each 6-12 months apart. The ELLs' average predicted standard score on an English receptive vocabulary at kindergarten was nearly 2 SDs below the mean for monolingual peers. Significant growth in the ELLs' receptive vocabulary was observed between preschool and 2nd grade, indicating that the ELLs were slowly closing the receptive vocabulary gap, although their average score remained below the standard score mean for age-matched monolingual peers. The ELLs demonstrated a significant decrease in Spanish receptive vocabulary standard scores over time. Initial Spanish receptive vocabulary was a significant predictor of growth in English receptive vocabulary. High initial Spanish receptive vocabulary was associated with greater growth in English receptive vocabulary and decelerated growth in Spanish receptive vocabulary. Gender was not a significant predictor of growth in either English or Spanish receptive vocabulary. ELLs from low socioeconomic backgrounds may be expected to perform lower in English compared with their monolingual English peers in kindergarten. Performance in Spanish at school entry may be useful in identifying children who require more intensive instructional support for English vocabulary growth. Findings substantiate the need for progress monitoring across the early school years.

Surface Management System Departure Event Data Analysis

NASA Technical Reports Server (NTRS)

Monroe, Gilena A.

2010-01-01

This paper presents a data analysis of the Surface Management System (SMS) performance of departure events, including push-back and runway departure events.The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry data source for SMS predictions.

Sex Differences in Familiality Effects on Neurocognitive Performance in Schizophrenia

PubMed Central

Calkins, Monica E.; Ray, Amrita; Gur, Ruben C.; Freedman, Robert; Green, Michael F.; Greenwood, Tiffany A.; Light, Gregory A.; Nuechterlein, Keith H.; Olincy, Ann; Radant, Allen D.; Seidman, Larry J.; Siever, Larry J.; Silverman, Jeremy M.; Stone, William S.; Sugar, Catherine; Swerdlow, Neal R.; Tsuang, Debby W.; Tsuang, Ming T.; Turetsky, Bruce I.; Braff, David L.; Lazzeroni, Laura C.; Gur, Raquel E.

2013-01-01

Background Numerous studies have documented that patients with schizophrenia show neurocognitive impairments, which are also heritable in schizophrenia families. In view of these findings, the current investigation tested the hypothesis that neurocognitive performance of schizophrenia probands can predict the neurocognitive performance of their unaffected family members. Methods Participants (n=1,967; schizophrenia=369; first-degree relatives=1,072; community comparison subjects=526) in the Consortium on the Genetics of Schizophrenia (COGS) were administered the Penn Computerized Neurocognitive Battery (CNB). Results Consistent with prior work, probands showed significant neurocognitive impairment, and neurocognitive ability was significantly heritable, across domains. On average, unaffected relatives did not differ from community comparison subjects in their neurocognitive performance. However, in 6 of 7 domains, probands’ score predicted the performance of their unaffected siblings. Male, but not female, probands’ performance was predictive of their unaffected relatives (siblings and mothers) performance, most consistently in face memory and spatial processing. Conclusions Using a novel approach in which individual probands are paired with their respective unaffected relatives within each family, we found that male probands’ performance predicted both sister and brother performance, an effect that was most powerfully observed for face memory and spatial processing. Results suggest that the familial transmission of sexually dimorphic neurocognitive domains, in which a particular sex tends to show a performance advantage over the other, may not itself be sex specific in schizophrenia families. PMID:23395246

Sex differences in familiality effects on neurocognitive performance in schizophrenia.

PubMed

Calkins, Monica E; Ray, Amrita; Gur, Ruben C; Freedman, Robert; Green, Michael F; Greenwood, Tiffany A; Light, Gregory A; Nuechterlein, Keith H; Olincy, Ann; Radant, Allen D; Seidman, Larry J; Siever, Larry J; Silverman, Jeremy M; Stone, William S; Sugar, Catherine; Swerdlow, Neal R; Tsuang, Debby W; Tsuang, Ming T; Turetsky, Bruce I; Braff, David L; Lazzeroni, Laura C; Gur, Raquel E

2013-05-15

Numerous studies have documented that patients with schizophrenia show neurocognitive impairments, which are also heritable in schizophrenia families. In view of these findings, the current investigation tested the hypothesis that neurocognitive performance of schizophrenia probands can predict the neurocognitive performance of their unaffected family members. Participants (n=1967; schizophrenia=369; first-degree relatives=1072; community comparison subjects=526) in the Consortium on the Genetics of Schizophrenia were administered the Penn Computerized Neurocognitive Battery. Consistent with prior work, probands showed significant neurocognitive impairment, and neurocognitive ability was significantly heritable across domains. On average, unaffected relatives did not differ from community comparison subjects in their neurocognitive performance. However, in six of seven domains, proband scores predicted the performance of their unaffected siblings. Male, but not female, proband performance was predictive of their unaffected relatives' (siblings and mothers) performance, most consistently in face memory and spatial processing. Using a novel approach in which individual probands are paired with their respective unaffected relatives within each family, we found that male proband performance predicted both sister and brother performance, an effect that was most powerfully observed for face memory and spatial processing. Results suggest that the familial transmission of sexually dimorphic neurocognitive domains, in which a particular sex tends to show a performance advantage over the other, may not itself be sex specific in schizophrenia families. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

PubMed

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-07-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

Validity of Treadmill-Derived Critical Speed on Predicting 5000-Meter Track-Running Performance.

PubMed

Nimmerichter, Alfred; Novak, Nina; Triska, Christoph; Prinz, Bernhard; Breese, Brynmor C

2017-03-01

Nimmerichter, A, Novak, N, Triska, C, Prinz, B, and Breese, BC. Validity of treadmill-derived critical speed on predicting 5,000-meter track-running performance. J Strength Cond Res 31(3): 706-714, 2017-To evaluate 3 models of critical speed (CS) for the prediction of 5,000-m running performance, 16 trained athletes completed an incremental test on a treadmill to determine maximal aerobic speed (MAS) and 3 randomly ordered runs to exhaustion at the [INCREMENT]70% intensity, at 110% and 98% of MAS. Critical speed and the distance covered above CS (D') were calculated using the hyperbolic speed-time (HYP), the linear distance-time (LIN), and the linear speed inverse-time model (INV). Five thousand meter performance was determined on a 400-m running track. Individual predictions of 5,000-m running time (t = [5,000-D']/CS) and speed (s = D'/t + CS) were calculated across the 3 models in addition to multiple regression analyses. Prediction accuracy was assessed with the standard error of estimate (SEE) from linear regression analysis and the mean difference expressed in units of measurement and coefficient of variation (%). Five thousand meter running performance (speed: 4.29 ± 0.39 m·s; time: 1,176 ± 117 seconds) was significantly better than the predictions from all 3 models (p < 0.0001). The mean difference was 65-105 seconds (5.7-9.4%) for time and -0.22 to -0.34 m·s (-5.0 to -7.5%) for speed. Predictions from multiple regression analyses with CS and D' as predictor variables were not significantly different from actual running performance (-1.0 to 1.1%). The SEE across all models and predictions was approximately 65 seconds or 0.20 m·s and is therefore considered as moderate. The results of this study have shown the importance of aerobic and anaerobic energy system contribution to predict 5,000-m running performance. Using estimates of CS and D' is valuable for predicting performance over race distances of 5,000 m.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Comparative values of medical school assessments in the prediction of internship performance.

PubMed

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

NASA Technical Reports Server (NTRS)

Sebok, Angelia; Wickens, Christopher; Sargent, Robert

2015-01-01

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.

Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

PubMed

Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

2015-01-01

Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

Predicting story goodness performance from cognitive measures following traumatic brain injury.

PubMed

Lê, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-05-01

This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Lê, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. One hundred sixty-seven individuals with TBI participated in the study. Story retellings were analyzed using the SGI protocol. Three cognitive measures--Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) Sorting Test, Wechsler Memory Scale--Third Edition (WMS-III; Wechsler, 1997) Working Memory Primary Index (WMI), and WMS-III Immediate Memory Primary Index (IMI)--were entered into a multiple linear regression model for each discourse measure. Two sets of regression analyses were performed, the first with the Sorting Test as the first predictor and the second with it as the last. The first set of regression analyses identified the Sorting Test and IMI as the only significant predictors of performance on measures of the SGI. The second set identified all measures as significant predictors when evaluating each step of the regression function. The cognitive variables predicted performance on the SGI measures, although there were differences in the amount of explained variance. The results (a) suggest that storytelling ability draws on a number of underlying skills and (b) underscore the importance of using discrete cognitive tasks rather than broad cognitive indices to investigate the cognitive substrates of discourse.

Performance-based measures and behavioral ratings of executive function in diagnosing attention-deficit/hyperactivity disorder in children.

PubMed

Tan, Alexander; Delgaty, Lauren; Steward, Kayla; Bunner, Melissa

2018-04-16

Deficits in real-world executive functioning (EF) are a frequent characteristic of attention-deficit/hyperactivity disorder (ADHD). However, the predictive value of using performance-based and behavioral rating measures of EF when diagnosing ADHD remains unclear. The current study investigates the use of performance-based EF measures and a parent-report questionnaire with established ecological validity and clinical utility when diagnosing ADHD. Participants included 21 healthy controls, 21 ADHD-primary inattentive, and 21 ADHD-combined type subjects aged 6-15 years. A brief neuropsychological battery was administered to each subject including common EF assessment measures. Significant differences were not found between groups on most performance-based EF measures, whereas significant differences (p < 0.05) were found on most parent-report behavioral rating scales. Furthermore, performance-based measures did not predict group membership above chance levels. Results further support differences in predictive value of EF performance-based measures compared to parent-report questionnaires when diagnosing ADHD. Further research must investigate the relationship between performance-based and behavioral rating measures when assessing EF in ADHD.

Grid orthogonality effects on predicted turbine midspan heat transfer and performance

NASA Technical Reports Server (NTRS)

Boyle, R. J.; Ameri, A. A.

1995-01-01

The effect of five different C type grid geometries on the predicted heat transfer and aerodynamic performance of a turbine stator is examined. Predictions were obtained using two flow analysis codes. One was a finite difference analysis, and the other was a finite volume analysis. Differences among the grids in terms of heat transfer and overall performance were small. The most significant difference among the five grids occurred in the prediction of pitchwise variation in total pressure. There was consistency between results obtained with each of the flow analysis codes when the same grid was used. A grid generating procedure in which the viscous grid is embedded within an inviscid type grid resulted in the best overall performance.

Stereotype threat can both enhance and impair older adults' memory.

PubMed

Barber, Sarah J; Mather, Mara

2013-12-01

Negative stereotypes about aging can impair older adults' memory via stereotype threat; however, the mechanisms underlying this phenomenon are unclear. In two experiments, we tested competing predictions derived from two theoretical accounts of stereotype threat: executive-control interference and regulatory fit. Older adults completed a working memory test either under stereotype threat about age-related memory declines or not under such threat. Monetary incentives were manipulated such that recall led to gains or forgetting led to losses. The executive-control-interference account predicts that stereotype threat decreases the availability of executive-control resources and hence should impair working memory performance. The regulatory-fit account predicts that threat induces a prevention focus, which should impair performance when gains are emphasized but improve performance when losses are emphasized. Results were consistent only with the regulatory-fit account. Although stereotype threat significantly impaired older adults' working memory performance when remembering led to gains, it significantly improved performance when forgetting led to losses.

Part A: Assessing the performance of the COMFA outdoor thermal comfort model on subjects performing physical activity

NASA Astrophysics Data System (ADS)

Kenny, Natasha A.; Warland, Jon S.; Brown, Robert D.; Gillespie, Terry G.

2009-09-01

This study assessed the performance of the COMFA outdoor thermal comfort model on subjects performing moderate to vigorous physical activity. Field tests were conducted on 27 subjects performing 30 min of steady-state activity (walking, running, and cycling) in an outdoor environment. The predicted COMFA budgets were compared to the actual thermal sensation (ATS) votes provided by participants during each 5-min interval. The results revealed a normal distribution in the subjects’ ATS votes, with 82% of votes received in categories 0 (neutral) to +2 (warm). The ATS votes were significantly dependent upon sex, air temperature, short and long-wave radiation, wind speed, and metabolic activity rate. There was a significant positive correlation between the ATS and predicted budgets (Spearman’s rho = 0.574, P < 0.01). However, the predicted budgets did not display a normal distribution, and the model produced erroneous estimates of the heat and moisture exchange between the human body and the ambient environment in 6% of the cases.

Mission possible? The performance of prosocially motivated employees depends on manager trustworthiness.

PubMed

Grant, Adam M; Sumanth, John J

2009-07-01

The authors propose that in mission-driven organizations, prosocially motivated employees are more likely to perform effectively when trust cues enhance their perceptions of task significance. The authors develop and test a model linking prosocial motivation, trust cues, task significance, and performance across 3 studies of fundraisers using 3 different objective performance measures. In Study 1, perceiving managers as trustworthy strengthened the relationship between employees' prosocial motivation and performance, measured in terms of calls made. This moderated relationship was mediated by employees' perceptions of task significance. Study 2 replicated the interaction of manager trustworthiness and prosocial motivation in predicting a new measure of performance: dollars raised. It also revealed 3-way interactions between prosocial motivation, manager trustworthiness, and dispositional trust propensity, such that high trust propensity compensated for low manager trustworthiness to strengthen the association between employees' prosocial motivation and performance. Study 3 replicated all of the previous mediation and moderation findings in predicting initiative taken by professional fundraisers. Implications for work motivation, work design, and trust in organizations are discussed.

Predictive validity of driving-simulator assessments following traumatic brain injury: a preliminary study.

PubMed

Lew, Henry L; Poole, John H; Lee, Eun Ha; Jaffe, David L; Huang, Hsiu-Chen; Brodd, Edward

2005-03-01

To evaluate whether driving simulator and road test evaluations can predict long-term driving performance, we conducted a prospective study on 11 patients with moderate to severe traumatic brain injury. Sixteen healthy subjects were also tested to provide normative values on the simulator at baseline. At their initial evaluation (time-1), subjects' driving skills were measured during a 30-minute simulator trial using an automated 12-measure Simulator Performance Index (SPI), while a trained observer also rated their performance using a Driving Performance Inventory (DPI). In addition, patients were evaluated on the road by a certified driving evaluator. Ten months later (time-2), family members observed patients driving for at least 3 hours over 4 weeks and rated their driving performance using the DPI. At time-1, patients were significantly impaired on automated SPI measures of driving skill, including: speed and steering control, accidents, and vigilance to a divided-attention task. These simulator indices significantly predicted the following aspects of observed driving performance at time-2: handling of automobile controls, regulation of vehicle speed and direction, higher-order judgment and self-control, as well as a trend-level association with car accidents. Automated measures of simulator skill (SPI) were more sensitive and accurate than observational measures of simulator skill (DPI) in predicting actual driving performance. To our surprise, the road test results at time-1 showed no significant relation to driving performance at time-2. Simulator-based assessment of patients with brain injuries can provide ecologically valid measures that, in some cases, may be more sensitive than a traditional road test as predictors of long-term driving performance in the community.

Predictors of outcomes following reablement in community-dwelling older adults.

PubMed

Tuntland, Hanne; Kjeken, Ingvild; Langeland, Eva; Folkestad, Bjarte; Espehaug, Birgitte; Førland, Oddvar; Aaslund, Mona Kristin

2017-01-01

Reablement is a rehabilitation intervention for community-dwelling older adults, which has recently been implemented in several countries. Its purpose is to improve functional ability in daily occupations (everyday activities) perceived as important by the older person. Performance and satisfaction with performance in everyday life are the major outcomes of reablement. However, the evidence base concerning which factors predict better outcomes and who receives the greatest benefit in reablement is lacking. The objective of this study was to determine the potential factors that predict occupational performance and satisfaction with that performance at 10 weeks follow-up. The sample in this study was derived from a nationwide clinical controlled trial evaluating the effects of reablement in Norway and consisted of 712 participants living in 34 municipalities. Multiple linear regression was used to investigate possible predictors of occupational performance (COPM-P) and satisfaction with that performance (COPM-S) at 10 weeks follow-up based on the Canadian Occupational Performance Measure (COPM). The results indicate that the factors that significantly predicted better COPM-P and COPM-S outcomes at 10 weeks follow-up were higher baseline scores of COPM-P and COPM-S respectively, female sex, having a fracture as the major health condition and high motivation for rehabilitation. Conversely, the factors that significantly predicted poorer COPM-P and COPM-S outcomes were having a neurological disease other than stroke, having dizziness/balance problems as the major health condition and having pain/discomfort. In addition, having anxiety/depression was a predictor of poorer COPM-P outcomes. The two regression models explained 38.3% and 38.8% of the total variance of the dependent variables of occupational performance and satisfaction with that performance, respectively. The results indicate that diagnosis, functional level, sex and motivation are significant predictors of outcomes following reablement.

Relations Among Student Attention Behaviors, Teacher Practices, and Beginning Word Reading Skill

PubMed Central

Sáez, Leilani; Folsom, Jessica Sidler; Al Otaiba, Stephanie; Schatschneider, Christopher

2011-01-01

The role of student attention for predicting kindergarten word reading was investigated among 432 students. Using SWAN behavior rating scores, we conducted an exploratory factor analysis, which yielded three distinct factors that reflected selective attention. In this study, we focused on the role of one of these factors, which we labeled attention-memory behaviors, for predicting reading performance. Teacher ratings of attention predicted word reading above and beyond the contribution of phonological awareness and vocabulary knowledge. In addition, the relations between four teacher practices and attention ratings for predicting reading performance were examined. Using HLM, significant interactions between student attention and teacher practices observed during literacy instruction were found. In general, as ratings of attention improved, better kindergarten word reading performance was associated with high levels of classroom behavior management. However, by mid-year, better word reading performance was not associated with high levels of teacher task- orienting. A significant three-way interaction was also found among attention, individualized instruction, and teacher task re-directions. The role of regulating kindergarten student attention to support beginning word reading skill development is discussed. PMID:22207616

Predictive Variables of Half-Marathon Performance for Male Runners.

PubMed

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A; García-López, Juan

2017-06-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO 2max , speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance.

Comparative evaluation of GPR versus APRI and FIB-4 in predicting different levels of liver fibrosis of chronic hepatitis B.

PubMed

Liu, D-P; Lu, W; Zhang, Z-Q; Wang, Y-B; Ding, R-R; Zhou, X-L; Huang, D; Li, X-F

2018-05-01

It is of great significance to develop and evaluate noninvasive indexes predicting the level of liver fibrosis. The aim of this study was to comparatively evaluate gamma-glutamyl transpeptidase-to-platelet ratio (GPR) versus aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on 4 factors (FIB-4) in predicting different levels of liver fibrosis of chronic hepatitis B (CHB) within the framework of HBeAg-positive and HBeAg-negative patients. A total of 1157 HBeAg-positive and 859 HBeAg-negative CHB patients were enrolled, among whom the pathological stage ≥S2, ≥S3, ≥S4 were defined as significant fibrosis, extensive fibrosis and cirrhosis, respectively. Receiver operating characteristic (ROC) curves were used to evaluate the performance of GPR, APRI and FIB-4 in predicting different levels of liver fibrosis. In HBeAg-positive patients, the area under ROC curves (AUROCs) of GPR in predicting extensive fibrosis and cirrhosis were both significantly larger than those of APRI (P = .0001 and P < .0001). In HBeAg-negative patients, the AUROCs of GPR in predicting significant fibrosis and cirrhosis were significantly larger than those of FIB-4 (P = .0006 and P = .0041). The AUROC of GPR in predicting extensive fibrosis was significantly larger than that of APRI and FIB-4 (P = .0320 and P = .0018). Using a cut-off of GPR > 0.500 as standard, the sensitivities and specificities of GPR in predicting significant fibrosis in HBeAg-positive patients were 59.6% and 81.2%, and for cirrhosis 80.9% and 63.8%, respectively; and those of HBeAg-negative patients were 60.3% and 78.3%, 84.5% and 66.1%, respectively. Regardless of HBeAg-positive or HBeAg-negative status, GPR had the best performance in predicting different levels of liver fibrosis. © 2017 John Wiley & Sons Ltd.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

Predicting Airport Screening Officers' Visual Search Competency With a Rapid Assessment.

PubMed

Mitroff, Stephen R; Ericson, Justin M; Sharpe, Benjamin

2018-03-01

Objective The study's objective was to assess a new personnel selection and assessment tool for aviation security screeners. A mobile app was modified to create a tool, and the question was whether it could predict professional screeners' on-job performance. Background A variety of professions (airport security, radiology, the military, etc.) rely on visual search performance-being able to detect targets. Given the importance of such professions, it is necessary to maximize performance, and one means to do so is to select individuals who excel at visual search. A critical question is whether it is possible to predict search competency within a professional search environment. Method Professional searchers from the USA Transportation Security Administration (TSA) completed a rapid assessment on a tablet-based X-ray simulator (XRAY Screener, derived from the mobile technology app Airport Scanner; Kedlin Company). The assessment contained 72 trials that were simulated X-ray images of bags. Participants searched for prohibited items and tapped on them with their finger. Results Performance on the assessment significantly related to on-job performance measures for the TSA officers such that those who were better XRAY Screener performers were both more accurate and faster at the actual airport checkpoint. Conclusion XRAY Screener successfully predicted on-job performance for professional aviation security officers. While questions remain about the underlying cognitive mechanisms, this quick assessment was found to significantly predict on-job success for a task that relies on visual search performance. Application It may be possible to quickly assess an individual's visual search competency, which could help organizations select new hires and assess their current workforce.

Examining Predictive Validity of Oral Reading Fluency Slope in Upper Elementary Grades Using Quantile Regression.

PubMed

Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon

2017-07-01

Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

PubMed Central

Sieberts, Solveig K.; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S. K.; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-ud-din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Mezlini, Aziz M.; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E.; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Calaza, Manuel; Elmarakeby, Haitham; Heath, Lenwood S.; Long, Quan; Moore, Jonathan D.; Opiyo, Stephen Obol; Savage, Richard S.; Zhu, Jun; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P.; Gerlag, Danielle; Huizinga, Tom W. J.; Kurreeman, Fina; Allaart, Cornelia F.; Louis Bridges Jr., S.; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K.; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M.

2016-01-01

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data. PMID:27549343

Only White Matter Hyperintensities Predicts Post-Stroke Cognitive Performances Among Cerebral Small Vessel Disease Markers: Results from the TABASCO Study.

PubMed

Molad, Jeremy; Kliper, Efrat; Korczyn, Amos D; Ben Assayag, Einor; Ben Bashat, Dafna; Shenhar-Tsarfaty, Shani; Aizenstein, Orna; Shopin, Ludmila; Bornstein, Natan M; Auriel, Eitan

2017-01-01

White matter hyperintensities (WMH) were shown to predict cognitive decline following stroke or transient ischemic attack (TIA). However, WMH are only one among other radiological markers of cerebral small vessel disease (SVD). The aim of this study was to determine whether adding other SVD markers to WMH improves prediction of post-stroke cognitive performances. Consecutive first-ever stroke or TIA patients (n = 266) from the Tel Aviv Acute Brain Stroke Cohort (TABASCO) study were enrolled. MRI scans were performed within seven days of stroke onset. We evaluated the relationship between cognitive performances one year following stroke, and previously suggested total SVD burden score including WMH, lacunes, cerebral microbleeds (CMB), and perivascular spaces (PVS). Significant negative associations were found between WMH and cognition (p < 0.05). Adding other SVD markers (lacunes, CMB, PVS) to WMH did not improve predication of post-stroke cognitive performances. Negative correlations between SVD burden score and cognitive scores were observed for global cognitive, memory, and visual spatial scores (all p < 0.05). However, following an adjustment for confounders, no associations remained significant. WMH score was associated with poor post-stroke cognitive performance. Adding other SVD markers or SVD burden score, however, did not improve prediction.

Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)

PubMed Central

Hammer, Eva M.; Kaufmann, Tobias; Kleih, Sonja C.; Blankertz, Benjamin; Kübler, Andrea

2014-01-01

Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person’s visuo-motor control ability; and (2) subject’s “attentional impulsivity”. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors. PMID:25147518

Predictive validity of pre-admission assessments on medical student performance.

PubMed

Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef; Mohamed, Ayman

2017-11-24

To examine the predictive validity of pre-admission variables on students' performance in a medical school in Saudi Arabia. In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students' progress test performance (p<0.001 and B=19.02). Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years.

Predictive validity of pre-admission assessments on medical student performance

PubMed Central

Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M. Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef

2017-01-01

Objectives To examine the predictive validity of pre-admission variables on students’ performance in a medical school in Saudi Arabia.  Methods In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. Results In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students’ progress test performance (p<0.001 and B=19.02). Conclusions Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years. PMID:29176032

Self-regulated learning and achievement by middle-school children.

PubMed

Sink, C A; Barnett, J E; Hixon, J E

1991-12-01

The relationship of self-regulated learning to the achievement test scores of 62 Grade 6 students was studied. Generally, the metacognitive and affective variables correlated significantly with teachers' grades and standardized test scores in mathematics, reading, and science. Planning and self-assessment significantly predicted the six measures of achievement. Step-wise multiple regression analyses using the metacognitive and affective variables largely indicate that students' and teachers' perceptions of scholastic ability and planning appear to be the most salient factors in predicting academic performance. The locus of control dimension had no utility in predicting classroom grades and performance on standardized measures of achievement. The implications of the findings for teaching and learning are discussed.

Has the UK Clinical Aptitude Test improved medical student selection?

PubMed

Wright, Sarah R; Bradley, Philip M

2010-11-01

In 2006, the United Kingdom Clinical Aptitude Test (UKCAT) was introduced as a new medical school admissions tool. The aim of this cohort study was to determine whether the UKCAT has made any improvements to the way medical students are selected. Regression analysis was performed in order to study the ability of previous school type and gender to predict UKCAT, personal statement or interview scores in two cohorts of accepted students. The ability of admissions scores and demographic data to predict performance on knowledge and skills examinations was also studied. Previous school type was not a significant predictor of either interview or UKCAT scores amongst students who had been accepted onto the programme (n = 307). However, it was a significant predictor of personal statement score, with students from independent and grammar schools performing better than students from state-maintained schools. Previous school type, personal statements and interviews were not significant predictors of knowledge examination performance. UKCAT scores were significant predictors of knowledge examination performance for all but one examination administered in the first 2 years of medical school. Admissions data explained very little about performance on skills (objective structured clinical examinations [OSCEs]) assessments. The use of personal statements as a basis for selection results in a bias towards students from independent and grammar schools. However, no evidence was found to suggest that students accepted from these schools perform any better than students from maintained schools on Year 1 and 2 medical school examinations. Previous school type did not predict interview or UKCAT scores of accepted students. UKCAT scores are predictive of Year 1 and 2 examination performance at this medical school, whereas interview scores are not. The results of this study challenge claims made by other authors that aptitude tests do not have a place in medical school selection in the UK. © Blackwell Publishing Ltd 2010.

A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules

PubMed Central

Ramakrishnan, Sridhar; Wesensten, Nancy J.; Balkin, Thomas J.; Reifman, Jaques

2016-01-01

Study Objectives: Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss—from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges—and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. Methods: We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. Results: The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. Conclusions: The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. Citation: Ramakrishnan S, Wesensten NJ, Balkin TJ, Reifman J. A unified model of performance: validation of its predictions across different sleep/wake schedules. SLEEP 2016;39(1):249–262. PMID:26518594

The validity of ACT-PEP test scores for predicting academic performance of registered nurses in BSN programs.

PubMed

Yang, J C; Noble, J

1990-01-01

This study investigated the validity of three American College Testing-Proficiency Examination Program (ACT-PEP) tests (Maternal and Child Nursing, Psychiatric/Mental Health Nursing, Adult Nursing) for predicting the academic performance of registered nurses (RNs) enrolled in bachelor's degree BSN programs nationwide. This study also examined RN students' performance on the ACT-PEP tests by their demographic characteristics: student's age, sex, race, student status (full- or part-time), and employment status (full- or part-time). The total sample for the three tests comprised 2,600 students from eight institutions nationwide. The median correlation coefficients between the three ACT-PEP tests and the semester grade point averages ranged from .36 to .56. Median correlation coefficients increased over time, supporting the stability of ACT-PEP test scores for predicting academic performance over time. The relative importance of selected independent variables for predicting academic performance was also examined; the most important variable for predicting academic performance was typically the ACT-PEP test score. Across the institutions, student demographic characteristics did not contribute significantly to explaining academic performance, over and above ACT-PEP scores.

Validity of the Optometry Admission Test in Predicting Performance in Schools and Colleges of Optometry.

ERIC Educational Resources Information Center

Kramer, Gene A.; Johnston, JoElle

1997-01-01

A study examined the relationship between Optometry Admission Test scores and pre-optometry or undergraduate grade point average (GPA) with first and second year performance in optometry schools. The test's predictive validity was limited but significant, and comparable to those reported for other admission tests. In addition, the scores…

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules.

PubMed

Ramakrishnan, Sridhar; Wesensten, Nancy J; Balkin, Thomas J; Reifman, Jaques

2016-01-01

Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss-from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges-and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. © 2016 Associated Professional Sleep Societies, LLC.

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

PubMed

Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

2009-02-15

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/. (c) 2008 Wiley-Liss, Inc.

Significant SNPs have limited prediction ability for thyroid cancer

PubMed Central

Guo, Shicheng; Wang, Yu-Long; Li, Yi; Jin, Li; Xiong, Momiao; Ji, Qing-Hai; Wang, Jiucun

2014-01-01

Recently, five thyroid cancer significantly associated genetic variants (rs965513, rs944289, rs116909374, rs966423, and rs2439302) have been discovered and validated in two independent GWAS and numerous case–control studies, which were conducted in different populations. We genotyped the above five single nucleotide polymorphisms (SNPs) in Han Chinese populations and performed thyroid cancer-risk predictions with nine machine learning methods. We found that four SNPs were significantly associated with thyroid cancer in Han Chinese population, while no polymorphism was observed for rs116909374. Small familial relative risks (1.02–1.05) and limited power to predict thyroid cancer (AUCs: 0.54–0.60) indicate limited clinical potential. Four significant SNPs have limited prediction ability for thyroid cancer. PMID:24591304

Prediction of the diffuse-field transmission loss of interior natural-ventilation openings and silencers.

PubMed

Bibby, Chris; Hodgson, Murray

2017-01-01

The work reported here, part of a study on the performance and optimal design of interior natural-ventilation openings and silencers ("ventilators"), discusses the prediction of the acoustical performance of such ventilators, and the factors that affect it. A wave-based numerical approach-the finite-element method (FEM)-is applied. The development of a FEM technique for the prediction of ventilator diffuse-field transmission loss is presented. Model convergence is studied with respect to mesh, frequency-sampling and diffuse-field convergence. The modeling technique is validated by way of predictions and the comparison of them to analytical and experimental results. The transmission-loss performance of crosstalk silencers of four shapes, and the factors that affect it, are predicted and discussed. Performance increases with flow-path length for all silencer types. Adding elbows significantly increases high-frequency transmission loss, but does not increase overall silencer performance which is controlled by low-to-mid-frequency transmission loss.

The big five personality traits and individual job performance growth trajectories in maintenance and transitional job stages.

PubMed

Thoresen, Carl J; Bradley, Jill C; Bliese, Paul D; Thoresen, Joseph D

2004-10-01

This study extends the literature on personality and job performance through the use of random coefficient modeling to test the validity of the Big Five personality traits in predicting overall sales performance and sales performance trajectories--or systematic patterns of performance growth--in 2 samples of pharmaceutical sales representatives at maintenance and transitional job stages (K. R. Murphy, 1989). In the maintenance sample, conscientiousness and extraversion were positively associated with between-person differences in total sales, whereas only conscientiousness predicted performance growth. In the transitional sample, agreeableness and openness to experience predicted overall performance differences and performance trends. All effects remained significant with job tenure statistically controlled. Possible explanations for these findings are offered, and theoretical and practical implications of findings are discussed. (c) 2004 APA, all rights reserved

Diagnostic performance of dual-energy CT stress myocardial perfusion imaging: direct comparison with cardiovascular MRI.

PubMed

Ko, Sung Min; Song, Meong Gun; Chee, Hyun Kun; Hwang, Hweung Kon; Feuchtner, Gudrun Maria; Min, James K

2014-12-01

The purpose of this study was to assess the diagnostic performance of stress perfusion dual-energy CT (DECT) and its incremental value when used with coronary CT angiography (CTA) for identifying hemodynamically significant coronary artery disease. One hundred patients with suspected or known coronary artery disease without chronic myocardial infarction detected with coronary CTA underwent stress perfusion DECT, stress cardiovascular perfusion MRI, and invasive coronary angiography (ICA). Stress perfusion DECT and cardiovascular stress perfusion MR images were used for detecting perfusion defects. Coronary CTA and ICA were evaluated in the detection of ≥50% coronary stenosis. The diagnostic performance of coronary CTA for detecting hemo-dynamically significant stenosis was assessed before and after stress perfusion DECT on a per-vessel basis with ICA and cardiovascular stress perfusion MRI as the reference standard. The performance of stress perfusion DECT compared with cardiovascular stress perfusion MRI on a per-vessel basis in the detection of perfusion defects was sensitivity, 89%; specificity, 74%; positive predictive value, 73%; negative predictive value, 90%. Per segment, these values were sensitivity, 76%; specificity, 80%; positive predictive value, 63%; and negative predictive value, 88%. Compared with ICA and cardiovascular stress perfusion MRI per vessel territory the sensitivity, specificity, positive predictive value, and negative predictive value of coronary CTA were 95%, 61%, 61%, and 95%. The values for stress perfusion DECT were 92%, 72%, 68%, and 94%. The values for coronary CTA and stress perfusion DECT were 88%, 79%, 73%, and 91%. The ROC AUC increased from 0.78 to 0.84 (p=0.02) with the use of coronary CTA and stress perfusion DECT compared with coronary CTA alone. Stress perfusion DECT plays a complementary role in enhancing the accuracy of coronary CTA for identifying hemodynamically significant coronary stenosis.

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

PubMed

Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

2014-03-01

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

Predictive validity of the HCR-20 for inpatient aggression: the effect of intellectual disability on accuracy.

PubMed

O'Shea, L E; Picchioni, M M; McCarthy, J; Mason, F L; Dickens, G L

2015-11-01

People with intellectual disability (ID) account for a large proportion of aggressive incidents in secure and forensic psychiatric services. Although the Historical, Clinical, Risk Management 20 (HCR-20) has good predictive validity in inpatient settings, it does not perform equally in all groups and there is little evidence for its efficacy in those with ID. A pseudo-prospective cohort study of the predictive efficacy of the HCR-20 for those with ID (n = 109) was conducted in a UK secure mental health setting using routinely collected risk data. Performance of the HCR-20 in the ID group was compared with a comparison group of adult inpatients without an ID (n = 504). Analysis controlled for potential covariates including security level, length of stay, gender and diagnosis. The HCR-20 total score was a significant predictor of any aggression and of physical aggression for both groups, although the area under the curve values did not reach the threshold for a large effect size. The clinical subscale performed significantly better in those without an ID compared with those with. The ID group had a greater number of relevant historical and risk management items. The clinicians' summary judgment significantly predicted both types of aggressive outcomes in the ID group, but did not predict either in those without an ID. This study demonstrates that, after controlling for a range of potential covariates, the HCR-20 is a significant predictor of inpatient aggression in people with an ID and performs as well as for a comparison group of mentally disordered individuals without ID. The potency of HCR-20 subscales and items varied between the ID and comparison groups suggesting important target areas for improved prediction and risk management interventions in those with ID. © 2015 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Predictive value of background experiences and visual spatial ability testing on laparoscopic baseline performance among residents entering postgraduate surgical training.

PubMed

Louridas, Marisa; Quinn, Lauren E; Grantcharov, Teodor P

2016-03-01

Emerging evidence suggests that despite dedicated practice, not all surgical trainees have the ability to reach technical competency in minimally invasive techniques. While selecting residents that have the ability to reach technical competence is important, evidence to guide the incorporation of technical ability into selection processes is limited. Therefore, the purpose of the present study was to evaluate whether background experiences and 2D-3D visual spatial test results are predictive of baseline laparoscopic skill for the novice surgical trainee. First-year residents were studied. Demographic data and background surgical and non-surgical experiences were obtained using a questionnaire. Visual spatial ability was evaluated using the PicSOr, cube comparison (CC) and card rotation (CR) tests. Technical skill was assessed using the camera navigation (LCN) task and laparoscopic circle cut (LCC) task. Resident performance on these technical tasks was compared and correlated with the questionnaire and visual spatial findings. Previous experience in observing laparoscopic procedures was associated with significantly better LCN performance, and experience in navigating the laparoscopic camera was associated with significantly better LCC task results. Residents who scored higher on the CC test demonstrated a more accurate LCN path length score (r s(PL) = -0.36, p = 0.03) and angle path (r s(AP) = -0.426, p = 0.01) score when completing the LCN task. No other significant correlations were found between the visual spatial tests (PicSOr, CC or CR) and LCC performance. While identifying selection tests for incoming surgical trainees that predict technical skill performance is appealing, the surrogate markers evaluated correlate with specific metrics of surgical performance related to a single task but do not appear to reliably predict technical performance of different laparoscopic tasks. Predicting the acquisition of technical skills will require the development of a series of evidence-based tests that measure a number of innate abilities as well as their inherent interactions.

Comparison of Predictive Factors for Postoperative Incontinence of Holmium Laser Enucleation of the Prostate by the Surgeons' Experience During Learning Curve.

PubMed

Shigemura, Katsumi; Tanaka, Kazushi; Yamamichi, Fukashi; Chiba, Koji; Fujisawa, Masato

2016-03-01

To detect predictive factors for postoperative incontinence following holmium laser enucleation of the prostate (HoLEP) according to surgeon experience (beginner or experienced) and preoperative clinical data. Of 224 patients, a total of 203 with available data on incontinence were investigated. The potential predictive factors for post-HoLEP incontinence included clinical factors, such as patient age, and preoperative urodynamic study results, including detrusor overactivity (DO). We also classified the surgeons performing the procedure according to their HoLEP experience: beginner (<21 cases) and experienced (≥21 cases). Our statistical data showed DO was a significant predictive factor at the super-short period (the next day of catheter removal: odds ratio [OR], 3.375; P=0.000). Additionally, patient age, surgeon mentorship (inverse correlation), and prostate volume were significant predictive factors at the 1-month interval after HoLEP (OR, 1.072; P=0.004; OR, 0.251; P=0.002; and OR, 1.008; P=0.049, respectively). With regards to surgeon experience, DO and preoperative International Prostate Symptom Score (inverse) at the super-short period, and patient age and mentorship (inverse correlation) at the 1-month interval after HoLEP (OR, 3.952; P=0.002; OR, 1.084; P=0.015; and OR,1.084; P=0.015; OR, 0.358; P=0.003, respectively) were significant predictive factors for beginners, and first desire to void (FDV) at 1 month after HoLEP (OR, 1.009; P=0.012) was a significant predictive factor for experienced surgeons in multivariate analysis. Preoperative DO, IPSS, patient age, and surgeon mentorship were significant predictive factors of postoperative patient incontinence for beginner surgeons, while FDV was a significant predictive factors for experienced surgeons. These findings should be taken into account by surgeons performing HoLEP to maximize the patient's quality of life with regards to urinary continence.

Predicting academic performance and clinical competency for international dental students: seeking the most efficient and effective measures.

PubMed

Stacey, D Graham; Whittaker, John M

2005-02-01

Measures used in the selection of international dental students to a U.S. D.D.S. program were examined to identify the grouping that most effectively and efficiently predicted academic performance and clinical competency. Archival records from the International Dental Program (IDP) at Loma Linda University provided data on 171 students who had trained in countries outside the United States. The students sought admission to the D.D.S. degree program, successful completion of which qualified them to sit for U.S. licensure. As with most dental schools, competition is high for admission to the D.D.S. program. The study's goal was to identify what measures contributed to a fair and accurate selection process for dental school applicants from other nations. Multiple regression analyses identified National Board Part II and dexterity measures as significant predictors of academic performance and clinical competency. National Board Part I, TOEFL, and faculty interviews added no significant additional help in predicting eventual academic performance and clinical competency.

Prosodic development in middle childhood and adolescence in high-functioning autism.

PubMed

Lyons, Megan; Schoen Simmons, Elizabeth; Paul, Rhea

2014-04-01

The present study aims to investigate the perception and production of several domains of prosodic performance in a cross-sectional sample of preadolescents and adolescents with and without high-functioning autism (HFA). To look at the role of language abilities on prosodic performance, the HFA groups were subdivided based on "high" and "low" language performance on the Clinical Evaluation of Language Fundamentals-Fourth Edition (CELF-4) (Semel, Wiig, & Secord). Social and cognitive abilities were also examined to determine their relationship to prosodic performance. No significant differences were seen in prosody scores in the younger versus older subgroups in typically developing (TD) group with age-appropriate language. There was small but significant improvement in performance with age in the groups with HFA. Comparing performance at each age level across diagnostic groups showed that preteens with HFA and higher language levels perform similarly to their TD peers on all prosodic tasks, whereas those with lower language skills scored significantly worse than both their higher language and TD peers when looking at composite perception and production findings. Teens with HFA showed no deficits on perception tasks; however, those with low language levels had difficulty on several production tasks when compared to the TD group. Regression analyses suggested that, for the preteen group with HFA, language was the strongest predictor of prosodic perception, whereas nonverbal IQ was most highly predictive of prosodic production. For adolescents with HFA, social skills significantly contributed to the prediction of prosodic perception and, along with language abilities, predicted prosodic production. Implications of these findings will be discussed. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Predicting Operator Execution Times Using CogTool

NASA Technical Reports Server (NTRS)

Santiago-Espada, Yamira; Latorella, Kara A.

2013-01-01

Researchers and developers of NextGen systems can use predictive human performance modeling tools as an initial approach to obtain skilled user performance times analytically, before system testing with users. This paper describes the CogTool models for a two pilot crew executing two different types of a datalink clearance acceptance tasks, and on two different simulation platforms. The CogTool time estimates for accepting and executing Required Time of Arrival and Interval Management clearances were compared to empirical data observed in video tapes and registered in simulation files. Results indicate no statistically significant difference between empirical data and the CogTool predictions. A population comparison test found no significant differences between the CogTool estimates and the empirical execution times for any of the four test conditions. We discuss modeling caveats and considerations for applying CogTool to crew performance modeling in advanced cockpit environments.

Validation and clinical utility of the executive function performance test in persons with traumatic brain injury.

PubMed

Baum, C M; Wolf, T J; Wong, A W K; Chen, C H; Walker, K; Young, A C; Carlozzi, N E; Tulsky, D S; Heaton, R K; Heinemann, A W

2017-07-01

This study examined the relationships between the Executive Function Performance Test (EFPT), the NIH Toolbox Cognitive Function tests, and neuropsychological executive function measures in 182 persons with traumatic brain injury (TBI) and 46 controls to evaluate construct, discriminant, and predictive validity. Construct validity: There were moderate correlations between the EFPT and the NIH Toolbox Crystallized (r = -.479), Fluid Tests (r = -.420), and Total Composite Scores (r = -.496). Discriminant validity: Significant differences were found in the EFPT total and sequence scores across control, complicated mild/moderate, and severe TBI groups. We found differences in the organisation score between control and severe, and between mild and severe TBI groups. Both TBI groups had significantly lower scores in safety and judgement than controls. Compared to the controls, the severe TBI group demonstrated significantly lower performance on all instrumental activities of daily living (IADL) tasks. Compared to the mild TBI group, the controls performed better on the medication task, the severe TBI group performed worse in the cooking and telephone tasks. Predictive validity: The EFPT predicted the self-perception of independence measured by the TBI-QOL (beta = -0.49, p < .001) for the severe TBI group. Overall, these data support the validity of the EFPT for use in individuals with TBI.

Do drug treatment variables predict cognitive performance in multidrug-treated opioid-dependent patients? A regression analysis study

PubMed Central

2012-01-01

Background Cognitive deficits and multiple psychoactive drug regimens are both common in patients treated for opioid-dependence. Therefore, we examined whether the cognitive performance of patients in opioid-substitution treatment (OST) is associated with their drug treatment variables. Methods Opioid-dependent patients (N = 104) who were treated either with buprenorphine or methadone (n = 52 in both groups) were given attention, working memory, verbal, and visual memory tests after they had been a minimum of six months in treatment. Group-wise results were analysed by analysis of variance. Predictors of cognitive performance were examined by hierarchical regression analysis. Results Buprenorphine-treated patients performed statistically significantly better in a simple reaction time test than methadone-treated ones. No other significant differences between groups in cognitive performance were found. In each OST drug group, approximately 10% of the attention performance could be predicted by drug treatment variables. Use of benzodiazepine medication predicted about 10% of performance variance in working memory. Treatment with more than one other psychoactive drug (than opioid or BZD) and frequent substance abuse during the past month predicted about 20% of verbal memory performance. Conclusions Although this study does not prove a causal relationship between multiple prescription drug use and poor cognitive functioning, the results are relevant for psychosocial recovery, vocational rehabilitation, and psychological treatment of OST patients. Especially for patients with BZD treatment, other treatment options should be actively sought. PMID:23121989

Academic Performance of First-Year Students at a College of Pharmacy in East Tennessee: Models for Prediction

ERIC Educational Resources Information Center

Clavier, Cheri Whitehead

2013-01-01

With the increase of students applying to pharmacy programs, it is imperative that admissions committees choose appropriate measures to analyze student readiness. The purpose of this research was to identify significant factors that predict the academic performance, defined as grade point average (GPA) at the end of the first professional year, of…

The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

PubMed

Xue, Fangzheng; Li, Qian; Li, Xiumin

2017-01-01

Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.

Relations among student attention behaviors, teacher practices, and beginning word reading skill.

PubMed

Sáez, Leilani; Folsom, Jessica Sidler; Al Otaiba, Stephanie; Schatschneider, Christopher

2012-01-01

The role of student attention for predicting kindergarten word reading was investigated among 432 students. Using Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale behavior rating scores, the authors conducted an exploratory factor analysis, which yielded three distinct factors that reflected selective attention. In this study, the authors focused on the role of one of these factors, which they labeled attention-memory, for predicting reading performance. Teacher ratings of attention-memory predicted word reading above and beyond the contribution of phonological awareness and vocabulary knowledge. In addition, the relations between four teacher practices and attention ratings for predicting reading performance were examined. Using hierarchical linear modeling, the authors found significant interactions between student attention and teacher practices observed during literacy instruction. In general, as ratings of attention improved, better kindergarten word reading performance was associated with high levels of classroom behavior management. However, better word reading performance was not associated with high levels of teacher task orienting. A significant three-way interaction was also found among attention, individualized instruction, and teacher task redirections. The role of regulating kindergarten student attention to support beginning word reading skill development is discussed.

Specification of variables predictive of victories in the sport of boxing.

PubMed

Warnick, Jason E; Warnick, Kyla

2007-08-01

Compared to other sports, very little research has been conducted on which variables can predict victory in the sport of boxing. This investigation examined whether boxers' age, weight change from their preceding contest, country of origin, total number of wins, total number of losses, performance in their preceding contest, or the possession of a championship title was predictive of a winning performance in a given bout. A 1-mo. sample of male professional boxing records for all contests held in the USA (N = 400) were collected from the BoxRec online database. Logistic regression analysis indicated that only boxers' age, total number of wins and losses, and the performance in the preceding contest predicted significant variance in outcome.

Performance in grade 12 mathematics and science predicts student nurses' performance in first year science modules at a university in the Western Cape.

PubMed

Mthimunye, Katlego D T; Daniels, Felicity M

2017-10-26

The demand for highly qualified and skilled nurses is increasing in South Africa as well as around the world. Having a background in science can create a significant advantage for students wishing to enrol for an undergraduate nursing qualification because nursing as profession is grounded in scientific evidence. The aim of this study was to investigate the predictive validity of grade 12 mathematics and science on the academic performance of first year student nurses in science modules. A quantitative research method using a cross-sectional predictive design was employed in this study. The participants included first year Bachelor of Nursing students enrolled at a university in the Western Cape, South Africa. Descriptive and inferential statistics were performed to analyse the data by using the IBM Statistical Package for Social Sciences versions 24. Descriptive analysis of all variables was performed as well as the Spearman's rank correlation test to describe the relationship among the study variables. Standard multiple linear regressions analysis was performed to determine the predictive validity of grade 12 mathematics and science on the academic performance of first year student nurses in science modules. The results of this study showed that grade 12 physical science is not a significant predictor (p > 0.062) of performance in first year science modules. The multiple linear regression revealed that grade 12 mathematics and life science grades explained 37.1% to 38.1% (R2 = 0.381 and adj R2 = 0.371) of the variation in the first year science grade distributions. Based on the results of the study it is evident that performance in grade 12 mathematics (β = 2.997) and life science (β = 3.175) subjects is a significant predictor (p < 0.001) of the performance in first year science modules for student nurses at the university identified for this study.

Impact of experimental design on PET radiomics in predicting somatic mutation status.

PubMed

Yip, Stephen S F; Parmar, Chintan; Kim, John; Huynh, Elizabeth; Mak, Raymond H; Aerts, Hugo J W L

2017-12-01

PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. 348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ). The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δ Overall <5%. The overall influence (δ Overall ) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56). The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human-Automation Interaction.

PubMed

Sebok, Angelia; Wickens, Christopher D

2017-03-01

The objectives were to (a) implement theoretical perspectives regarding human-automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance. Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation. The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system. Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions. The three model-based tools offer useful ways to predict operator performance in complex systems. The three tools offer ways to predict the effects of different automation designs on operator performance.

RRCRank: a fusion method using rank strategy for residue-residue contact prediction.

PubMed

Jing, Xiaoyang; Dong, Qiwen; Lu, Ruqian

2017-09-02

In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair. First, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics. The learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment.

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

Organizational Performance and Organizational Level Training and Support.

ERIC Educational Resources Information Center

Russell, James S.; And Others

1985-01-01

Examined relations among retail sales training, organizational support, and store performance and examined whether training interacts with organizational support to predict store performance. Results indicated that training and organizational support were significantly correlated with both measures of store performance, although the relationship…

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

NASA Astrophysics Data System (ADS)

Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.

2017-03-01

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process

NASA Technical Reports Server (NTRS)

Williams, David H.; Green, Steven M.

1998-01-01

Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.

Influence of Wind Model Performance on Wave Forecasts of the Naval Oceanographic Office

NASA Astrophysics Data System (ADS)

Gay, P. S.; Edwards, K. L.

2017-12-01

Significant discrepancies between the Naval Oceanographic Office's significant wave height (SWH) predictions and observations have been noted in some model domains. The goal of this study is to evaluate these discrepancies and identify to what extent inaccuracies in the wind predictions may explain inaccuracies in SWH predictions. A one-year time series of data is evaluated at various locations in Southern California and eastern Florida. Correlations are generally quite good, ranging from 73% at Pendleton to 88% at both Santa Barbara, California, and Cape Canaveral, Florida. Correlations for month-long periods off Southern California drop off significantly in late spring through early autumn - less so off eastern Florida - likely due to weaker local wind seas and generally smaller SWH in addition to the influence of remotely-generated swell, which may not propagate accurately into and through the wave models. The results of this study suggest that it is likely that a change in meteorological and/or oceanographic conditions explains the change in model performance, partially as a result of a seasonal reduction in wind model performance in the summer months.

Seventy-meter antenna performance predictions: GTD analysis compared with traditional ray-tracing methods

NASA Technical Reports Server (NTRS)

Schredder, J. M.

1988-01-01

A comparative analysis was performed, using both the Geometrical Theory of Diffraction (GTD) and traditional pathlength error analysis techniques, for predicting RF antenna gain performance and pointing corrections. The NASA/JPL 70 meter antenna with its shaped surface was analyzed for gravity loading over the range of elevation angles. Also analyzed were the effects of lateral and axial displacements of the subreflector. Significant differences were noted between the predictions of the two methods, in the effect of subreflector displacements, and in the optimal subreflector positions to focus a gravity-deformed main reflector. The results are of relevance to future design procedure.

Hardiness commitment, gender, and age differentiate university academic performance.

PubMed

Sheard, Michael

2009-03-01

The increasing diversity of students, particularly in age, attending university has seen a concomitant interest in factors predicting academic success. This 2-year correlational study examined whether age, gender (demographic variables), and hardiness (cognitive/emotional variable) differentiate and predict university final degree grade point average (GPA) and final-year dissertation mark. Data are reported from a total of 134 university undergraduate students. Participants provided baseline data in questionnaires administered during the first week of their second year of undergraduate study and gave consent for their academic progress to be tracked. Final degree GPA and dissertation mark were the academic performance criteria. Mature-age students achieved higher final degree GPA compared to young undergraduates. Female students significantly outperformed their male counterparts in each measured academic assessment criteria. Female students also reported a significantly higher mean score on hardiness commitment compared to male students. commitment was the most significant positive correlate of academic achievement. Final degree GPA and dissertation mark were significantly predicted by commitment, and commitment and gender, respectively. The findings have implications for universities targeting academic support services to maximize student scholastic potential. Future research should incorporate hardiness, gender, and age with other variables known to predict academic success.

The honeymoon effect in job performance - Temporal increases in the predictive power of achievement motivation

NASA Technical Reports Server (NTRS)

Helmreich, Robert L.; Sawin, Linda L.; Carsrud, Alan L.

1986-01-01

Correlations between a job performance criterion and personality measures reflecting achievement motivation and an interpersonal orientation were examined at three points in time after completion of job training for a sample of airline reservations agents. Although correlations between the personality predictors and performance were small and nonsignificant for the 3-month period after beginning the job, by the end of six and eight months a number of significant relationships had emerged. Implications for the utility of personality measures in selection and performance prediction are discussed.

Predictors of outcomes following reablement in community-dwelling older adults

PubMed Central

Tuntland, Hanne; Kjeken, Ingvild; Langeland, Eva; Folkestad, Bjarte; Espehaug, Birgitte; Førland, Oddvar; Aaslund, Mona Kristin

2017-01-01

Background Reablement is a rehabilitation intervention for community-dwelling older adults, which has recently been implemented in several countries. Its purpose is to improve functional ability in daily occupations (everyday activities) perceived as important by the older person. Performance and satisfaction with performance in everyday life are the major outcomes of reablement. However, the evidence base concerning which factors predict better outcomes and who receives the greatest benefit in reablement is lacking. Objective The objective of this study was to determine the potential factors that predict occupational performance and satisfaction with that performance at 10 weeks follow-up. Methods The sample in this study was derived from a nationwide clinical controlled trial evaluating the effects of reablement in Norway and consisted of 712 participants living in 34 municipalities. Multiple linear regression was used to investigate possible predictors of occupational performance (COPM-P) and satisfaction with that performance (COPM-S) at 10 weeks follow-up based on the Canadian Occupational Performance Measure (COPM). Results The results indicate that the factors that significantly predicted better COPM-P and COPM-S outcomes at 10 weeks follow-up were higher baseline scores of COPM-P and COPM-S respectively, female sex, having a fracture as the major health condition and high motivation for rehabilitation. Conversely, the factors that significantly predicted poorer COPM-P and COPM-S outcomes were having a neurological disease other than stroke, having dizziness/balance problems as the major health condition and having pain/discomfort. In addition, having anxiety/depression was a predictor of poorer COPM-P outcomes. The two regression models explained 38.3% and 38.8% of the total variance of the dependent variables of occupational performance and satisfaction with that performance, respectively. Conclusion The results indicate that diagnosis, functional level, sex and motivation are significant predictors of outcomes following reablement. PMID:28096664

Automated Clinical Assessment from Smart home-based Behavior Data

PubMed Central

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-01-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

Prediction of Student Performance in Academic and Military Learning Environment: Use of Multiple Linear Regression Predictive Model and Hypothesis Testing

ERIC Educational Resources Information Center

Khan, Wasi Z.; Al Zubaidy, Sarim

2017-01-01

The variance in students' academic performance in a civilian institute and in a military technological institute could be linked to the environment of the competition available to the students. The magnitude of talent, domain of skills and volume of efforts students put are identical in both type of institutes. The significant factor is the…

Image processing system performance prediction and product quality evaluation

NASA Technical Reports Server (NTRS)

Stein, E. K.; Hammill, H. B. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

The UKCAT-12 study: educational attainment, aptitude test performance, demographic and socio-economic contextual factors as predictors of first year outcome in a cross-sectional collaborative study of 12 UK medical schools.

PubMed

McManus, I C; Dewberry, Chris; Nicholson, Sandra; Dowell, Jonathan S

2013-11-14

Most UK medical schools use aptitude tests during student selection, but large-scale studies of predictive validity are rare. This study assesses the United Kingdom Clinical Aptitude Test (UKCAT), and its four sub-scales, along with measures of educational attainment, individual and contextual socio-economic background factors, as predictors of performance in the first year of medical school training. A prospective study of 4,811 students in 12 UK medical schools taking the UKCAT from 2006 to 2008 as a part of the medical school application, for whom first year medical school examination results were available in 2008 to 2010. UKCAT scores and educational attainment measures (General Certificate of Education (GCE): A-levels, and so on; or Scottish Qualifications Authority (SQA): Scottish Highers, and so on) were significant predictors of outcome. UKCAT predicted outcome better in female students than male students, and better in mature than non-mature students. Incremental validity of UKCAT taking educational attainment into account was significant, but small. Medical school performance was also affected by sex (male students performing less well), ethnicity (non-White students performing less well), and a contextual measure of secondary schooling, students from secondary schools with greater average attainment at A-level (irrespective of public or private sector) performing less well. Multilevel modeling showed no differences between medical schools in predictive ability of the various measures. UKCAT sub-scales predicted similarly, except that Verbal Reasoning correlated positively with performance on Theory examinations, but negatively with Skills assessments. This collaborative study in 12 medical schools shows the power of large-scale studies of medical education for answering previously unanswerable but important questions about medical student selection, education and training. UKCAT has predictive validity as a predictor of medical school outcome, particularly in mature applicants to medical school. UKCAT offers small but significant incremental validity which is operationally valuable where medical schools are making selection decisions based on incomplete measures of educational attainment. The study confirms the validity of using all the existing measures of educational attainment in full at the time of selection decision-making. Contextual measures provide little additional predictive value, except that students from high attaining secondary schools perform less well, an effect previously shown for UK universities in general.

The UKCAT-12 study: educational attainment, aptitude test performance, demographic and socio-economic contextual factors as predictors of first year outcome in a cross-sectional collaborative study of 12 UK medical schools

PubMed Central

2013-01-01

Background Most UK medical schools use aptitude tests during student selection, but large-scale studies of predictive validity are rare. This study assesses the United Kingdom Clinical Aptitude Test (UKCAT), and its four sub-scales, along with measures of educational attainment, individual and contextual socio-economic background factors, as predictors of performance in the first year of medical school training. Methods A prospective study of 4,811 students in 12 UK medical schools taking the UKCAT from 2006 to 2008 as a part of the medical school application, for whom first year medical school examination results were available in 2008 to 2010. Results UKCAT scores and educational attainment measures (General Certificate of Education (GCE): A-levels, and so on; or Scottish Qualifications Authority (SQA): Scottish Highers, and so on) were significant predictors of outcome. UKCAT predicted outcome better in female students than male students, and better in mature than non-mature students. Incremental validity of UKCAT taking educational attainment into account was significant, but small. Medical school performance was also affected by sex (male students performing less well), ethnicity (non-White students performing less well), and a contextual measure of secondary schooling, students from secondary schools with greater average attainment at A-level (irrespective of public or private sector) performing less well. Multilevel modeling showed no differences between medical schools in predictive ability of the various measures. UKCAT sub-scales predicted similarly, except that Verbal Reasoning correlated positively with performance on Theory examinations, but negatively with Skills assessments. Conclusions This collaborative study in 12 medical schools shows the power of large-scale studies of medical education for answering previously unanswerable but important questions about medical student selection, education and training. UKCAT has predictive validity as a predictor of medical school outcome, particularly in mature applicants to medical school. UKCAT offers small but significant incremental validity which is operationally valuable where medical schools are making selection decisions based on incomplete measures of educational attainment. The study confirms the validity of using all the existing measures of educational attainment in full at the time of selection decision-making. Contextual measures provide little additional predictive value, except that students from high attaining secondary schools perform less well, an effect previously shown for UK universities in general. PMID:24229380

Anosognosia, neglect, extinction and lesion site predict impairment of daily living after right-hemispheric stroke.

PubMed

Vossel, Simone; Weiss, Peter H; Eschenbeck, Philipp; Fink, Gereon R

2013-01-01

Right-hemispheric stroke can give rise to manifold neuropsychological deficits, in particular, impairments of spatial perception which are often accompanied by reduced self-awareness of these deficits (anosognosia). To date, the specific contribution of these deficits to a patient's difficulties in daily life activities remains to be elucidated. In 55 patients with right-hemispheric stroke we investigated the predictive value of different neglect-related symptoms, visual extinction and anosognosia for the performance of standardized activities of daily living (ADL). The additional impact of lesion location was examined using voxel-based lesion-symptom mapping. Step-wise linear regression revealed that anosognosia for visuospatial deficits was the most important predictor for performance in standardized ADL. In addition, motor-intentional and perceptual-attentional neglect, extinction and cancellation task performance significantly predicted ADL performance. Lesions comprising the right frontal and cingulate cortex and adjacent white matter explained additional variance in the performance of standardized ADL, in that damage to these areas was related to lower performance than predicted by the regression model only. Our data show a decisive role of anosognosia for visuospatial deficits for impaired ADL and therefore outcome/disability after stroke. The findings further demonstrate that the severity of neglect and extinction also predicts ADL performance. Our results thus strongly suggest that right-hemispheric stroke patients should not only be routinely assessed for neglect and extinction but also for anosognosia to initiate appropriate rehabilitative treatment. The observation that right frontal lesions explain additional variance in ADL most likely reflects that dysfunction of the supervisory system also significantly impacts upon rehabilitation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Fitness for duty: A 3 minute version of the Psychomotor Vigilance Test predicts fatigue related declines in luggage screening performance

PubMed Central

Basner, Mathias; Rubinstein, Joshua

2011-01-01

Objective To evaluate the ability of a 3-min Psychomotor Vigilance Test (PVT) to predict fatigue related performance decrements on a simulated luggage screening task (SLST). Methods Thirty-six healthy non-professional subjects (mean age 30.8 years, 20 female) participated in a 4 day laboratory protocol including a 34 hour period of total sleep deprivation with PVT and SLST testing every 2 hours. Results Eleven and 20 lapses (355 ms threshold) on the PVT optimally divided SLST performance into high, medium, and low performance bouts with significantly decreasing threat detection performance A′. Assignment to the different SLST performance groups replicated homeostatic and circadian patterns during total sleep deprivation. Conclusions The 3 min PVT was able to predict performance on a simulated luggage screening task. Fitness-for-duty feasibility should now be tested in professional screeners and operational environments. PMID:21912278

Fitness for duty: a 3-minute version of the Psychomotor Vigilance Test predicts fatigue-related declines in luggage-screening performance.

PubMed

Basner, Mathias; Rubinstein, Joshua

2011-10-01

To evaluate the ability of a 3-minute Psychomotor Vigilance Test (PVT) to predict fatigue-related performance decrements on a simulated luggage-screening task (SLST). Thirty-six healthy nonprofessional subjects (mean age = 30.8 years, 20 women) participated in a 4-day laboratory protocol including a 34-hour period of total sleep deprivation with PVT and SLST testing every 2 hours. Eleven and 20 lapses (355-ms threshold) on the PVT optimally divided SLST performance into high-, medium-, and low-performance bouts with significantly decreasing threat detection performance A'. Assignment to the different SLST performance groups replicated homeostatic and circadian patterns during total sleep deprivation. The 3-minute PVT was able to predict performance on a simulated luggage-screening task. Fitness-for-duty feasibility should now be tested in professional screeners and operational environments.

Longitudinal Study-Based Dementia Prediction for Public Health

PubMed Central

Kim, HeeChel; Chun, Hong-Woo; Kim, Seonho; Coh, Byoung-Youl; Kwon, Oh-Jin; Moon, Yeong-Ho

2017-01-01

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly. PMID:28867810

Evaluating pictogram prediction in a location-aware augmentative and alternative communication system.

PubMed

Garcia, Luís Filipe; de Oliveira, Luís Caldas; de Matos, David Martins

2016-01-01

This study compared the performance of two statistical location-aware pictogram prediction mechanisms, with an all-purpose (All) pictogram prediction mechanism, having no location knowledge. The All approach had a unique language model under all locations. One of the location-aware alternatives, the location-specific (Spec) approach, made use of specific language models for pictogram prediction in each location of interest. The other location-aware approach resulted from combining the Spec and the All approaches, and was designated the mixed approach (Mix). In this approach, the language models acquired knowledge from all locations, but a higher relevance was assigned to the vocabulary from the associated location. Results from simulations showed that the Mix and Spec approaches could only outperform the baseline in a statistically significant way if pictogram users reuse more than 50% and 75% of their sentences, respectively. Under low sentence reuse conditions there were no statistically significant differences between the location-aware approaches and the All approach. Under these conditions, the Mix approach performed better than the Spec approach in a statistically significant way.

Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion

PubMed Central

Li, Han; Liu, Yashu; Gong, Pinghua; Zhang, Changshui; Ye, Jieping

2014-01-01

Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features. PMID:24416143

Predictive Variables of Half-Marathon Performance for Male Runners

PubMed Central

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A.; García-López, Juan

2017-01-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance. Key points The present study obtained four equations involving anthropometric, training, physiological and biomechanical variables to estimate half-marathon performance. These equations were validated in a different population, demonstrating narrows ranges of prediction than previous studies and also their consistency. As a novelty, some biomechanical variables (i.e. step length and step rate at RCT, and maximal step length) have been related to half-marathon performance. PMID:28630571

The predictive validity of a situational judgement test, a clinical problem solving test and the core medical training selection methods for performance in specialty training .

PubMed

Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David

2017-02-01

The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.

Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years.

PubMed

Selzam, Saskia; Dale, Philip S; Wagner, Richard K; DeFries, John C; Cederlöf, Martin; O'Reilly, Paul F; Krapohl, Eva; Plomin, Robert

2017-07-04

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education ( EduYears ) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years

PubMed Central

Selzam, Saskia; Dale, Philip S.; Wagner, Richard K.; DeFries, John C.; Cederlöf, Martin; O’Reilly, Paul F.; Krapohl, Eva; Plomin, Robert

2017-01-01

ABSTRACT It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (EduYears) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education. PMID:28706435

Using self-reported and objective measures of self-control to predict exercise and academic behaviors among first-year university students.

PubMed

Stork, Matthew J; Graham, Jeffrey D; Bray, Steven R; Martin Ginis, Kathleen A

2017-07-01

Thirty students (mean age = 18 ± 0.5 years) completed self-report (Self-Control Scale) and objective (isometric handgrip squeeze performance) measures of self-control, provided their exercise and academic (study/schoolwork) plans for the next month, and then logged these behaviors over the subsequent 4-week period. Trait self-control predicted exercise and academic behavior. Handgrip squeeze performance predicted academic behavior and adherence to academic plans. Further, regression analysis revealed that trait self-control and handgrip performance explained significant variance in academic behavior. These findings provide a new understanding of how different self-control measures can be used to predict first-year students' participation in, and adherence to, exercise and academic behaviors concurrently.

Pre-admission criteria and pre-clinical achievement: Can they predict medical students performance in the clinical phase?

PubMed

Salem, Raneem O; Al-Mously, Najwa; AlFadil, Sara; Baalash, Amal

2016-01-01

Various factors affect medical students' performance during clinical phase. Identifying these factors would help in mentoring weak students and help in selection process for residency programmes. Our study objective is to evaluate the impact of pre-admission criteria, and pre-clinical grade point average (GPA) on undergraduate medical students' performance during clinical phase. This study has a cross-sectional design that includes fifth- and sixth-year female medical students (71). Data of clinical and pre-clinical GPA in medical school and pre-admission to medical school tests scores were collected. A significant correlation between clinical GPA with the pre-clinical GPA was observed (p < 0.05). Such significant correlation was not seen with other variables under study. A regression analysis was performed, and the only significant predictor of students clinical performance was the pre-clinical GPA (p < 0.001). However, no significant difference between students' clinical and pre-clinical GPA for both cohorts was observed (p > 0.05). Pre-clinical GPA is strongly correlated with and can predict medical students' performance during clinical years. Our study highlighted the importance of evaluating the academic performances of students in pre-clinical years before they move into clinical years in order to identify weak students to mentor them and monitor their progress.

Assessing the validity of sales self-efficacy: a cautionary tale.

PubMed

Gupta, Nina; Ganster, Daniel C; Kepes, Sven

2013-07-01

We developed a focused, context-specific measure of sales self-efficacy and assessed its incremental validity against the broad Big 5 personality traits with department store salespersons, using (a) both a concurrent and a predictive design and (b) both objective sales measures and supervisory ratings of performance. We found that in the concurrent study, sales self-efficacy predicted objective and subjective measures of job performance more than did the Big 5 measures. Significant differences between the predictability of subjective and objective measures of performance were not observed. Predictive validity coefficients were generally lower than concurrent validity coefficients. The results suggest that there are different dynamics operating in concurrent and predictive designs and between broad and contextualized measures; they highlight the importance of distinguishing between these designs and measures in meta-analyses. The results also point to the value of focused, context-specific personality predictors in selection research. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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

PubMed

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

2015-05-26

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

Cognitive predictors of skilled performance with an advanced upper limb multifunction prosthesis: a preliminary analysis.

PubMed

Hancock, Laura; Correia, Stephen; Ahern, David; Barredo, Jennifer; Resnik, Linda

2017-07-01

Purpose The objectives were to 1) identify major cognitive domains involved in learning to use the DEKA Arm; 2) specify cognitive domain-specific skills associated with basic versus advanced users; and 3) examine whether baseline memory and executive function predicted learning. Method Sample included 35 persons with upper limb amputation. Subjects were administered a brief neuropsychological test battery prior to start of DEKA Arm training, as well as physical performance measures at the onset of, and following training. Multiple regression models controlling for age and including neuropsychological tests were developed to predict physical performance scores. Prosthetic performance scores were divided into quartiles and independent samples t-tests compared neuropsychological test scores of advanced scorers and basic scorers. Baseline neuropsychological test scores were used to predict change in scores on physical performance measures across time. Results Cognitive domains of attention and processing speed were statistically significantly related to proficiency of DEKA Arm use and predicted level of proficiency. Conclusions Results support use of neuropsychological tests to predict learning and use of a multifunctional prosthesis. Assessment of cognitive status at the outset of training may help set expectations for the duration and outcomes of treatment. Implications for Rehabilitation Cognitive domains of attention and processing speed were significantly related to level of proficiencyof an advanced multifunctional prosthesis (the DEKA Arm) after training. Results provide initial support for the use of neuropsychological tests to predict advanced learningand use of a multifunctional prosthesis in upper-limb amputees. Results suggest that assessment of patients' cognitive status at the outset of upper limb prosthetictraining may, in the future, help patients, their families and therapists set expectations for theduration and intensity of training and may help set reasonable proficiency goals.

Yo-Yo IR1 vs. incremental continuous running test for prediction of 3000-m performance.

PubMed

Schmitz, Boris; Klose, Andreas; Schelleckes, Katrin; Jekat, Charlotte M; Krüger, Michael; Brand, Stefan-Martin

2017-11-01

This study aimed to compare physiological responses during the Yo-Yo intermittent recovery level 1 (Yo-Yo IR1) Test and an incremental continuous running field Test (ICRT) and to analyze their predictive value on 3000-m running performance. Forty moderately trained individuals (18 females) performed the ICRT and Yo-Yo IR1 Test to exhaustion. The ICRT was performed as graded running test with an increase of 2.0 km·h-1 after each 3 min interval for lactate diagnostic. In both tests, blood lactate levels were determined after the test and at 2 and 5 min of recovery. Heart rate (HR) was recorded to monitor differences in HR slopes and HR recovery. Comparison revealed a correlation between ICRT and Yo-Yo IR1 Test performance (R2=0.83, P<0.001), while significant differences in HRmax existed (Yo-Yo IR1, 189±10 bpm; ICRT, 195±16 bpm; P<0.005; ES=0.5). Maximum lactate levels were also different between test (Yo-Yo IR1, 10.1±2.1 mmol∙L-1; ICRT, 11.7±2.4 mmol∙L-1; P<0.01; ES=0.7). Significant inverse correlations were found between the Yo-Yo IR1 Test performance and 3000 m running time (R2=0.77, P<0.0001) as well as the ICRT and 3000 m time (R2=0.90, P<0.0001). Our data suggest that ICRT and Yo-Yo IR1 test are useful field test methods for the prediction of competitive running performances such as 3000-m runs but maximum HR and blood lactate values differ significantly. The ICRT may have higher predictive power for middle- to long- distance running performance such as 3000-m runs offering a reliable test for coaches in the recruitment of athletes or supervision of training concepts.

Clinical Performance and Admission Variables as Predictors of Passage of the National Physical Therapy Examination.

PubMed

Meiners, Kelly M; Rush, Douglas K

2017-01-01

Prior studies have explored variables that had predictive relationships with National Physical Therapy Examination (NPTE) score or NPTE failure. The purpose of this study was to explore whether certain variables were predictive of test-takers' first-time score on the NPTE. The population consisted of 134 students who graduated from the university's Professional DPT Program in 2012 to 2014. This quantitative study used a retrospective design. Two separate data analyses were conducted. First, hierarchical linear multiple regression (HMR) analysis was performed to determine which variables were predictive of first-time NPTE score. Second, a correlation analysis was performed on all 18 Physical Therapy Clinical Performance Instrument (PT CPI) 2006 category scores obtained during the first long-term clinical rotation, overall PT CPI 2006 score, and NPTE passage. With all variables entered, the HMR model predicted 39% of the variance seen in NPTE scores. The HMR results showed that physical therapy program first-year GPA (1PTGPA) was the strongest predictor and explained 24% of the variance in NPTE scores (b=0.572, p<0.001). The correlational analysis found no statistically significant correlation between the 18 PT CPI 2006 category scores, overall PT CPI 2006 score, and NPTE passage. As 1PTGPA had the most significant contribution to prediction of NPTE scores, programs need to monitor first-year students who display academic difficulty. PT CPI version 2006 scores were significantly correlated with each other, but not with NPTE score or NPTE passage. Both tools measure many of the same professional requirements but use different modes of assessment, and they may be considered complementary tools to gain a full picture of both the student's ability and skills.

Seismic activity prediction using computational intelligence techniques in northern Pakistan

NASA Astrophysics Data System (ADS)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Validity of the MCAT in Predicting Performance in the First Two Years of Medical School.

ERIC Educational Resources Information Center

Jones, Robert F.; Thomae-Forgues, Maria

1984-01-01

The first systematic summary of predictive validity research on the new Medical College Admission Test (MCAT) is presented. The results show that MCAT scores have significant predictive validity with respect to first- and second-year medical school course grades. Further directions for MCAT validity research are described. (Author/MLW)

Online physician ratings fail to predict actual performance on measures of quality, value, and peer review.

PubMed

Daskivich, Timothy J; Houman, Justin; Fuller, Garth; Black, Jeanne T; Kim, Hyung L; Spiegel, Brennan

2018-04-01

Patients use online consumer ratings to identify high-performing physicians, but it is unclear if ratings are valid measures of clinical performance. We sought to determine whether online ratings of specialist physicians from 5 platforms predict quality of care, value of care, and peer-assessed physician performance. We conducted an observational study of 78 physicians representing 8 medical and surgical specialties. We assessed the association of consumer ratings with specialty-specific performance scores (metrics including adherence to Choosing Wisely measures, 30-day readmissions, length of stay, and adjusted cost of care), primary care physician peer-review scores, and administrator peer-review scores. Across ratings platforms, multivariable models showed no significant association between mean consumer ratings and specialty-specific performance scores (β-coefficient range, -0.04, 0.04), primary care physician scores (β-coefficient range, -0.01, 0.3), and administrator scores (β-coefficient range, -0.2, 0.1). There was no association between ratings and score subdomains addressing quality or value-based care. Among physicians in the lowest quartile of specialty-specific performance scores, only 5%-32% had consumer ratings in the lowest quartile across platforms. Ratings were consistent across platforms; a physician's score on one platform significantly predicted his/her score on another in 5 of 10 comparisons. Online ratings of specialist physicians do not predict objective measures of quality of care or peer assessment of clinical performance. Scores are consistent across platforms, suggesting that they jointly measure a latent construct that is unrelated to performance. Online consumer ratings should not be used in isolation to select physicians, given their poor association with clinical performance.

Perceived ability and social support as mediators of achievement motivation and performance anxiety.

PubMed

Abrahamsen, F E; Roberts, G C; Pensgaard, A M; Ronglan, L T

2008-12-01

The present study is founded on achievement goal theory (AGT) and examines the relationship between motivation, social support and performance anxiety with team handball players (n=143) from 10 elite teams. Based on these theories and previous findings, the study has three purposes. First, it was predicted that the female athletes (n=69) would report more performance worries and more social support use than males (n=74). The findings support the hypothesis for anxiety, but not for social support use. However, females report that they felt social support was more available than males. Second, we predicted and found a positive relationship between the interaction of ego orientation and perceptions of a performance climate on performance anxiety, but only for females. As predicted, perceived ability mediated this relationship. Finally, we predicted that perceptions of a performance climate were related to the view that social support was less available especially for the male athletes. Simple correlation supports this prediction, but the regression analyses did not reach significance. Thus, we could not test for mediation of social support between motivational variables and anxiety. The results illustrate that fostering a mastery climate helps elite athletes tackle competitive pressure.

A Predictive Validity Study of Creative and Effective Managerial Performance.

ERIC Educational Resources Information Center

Moffie, D. J.; Goodner, Susan

This study tests the following hypotheses concerning the job creativity of managers: (1) There is a significant relationship between psychological test scores secured on subjects 15 to 20 years ago and creative performance on the job today, (2) there is a significant relationship between biographical information secured from subjects at the time…

Development and in-flight performance of the Mariner 9 spacecraft propulsion system

NASA Technical Reports Server (NTRS)

Evans, D. D.; Cannova, R. D.; Cork, M. J.

1972-01-01

On November 14, 1971, Mariner 9 was decelerated into orbit about Mars by a 1334-newton (300-lbf) liquid bipropellant propulsion system. The development and in-flight performance are described and summarized of this pressure-fed, nitrogen tetroxide/monomethyl hydrazine bipropellant system. The design of all Mariner propulsion subsystems has been predicated upon the premise that simplicity of approach, coupled with thorough qualification and margin-limits testing, is the key to cost-effective reliability. The qualification test program and analytical modeling of the Mariner 9 subsystem are discussed. Since the propulsion subsystem is modular in nature, it was completely checked, serviced, and tested independent of the spacecraft. Proper prediction of in-flight performance required the development of three significant modeling tools to predict and account for nitrogen saturation of the propellant during the six-month coast period and to predict and statistically analyze in-flight data. The flight performance of the subsystem was excellent, as were the performance prediction correlations. These correlations are presented.

Language and theory of mind in autism spectrum disorder: the relationship between complement syntax and false belief task performance.

PubMed

Lind, Sophie E; Bowler, Dermot M

2009-06-01

This study aimed to test the hypothesis that children with autism spectrum disorder (ASD) use their knowledge of complement syntax as a means of "hacking out" solutions to false belief tasks, despite lacking a representational theory of mind (ToM). Participants completed a "memory for complements" task, a measure of receptive vocabulary, and traditional location change and unexpected contents false belief tasks. Consistent with predictions, the correlation between complement syntax score and location change task performance was significantly stronger within the ASD group than within the comparison group. However, contrary to predictions, complement syntax score was not significantly correlated with unexpected contents task performance within either group. Possible explanations for this pattern of results are considered.

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

NASA Astrophysics Data System (ADS)

Mainsah, B. O.; Reeves, G.; Collins, L. M.; Throckmorton, C. S.

2017-08-01

Objective. The role of a brain-computer interface (BCI) is to discern a user’s intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. Significance. By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

Predicting space telerobotic operator training performance from human spatial ability assessment

NASA Astrophysics Data System (ADS)

Liu, Andrew M.; Oman, Charles M.; Galvan, Raquel; Natapoff, Alan

2013-11-01

Our goal was to determine whether existing tests of spatial ability can predict an astronaut's qualification test performance after robotic training. Because training astronauts to be qualified robotics operators is so long and expensive, NASA is interested in tools that can predict robotics performance before training begins. Currently, the Astronaut Office does not have a validated tool to predict robotics ability as part of its astronaut selection or training process. Commonly used tests of human spatial ability may provide such a tool to predict robotics ability. We tested the spatial ability of 50 active astronauts who had completed at least one robotics training course, then used logistic regression models to analyze the correlation between spatial ability test scores and the astronauts' performance in their evaluation test at the end of the training course. The fit of the logistic function to our data is statistically significant for several spatial tests. However, the prediction performance of the logistic model depends on the criterion threshold assumed. To clarify the critical selection issues, we show how the probability of correct classification vs. misclassification varies as a function of the mental rotation test criterion level. Since the costs of misclassification are low, the logistic models of spatial ability and robotic performance are reliable enough only to be used to customize regular and remedial training. We suggest several changes in tracking performance throughout robotics training that could improve the range and reliability of predictive models.

Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194

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

Ahrens, J.S.

For over fifteen years Sandia National Laboratories has been involved in laboratory testing of biometric identification devices. The key concept of biometric identification devices is the ability for the system to identify some unique aspect of the individual rather than some object a person may be carrying or some password they are required to know. Tests were conducted to verify manufacturer`s performance claims, to determine strengths/weaknesses of devices, and to determine devices that meet the US Department of energy`s needs. However, during recent field installation, significantly different performance was observed than was predicted by laboratory tests. Although most people usingmore » the device believed it operated adequately, the performance observed was over an order of magnitude worse than predicted. The search for reasons behind this gap between the predicted and the actual performance has revealed many possible contributing factors. As engineers, the most valuable lesson to be learned from this experience is the value of scientists and engineers with (1) common sense, (2) knowledge of human behavior, (3) the ability to observe the real world, and (4) the capability to realize the significant differences between controlled experiments and actual installations.« less

Can pre-season fitness measures predict time to injury in varsity athletes?: a retrospective case control study

PubMed Central

2012-01-01

Background The ability to determine athletic performance in varsity athletes using preseason measures has been established. The ability of pre-season performance measures and athlete’s exposure to predict the incidence of injuries is unclear. Thus our purpose was to determine the ability of pre-season measures of athletic performance to predict time to injury in varsity athletes. Methods Male and female varsity athletes competing in basketball, volleyball and ice hockey participated in this study. The main outcome measures were injury prevalence, time to injury (based on calculated exposure) and pre-season fitness measures as predictors of time to injury. Fitness measures were Apley’s range of motion, push-up, curl-ups, vertical jump, modified Illinois agility, and sit-and-reach. Cox regression models were used to identify which baseline fitness measures were predictors of time to injury. Results Seventy-six percent of the athletes reported 1 or more injuries. Mean times to initial injury were significantly different for females and males (40.6% and 66.1% of the total season (p < 0.05), respectively). A significant univariate correlation was observed between push-up performance and time to injury (Pearson’s r = 0.332, p < 0.01). No preseason fitness measure impacted the hazard of injury. Regardless of sport, female athletes had significantly shorter time to injury than males (Hazard Ratio = 2.2, p < 0.01). Athletes playing volleyball had significantly shorter time to injury (Hazard Ratio = 4.2, p < 0.01) compared to those playing hockey or basketball. Conclusions When accounting for exposure, gender, sport and fitness measures, prediction of time to injury was influenced most heavily by gender and sport. PMID:22824555

Examining the relationships between span of control and manager job and unit performance outcomes.

PubMed

Wong, Carol A; Elliott-Miller, Pat; Laschinger, Heather; Cuddihy, Michael; Meyer, Raquel M; Keatings, Margaret; Burnett, Camille; Szudy, Natalie

2015-03-01

Our aim was to examine the combination of frontline manager (FLM) personal characteristics and span of control (SOC) on their job and unit performance outcomes. Healthcare downsizing and reform have contributed to larger spans for FLMs in Canadian hospitals and increased concerns about manager workload. Despite a heightened awareness of SOC issues among decision makers, there is limited empirical evidence related to the effects of SOC on outcomes. A non-experimental predictive survey design was used to examine FLM SOC in 14 Canadian academic hospitals. Managers (n = 121) completed an online survey of work characteristics and The Ottawa Hospital (TOH) SOC tool. Unit turnover data were collected from organisational databases. The combination of SOC and core self-evaluation significantly predicted role overload, work control and job satisfaction, but only SOC predicted unit adverse outcomes and neither significantly predicted unit turnover. The findings contribute to an understanding of connections between the combination of SOC and core self-evaluation and manager job and unit performance outcomes. Organisational strategies to create manageable FLM SOC are essential to ensure exemplary job and unit outcomes. Core self-evaluation is a personality characteristic that may enhance manager performance in the face of high spans of control. © 2013 John Wiley & Sons Ltd.

Factors Associated with Postoperative Diabetes Insipidus after Pituitary Surgery.

PubMed

Faltado, Antonio L; Macalalad-Josue, Anna Angelica; Li, Ralph Jason S; Quisumbing, John Paul M; Yu, Marc Gregory Y; Jimeno, Cecilia A

2017-12-01

Determining risk factors for diabetes insipidus (DI) after pituitary surgery is important in improving patient care. Our objective is to determine the factors associated with DI after pituitary surgery. We reviewed records of patients who underwent pituitary surgery from 2011 to 2015 at Philippine General Hospital. Patients with preoperative DI were excluded. Multiple logistic regression analysis was performed and a predictive model was generated. The discrimination abilities of the predictive model and individual variables were assessed using the receiving operator characteristic curve. A total of 230 patients were included. The rate of postoperative DI was 27.8%. Percent change in serum Na (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.15 to 1.69); preoperative serum Na (OR, 1.19; 95% CI, 1.02 to 1.40); and performance of craniotomy (OR, 5.48; 95% CI, 1.60 to 18.80) remained significantly associated with an increased incidence of postoperative DI, while percent change in urine specific gravity (USG) (OR, 0.53; 95% CI, 0.33 to 0.87) and meningioma on histopathology (OR, 0.05; 95% CI, 0.04 to 0.70) were significantly associated with a decreased incidence. The predictive model generated has good diagnostic accuracy in predicting postoperative DI with an area under curve of 0.83. Greater percent change in serum Na, preoperative serum Na, and performance of craniotomy significantly increased the likelihood of postoperative DI while percent change in USG and meningioma on histopathology were significantly associated with a decreased incidence. The predictive model can be used to generate a scoring system in estimating the risk of postoperative DI. Copyright © 2017 Korean Endocrine Society

Prediction of Osteopathic Medical School Performance on the basis of MCAT score, GPA, sex, undergraduate major, and undergraduate institution.

PubMed

Dixon, Donna

2012-04-01

The relationships of students' preadmission academic variables, sex, undergraduate major, and undergraduate institution to academic performance in medical school have not been thoroughly examined. To determine the ability of students' preadmission academic variables to predict osteopathic medical school performance and whether students' sex, undergraduate major, or undergraduate institution influence osteopathic medical school performance. The study followed students who graduated from New York College of Osteopathic Medicine of New York Institute of Technology in Old Westbury between 2003 and 2006. Student preadmission data were Medical College Admission Test (MCAT) scores, undergraduate grade point averages (GPAs), sex, undergraduate major, and undergraduate institutional selectivity. Medical school performance variables were GPAs, clinical performance (ie, clinical subject examinations and clerkship evaluations), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Clinical Evaluation (CE). Data were analyzed with Pearson product moment correlation coefficients and multivariate linear regression analyses. Differences between student groups were compared with the independent-samples, 2-tailed t test. A total of 737 students were included. All preadmission academic variables, except nonscience undergraduate GPA, were statistically significant predictors of performance on COMLEX-USA Level 1, and all preadmission academic variables were statistically significant predictors of performance on COMLEX-USA Level 2-CE. The MCAT score for biological sciences had the highest correlation among all variables with COMLEX-USA Level 1 performance (Pearson r=0.304; P<.001) and Level 2-CE performance (Pearson r=0.272; P<.001). All preadmission variables were moderately correlated with the mean clinical subject examination scores. The mean clerkship evaluation score was moderately correlated with mean clinical examination results (Pearson r=0.267; P<.001) and COMLEX-USA Level 2-CE performance (Pearson r=0.301; P<.001). Clinical subject examination scores were highly correlated with COMLEX-USA Level 2-CE scores (Pearson r=0.817; P<.001). No statistically significant difference in medical school performance was found between students with science and nonscience undergraduate majors, nor was undergraduate institutional selectivity a factor influencing performance. Students' preadmission academic variables were predictive of osteopathic medical school performance, including GPAs, clinical performance, and COMLEX-USA Level 1 and Level 2-CE results. Clinical performance was predictive of COMLEX-USA Level 2-CE performance.

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.

Poor performances of EuroSCORE and CARE score for prediction of perioperative mortality in octogenarians undergoing aortic valve replacement for aortic stenosis.

PubMed

Chhor, Vibol; Merceron, Sybille; Ricome, Sylvie; Baron, Gabriel; Daoud, Omar; Dilly, Marie-Pierre; Aubier, Benjamin; Provenchere, Sophie; Philip, Ivan

2010-08-01

Although results of cardiac surgery are improving, octogenarians have a higher procedure-related mortality and more complications with increased length of stay in ICU. Consequently, careful evaluation of perioperative risk seems necessary. The aims of our study were to assess and compare the performances of EuroSCORE and CARE score in the prediction of perioperative mortality among octogenarians undergoing aortic valve replacement for aortic stenosis and to compare these predictive performances with those obtained in younger patients. This retrospective study included all consecutive patients undergoing cardiac surgery in our institution between November 2005 and December 2007. For each patient, risk assessment for mortality was performed using logistic EuroSCORE, additive EuroSCORE and CARE score. The main outcome measure was early postoperative mortality. Predictive performances of these scores were assessed by calibration and discrimination using goodness-of-fit test and area under the receiver operating characteristic curve, respectively. During this 2-year period, we studied 2117 patients, among whom 134/211 octogenarians and 335/1906 nonoctogenarians underwent an aortic valve replacement for aortic stenosis. When considering patients with aortic stenosis, discrimination was poor in octogenarians and the difference from nonoctogenarians was significant for each score (0.58, 0.59 and 0.56 vs. 0.82, 0.81 and 0.77 for additive EuroSCORE, logistic EuroSCORE and CARE score in octogenarians and nonoctogenarians, respectively, P < 0.05). Moreover, in the whole cohort, logistic EuroSCORE significantly overestimated mortality among octogenarians. Predictive performances of these scores are poor in octogenarians undergoing cardiac surgery, especially aortic valve replacement. Risk assessment and therapeutic decisions in octogenarians should not be made with these scoring systems alone.

Predicting Success in Psychological Statistics Courses.

PubMed

Lester, David

2016-06-01

Many students perform poorly in courses on psychological statistics, and it is useful to be able to predict which students will have difficulties. In a study of 93 undergraduates enrolled in Statistical Methods (18 men, 75 women; M age = 22.0 years, SD = 5.1), performance was significantly associated with sex (female students performed better) and proficiency in algebra in a linear regression analysis. Anxiety about statistics was not associated with course performance, indicating that basic mathematical skills are the best correlate for performance in statistics courses and can usefully be used to stream students into classes by ability. © The Author(s) 2016.

Vital Signs: How Early Can Resident Evaluation Predict Acquisition of Competency in Surgical Pathology?

PubMed Central

Ducatman, Barbara S.; Williams, H. James; Hobbs, Gerald; Gyure, Kymberly A.

2009-01-01

Objectives To determine whether a longitudinal, case-based evaluation system can predict acquisition of competency in surgical pathology and how trainees at risk can be identified early. Design Data were collected for trainee performance on surgical pathology cases (how well their diagnosis agreed with the faculty diagnosis) and compared with training outcomes. Negative training outcomes included failure to complete the residency, failure to pass the anatomic pathology component of the American Board of Pathology examination, and/or failure to obtain or hold a position immediately following training. Findings Thirty-three trainees recorded diagnoses for 54 326 surgical pathology cases, with outcome data available for 15 residents. Mean case-based performance was significantly higher for those with positive outcomes, and outcome status could be predicted as early as postgraduate year-1 (P  =  .0001). Performance on the first postgraduate year-1 rotation was significantly associated with the outcome (P  =  .02). Although trainees with unsuccessful outcomes improved their performance more rapidly, they started below residents with successful outcomes and did not make up the difference during training. There was no significant difference in Step 1 or 2 United States Medical Licensing Examination (USMLE) scores when compared with performance or final outcomes (P  =  .43 and P  =  .68, respectively) and the resident in-service examination (RISE) had limited predictive ability. Discussion Differences between successful- and unsuccessful-outcome residents were most evident in early residency, ideal for designing interventions or counseling residents to consider another specialty. Conclusion Our longitudinal case-based system successfully identified trainees at risk for failure to acquire critical competencies for surgical pathology early in the program. PMID:21975705

The Prediction of Students' Academic Performance With Fluid Intelligence in Giving Special Consideration to the Contribution of Learning.

PubMed

Ren, Xuezhu; Schweizer, Karl; Wang, Tengfei; Xu, Fen

2015-01-01

The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.

The Prediction of Students’ Academic Performance With Fluid Intelligence in Giving Special Consideration to the Contribution of Learning

PubMed Central

Ren, Xuezhu; Schweizer, Karl; Wang, Tengfei; Xu, Fen

2015-01-01

The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school. PMID:26435760

5-HTTLPR Genotype Moderates the Effects of Past Ecstasy Use on Verbal Memory Performance in Adolescent and Emerging Adults: A Pilot Study.

PubMed

Wright, Natasha E; Strong, Judith A; Gilbart, Erika R; Shollenbarger, Skyler G; Lisdahl, Krista M

2015-01-01

Ecstasy use is associated with memory deficits. Serotonin transporter gene (5-HTTLPR) polymorphisms have been linked with memory function in healthy samples. The present pilot study investigated the influence of 5-HTTLPR polymorphisms on memory performance in ecstasy users, marijuana-using controls, and non-drug-using controls, after a minimum of 7 days of abstinence. Data were collected from 116 young adults (18-25 years-old), including 45 controls, 42 marijuana users, and 29 ecstasy users, and were balanced for 5-HTTLPR genotype. Participants were abstinent seven days prior to completing memory testing. Three MANCOVAs and one ANCOVA were run to examine whether drug group, 5-HTTLPR genotype, and their interactions predicted verbal and visual memory after controlling for gender, past year alcohol use, other drug use, and nicotine cotinine levels. MANCOVA and ANCOVA analysis revealed a significant interaction between drug group and genotype (p = .03) such that ecstasy users with the L/L genotype performed significantly worse on CVLT-2 total recall (p = .05), short (p = .008) and long delay free recall (p = .01), and recognition (p = .006), with the reverse pattern found in controls. Ecstasy did not significantly predict visual memory. 5-HTTLPR genotype significantly predicted memory for faces (p = .02); short allele carriers performed better than those with L/L genotype. 5-HTTLPR genotype moderated the effects of ecstasy on verbal memory, with L/L carriers performing worse compared to controls. Future research should continue to examine individual differences in ecstasy's impact on neurocognitive performance as well as relationships with neuronal structure. Additional screening and prevention efforts focused on adolescents and emerging adults are necessary to prevent ecstasy consumption.

5-HTTLPR Genotype Moderates the Effects of Past Ecstasy Use on Verbal Memory Performance in Adolescent and Emerging Adults: A Pilot Study

PubMed Central

Wright, Natasha E.; Strong, Judith A.; Gilbart, Erika R.; Shollenbarger, Skyler G.; Lisdahl, Krista M.

2015-01-01

Objective Ecstasy use is associated with memory deficits. Serotonin transporter gene (5-HTTLPR) polymorphisms have been linked with memory function in healthy samples. The present pilot study investigated the influence of 5-HTTLPR polymorphisms on memory performance in ecstasy users, marijuana-using controls, and non-drug-using controls, after a minimum of 7 days of abstinence. Method Data were collected from 116 young adults (18–25 years-old), including 45 controls, 42 marijuana users, and 29 ecstasy users, and were balanced for 5-HTTLPR genotype. Participants were abstinent seven days prior to completing memory testing. Three MANCOVAs and one ANCOVA were run to examine whether drug group, 5-HTTLPR genotype, and their interactions predicted verbal and visual memory after controlling for gender, past year alcohol use, other drug use, and nicotine cotinine levels. Results MANCOVA and ANCOVA analysis revealed a significant interaction between drug group and genotype (p = .03) such that ecstasy users with the L/L genotype performed significantly worse on CVLT-2 total recall (p = .05), short (p = .008) and long delay free recall (p = .01), and recognition (p = .006), with the reverse pattern found in controls. Ecstasy did not significantly predict visual memory. 5-HTTLPR genotype significantly predicted memory for faces (p = .02); short allele carriers performed better than those with L/L genotype. Conclusions 5-HTTLPR genotype moderated the effects of ecstasy on verbal memory, with L/L carriers performing worse compared to controls. Future research should continue to examine individual differences in ecstasy’s impact on neurocognitive performance as well as relationships with neuronal structure. Additional screening and prevention efforts focused on adolescents and emerging adults are necessary to prevent ecstasy consumption. PMID:26231032

Internal Flow Analysis of Large L/D Solid Rocket Motors

NASA Technical Reports Server (NTRS)

Laubacher, Brian A.

2000-01-01

Traditionally, Solid Rocket Motor (SRM) internal ballistic performance has been analyzed and predicted with either zero-dimensional (volume filling) codes or one-dimensional ballistics codes. One dimensional simulation of SRM performance is only necessary for ignition modeling, or for motors that have large length to port diameter ratios which exhibit an axial "pressure drop" during the early burn times. This type of prediction works quite well for many types of motors, however, when motor aspect ratios get large, and port to throat ratios get closer to one, two dimensional effects can become significant. The initial propellant grain configuration for the Space Shuttle Reusable Solid Rocket Motor (RSRM) was analyzed with 2-D, steady, axi-symmetric computational fluid dynamics (CFD). The results of the CFD analysis show that the steady-state performance prediction at the initial burn geometry, in general, agrees well with 1-D transient prediction results at an early time, however, significant features of the 2-D flow are captured with the CFD results that would otherwise go unnoticed. Capturing these subtle differences gives a greater confidence to modeling accuracy, and additional insight with which to model secondary internal flow effects like erosive burning. Detailed analysis of the 2-D flowfield has led to the discovery of its hidden 1-D isentropic behavior, and provided the means for a thorough and simplified understanding of internal solid rocket motor flow. Performance parameters such as nozzle stagnation pressure, static pressure drop, characteristic velocity, thrust and specific impulse are discussed in detail and compared for different modeling and prediction methods. The predicted performance using both the 1-D codes and the CFD results are compared with measured data obtained from static tests of the RSRM. The differences and limitations of predictions using ID and 2-D flow fields are discussed and some suggestions for the design of large L/D motors and more critically, motors with port to throat ratios near one, are covered.

Does the Value of Dynamic Assessment in Predicting End-of-First-Grade Mathematics Performance Differ as a Function of English Language Proficiency?

PubMed Central

Seethaler, Pamela M.; Fuchs, Lynn S.; Fuchs, Douglas; Compton, Donald L.

2015-01-01

The purpose of this study was to assess the added value of dynamic assessment (DA) beyond more conventional static measures for predicting individual differences in year-end 1st-grade calculation (CA) and word-problem (WP) performance, as a function of limited English proficiency (LEP) status. At the start of 1st grade, students (129 LEP; 163 non-LEP) were assessed on a brief static mathematics test, an extended static mathematics test, static tests of domain-general abilities associated with CAs and WPs (vocabulary; reasoning), and DA. Near end of 1st grade, they were assessed on CA and WP. Regression analyses indicated that the value of the predictor depends on the predicted outcome and LEP status. In predicting CAs, the extended mathematics test and DA uniquely explained variance for LEP children, with stronger predictive value for the extended mathematics test; for non-LEP children, the extended mathematics test was the only significant predictor. However, in predicting WPs, only DA and vocabulary were uniquely predictive for LEP children, with stronger value for DA; for non-LEP children, the extended mathematics test and DA were comparably uniquely predictive. Neither the brief static mathematics test nor reasoning was significant in predicting either outcome. The potential value of a gated screening process, using an extended mathematics assessment to predict CAs and using DA to predict WPs, is discussed. PMID:26523068

Evidence for an Explanation Advantage in Naïve Biological Reasoning

PubMed Central

Legare, Cristine H.; Wellman, Henry M.; Gelman, Susan A.

2013-01-01

The present studies compare young children's explanations and predictions for the biological phenomenon of contamination. In Study 1, 36 preschoolers and 24 adults heard vignettes concerning contamination, and were asked either to make a prediction or to provide an explanation. Even 3-year-olds readily supplied contamination-based explanations, and most children mentioned an unseen mechanism (germs, contact through bodily fluids). Moreover, unlike adults who performed at ceiling across both explanation and prediction tasks, children were significantly more accurate with their explanations than their predictions. In Study 2, we varied the strength of cues regarding the desirability of the contaminated substance (N = 24 preschoolers). Although desirability affected responses, for both levels of desirability participants were significantly more accurate on explanation than prediction questions. Altogether, these studies demonstrate a significant “explanation advantage” for children's reasoning in the domain of everyday biology. PMID:18710700

Scoring annual earthquake predictions in China

NASA Astrophysics Data System (ADS)

Zhuang, Jiancang; Jiang, Changsheng

2012-02-01

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

Reliability of didactic grades to predict practical skills in an undergraduate dental college in Saudi Arabia.

PubMed

Zawawi, Khalid H; Afify, Ahmed R; Yousef, Mohammed K; Othman, Hisham I; Al-Dharrab, Ayman A

2015-01-01

This longitudinal study was aimed to investigate the association between didactic grades and practical skills for dental students and whether didactic grades can reliability predict the dental students' practical performance. Didactic and practical grades for graduates from the Faculty of Dentistry, King Abdulaziz University, between the years 2009 and 2011 were collected. Four courses were selected: Dental Anatomy, Operative Dentistry, Prosthodontics, and Orthodontics. Pearson product-moment correlation analyses between didactic and practical scores were conducted. There was only a significant correlation between didactic and practical scores for the Dental Anatomy course (P<0.001). There was also a significant correlation between all four subjects in the didactic scores (P<0.001). Only the scores of male students showed a significant correlation in the Operative Dentistry course (P<0.001). There were no correlations between Orthodontic grades. Moreover, a poor degree of reliability was found between didactic and practical scores for all subjects. Based on the findings of this study, the relationship between didactic grades and practical performance is course specific. Didactic grades do not reliably predict the students' practical skills. Measuring practical performances should be independent from didactic grading.

Reliability of didactic grades to predict practical skills in an undergraduate dental college in Saudi Arabia

PubMed Central

Zawawi, Khalid H; Afify, Ahmed R; Yousef, Mohammed K; Othman, Hisham I; Al-Dharrab, Ayman A

2015-01-01

Objectives This longitudinal study was aimed to investigate the association between didactic grades and practical skills for dental students and whether didactic grades can reliability predict the dental students’ practical performance. Materials and methods Didactic and practical grades for graduates from the Faculty of Dentistry, King Abdulaziz University, between the years 2009 and 2011 were collected. Four courses were selected: Dental Anatomy, Operative Dentistry, Prosthodontics, and Orthodontics. Pearson product-moment correlation analyses between didactic and practical scores were conducted. Results There was only a significant correlation between didactic and practical scores for the Dental Anatomy course (P<0.001). There was also a significant correlation between all four subjects in the didactic scores (P<0.001). Only the scores of male students showed a significant correlation in the Operative Dentistry course (P<0.001). There were no correlations between Orthodontic grades. Moreover, a poor degree of reliability was found between didactic and practical scores for all subjects. Conclusion Based on the findings of this study, the relationship between didactic grades and practical performance is course specific. Didactic grades do not reliably predict the students’ practical skills. Measuring practical performances should be independent from didactic grading. PMID:25878519

Off-Ice Anaerobic Power Does Not Predict On-Ice Repeated Shift Performance in Hockey.

PubMed

Peterson, Ben J; Fitzgerald, John S; Dietz, Calvin C; Ziegler, Kevin S; Baker, Sarah E; Snyder, Eric M

2016-09-01

Peterson, BJ, Fitzgerald, JS, Dietz, CC, Ziegler, KS, Baker, SE, and Snyder, EM. Off-ice anaerobic power does not predict on-ice repeated shift performance in hockey. J Strength Cond Res 30(9): 2375-2381, 2016-Anaerobic power is a significant predictor of acceleration and top speed in team sport athletes. Historically, these findings have been applied to ice hockey although recent research has brought their validity for this sport into question. As ice hockey emphasizes the ability to repeatedly produce power, single bout anaerobic power tests should be examined to determine their ability to predict on-ice performance. We tested whether conventional off-ice anaerobic power tests could predict on-ice acceleration, top speed, and repeated shift performance. Forty-five hockey players, aged 18-24 years, completed anthropometric, off-ice, and on-ice tests. Anthropometric and off-ice testing included height, weight, body composition, vertical jump, and Wingate tests. On-ice testing consisted of acceleration, top speed, and repeated shift fatigue tests. Vertical jump (VJ) (r = -0.42; r = -0.58), Wingate relative peak power (WRPP) (r = -0.32; r = -0.43), and relative mean power (WRMP) (r = -0.34; r = -0.48) were significantly correlated (p ≤ 0.05) to on-ice acceleration and top speed, respectively. Conversely, none of the off-ice tests correlated with on-ice repeated shift performance, as measured by first gate, second gate, or total course fatigue; VJ (r = 0.06; r = 0.13; r = 0.09), WRPP (r = 0.06; r = 0.14; r = 0.10), or WRMP (r = -0.10; r = -0.01; r = -0.01). Although conventional off-ice anaerobic power tests predict single bout on-ice acceleration and top speed, they neither predict the repeated shift ability of the player, nor are good markers for performance in ice hockey.

Neurocognitive and Behavioral Predictors of Math Performance in Children with and without ADHD

PubMed Central

Antonini, Tanya N.; O’Brien, Kathleen M.; Narad, Megan E.; Langberg, Joshua M.; Tamm, Leanne; Epstein, Jeff N.

2014-01-01

Objective: This study examined neurocognitive and behavioral predictors of math performance in children with and without attention-deficit/hyperactivity disorder (ADHD). Method: Neurocognitive and behavioral variables were examined as predictors of 1) standardized mathematics achievement scores,2) productivity on an analog math task, and 3) accuracy on an analog math task. Results: Children with ADHD had lower achievement scores but did not significantly differ from controls on math productivity or accuracy. N-back accuracy and parent-rated attention predicted math achievement. N-back accuracy and observed attention predicted math productivity. Alerting scores on the Attentional Network Task predicted math accuracy. Mediation analyses indicated that n-back accuracy significantly mediated the relationship between diagnostic group and math achievement. Conclusion: Neurocognition, rather than behavior, may account for the deficits in math achievement exhibited by many children with ADHD. PMID:24071774

Neurocognitive and Behavioral Predictors of Math Performance in Children With and Without ADHD.

PubMed

Antonini, Tanya N; Kingery, Kathleen M; Narad, Megan E; Langberg, Joshua M; Tamm, Leanne; Epstein, Jeffery N

2016-02-01

This study examined neurocognitive and behavioral predictors of math performance in children with and without ADHD. Neurocognitive and behavioral variables were examined as predictors of (a) standardized mathematics achievement scores, (b) productivity on an analog math task, and (c) accuracy on an analog math task. Children with ADHD had lower achievement scores but did not significantly differ from controls on math productivity or accuracy. N-back accuracy and parent-rated attention predicted math achievement. N-back accuracy and observed attention predicted math productivity. Alerting scores on the attentional network task predicted math accuracy. Mediation analyses indicated that n-back accuracy significantly mediated the relationship between diagnostic group and math achievement. Neurocognition, rather than behavior, may account for the deficits in math achievement exhibited by many children with ADHD. © The Author(s) 2013.

Building a generalized distributed system model

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Foudriat, E. C.

1991-01-01

A modeling tool for both analysis and design of distributed systems is discussed. Since many research institutions have access to networks of workstations, the researchers decided to build a tool running on top of the workstations to function as a prototype as well as a distributed simulator for a computing system. The effects of system modeling on performance prediction in distributed systems and the effect of static locking and deadlocks on the performance predictions of distributed transactions are also discussed. While the probability of deadlock is considerably small, its effects on performance could be significant.

Improving real-time efficiency of case-based reasoning for medical diagnosis.

PubMed

Park, Yoon-Joo

2014-01-01

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.

Verbal Memory Declines More Rapidly with Age in HIV Infected versus Uninfected Adults

PubMed Central

Seider, Talia R.; Luo, Xi; Gongvatana, Assawin; Devlin, Kathryn N.; de la Monte, Suzanne M.; Chasman, Jesse D.; Yan, Peisi; Tashima, Karen T.; Navia, Bradford; Cohen, Ronald A.

2015-01-01

Objectives In the current era of effective antiretroviral treatment, the number of older adults living with HIV is rapidly increasing. This study investigated the combined influence of age and HIV infection on longitudinal changes in verbal and visuospatial learning and memory. Methods In this longitudinal, case-control design, 54 HIV seropositive and 30 seronegative individuals aged 40–74 received neurocognitive assessments at baseline visits and again one year later. Assessment included tests of verbal and visuospatial learning and memory. Linear regression was used to predict baseline performance and longitudinal change on each test using HIV serostatus, age, and their interaction as predictors. MANOVA was used to assess the effects of these predictors on overall baseline performance and overall longitudinal change. Results The interaction of HIV and age significantly predicted longitudinal change in verbal memory performance, as did HIV status, indicating that although the seropositive group declined more than the seronegative group overall, the rate of decline depended on age such that greater age was associated with a greater decline in this group. The regression models for visuospatial learning and memory were significant at baseline, but did not predict change over time. HIV status significantly predicted overall baseline performance and overall longitudinal change. Conclusions This is the first longitudinal study focused on the effects of age and HIV on memory. Findings suggest that age and HIV interact to produce larger declines in verbal memory over time. Further research is needed to gain a greater understanding of the effects of HIV on the aging brain. PMID:24645772

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

PubMed

Nielsen, Anne; Hansen, Mikkel Bo; Tietze, Anna; Mouridsen, Kim

2018-06-01

Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNN deep ) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNN deep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNN Tmax ), a shallow CNN based on a combination of 9 different biomarkers (CNN shallow ), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10 -6 mm 2 /s (ADC thres ). To assess whether CNN deep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNN deep, -rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. CNN deep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P =0.005), CNN Tmax (AUC=0.72±0.14; P <0.003), and ADC thres (AUC=0.66±0.13; P <0.0001) and a substantially better performance than CNN shallow (AUC=0.85±0.11; P =0.063). Measured by contrast, CNN deep improves the predictions significantly, showing superiority to all other methods ( P ≤0.003). CNN deep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different ( P =0.048). The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans. © 2018 American Heart Association, Inc.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

The diagnostic performance of shear-wave elastography for liver fibrosis in children and adolescents: A systematic review and diagnostic meta-analysis.

PubMed

Kim, Jeong Rye; Suh, Chong Hyun; Yoon, Hee Mang; Lee, Jin Seong; Cho, Young Ah; Jung, Ah Young

2018-03-01

To assess the diagnostic performance of shear-wave elastography for determining the severity of liver fibrosis in children and adolescents. An electronic literature search of PubMed and EMBASE was conducted. Bivariate modelling and hierarchical summary receiver-operating-characteristic modelling were performed to evaluate the diagnostic performance of shear-wave elastography. Meta-regression and subgroup analyses according to the modality of shear-wave imaging and the degree of liver fibrosis were also performed. Twelve eligible studies with 550 patients were included. Shear-wave elastography showed a summary sensitivity of 81 % (95 % CI: 71-88) and a specificity of 91 % (95 % CI: 83-96) for the prediction of significant liver fibrosis. The number of measurements of shear-wave elastography performed was a significant factor influencing study heterogeneity. Subgroup analysis revealed shear-wave elastography to have an excellent diagnostic performance according to each degree of liver fibrosis. Supersonic shear imaging (SSI) had a higher sensitivity (p<.01) and specificity (p<.01) than acoustic radiation force impulse imaging (ARFI). Shear-wave elastography is an excellent modality for the evaluation of the severity of liver fibrosis in children and adolescents. Compared with ARFI, SSI showed better diagnostic performance for prediction of significant liver fibrosis. • Shear-wave elastography is beneficial for determining liver fibrosis severity in children. • Shear-wave elastography showed summary sensitivity of 81 %, specificity of 91 %. • SSI showed better diagnostic performance than ARFI for significant liver fibrosis.

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

The mouse beam walking assay offers improved sensitivity over the mouse rotarod in determining motor coordination deficits induced by benzodiazepines.

PubMed

Stanley, Joanna L; Lincoln, Rachael J; Brown, Terry A; McDonald, Louise M; Dawson, Gerard R; Reynolds, David S

2005-05-01

The mouse rotarod test of motor coordination/sedation is commonly used to predict clinical sedation caused by novel drugs. However, past experience suggests that it lacks the desired degree of sensitivity to be predictive of effects in humans. For example, the benzodiazepine, bretazenil, showed little impairment of mouse rotarod performance, but marked sedation in humans. The aim of the present study was to assess whether the mouse beam walking assay demonstrates: (i) an increased sensitivity over the rotarod and (ii) an increased ability to predict clinically sedative doses of benzodiazepines. The study compared the effects of the full benzodiazepine agonists, diazepam and lorazepam, and the partial agonist, bretazenil, on the mouse rotarod and beam walking assays. Diazepam and lorazepam significantly impaired rotarod performance, although relatively high GABA-A receptor occupancy was required (72% and 93%, respectively), whereas beam walking performance was significantly affected at approximately 30% receptor occupancy. Bretazenil produced significant deficits at 90% and 53% receptor occupancy on the rotarod and beam walking assays, respectively. The results suggest that the mouse beam walking assay is a more sensitive tool for determining benzodiazepine-induced motor coordination deficits than the rotarod. Furthermore, the GABA-A receptor occupancy values at which significant deficits were determined in the beam walking assay are comparable with those observed in clinical positron emission tomography studies using sedative doses of benzodiazepines. These data suggest that the beam walking assay may be able to more accurately predict the clinically sedative doses of novel benzodiazepine-like drugs.

Adolescent Summaries of Narrative and Expository Discourse: Differences and Predictors.

PubMed

Lundine, Jennifer P; Harnish, Stacy M; McCauley, Rebecca J; Blackett, Deena Schwen; Zezinka, Alexandra; Chen, Wei; Fox, Robert A

2018-05-03

Summarizing expository passages is a critical academic skill that is understudied in language research. The purpose of this study was to compare the quality of verbal summaries produced by adolescents for 3 different discourse types and to determine whether a composite measure of cognitive skill or a test of expressive syntax predicted their performance. Fifty adolescents listened to, and then verbally summarized, 1 narrative and 2 expository lectures (compare-contrast and cause-effect). They also participated in testing that targeted expressive syntax and 5 cognitive subdomains. Summary quality scores were significantly different across discourse types, with a medium effect size. Analyses revealed significantly higher summary quality scores for cause-effect than compare-contrast summaries. Although the composite cognitive measure contributed significantly to the prediction of quality scores for both types of expository summaries, the expressive syntax score only contributed significantly to the quality scores for narrative summaries. These results support previous research indicating that type of expository discourse may impact student performance. These results also show, for the first time, that cognition may play a predictive role in determining summary quality for expository but not narrative passages in this population. In addition, despite the more complex syntax commonly associated with exposition versus narratives, an expressive syntax score was only predictive of performance on narrative summaries. These findings provide new information, questions, and directions for future research for those who study academic discourse and for professionals who must identify and manage the problems of students struggling with different types of academic discourse. https://doi.org/10.23641/asha.6167879.

A new predictive model for continuous positive airway pressure in the treatment of obstructive sleep apnea.

PubMed

Ebben, Matthew R; Narizhnaya, Mariya; Krieger, Ana C

2017-05-01

Numerous mathematical formulas have been developed to determine continuous positive airway pressure (CPAP) without an in-laboratory titration study. Recent studies have shown that style of CPAP mask can affect the optimal pressure requirement. However, none of the current models take mask style into account. Therefore, the goal of this study was to develop new predictive models of CPAP that take into account the style of mask interface. Data from 200 subjects with attended CPAP titrations during overnight polysomnograms using nasal masks and 132 subjects using oronasal masks were randomized and split into either a model development or validation group. Predictive models were then created in each model development group and the accuracy of the models was then tested in the model validation groups. The correlation between our new oronasal model and laboratory determined optimal CPAP was significant, r = 0.61, p < 0.001. Our nasal formula was also significantly related to laboratory determined optimal CPAP, r = 0.35, p < 0.001. The oronasal model created in our study significantly outperformed the original CPAP predictive model developed by Miljeteig and Hoffstein, z = 1.99, p < 0.05. The predictive performance of our new nasal model did not differ significantly from Miljeteig and Hoffstein's original model, z = -0.16, p < 0.90. The best predictors for the nasal mask group were AHI, lowest SaO2, and neck size, whereas the top predictors in the oronasal group were AHI and lowest SaO2. Our data show that predictive models of CPAP that take into account mask style can significantly improve the formula's accuracy. Most of the past models likely focused on model development with nasal masks (mask style used for model development was not typically reported in previous investigations) and are not well suited for patients using an oronasal interface. Our new oronasal CPAP prediction equation produced significantly improved performance compared to the well-known Miljeteig and Hoffstein formula in patients titrated on CPAP with an oronasal mask and was also significantly related to laboratory determined optimal CPAP.

Predicting Student Performance in Statewide High-Stakes Tests for Middle School Mathematics Using the Results from Third Party Testing Instruments

ERIC Educational Resources Information Center

Meylani, Rusen; Bitter, Gary G.; Castaneda, Rene

2014-01-01

In this study regression and neural networks based methods are used to predict statewide high-stakes test results for middle school mathematics using the scores obtained from third party tests throughout the school year. Such prediction is of utmost significance for school districts to live up to the state's educational standards mandated by the…

Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

2016-07-01

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

PubMed Central

Wang, Chih-Wei; Liu, Yi-Jui; Lee, Yi-Hsiung; Hueng, Dueng-Yuan; Fan, Hueng-Chuen; Yang, Fu-Chi; Hsueh, Chun-Jen; Kao, Hung-Wen; Juan, Chun-Jung; Hsu, Hsian-He

2014-01-01

Purpose To investigate the performance of hematoma shape, hematoma size, Glasgow coma scale (GCS) score, and intracerebral hematoma (ICH) score in predicting the 30-day mortality for ICH patients. To examine the influence of the estimation error of hematoma size on the prediction of 30-day mortality. Materials and Methods This retrospective study, approved by a local institutional review board with written informed consent waived, recruited 106 patients diagnosed as ICH by non-enhanced computed tomography study. The hemorrhagic shape, hematoma size measured by computer-assisted volumetric analysis (CAVA) and estimated by ABC/2 formula, ICH score and GCS score was examined. The predicting performance of 30-day mortality of the aforementioned variables was evaluated. Statistical analysis was performed using Kolmogorov-Smirnov tests, paired t test, nonparametric test, linear regression analysis, and binary logistic regression. The receiver operating characteristics curves were plotted and areas under curve (AUC) were calculated for 30-day mortality. A P value less than 0.05 was considered as statistically significant. Results The overall 30-day mortality rate was 15.1% of ICH patients. The hematoma shape, hematoma size, ICH score, and GCS score all significantly predict the 30-day mortality for ICH patients, with an AUC of 0.692 (P = 0.0018), 0.715 (P = 0.0008) (by ABC/2) to 0.738 (P = 0.0002) (by CAVA), 0.877 (P<0.0001) (by ABC/2) to 0.882 (P<0.0001) (by CAVA), and 0.912 (P<0.0001), respectively. Conclusion Our study shows that hematoma shape, hematoma size, ICH scores and GCS score all significantly predict the 30-day mortality in an increasing order of AUC. The effect of overestimation of hematoma size by ABC/2 formula in predicting the 30-day mortality could be remedied by using ICH score. PMID:25029592

Method for evaluation of predictive models of microwave ablation via post-procedural clinical imaging

NASA Astrophysics Data System (ADS)

Collins, Jarrod A.; Brown, Daniel; Kingham, T. Peter; Jarnagin, William R.; Miga, Michael I.; Clements, Logan W.

2015-03-01

Development of a clinically accurate predictive model of microwave ablation (MWA) procedures would represent a significant advancement and facilitate an implementation of patient-specific treatment planning to achieve optimal probe placement and ablation outcomes. While studies have been performed to evaluate predictive models of MWA, the ability to quantify the performance of predictive models via clinical data has been limited to comparing geometric measurements of the predicted and actual ablation zones. The accuracy of placement, as determined by the degree of spatial overlap between ablation zones, has not been achieved. In order to overcome this limitation, a method of evaluation is proposed where the actual location of the MWA antenna is tracked and recorded during the procedure via a surgical navigation system. Predictive models of the MWA are then computed using the known position of the antenna within the preoperative image space. Two different predictive MWA models were used for the preliminary evaluation of the proposed method: (1) a geometric model based on the labeling associated with the ablation antenna and (2) a 3-D finite element method based computational model of MWA using COMSOL. Given the follow-up tomographic images that are acquired at approximately 30 days after the procedure, a 3-D surface model of the necrotic zone was generated to represent the true ablation zone. A quantification of the overlap between the predicted ablation zones and the true ablation zone was performed after a rigid registration was computed between the pre- and post-procedural tomograms. While both model show significant overlap with the true ablation zone, these preliminary results suggest a slightly higher degree of overlap with the geometric model.

The correlation between fundamental characteristics and first-time performance in laparoscopic tasks.

PubMed

Harrington, Cuan M; Bresler, Richard; Ryan, Donncha; Dicker, Patrick; Traynor, Oscar; Kavanagh, Dara O

2018-04-01

The ability of characteristics to predict first time performance in laparoscopic tasks is not well described. Videogame experience predicts positive performance in laparoscopic experiences but its mechanism and confounding-association with aptitude remains to be elucidated. This study sought to evaluate for innate predictors of laparoscopic performance in surgically naive individuals with minimal videogame exposure. Participants with no prior laparoscopic exposure and minimal videogaming experience were recruited consecutively from preclinical years at a medical university. Participants completed four visuospatial, one psychomotor aptitude test and an electronic survey, followed by four laparoscopic tasks on a validated Virtual Reality simulator (LAP Mentor™). Twenty eligible individuals participated with a mean age of 20.8 (±3.8) years. Significant intra-aptitude performance correlations were present amongst 75% of the visuospatial tests. These visuospatial aptitudes correlated significantly with multiple laparoscopic task metrics: number of movements of a dominant instrument (r s  ≥ -0.46), accuracy rate of clip placement (r s  ≥ 0.50) and time taken (r s  ≥ -0.47) (p < 0.05). Musical Instrument experience predicted higher average speed of instruments (r s  ≥ 0.47) (p < 0.05). Participant's revised competitive index level predicted lower proficiency in laparoscopic metrics including: pathlength, economy and number of movements of dominant instrument (r s  ≥ 0.46) (p < 0.05). Multiple visuospatial aptitudes and innate competitive level influenced baseline laparoscopic performances across several tasks in surgically naïve individuals. Copyright © 2017 Elsevier Inc. All rights reserved.

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

An improved method to detect correct protein folds using partial clustering.

PubMed

Zhou, Jianjun; Wishart, David S

2013-01-16

Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient "partial" clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either C(α) RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

An improved method to detect correct protein folds using partial clustering

PubMed Central

2013-01-01

Background Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. PMID:23323835

Development and in-flight performance of the Mariner 9 spacecraft propulsion system

NASA Technical Reports Server (NTRS)

Evans, D. D.; Cannova, R. D.; Cork, M. J.

1973-01-01

On November 14, 1971, Mariner 9 was decelerated into orbit about Mars by a 1334 N (300 lbf) liquid bipropellant propulsion system. This paper describes and summarizes the development and in-flight performance of this pressure-fed, nitrogen tetroxide/monomethyl hydrazine bipropellant system. The design of all Mariner propulsion subsystems has been predicted upon the premise that simplicity of approach, coupled with thorough qualification and margin-limits testing, is the key to cost-effective reliability. The qualification test program and analytical modeling are also discussed. Since the propulsion subsystem is modular in nature, it was completely checked, serviced, and tested independent of the spacecraft. Proper prediction of in-flight performance required the development of three significant modeling tools to predict and account for nitrogen saturation of the propellant during the six-month coast period and to predict and statistically analyze in-flight data.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach?

PubMed

Mossadegh, Somayyeh; He, Shan; Parker, Paul

2016-05-01

Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS, and the Revised Trauma Score in virtually all areas; positive predictive value 0.8941, specificity 0.9027, accuracy 0.9056, and area under curve 0.9059. A two-sample t test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p < 0.001). With limited resources and the simplest of Bayesian methodologies, we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for our military injured, but also for civilian trauma victims. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Analysis of free modeling predictions by RBO aleph in CASP11.

PubMed

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2016-09-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Person-Environment Congruence as a Predictor of Customer Service Performance.

ERIC Educational Resources Information Center

Fritzsche, Barbara A.; Powell, Amy B.; Hoffman, Russell

1999-01-01

Customer service representatives (n=90) completed the Position Classification Inventory (PCI), Self-Directed Search, and a cognitive ability test. PCI was similar to the Dictionary of Holland Occupational Codes in predicting performance. Cognitive ability was not significantly correlated with performance. Person/environment fit was supported as a…

The Gender Difference: Validity of Standardized Admission Tests in Predicting MBA Performance.

ERIC Educational Resources Information Center

Hancock, Terence

1999-01-01

Of 120 female and 149 male master of business administration (MBA) students, women performed significantly less well on the Graduate Management Admission Test (GMAT). There were no differences in overall MBA grade point average, indicating no strong correlation between the GMAT and MBA performance. (SK)

Using Absenteeism and Performance To Predict Employee Turnover: Early Detection through Company Records.

ERIC Educational Resources Information Center

Morrow, Paula C.; McElroy, James C.; Laczniak, Kathleen S.; Fenton, James B.

1999-01-01

Results of a comparison of 113 insurance company employees who left voluntarily with 113 who stayed supported a relationship between absenteeism, performance ratings, and voluntary turnover. There was no significant interaction effect. (SK)

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

PubMed

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

2018-02-01

Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

A statistical model for predicting muscle performance

NASA Astrophysics Data System (ADS)

Byerly, Diane Leslie De Caix

The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing injury.

Role of Social Performance in Predicting Learning Problems: Prediction of Risk Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Del Prette, Zilda Aparecida Pereira; Prette, Almir Del; De Oliveira, Lael Almeida; Gresham, Frank M.; Vance, Michael J.

2012-01-01

Social skills are specific behaviors that individuals exhibit in order to successfully complete social tasks whereas social competence represents judgments by significant others that these social tasks have been successfully accomplished. The present investigation identified the best sociobehavioral predictors obtained from different raters…

Performance Trends During Sleep Deprivation on a Tilt-Based Control Task.

PubMed

Bolkhovsky, Jeffrey B; Ritter, Frank E; Chon, Ki H; Qin, Michael

2018-07-01

Understanding human behavior under the effects of sleep deprivation allows for the mitigation of risk due to reduced performance. To further this goal, this study investigated the effects of short-term sleep deprivation using a tilt-based control device and examined whether existing user models accurately predict targeting performance. A task in which the user tilts a surface to roll a ball into a target was developed to examine motor performance. A model was built to predict human performance for this task under various levels of sleep deprivation. Every 2 h, 10 subjects completed the task until they reached 24 h of wakefulness. Performance measurements of this task, which were based on Fitts' law, included movement time, task throughput, and time intercept. The model predicted significant performance decrements over the 24-h period with an increase in movement time (R2 = 0.61), a decrease in throughput (R2 = 0.57), and an increase in time intercept (R2 = 0.60). However, it was found that in experimental trials there was no significant change in movement time (R2 = 0.11), throughput (R2 = 0.15), or time intercept (R2 = 0.27). The results found were unexpected as performance decrement is frequently reported during sleep deprivation. These findings suggest a reexamination of the initial thought of sleep loss leading to a decrement in all aspects of performance.Bolkovsky JB, Ritter FE, Chon KH, Qin M. Performance trends during sleep deprivation on a tilt-based control task. Aerosp Med Hum Perform. 2018; 89(7):626-633.

Can risk assessment predict suicide in secondary mental healthcare? Findings from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register.

PubMed

Lopez-Morinigo, Javier-David; Fernandes, Andrea C; Shetty, Hitesh; Ayesa-Arriola, Rosa; Bari, Ashraful; Stewart, Robert; Dutta, Rina

2018-06-02

The predictive value of suicide risk assessment in secondary mental healthcare remains unclear. This study aimed to investigate the extent to which clinical risk assessment ratings can predict suicide among people receiving secondary mental healthcare. Retrospective inception cohort study (n = 13,758) from the South London and Maudsley NHS Foundation Trust (SLaM) (London, UK) linked with national mortality data (n = 81 suicides). Cox regression models assessed survival from the last suicide risk assessment and ROC curves evaluated the performance of risk assessment total scores. Hopelessness (RR = 2.24, 95% CI 1.05-4.80, p = 0.037) and having a significant loss (RR = 1.91, 95% CI 1.03-3.55, p = 0.041) were significantly associated with suicide in the multivariable Cox regression models. However, screening statistics for the best cut-off point (4-5) of the risk assessment total score were: sensitivity 0.65 (95% CI 0.54-0.76), specificity 0.62 (95% CI 0.62-0.63), positive predictive value 0.01 (95% CI 0.01-0.01) and negative predictive value 0.99 (95% CI 0.99-1.00). Although suicide was linked with hopelessness and having a significant loss, risk assessment performed poorly to predict such an uncommon outcome in a large case register of patients receiving secondary mental healthcare.

Vehicle integration effects on hypersonic waveriders. M.S. Thesis - George Washington Univ.

NASA Technical Reports Server (NTRS)

Cockrell, Charles Edward, Jr.

1994-01-01

The integration of a class of hypersonic high-lift configurations known as waveriders into hypersonic cruise vehicles was evaluated. Waveriders offer advantages in aerodynamic performance and propulsion/airframe integration (PAI) characteristics over conventional hypersonic shapes. A wind-tunnel model was developed which integrates realistic vehicle components with two waverider shapes, referred to as the 'straight-wing' and 'cranked-wing' shapes. Both shapes were conical-flow-derived waveriders at a design Mach number of 4.0. The cranked-wing shape was designed to provide advantages in subsonic performance and directional stability over conventional waveriders. Experimental data and limited computational fluid dynamics (CFD) predictions were obtained over a Mach number range of 2.3 to 4.63 at a Reynolds number of 2.0x10(exp 6) per foot. The CFD predictions and flow visualization data confirmed the shock attachment characteristics of the baseline waverider shapes and illustrated the waverider flow-field properties. Both CFD predictions and experimental data showed that no significant performance degradations occur at off-design Mach numbers for the waverider shapes and the integrated configurations. The experimental data showed that the effects of adding a realistic canopy were minimal. The effects of adding engine components were to increase the drag and thus degrade the aerodynamic performance of the configuration. A significant degradation in aerodynamic performance was observed when 0 degree control surfaces were added to close the blunt base of the waverider to a sharp trailing edge. A comparison of the fully-integrated waverider models to the baseline shapes showed that the performance was significantly degraded when all of the components were added to the waveriders. The fully-integrated configurations studied here do not offer significant performance advantages over conventional hypersonic vehicles, but still offer advantages in air-breathing propulsion integration. Additionally, areas are identified in this study where improvements could be made to enhance the performance. Both fully-integrated configurations are longitudinally unstable over the Mach number range studied for unpowered conditions. The cranked-wing fully-integrated configuration provided significantly better lateral-directional stability characteristics than the straight-wing configuration.

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Predicting Positive Education Outcomes for Emerging Adults in Mental Health Systems of Care.

PubMed

Brennan, Eileen M; Nygren, Peggy; Stephens, Robert L; Croskey, Adrienne

2016-10-01

Emerging adults who receive services based on positive youth development models have shown an ability to shape their own life course to achieve positive goals. This paper reports secondary data analysis from the Longitudinal Child and Family Outcome Study including 248 culturally diverse youth ages 17 through 22 receiving mental health services in systems of care. After 12 months of services, school performance was positively related to youth ratings of school functioning and service participation and satisfaction. Regression analysis revealed ratings of young peoples' perceptions of school functioning, and their experience in services added to the significant prediction of satisfactory school performance, even controlling for sex and attendance. Finally, in addition to expected predictors, participation in planning their own services significantly predicted enrollment in higher education for those who finished high school. Findings suggest that programs and practices based on positive youth development approaches can improve educational outcomes for emerging adults.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

NASA Astrophysics Data System (ADS)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

Biological Interactions and Simulated Climate Change Modulates the Ecophysiological Performance of Colobanthus quitensis in the Antarctic Ecosystem

PubMed Central

Torres-Díaz, Cristian; Gallardo-Cerda, Jorge; Lavin, Paris; Oses, Rómulo; Carrasco-Urra, Fernando; Atala, Cristian; Acuña-Rodríguez, Ian S.; Convey, Peter; Molina-Montenegro, Marco A.

2016-01-01

Most climate and environmental change models predict significant increases in temperature and precipitation by the end of the 21st Century, for which the current functional output of certain symbioses may also be altered. In this context we address the following questions: 1) How the expected changes in abiotic factors (temperature, and water) differentially affect the ecophysiological performance of the plant Colobanthus quitensis? and 2) Will this environmental change indirectly affect C. quitensis photochemical performance and biomass accumulation by modifying its association with fungal endophytes? Plants of C. quitensis from King George Island in the South Shetland archipelago (62°09′ S), and Lagotellerie Island in the Antarctic Peninsula (65°53′ S) were put under simulated abiotic conditions in growth chambers following predictive models of global climate change (GCC). The indirect effect of GCC on the interaction between C. quitensis and fungal endophytes was assessed in a field experiment carried out in the Antarctica, in which we eliminated endophytes under contemporary conditions and applied experimental watering to simulate increased precipitation input. We measured four proxies of plant performance. First, we found that warming (+W) significantly increased plant performance, however its effect tended to be less than watering (+W) and combined warming and watering (+T°+W). Second, the presence of fungal endophytes improved plant performance, and its effect was significantly decreased under experimental watering. Our results indicate that both biotic and abiotic factors affect ecophysiological performance, and the directions of these influences will change with climate change. Our findings provide valuable information that will help to predict future population spread and evolution through using ecological niche models under different climatic scenarios. PMID:27776181

Biological Interactions and Simulated Climate Change Modulates the Ecophysiological Performance of Colobanthus quitensis in the Antarctic Ecosystem.

PubMed

Torres-Díaz, Cristian; Gallardo-Cerda, Jorge; Lavin, Paris; Oses, Rómulo; Carrasco-Urra, Fernando; Atala, Cristian; Acuña-Rodríguez, Ian S; Convey, Peter; Molina-Montenegro, Marco A

2016-01-01

Most climate and environmental change models predict significant increases in temperature and precipitation by the end of the 21st Century, for which the current functional output of certain symbioses may also be altered. In this context we address the following questions: 1) How the expected changes in abiotic factors (temperature, and water) differentially affect the ecophysiological performance of the plant Colobanthus quitensis? and 2) Will this environmental change indirectly affect C. quitensis photochemical performance and biomass accumulation by modifying its association with fungal endophytes? Plants of C. quitensis from King George Island in the South Shetland archipelago (62°09' S), and Lagotellerie Island in the Antarctic Peninsula (65°53' S) were put under simulated abiotic conditions in growth chambers following predictive models of global climate change (GCC). The indirect effect of GCC on the interaction between C. quitensis and fungal endophytes was assessed in a field experiment carried out in the Antarctica, in which we eliminated endophytes under contemporary conditions and applied experimental watering to simulate increased precipitation input. We measured four proxies of plant performance. First, we found that warming (+W) significantly increased plant performance, however its effect tended to be less than watering (+W) and combined warming and watering (+T°+W). Second, the presence of fungal endophytes improved plant performance, and its effect was significantly decreased under experimental watering. Our results indicate that both biotic and abiotic factors affect ecophysiological performance, and the directions of these influences will change with climate change. Our findings provide valuable information that will help to predict future population spread and evolution through using ecological niche models under different climatic scenarios.

Sonographic findings predictive of central lymph node metastasis in patients with papillary thyroid carcinoma: influence of associated chronic lymphocytic thyroiditis on the diagnostic performance of sonography.

PubMed

Yoo, Yeon Hwa; Kim, Jeong-Ah; Son, Eun Ju; Youk, Ji Hyun; Kwak, Jin Young; Kim, Eun-Kyung; Park, Cheong Soo

2013-12-01

To analyze sonographic findings suggesting central lymph node metastasis of papillary thyroid carcinoma and to evaluate the influence of associated chronic lymphocytic thyroiditis on the diagnostic performance of sonography for predicting central lymph node metastasis. A total of 124 patients (101 female and 23 male; mean age, 47.5 years; range, 21-74 years) underwent sonographically guided fine-needle aspiration in central lymph nodes from January 2008 to July 2011. Sonographic features of size, shape, margin, thickening of the cortex, cortical echogenicity, presence of a hilum, cystic changes, calcification, and vascularity of enlarged lymph nodes were analyzed before fine-needle aspiration and classified into 2 categories (probably benign and suspicious). Sonographic findings were correlated with the pathologic diagnosis and associated chronic lymphocytic thyroiditis. Receiver operating characteristic curve analysis was performed to assess the diagnostic performance of sonography for predicting central lymph node metastasis according to the associated thyroiditis. Fifty-one lymph nodes (39.5%) were malignant, and 73 (60.5%) were benign. On univariate analysis, size, shape, margin, cortical thickening, cortical echogenicity, cystic changes, calcification, and vascularity were significantly different between the benign and metastatic nodes (P < .05). On multivariate analysis, eccentric cortical thickening (odds ratio, 26.59; 95% confidence interval [CI], 3.26-216.66) and hyper echogenicity of the cortex (odds ratio, 18.46; 95% CI, 2.44-139.64) were significantly associated with malignant nodes (P < .05). The area under the curve values for sonography for predicting metastasis were 0.756 (95% CI, 0.618-0.894) in chronic lymphocytic thyroiditis-positive patients and 0.971 (95% CI, 0.938-1.000) in negative patients. Eccentric cortical thickening and cortical hyperechogenicity were the sonographic findings predictive of central lymph node metastasis from papillary thyroid carcinoma. The diagnostic performance of sonography for predicting metastasis was superior in chronic lymphocytic thyroiditis-negative patients than in positive patients.

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed Central

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-01-01

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions. Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. PMID:29172263

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-11-26

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions . Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. Creative Commons Attribution License

[Academic performance in first year medical students: an explanatory multivariate model].

PubMed

Urrutia Aguilar, María Esther; Ortiz León, Silvia; Fouilloux Morales, Claudia; Ponce Rosas, Efrén Raúl; Guevara Guzmán, Rosalinda

2014-12-01

Current education is focused in intellectual, affective, and ethical aspects, thus acknowledging their significance in students´ metacognition. Nowadays, it is known that an adequate and motivating environment together with a positive attitude towards studies is fundamental to induce learning. Medical students are under multiple stressful, academic, personal, and vocational situations. To identify psychosocial, vocational, and academic variables of 2010-2011 first year medical students at UNAM that may help predict their academic performance. Academic surveys of psychological and vocational factors were applied; an academic follow-up was carried out to obtain a multivariate model. The data were analyzed considering descriptive, comparative, correlative, and predictive statistics. The main variables that affect students´ academic performance are related to previous knowledge and to psychological variables. The results show the significance of implementing institutional programs to support students throughout their college adaptation.

A Measurement and Simulation Based Methodology for Cache Performance Modeling and Tuning

NASA Technical Reports Server (NTRS)

Waheed, Abdul; Yan, Jerry; Saini, Subhash (Technical Monitor)

1998-01-01

We present a cache performance modeling methodology that facilitates the tuning of uniprocessor cache performance for applications executing on shared memory multiprocessors by accurately predicting the effects of source code level modifications. Measurements on a single processor are initially used for identifying parts of code where cache utilization improvements may significantly impact the overall performance. Cache simulation based on trace-driven techniques can be carried out without gathering detailed address traces. Minimal runtime information for modeling cache performance of a selected code block includes: base virtual addresses of arrays, virtual addresses of variables, and loop bounds for that code block. Rest of the information is obtained from the source code. We show that the cache performance predictions are as reliable as those obtained through trace-driven simulations. This technique is particularly helpful to the exploration of various "what-if' scenarios regarding the cache performance impact for alternative code structures. We explain and validate this methodology using a simple matrix-matrix multiplication program. We then apply this methodology to predict and tune the cache performance of two realistic scientific applications taken from the Computational Fluid Dynamics (CFD) domain.

Predictive models of safety based on audit findings: Part 2: Measurement of model validity.

PubMed

Hsiao, Yu-Lin; Drury, Colin; Wu, Changxu; Paquet, Victor

2013-07-01

Part 1 of this study sequence developed a human factors/ergonomics (HF/E) based classification system (termed HFACS-MA) for safety audit findings and proved its measurement reliability. In Part 2, we used the human error categories of HFACS-MA as predictors of future safety performance. Audit records and monthly safety incident reports from two airlines submitted to their regulatory authority were available for analysis, covering over 6.5 years. Two participants derived consensus results of HF/E errors from the audit reports using HFACS-MA. We adopted Neural Network and Poisson regression methods to establish nonlinear and linear prediction models respectively. These models were tested for the validity of prediction of the safety data, and only Neural Network method resulted in substantially significant predictive ability for each airline. Alternative predictions from counting of audit findings and from time sequence of safety data produced some significant results, but of much smaller magnitude than HFACS-MA. The use of HF/E analysis of audit findings provided proactive predictors of future safety performance in the aviation maintenance field. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

The role of the thalamus and hippocampus in episodic memory performance in patients with multiple sclerosis.

PubMed

Koenig, Katherine A; Rao, Stephen M; Lowe, Mark J; Lin, Jian; Sakaie, Ken E; Stone, Lael; Bermel, Robert A; Trapp, Bruce D; Phillips, Micheal D

2018-03-01

Episodic memory loss is one of the most common cognitive symptoms in patients with multiple sclerosis (MS), but the pathophysiology of this symptom remains unclear. Both the hippocampus and thalamus have been implicated in episodic memory and show regional atrophy in patients with MS. In this work, we used functional magnetic resonance imaging (fMRI) during a verbal episodic memory task, lesion load, and volumetric measures of the hippocampus and thalamus to assess the relative contributions to verbal and visual-spatial episodic memory. Functional activation, lesion load, and volumetric measures from 32 patients with MS and 16 healthy controls were used in a predictive analysis of episodic memory function. After adjusting for disease duration, immediate recall performance on a visual-spatial episodic memory task was significantly predicted by hippocampal volume ( p < 0.003). Delayed recall on the same task was significantly predicted by volume of the left thalamus ( p < 0.003). For both memory measures, functional activation of the thalamus during encoding was more predictive than that of volume measures ( p < 0.002). Our results suggest that functional activation may be useful as a predictive measure of episodic memory loss in patients with MS.

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players.

PubMed

Janot, Jeffrey M; Beltz, Nicholas M; Dalleck, Lance D

2015-09-01

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key pointsThe 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed.In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity.Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams.

Surgery residency curriculum examination scores predict future American Board of Surgery in-training examination performance.

PubMed

Webb, Travis P; Paul, Jasmeet; Treat, Robert; Codner, Panna; Anderson, Rebecca; Redlich, Philip

2014-01-01

A protected block curriculum (PBC) with postcurriculum examinations for all surgical residents has been provided to assure coverage of core curricular topics. Biannual assessment of resident competency will soon be required by the Next Accreditation System. To identify opportunities for early medical knowledge assessment and interventions, we examined whether performance in postcurriculum multiple-choice examinations (PCEs) is predictive of performance in the American Board of Surgery In-Training Examination (ABSITE) and clinical service competency assessments. Retrospective single-institutional education research study. Academic general surgery residency program. A total of 49 surgical residents. Data for PGY1 and PGY2 residents participating in the 2008 to 2012 PBC are included. Each resident completed 6 PCEs during each year. The results of 6 examinations were correlated to percentage-correct ABSITE scores and clinical assessments based on the 6 Accreditation Council for Graduate Medical Education core competencies. Individual ABSITE performance was compared between PGY1 and PGY2. Statistical analysis included multivariate linear regression and bivariate Pearson correlations. A total of 49 residents completed the PGY1 PBC and 36 completed the PGY2 curriculum. Linear regression analysis of percentage-correct ABSITE and PCE scores demonstrated a statistically significant correlation between the PGY1 PCE 1 score and the subsequent PGY1 ABSITE score (p = 0.037, β = 0.299). Similarly, the PGY2 PCE 1 score predicted performance in the PGY2 ABSITE (p = 0.015, β = 0.383). The ABSITE scores correlated between PGY1 and PGY2 with statistical significance, r = 0.675, p = 0.001. Performance on the 6 Accreditation Council for Graduate Medical Education core competencies correlated between PGY1 and PGY2, r = 0.729, p = 0.001, but did not correlate with PCE scores during either years. Within a mature PBC, early performance in a PGY1 and PGY2 PCE is predictive of performance in the respective ABSITE. This information can be used for formative assessment and early remediation of residents who are predicted to be at risk for poor performance in the ABSITE. Copyright © 2014 Association of Program Directors in Surgery. All rights reserved.

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

PubMed

Mainsah, B O; Reeves, G; Collins, L M; Throckmorton, C S

2017-08-01

The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

Total lung capacity, residual volume and predicted residual volume in a densitometric study of older men.

PubMed Central

Latin, R W; Ruhling, R O

1986-01-01

Results of investigations using various lung volumes for hydrostatic weighing determinations (HWD) appear to be inconclusive. Often, these lung volumes are predicted and not clinically determined. For this reason, total lung capacity (TLC), a measured residual volume (RV), and a predicted residual volume (PRV) were used during HWDs to compare the techniques. Twenty-five older men, 56 to 70 years (means +/- 62.1 + 4.2 years) performed HWDs at RV (10 trials) and at TLC (3-5 trials). Values for body density and fat free mass were not significantly different between RV and TLC; both values were, however, significantly different from those derived using PRV. There were statistically significant differences (p less than 0.05) between all 3 per cent body fat values but the 1.1 per cent difference between TLC and RV may not be physiologically important. It was concluded that TLC and RV may be used comparably during HWDs, but a PRV may produce significantly different values. Since HWD at TLC is easily performed and circumvents the difficulties associated with the RV technique, it may be the preferred method for older subjects. PMID:3730758

Can We Predict Technical Aptitude?: A Systematic Review.

PubMed

Louridas, Marisa; Szasz, Peter; de Montbrun, Sandra; Harris, Kenneth A; Grantcharov, Teodor P

2016-04-01

To identify background characteristics and cognitive tests that may predict surgical trainees' future technical performance, and therefore be used to supplement existing surgical residency selection criteria. Assessment of technical skills is not commonly incorporated as part of the selection process for surgical trainees in North America. Emerging evidence, however, suggests that not all trainees are capable of reaching technical competence. Therefore, incorporating technical aptitude into selection processes may prove useful. A systematic search was carried out of the MEDLINE, PsycINFO, and Embase online databases to identify all studies that assessed associations between surrogate markers of innate technical abilities in surgical trainees, and whether these abilities correlate with technical performance. The quality of each study was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation system. A total of 8035 records were identified. After screening by title, abstract, and full text, 52 studies were included. Very few surrogate markers were found to predict technical performance. Significant associations with technical performance were seen for 1 of 23 participant-reported surrogate markers, 2 of 25 visual spatial tests, and 2 of 19 dexterity tests. The assessment of trainee Basic Performance Resources predicted technical performance in 62% and 75% of participants. To date, no single test has been shown to reliably predict the technical performance of surgical trainees. Strategies that rely on assessing multiple innate abilities, their interaction, and their relationship with technical skill may ultimately be more likely to serve as reliable predictors of future surgical performance.

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

PubMed

Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

2018-05-01

Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

PubMed Central

Genders, Tessa S. S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; van Mieghem, Carlos A. G.; de Feyter, Pim J.

2010-01-01

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. PMID:20559838

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

PubMed

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

2012-12-01

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

Predictability of physiological testing and the role of maturation in talent identification for adolescent team sports.

PubMed

Pearson, D T; Naughton, G A; Torode, M

2006-08-01

Entrepreneurial marketing of sport increases demands on sport development officers to identify talented individuals for specialist development at the youngest possible age. Talent identification results in the streamlining of resources to produce optimal returns from a sports investment. However, the process of talent identification for team sports is complex and success prediction is imperfect. The aim of this review is to describe existing practices in physiological tests used for talent identification in team sports and discuss the impact of maturity-related differences on the long term outcomes particularly for male participants. Maturation is a major confounding variable in talent identification during adolescence. A myriad of hormonal changes during puberty results in physical and physiological characteristics important for sporting performance. Significant changes during puberty make the prediction of adult performance difficult from adolescent data. Furthermore, for talent identification programs to succeed, valid and reliable testing procedures must be accepted and implemented in a range of performance-related categories. Limited success in scientifically based talent identification is evident in a range of team sports. Genetic advances challenge the ethics of talent identification in adolescent sport. However, the environment remains a significant component of success prediction in sport. Considerations for supporting talented young male athletes are discussed.

Application of Athletic Movement Tests that Predict Injury Risk in a Military Population: Development of Normative Data.

PubMed

Teyhen, Deydre S; Shaffer, Scott W; Butler, Robert J; Goffar, Stephen L; Kiesel, Kyle B; Rhon, Daniel I; Boyles, Robert E; McMillian, Daniel J; Williamson, Jared N; Plisky, Phillip J

2016-10-01

Performance on movement tests helps to predict injury risk in a variety of physically active populations. Understanding baseline measures for normal is an important first step. Determine differences in physical performance assessments and describe normative values for these tests based on military unit type. Assessment of power, balance, mobility, motor control, and performance on the Army Physical Fitness Test were assessed in a cohort of 1,466 soldiers. Analysis of variance was performed to compare the results based on military unit type (Rangers, Combat, Combat Service, and Combat Service Support) and analysis of covariance was performed to determine the influence of age and gender. Rangers performed the best on all performance and fitness measures (p < 0.05). Combat soldiers performed better than Combat Service and Service Support soldiers on several physical performance tests and the Army Physical Fitness Test (p < 0.05). Performance in Combat Service and Service Support soldiers was equivalent on most measures (p < 0.05). Functional performance and level of fitness varied significantly by military unit type. Understanding these differences will provide a foundation for future injury prediction and prevention strategies. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

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

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Age and visual impairment decrease driving performance as measured on a closed-road circuit.

PubMed

Wood, Joanne M

2002-01-01

In this study the effects of visual impairment and age on driving were investigated and related to visual function. Participants were 139 licensed drivers (young, middle-aged, and older participants with normal vision, and older participants with ocular disease). Driving performance was assessed during the daytime on a closed-road driving circuit. Visual performance was assessed using a vision testing battery. Age and visual impairment had a significant detrimental effect on recognition tasks (detection and recognition of signs and hazards), time to complete driving tasks (overall course time, reversing, and maneuvering), maneuvering ability, divided attention, and an overall driving performance index. All vision measures were significantly affected by group membership. A combination of motion sensitivity, useful field of view (UFOV), Pelli-Robson letter contrast sensitivity, and dynamic acuity could predict 50% of the variance in overall driving scores. These results indicate that older drivers with either normal vision or visual impairment had poorer driving performance compared with younger or middle-aged drivers with normal vision. The inclusion of tests such as motion sensitivity and the UFOV significantly improve the predictive power of vision tests for driving performance. Although such measures may not be practical for widespread screening, their application in selected cases should be considered.

Validity Evidence for Games as Assessment Environments. CRESST Report 773

ERIC Educational Resources Information Center

Delacruz, Girlie C.; Chung, Gregory K. W. K.; Baker, Eva L.

2010-01-01

This study provides empirical evidence of a highly specific use of games in education--the assessment of the learner. Linear regressions were used to examine the predictive and convergent validity of a math game as assessment of mathematical understanding. Results indicate that prior knowledge significantly predicts game performance. Results also…

Prediction and Stability of Reading Problems in Middle Childhood

ERIC Educational Resources Information Center

Ritchey, Kristen D.; Silverman, Rebecca D.; Schatschneider, Christopher; Speece, Deborah L.

2015-01-01

The longitudinal prediction of reading problems from fourth grade to sixth grade was investigated with a sample of 173 students. Reading problems at the end of sixth grade were defined by significantly below average performance (= 15th percentile) on reading factors defining word reading, fluency, and reading comprehension. Sixth grade poor reader…

A Trillion-Dollar Question: What Predicts Student Loan Delinquencies?

ERIC Educational Resources Information Center

Mezza, Alvaro; Sommer, Kamila

2016-01-01

The recent significant increase in student loan delinquencies has generated interest in understanding the key factors predicting the non-performance of these loans. However, despite the large size of the student loan market, existing analyses have been limited by lack of data. This paper studies predictors of student loan delinquencies using a…

CAN UPPER EXTREMITY FUNCTIONAL TESTS PREDICT THE SOFTBALL THROW FOR DISTANCE: A PREDICTIVE VALIDITY INVESTIGATION

PubMed Central

Hanney, William J.; Kolber, Morey J.; Davies, George J.; Riemann, Bryan

2011-01-01

Introduction: Understanding the relationships between performance tests and sport activity is important to the rehabilitation specialist. The purpose of this study was two- fold: 1) To identify if relationships exist between tests of upper body strength and power (Single Arm Seated Shot Put, Timed Push-Up, Timed Modified Pull-Up, and The Davies Closed Kinetic Chain Upper Extremity Stability Test, and the softball throw for distance), 2) To determine which variable or group of variables best predicts the performance of a sport specific task (the softball throw for distance). Methods: One hundred eighty subjects (111 females and 69 males, aged 18-45 years) performed the 5 upper extremity tests. The Pearson product moment correlation and a stepwise regression were used to determine whether relationships existed between performance on the tests and which upper extremity test result best explained the performance on the softball throw for distance. Results: There were significant correlations (r=.33 to r=.70, p=0.001) between performance on all of the tests. The modified pull-up test was the best predictor of the performance on the softball throw for distance (r2= 48.7), explaining 48.7% of variation in performance. When weight, height, and age were added to the regression equation the r2 values increased to 64.5, 66.2, and 67.5 respectively. Conclusion: The results of this study indicate that several upper extremity tests demonstrate significant relationships with one another and with the softball throw for distance. The modified pull up test was the best predictor of performance on the softball throw for distance. PMID:21712942

Temporal Learning Analytics for Adaptive Assessment

ERIC Educational Resources Information Center

Papamitsiou, Zacharoula; Economides, Anastasios A.

2014-01-01

Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine…

Associative cueing of attention through implicit feature-location binding.

PubMed

Girardi, Giovanna; Nico, Daniele

2017-09-01

In order to assess associative learning between two task-irrelevant features in cueing spatial attention, we devised a task in which participants have to make an identity comparison between two sequential visual stimuli. Unbeknownst to them, location of the second stimulus could be predicted by the colour of the first or a concurrent sound. Albeit unnecessary to perform the identity-matching judgment the predictive features thus provided an arbitrary association favouring the spatial anticipation of the second stimulus. A significant advantage was found with faster responses at predicted compared to non-predicted locations. Results clearly demonstrated an associative cueing of attention via a second-order arbitrary feature/location association but with a substantial discrepancy depending on the sensory modality of the predictive feature. With colour as predictive feature, significant advantages emerged only after the completion of three blocks of trials. On the contrary, sound affected responses from the first block of trials and significant advantages were manifest from the beginning of the second. The possible mechanisms underlying the associative cueing of attention in both conditions are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

PubMed Central

Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

2018-01-01

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures.

PubMed

Nayak, Tapsya; Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J; Dong, Bing; Huang, Yufei

2018-04-23

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R ² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

Predicting performance and injury resilience from movement quality and fitness scores in a basketball team over 2 years.

PubMed

McGill, Stuart M; Andersen, Jordan T; Horne, Arthur D

2012-07-01

The purpose of this study was to see if specific tests of fitness and movement quality could predict injury resilience and performance in a team of basketball players over 2 years (2 playing seasons). It was hypothesized that, in a basketball population, movement and fitness scores would predict performance scores and that movement and fitness scores would predict injury resilience. A basketball team from a major American university (N = 14) served as the test population in this longitudinal trial. Variables linked to fitness, movement ability, speed, strength, and agility were measured together with some National Basketball Association (NBA) combine tests. Dependent variables of performance indicators (such as games and minutes played, points scored, assists, rebounds, steal, and blocks) and injury reports were tracked for the subsequent 2 years. Results showed that better performance was linked with having a stiffer torso, more mobile hips, weaker left grip strength, and a longer standing long jump, to name a few. Of the 3 NBA combine tests administered here, only a faster lane agility time had significant links with performance. Some movement qualities and torso endurance were not linked. No patterns with injury emerged. These observations have implications for preseason testing and subsequent training programs in an attempt to reduce future injury and enhance playing performance.

MODIFIED FUNCTIONAL MOVEMENT SCREENING AS A PREDICTOR OF TACTICAL PERFORMANCE POTENTIAL IN RECREATIONALLY ACTIVE ADULTS.

PubMed

Glass, Stephen M; Ross, Scott E

2015-10-01

Failure to meet minimum performance standards is a leading cause of attrition from basic combat training. A standardized assessment such as the Functional Movement Screen™ (FMS™) could help identify movement behaviors relevant to physical performance in tactical occupations. Previous work has demonstrated only marginal association between FMS™ tests and performance outcomes, but adding a load challenge to this movement assessment may help highlight performance-limiting behaviors. The purposes of this investigation were to quantify the effect of load on FMS™ tests and determine the extent to which performance outcomes could be predicted using scores from both loaded and unloaded FMS™ conditions. Crossover Trial. Thirteen female and six male recreationally active college students (21 ± 1.37 years, 168 ± 9.8 cm, 66 ± 12.25 kg) completed the FMS™ under (1) a control condition (FMS™C), and (2) an 18.10kg weight vest condition (FMS™W). Balance was assessed using a force plate in double-legged stance and tactical physical performance was evaluated via completion times in a battery of field tests. For each condition, penalized regression was used to select models from the seven FMS™ component tests to predict balance and performance outcomes. Data were collected during a single session lasting approximately three hours per participant. For balance, significant predictors were identified from both conditions but primarily predicted poorer balance with increasing FMS™ scores. For tactical performance, models were retained almost exclusively from FMS™W and generally predicted better performance with higher item scores. The current results suggest that FMS™ screening with an external load could help predict performance relevant to tactical occupations. Sports medicine and fitness professionals interested in performance outcomes may consider assessing movement behaviors under a load. 3.

Interactions of age and cognitive functions in predicting decision making under risky conditions over the life span.

PubMed

Brand, Matthias; Schiebener, Johannes

2013-01-01

Little is known about how normal healthy aging affects decision-making competence. In this study 538 participants (age 18-80 years) performed the Game of Dice Task (GDT). Subsamples also performed the Iowa Gambling Task as well as tasks measuring logical thinking and executive functions. In a moderated regression analysis, the significant interaction between age and executive components indicates that older participants with good executive functioning perform well on the GDT, while older participants with reduced executive functions make more risky choices. The same pattern emerges for the interaction of age and logical thinking. Results demonstrate that age and cognitive functions act in concert in predicting the decision-making performance.

Iowa Gambling Task performance and emotional distress interact to predict risky sexual behavior in individuals with dual substance and HIV diagnoses

PubMed Central

Wardle, Margaret C.; Gonzalez, Raul; Bechara, Antoine; Martin-Thormeyer, Eileen M.

2013-01-01

HIV+ substance-dependent individuals (SDIs) show emotional distress and executive impairment, but in isolation these poorly predict sexual risk. We hypothesized that an executive measure sensitive to emotional aspects of judgment (Iowa Gambling Task; IGT) would identify HIV+ SDIs whose sexual risks were influenced by emotional distress. We assessed emotional distress and performance on several executive tasks in 190 HIV+ SDIs. IGT performance interacted significantly with emotional distress, such that only in better performers were distress and risk related. Our results are interpreted using the somatic marker hypothesis and indicate that the IGT identifies HIV+ SDIs for whom psychological distress influences HIV risk. PMID:20480423

The role of visual attention in predicting driving impairment in older adults.

PubMed

Hoffman, Lesa; McDowd, Joan M; Atchley, Paul; Dubinsky, Richard

2005-12-01

This study evaluated the role of visual attention (as measured by the DriverScan change detection task and the Useful Field of View Test [UFOV]) in the prediction of driving impairment in 155 adults between the ages of 63 and 87. In contrast to previous research, participants were not oversampled for visual impairment or history of automobile accidents. Although a history of automobile accidents within the past 3 years could not be predicted using any variable, driving performance in a low-fidelity simulator could be significantly predicted by performance in the change detection task and by the divided and selection attention subtests of the UFOV in structural equation models. The sensitivity and specificity of each measure in identifying at-risk drivers were also evaluated with receiver operating characteristic curves.

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study.

PubMed

Husbands, Adrian; Mathieson, Alistair; Dowell, Jonathan; Cleland, Jennifer; MacKenzie, Rhoda

2014-04-23

The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson's correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT's role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life.

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study

PubMed Central

2014-01-01

Background The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. Methods The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson’s correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Results Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Conclusions Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT’s role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life. PMID:24762134

Environments predicting intermittent shortening access reduce operant performance but not home cage binge size in rats

PubMed Central

Wojnicki, F.H.E.; Babbs, R.K.; Corwin, R.L.W

2013-01-01

When non-food-deprived rats are given brief access to vegetable shortening (a semi-solid fat used in baked products) on an intermittent basis (Monday, Wednesday, Friday), they consume significantly more and emit more operant responses for shortening than a separate group of rats given brief access to shortening every day. Since both groups are traditionally housed in the same room, it is possible that the environmental cues associated with placing shortening in the cages (e.g., investigator in room, cages opening and closing, etc.) provide predictable cues to the daily group, but unpredictable cues to the intermittent group. The present study examined the effects of providing predictable environmental cues to an isolated intermittent group in order to examine the independent contributions of intermittency and predictability on intake and operant performance. Two groups of rats were housed in the same room, with one group provided 30-min intermittent (INT) access and the second group provided 30-min daily access (D) to shortening. A third group (ISO) of rats was housed in a room by themselves in which all environmental cues associated with intermittent shortening availability were highly predictable. After five weeks of home cage shortening access, all rats were then exposed to several different operant schedules of reinforcement. The INT and ISO groups consumed significantly more shortening in the home cage than the D group. In contrast, the INT group earned significantly more reinforcers than both the ISO and D groups under all but one of the reinforcement schedules, while ISO and D did not differ. These data indicate that intermittent access will generate binge-type eating in the home cage independent of cue predictability. However, predictable cues in the home cage reduce operant responding independent of intermittent access. PMID:23535243

Predicting dire outcomes of patients with community acquired pneumonia.

PubMed

Cooper, Gregory F; Abraham, Vijoy; Aliferis, Constantin F; Aronis, John M; Buchanan, Bruce G; Caruana, Richard; Fine, Michael J; Janosky, Janine E; Livingston, Gary; Mitchell, Tom; Monti, Stefano; Spirtes, Peter

2005-10-01

Community-acquired pneumonia (CAP) is an important clinical condition with regard to patient mortality, patient morbidity, and healthcare resource utilization. The assessment of the likely clinical course of a CAP patient can significantly influence decision making about whether to treat the patient as an inpatient or as an outpatient. That decision can in turn influence resource utilization, as well as patient well being. Predicting dire outcomes, such as mortality or severe clinical complications, is a particularly important component in assessing the clinical course of patients. We used a training set of 1601 CAP patient cases to construct 11 statistical and machine-learning models that predict dire outcomes. We evaluated the resulting models on 686 additional CAP-patient cases. The primary goal was not to compare these learning algorithms as a study end point; rather, it was to develop the best model possible to predict dire outcomes. A special version of an artificial neural network (NN) model predicted dire outcomes the best. Using the 686 test cases, we estimated the expected healthcare quality and cost impact of applying the NN model in practice. The particular, quantitative results of this analysis are based on a number of assumptions that we make explicit; they will require further study and validation. Nonetheless, the general implication of the analysis seems robust, namely, that even small improvements in predictive performance for prevalent and costly diseases, such as CAP, are likely to result in significant improvements in the quality and efficiency of healthcare delivery. Therefore, seeking models with the highest possible level of predictive performance is important. Consequently, seeking ever better machine-learning and statistical modeling methods is of great practical significance.

Does Nutrition Knowledge and Practice of Athletes Translate to Enhanced Athletic Performance? Cross-Sectional Study Amongst Nigerian Undergraduate Athletes

PubMed Central

Folasire, Oluyemisi F.; Akomolafe, Abiola A.; Sanusi, Rasaki A.

2015-01-01

Introduction and Objectives: Nutrition knowledge of an athlete, as well as practice, is expected to influence athlete’s performance. The study assessed the nutrition knowledge and practice as well as athletes’ performance and identified the factors predicting the athletes’ performance. Methodology: A cross-sectional survey, involved 110 purposively selected undergraduate athletes (47 females, 63 males) of University of Ibadan, Nigeria, between July 2013 and December 2013. A semi-structured, self-administered questionnaire assessed the nutrition knowledge and practice. 24-hr diet recall and food frequency questionnaire were done. Anthropometric measurements were taken; body composition was determined by bioelectrical impedance analysis method. Handgrip strength (HGS), as an indirect measure of athlete performance, was assessed with the hand dynamometer. Chi-square and t-test analysis were used for the bivariate analysis. Pearson correlation and simple linear regression were used to determine relationships and predict athletic performance. The level of statistical significance was p<0.05. Results: More than half (58.2%) had good nutrition knowledge (NK), and 62.7% had good nutrition practices (NP). Majority (75.4%) had normal handgrip strength (HGS). More than 70.0% frequently do not consume cereals, roots and tubers, fruits and vegetables, legumes/nuts. About 30.0-40.0% frequently do not consume eggs/milk, meat/fish. Having good NK was significantly associated with good NP (χ2 = 15.520, p=0.000), but not with athlete’s performance (HGS). There is no significant correlation between NK, NP, and HGS. There is a significant positive correlation between HGS and lean muscle mass (LMM) (r=.670, p=0.000), weight (r=.492, p=0.000), height (r=.521, p=0.000) and energy intake (r=.386, p=0.000). There is a significant negative correlation between HGS and percentage body fat (r=-.400, p=0.000). Athletes’ performance was significantly predicted by the resting metabolic rate (β= .454 C.I=0.011 to 0.045, p=0.003), Lean muscle mass (β =.297 C.I=.059 to 0.562, p=0.024) and the weight (β =.228, C.I=1.852 to .489, p=0.047). Conclusion: Having good nutrition knowledge or practice did not directly determine athletic performance. However, there is the need for nutrition education interventions, to improve athlete’s performance by promoting adequate energy intake, lean muscle mass and appropriate weight gain in athletes. PMID:26156896

A prediction of 3-D viscous flow and performance of the NASA Low-Speed Centrifugal Compressor

NASA Technical Reports Server (NTRS)

Moore, John; Moore, Joan G.

1990-01-01

A prediction of the three-dimensional turbulent flow in the NASA Low-Speed Centrifugal Compressor Impeller has been made. The calculation was made for the compressor design conditions with the specified uniform tip clearance gap. The predicted performance is significantly worse than that predicted in the NASA design study. This is explained by the high tip leakage flow in the present calculation and by the different model adopted for tip leakage flow mixing. The calculation gives an accumulation of high losses in the shroud/pressure-side quadrant near the exit of the impeller. It also predicts a region of meridional backflow near the shroud wall. Both of these flow features should be extensive enough in the NASA impeller to allow detailed flow measurements, leading to improved flow modeling. Recommendations are made for future flow studies in the NASA impeller.

A prediction of 3-D viscous flow and performance of the NASA low-speed centrifugal compressor

NASA Technical Reports Server (NTRS)

Moore, John; Moore, Joan G.

1989-01-01

A prediction of the 3-D turbulent flow in the NASA Low-Speed Centrifugal Compressor Impeller has been made. The calculation was made for the compressor design conditions with the specified uniform tip clearance gap. The predicted performance is significantly worse than that predicted in the NASA design study. This is explained by the high tip leakage flow in the present calculation and by the different model adopted for tip leakage flow mixing. The calculation gives an accumulation for high losses in the shroud/pressure-side quadrant near the exit of the impeller. It also predicts a region of meridional backflow near the shroud wall. Both of these flow features should be extensive enough in the NASA impeller to allow detailed flow measurements, leading to improved flow modelling. Recommendations are made for future flow studies in the NASA impeller.

Motion compensation via redundant-wavelet multihypothesis.

PubMed

Fowler, James E; Cui, Suxia; Wang, Yonghui

2006-10-01

Multihypothesis motion compensation has been widely used in video coding with previous attention focused on techniques employing predictions that are diverse spatially or temporally. In this paper, the multihypothesis concept is extended into the transform domain by using a redundant wavelet transform to produce multiple predictions that are diverse in transform phase. The corresponding multiple-phase inverse transform implicitly combines the phase-diverse predictions into a single spatial-domain prediction for motion compensation. The performance advantage of this redundant-wavelet-multihypothesis approach is investigated analytically, invoking the fact that the multiple-phase inverse involves a projection that significantly reduces the power of a dense-motion residual modeled as additive noise. The analysis shows that redundant-wavelet multihypothesis is capable of up to a 7-dB reduction in prediction-residual variance over an equivalent single-phase, single-hypothesis approach. Experimental results substantiate the performance advantage for a block-based implementation.

Visuospatial and psychomotor aptitude predicts endovascular performance of inexperienced individuals on a virtual reality simulator.

PubMed

Van Herzeele, Isabelle; O'Donoghue, Kevin G L; Aggarwal, Rajesh; Vermassen, Frank; Darzi, Ara; Cheshire, Nicholas J W

2010-04-01

This study evaluated virtual reality (VR) simulation for endovascular training of medical students to determine whether innate perceptual, visuospatial, and psychomotor aptitude (VSA) can predict initial and plateau phase of technical endovascular skills acquisition. Twenty medical students received didactic and endovascular training on a commercially available VR simulator. Each student treated a series of 10 identical noncomplex renal artery stenoses endovascularly. The simulator recorded performance data instantly and objectively. An experienced interventionalist rated the performance at the initial and final sessions using generic (out of 40) and procedure-specific (out of 30) rating scales. VSA were tested with fine motor dexterity (FMD, Perdue Pegboard), psychomotor ability (minimally invasive virtual reality surgical trainer [MIST-VR]), image recall (Rey-Osterrieth), and organizational aptitude (map-planning). VSA performance scores were correlated with the assessment parameters of endovascular skills at commencement and completion of training. Medical students exhibited statistically significant learning curves from the initial to the plateau performance for contrast usage (medians, 28 vs 17 mL, P < .001), total procedure time (2120 vs 867 seconds, P < .001), and fluoroscopy time (993 vs. 507 seconds, P < .001). Scores on generic and procedure-specific rating scales improved significantly (10 vs 25, P < .001; 8 vs 17 P < .001). Significant correlations were noted for FMD with initial and plateau sessions for fluoroscopy time (r(s) = -0.564, P = .010; r(s) = -.449, P = .047). FMD correlated with procedure-specific scores at the initial session (r(s) = .607, P = .006). Image recall correlated with generic skills at the end of training (r(s) = .587, P = .006). Simulator-based training in endovascular skills improved performance in medical students. There were significant correlations between initial endovascular skill and fine motor dexterity as well as with image recall at end of the training period. In addition to current recruitment strategies, VSA may be a useful tool for predictive validity studies.

Longitudinal motor performance development in early adolescence and its relationship to adult success: An 8-year prospective study of highly talented soccer players

PubMed Central

Kelava, Augustin; Raabe, Johannes; Höner, Oliver

2018-01-01

Several talent identification and development (TID) programs in soccer have implemented diagnostics to measure players’ motor performance. Yet, there is a lack of research investigating the relationship between motor development in adolescence and future, adult performance. This longitudinal study analyzed the three-year development of highly talented young soccer players’ speed abilities and technical skills and examined the relevance of this development to their adult success. The current research sample consisted of N = 1,134 players born between 1993 and 1995 who were selected for the German Soccer Association’s TID program and participated in nationwide motor diagnostics (sprinting, agility, dribbling, ball control, shooting) four times between the Under 12 (U12) and Under 15 (U15) age class. Relative age (RA) was assessed for all players, and a total motor score was calculated based on performances in the individual tests. In order to investigate players’ future success, participants were divided into two groups according to their adult performance level (APL) in the 2014/2015 season: Elite (1st-5th German division; N = 145, 12.8%) and non-elite players (lower divisions; N = 989, 87.2%). Using multilevel regression analyses each motor performance was predicted by Time, Time2 (level-1 predictors), APL, and RA (level-2 covariates) with simultaneous consideration for interaction effects between the respective variables. Time and Time2 were significant predictors for each test performance. A predictive value for RA was confirmed for sprinting and the total motor score. A significant relationship between APL and the motor score as well as between APL and agility, dribbling, ball control, and shooting emerged. Interaction effects distinctly failed to reach significance. The study found a non-linear improvement in players’ performance for all considered motor performance factors over a three-year period from early to middle adolescence. While their predictive value for future success was confirmed by a significant relationship between APL and most of the considered factors, there was no significant interaction between APL and Time. These findings indicate that future elite players had already been better at the beginning of the TID program and maintained this high level throughout their promotion from U12 to U15. PMID:29723200

Longitudinal motor performance development in early adolescence and its relationship to adult success: An 8-year prospective study of highly talented soccer players.

PubMed

Leyhr, Daniel; Kelava, Augustin; Raabe, Johannes; Höner, Oliver

2018-01-01

Several talent identification and development (TID) programs in soccer have implemented diagnostics to measure players' motor performance. Yet, there is a lack of research investigating the relationship between motor development in adolescence and future, adult performance. This longitudinal study analyzed the three-year development of highly talented young soccer players' speed abilities and technical skills and examined the relevance of this development to their adult success. The current research sample consisted of N = 1,134 players born between 1993 and 1995 who were selected for the German Soccer Association's TID program and participated in nationwide motor diagnostics (sprinting, agility, dribbling, ball control, shooting) four times between the Under 12 (U12) and Under 15 (U15) age class. Relative age (RA) was assessed for all players, and a total motor score was calculated based on performances in the individual tests. In order to investigate players' future success, participants were divided into two groups according to their adult performance level (APL) in the 2014/2015 season: Elite (1st-5th German division; N = 145, 12.8%) and non-elite players (lower divisions; N = 989, 87.2%). Using multilevel regression analyses each motor performance was predicted by Time, Time2 (level-1 predictors), APL, and RA (level-2 covariates) with simultaneous consideration for interaction effects between the respective variables. Time and Time2 were significant predictors for each test performance. A predictive value for RA was confirmed for sprinting and the total motor score. A significant relationship between APL and the motor score as well as between APL and agility, dribbling, ball control, and shooting emerged. Interaction effects distinctly failed to reach significance. The study found a non-linear improvement in players' performance for all considered motor performance factors over a three-year period from early to middle adolescence. While their predictive value for future success was confirmed by a significant relationship between APL and most of the considered factors, there was no significant interaction between APL and Time. These findings indicate that future elite players had already been better at the beginning of the TID program and maintained this high level throughout their promotion from U12 to U15.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Predicted versus observed 30-day perioperative outcomes using the ACS NSQIP surgical risk calculator in patients undergoing partial nephrectomy for renal cell carcinoma.

PubMed

Blair, Brian M; Lehman, Erik B; Jafri, Syed M; Kaag, Matthew G; Raman, Jay D

2018-06-04

The purpose of the study was to evaluate the accuracy of the American College of Surgeons NSQIP Surgical Risk Calculator for predicting risk-adjusted 30-day outcomes for patients undergoing partial nephrectomy (PN) for renal cell carcinoma (RCC). A single institution, multi-surgeon, prospectively maintained database was queried for patients undergoing PN for RCC from 1998 to 2015. 21 preoperative factors were analyzed for each patient with predicted risk for 30-day complications, mortality, and length of stay (LOS) calculated. Differences between the mean predicted risk and observed rate of surgical outcomes were determined using two-sided one-sample t test with significance at p < 0.05. Subgroup analyses of outcomes stratified by surgical approach were also performed. 470 patients undergoing PN for RCC were analyzed. Comparing NSQIP predicted to observed outcomes, clinically significant underestimations occurred with rates of overall complications (9.16 vs. 16.81%, p < 0.001), surgical site infections [SSI] (1.65 vs. 2.77%, p < 0.001), urinary tract infection [UTI] (1.41 vs. 3.40%, p < 0.001), and LOS (3.25 vs. 3.73 days, p < 0.001). On subgroup analysis, 209 open PN and 261 minimally invasive PN (MIPN) were performed. The NSQIP calculator consistently underestimated overall complications, SSI, UTI, and LOS (p < 0.001) among both surgical approaches, while overestimating MIPN severe complications (p < 0.001). Clinically important differences persisted when stratifying the MIPN group by laparoscopic (N = 111) and robotic (N = 150) approaches. The ACS NSQIP Surgical Risk Calculator had significant discrepancies among observed and predicted outcomes. Additional analyses confirmed these differences remained significant irrespective of surgical approach. These findings emphasize the need for urologic oncology-specific calculators to better predict surgical outcomes in this complex patient population.

Flight Test Results: CTAS Cruise/Descent Trajectory Prediction Accuracy for En route ATC Advisories

NASA Technical Reports Server (NTRS)

Green, S.; Grace, M.; Williams, D.

1999-01-01

The Center/TRACON Automation System (CTAS), under development at NASA Ames Research Center, is designed to assist controllers with the management and control of air traffic transitioning to/from congested airspace. This paper focuses on the transition from the en route environment, to high-density terminal airspace, under a time-based arrival-metering constraint. Two flight tests were conducted at the Denver Air Route Traffic Control Center (ARTCC) to study trajectory-prediction accuracy, the key to accurate Decision Support Tool advisories such as conflict detection/resolution and fuel-efficient metering conformance. In collaboration with NASA Langley Research Center, these test were part of an overall effort to research systems and procedures for the integration of CTAS and flight management systems (FMS). The Langley Transport Systems Research Vehicle Boeing 737 airplane flew a combined total of 58 cruise-arrival trajectory runs while following CTAS clearance advisories. Actual trajectories of the airplane were compared to CTAS and FMS predictions to measure trajectory-prediction accuracy and identify the primary sources of error for both. The research airplane was used to evaluate several levels of cockpit automation ranging from conventional avionics to a performance-based vertical navigation (VNAV) FMS. Trajectory prediction accuracy was analyzed with respect to both ARTCC radar tracking and GPS-based aircraft measurements. This paper presents detailed results describing the trajectory accuracy and error sources. Although differences were found in both accuracy and error sources, CTAS accuracy was comparable to the FMS in terms of both meter-fix arrival-time performance (in support of metering) and 4D-trajectory prediction (key to conflict prediction). Overall arrival time errors (mean plus standard deviation) were measured to be approximately 24 seconds during the first flight test (23 runs) and 15 seconds during the second flight test (25 runs). The major source of error during these tests was found to be the predicted winds aloft used by CTAS. Position and velocity estimates of the airplane provided to CTAS by the ATC Host radar tracker were found to be a relatively insignificant error source for the trajectory conditions evaluated. Airplane performance modeling errors within CTAS were found to not significantly affect arrival time errors when the constrained descent procedures were used. The most significant effect related to the flight guidance was observed to be the cross-track and turn-overshoot errors associated with conventional VOR guidance. Lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and aircraft performance model errors.

Predicting dementia using socio-demographic characteristics and the Free and Cued Selective Reminding Test in the general population.

PubMed

Mura, Thibault; Baramova, Marieta; Gabelle, Audrey; Artero, Sylvaine; Dartigues, Jean-François; Amieva, Hélène; Berr, Claudine

2017-03-23

Our study aimed to determine whether the consideration of socio-demographic features improves the prediction of Alzheimer's dementia (AD) at 5 years when using the Free and Cued Selective Reminding Test (FCSRT) in the general older population. Our analyses focused on 2558 subjects from the prospective Three-City Study, a cohort of community-dwelling individuals aged 65 years and over, with FCSRT scores. Four "residual scores" and "risk scores" were built that included the FCSRT scores and socio-demographic variables. The predictive performance of crude, residual and risk scores was analyzed by comparing the areas under the ROC curve (AUC). In total, 1750 subjects were seen 5 years after completing the FCSRT. AD was diagnosed in 116 of them. Compared with the crude free-recall score, the predictive performances of the residual score and of the risk score were not significantly improved (AUC: 0.83 vs 0.82 and 0.88 vs 0.89 respectively). Using socio-demographic features in addition to the FCSRT does not improve its predictive performance for dementia or AD.

Destination memory and cognitive theory of mind in normal ageing.

PubMed

El Haj, Mohamad; Raffard, Stéphane; Gély-Nargeot, Marie-Christine

2016-01-01

Destination memory is the ability to remember the destination to which a piece of information has been addressed (e.g., "Did I tell you about the promotion?"). This ability is found to be impaired in normal ageing. Our work aimed to link this deterioration to the decline in theory of mind. Forty younger adults (M age = 23.13 years, SD = 4.00) and 36 older adults (M age = 69.53 years, SD = 8.93) performed a destination memory task. They also performed the False-belief test addressing cognitive theory of mind and the Reading the mind in the eyes test addressing affective theory of mind. Results showed significant deterioration in destination memory, cognitive theory of mind and affective theory of mind in the older adults. The older adults' performance on destination memory was significantly correlated with and predicted by their performance on cognitive theory of mind. Difficulties in the ability to interpret and predict others' mental states are related to destination memory decline in older adults.

Measuring and Predicting Tag Importance for Image Retrieval.

PubMed

Li, Shangwen; Purushotham, Sanjay; Chen, Chen; Ren, Yuzhuo; Kuo, C-C Jay

2017-12-01

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience. PMID:28959198

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

Wosnik, Martin; Bachant, Pete; Neary, Vincent Sinclair

CACTUS, developed by Sandia National Laboratories, is an open-source code for the design and analysis of wind and hydrokinetic turbines. While it has undergone extensive validation for both vertical axis and horizontal axis wind turbines, and it has been demonstrated to accurately predict the performance of horizontal (axial-flow) hydrokinetic turbines, its ability to predict the performance of crossflow hydrokinetic turbines has yet to be tested. The present study addresses this problem by comparing the predicted performance curves derived from CACTUS simulations of the U.S. Department of Energy’s 1:6 scale reference model crossflow turbine to those derived by experimental measurements inmore » a tow tank using the same model turbine at the University of New Hampshire. It shows that CACTUS cannot accurately predict the performance of this crossflow turbine, raising concerns on its application to crossflow hydrokinetic turbines generally. The lack of quality data on NACA 0021 foil aerodynamic (hydrodynamic) characteristics over the wide range of angles of attack (AoA) and Reynolds numbers is identified as the main cause for poor model prediction. A comparison of several different NACA 0021 foil data sources, derived using both physical and numerical modeling experiments, indicates significant discrepancies at the high AoA experienced by foils on crossflow turbines. Users of CACTUS for crossflow hydrokinetic turbines are, therefore, advised to limit its application to higher tip speed ratios (lower AoA), and to carefully verify the reliability and accuracy of their foil data. Accurate empirical data on the aerodynamic characteristics of the foil is the greatest limitation to predicting performance for crossflow turbines with semi-empirical models like CACTUS. Future improvements of CACTUS for crossflow turbine performance prediction will require the development of accurate foil aerodynamic characteristic data sets within the appropriate ranges of Reynolds numbers and AoA.« less

Predictive value of age of walking for later motor performance in children with mental retardation.

PubMed

Kokubun, M; Haishi, K; Okuzumi, H; Hosobuchi, T; Koike, T

1996-12-01

The purpose of the present study was to clarify the predictive value of age of walking for later motor performance in children with mental retardation. While paying due attention to other factors, our investigation focused on the relationship between a subject's age of walking, and his or her subsequent beam-walking performance. The subjects were 85 children with mental retardation with an average age of 13 years and 3 months. Beam-walking performance was measured by a procedure developed by the authors. Five low beams (5 cm) which varied in width (12.5, 10, 7.5, 5 and 2.5 cm) were employed. The performance of subjects was scored from zero to five points according to the width of the beam that they were able to walk without falling off. From the results of multiple regression analysis, three independent variables were found to be significantly related to beam-walking performance. The age of walking was the most basic variable: partial correlation coefficient (PCC) = -45; standardized partial regression coefficient (SPRC) = -0.41. The next variable in importance was walking duration (PCC = 0.38; SPRC = 0.31). The autism variable also contributed significantly (PCC = 0.28; SPRC = 0.22). Therefore, within the age range used in the present study, the age of walking in children with mental retardation was thought to have sufficient predictive value, even when the variables which might have possibly affected their subsequent performance were taken into consideration; the earlier the age of walking, the better the beam-walking performance.

Value of intracochlear electrically evoked auditory brainstem response after cochlear implantation in patients with narrow internal auditory canal.

PubMed

Song, Mee Hyun; Bae, Mi Ran; Kim, Hee Nam; Lee, Won-Sang; Yang, Won Sun; Choi, Jae Young

2010-08-01

Cochlear implantation in patients with narrow internal auditory canal (IAC) can result in variable outcomes; however, preoperative evaluations have limitations in accurately predicting outcomes. In this study, we analyzed the outcomes of cochlear implantation in patients with narrow IAC and correlated the intracochlear electrically evoked auditory brainstem response (EABR) findings to postoperative performance to determine the prognostic significance of intracochlear EABR. Retrospective case series at a tertiary hospital. Thirteen profoundly deaf patients with narrow IAC who received cochlear implantation from 2002 to 2008 were included in this study. Postoperative performance was evaluated after at least 12 months of follow-up, and postoperative intracochlear EABR was measured to determine its correlation with outcome. The clinical significance of electrically evoked compound action potential (ECAP) was also analyzed. Patients with narrow IAC showed postoperative auditory performances ranging from CAP 0 to 4 after cochlear implantation. Intracochlear EABR measured postoperatively demonstrated prognostic value in the prediction of long-term outcomes, whereas ECAP measurements failed to show a significant correlation with outcome. Consistent with the advantages of intracochlear EABR over extracochlear EABR, this study demonstrates that intracochlear EABR has prognostic significance in predicting long-term outcomes in patients with narrow IAC. Intracochlear EABR measured either intraoperatively or in the early postoperative period may play an important role in deciding whether to continue with auditory rehabilitation using a cochlear implant or to switch to an auditory brainstem implant so as not to miss the optimal timing for language development.

Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

PubMed Central

Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

2014-01-01

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Limited potential of genetic predisposition scores to predict muscle mass and strength performance in Flemish Caucasians between 19 and 73 years of age.

PubMed

Charlier, Ruben; Caspers, Maarten; Knaeps, Sara; Mertens, Evelien; Lambrechts, Diether; Lefevre, Johan; Thomis, Martine

2017-03-01

Since both muscle mass and strength performance are polygenic in nature, the current study compared four genetic predisposition scores (GPS) in their ability to predict these phenotypes. Data were gathered within the framework of the first-generation Flemish Policy Research Centre "Sport, Physical Activity and Health" (2002-2004). Results are based on muscle characteristics data of 565 Flemish Caucasians (19-73 yr, 365 men). Skeletal muscle mass was determined from bioelectrical impedance. The Biodex dynamometer was used to measure isometric (PT static120° ) and isokinetic strength (PT dynamic60° and PT dynamic240° ), ballistic movement speed (S 20% ), and muscular endurance (Work) of the knee extensors. Genotyping was done for 153 gene variants, selected on the basis of a literature search and the expression quantitative trait loci of selected genes. Four GPS were designed: a total GPS (based on the sum of all 153 variants, each favorable allele = score 1), a data-driven and weighted GPS [respectively, the sum of favorable alleles of those variants with significant b-coefficients in stepwise regression (GPS dd ), and the sum of these variants weighted with their respective partial r 2 (GPS w )], and an elastic net GPS (based on the variants that were selected by an elastic net regularization; GPS en ). It was found that four different models for a GPS were able to significantly predict up to ~7% of the variance in strength performance. GPS en made the best prediction of SMM and Work. However, this was not the case for the remaining strength performance parameters, where best predictions were made by GPS dd and GPS w . Copyright © 2017 the American Physiological Society.

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.

Four hundred or more participants needed for stable contingency table estimates of clinical prediction rule performance.

PubMed

Kent, Peter; Boyle, Eleanor; Keating, Jennifer L; Albert, Hanne B; Hartvigsen, Jan

2017-02-01

To quantify variability in the results of statistical analyses based on contingency tables and discuss the implications for the choice of sample size for studies that derive clinical prediction rules. An analysis of three pre-existing sets of large cohort data (n = 4,062-8,674) was performed. In each data set, repeated random sampling of various sample sizes, from n = 100 up to n = 2,000, was performed 100 times at each sample size and the variability in estimates of sensitivity, specificity, positive and negative likelihood ratios, posttest probabilities, odds ratios, and risk/prevalence ratios for each sample size was calculated. There were very wide, and statistically significant, differences in estimates derived from contingency tables from the same data set when calculated in sample sizes below 400 people, and typically, this variability stabilized in samples of 400-600 people. Although estimates of prevalence also varied significantly in samples below 600 people, that relationship only explains a small component of the variability in these statistical parameters. To reduce sample-specific variability, contingency tables should consist of 400 participants or more when used to derive clinical prediction rules or test their performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Plasma inflammatory and immune proteins as predictors of intra-amniotic infection and spontaneous preterm delivery in women with preterm labor: a retrospective study.

PubMed

Park, Hyunsoo; Park, Kyo Hoon; Kim, Yu Mi; Kook, Song Yi; Jeon, Se Jeong; Yoo, Ha-Na

2018-05-09

We investigated whether various inflammatory and immune proteins in plasma predict intra-amniotic infection and imminent preterm delivery in women with preterm labor and compared their predictive ability with that of amniotic fluid (AF) interleukin (IL)-6 and serum C-reactive protein (CRP). This retrospective cohort study included 173 consecutive women with preterm labor who underwent amniocentesis for diagnosis of infection and/or inflammation in the AF. The AF was cultured, and assayed for IL-6. CRP levels and cervical length by transvaginal ultrasound were measured at the time of amniocentesis. The stored maternal plasma was assayed for IL-6, matrix metalloproteinase (MMP)-9, and complements C3a and C5a using ELISA kits. The primary and secondary outcome criteria were positive AF cultures and spontaneous preterm delivery (SPTD) within 48 h, respectively. Univariate, multivariate, and receiver operating characteristic analysis were used for the statistical analysis. In bivariate analyses, elevated plasma IL-6 level was significantly associated with intra-amniotic infection and imminent preterm delivery, whereas elevated plasma levels of MMP-9, C3a, and C5a were not associated with these two outcomes. On multivariate analyses, an elevated plasma IL-6 level was significantly associated with intra-amniotic infection and imminent preterm delivery after adjusting for confounders, including high serum CRP levels and short cervical length. In predicting intra-amniotic infection, the area under the curve (AUC) was significantly lower for plasma IL-6 than for AF IL-6 but was similar to that for serum CRP. Differences in the AUCs between plasma IL-6, AF IL-6, and serum CRP were not statistically significant in predicting imminent preterm delivery. Maternal plasma IL-6 independently predicts intra-amniotic infection in women with preterm labor; however, it has worse diagnostic performance than that of AF IL-6 and similar performance to that of serum CRP. To predict imminent preterm delivery, plasma IL-6 had an overall diagnostic performance similar to that of AF IL-6 and serum CRP. Plasma MMP-9, C3a, and C5a levels could not predict intra-amniotic infection or imminent preterm delivery.

Genomewide predictions from maize single-cross data.

PubMed

Massman, Jon M; Gordillo, Andres; Lorenzana, Robenzon E; Bernardo, Rex

2013-01-01

Maize (Zea mays L.) breeders evaluate many single-cross hybrids each year in multiple environments. Our objective was to determine the usefulness of genomewide predictions, based on marker effects from maize single-cross data, for identifying the best untested single crosses and the best inbreds within a biparental cross. We considered 479 experimental maize single crosses between 59 Iowa Stiff Stalk Synthetic (BSSS) inbreds and 44 non-BSSS inbreds. The single crosses were evaluated in multilocation experiments from 2001 to 2009 and the BSSS and non-BSSS inbreds had genotypic data for 669 single nucleotide polymorphism (SNP) markers. Single-cross performance was predicted by a previous best linear unbiased prediction (BLUP) approach that utilized marker-based relatedness and information on relatives, and from genomewide marker effects calculated by ridge-regression BLUP (RR-BLUP). With BLUP, the mean prediction accuracy (r(MG)) of single-cross performance was 0.87 for grain yield, 0.90 for grain moisture, 0.69 for stalk lodging, and 0.84 for root lodging. The BLUP and RR-BLUP models did not lead to r(MG) values that differed significantly. We then used the RR-BLUP model, developed from single-cross data, to predict the performance of testcrosses within 14 biparental populations. The r(MG) values within each testcross population were generally low and were often negative. These results were obtained despite the above-average level of linkage disequilibrium, i.e., r(2) between adjacent markers of 0.35 in the BSSS inbreds and 0.26 in the non-BSSS inbreds. Overall, our results suggested that genomewide marker effects estimated from maize single crosses are not advantageous (cofmpared with BLUP) for predicting single-cross performance and have erratic usefulness for predicting testcross performance within a biparental cross.

The baseline serum value of α-amylase is a significant predictor of distance running performance.

PubMed

Lippi, Giuseppe; Salvagno, Gian Luca; Danese, Elisa; Tarperi, Cantor; La Torre, Antonio; Guidi, Gian Cesare; Schena, Federico

2015-02-01

This study was planned to investigate whether serum α-amylase concentration may be associated with running performance, physiological characteristics and other clinical chemistry analytes in a large sample of recreational athletes undergoing distance running. Forty-three amateur runners successfully concluded a 21.1 km half-marathon at 75%-85% of their maximal oxygen uptake (VO2max). Blood was drawn during warm up and 15 min after conclusion of the run. After correction for body weight change, significant post-run increases were observed for serum values of alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, creatine kinase (CK), iron, lactate dehydrogenase (LDH), triglycerides, urea and uric acid, whereas the values of body weight, glomerular filtration rate, total and low density lipoprotein-cholesterol were significantly decreased. The concentration of serum α-amylase was unchanged. In univariate analysis, significant associations with running performance were found for gender, VO2max, training regimen and pre-run serum values of α-amylase, CK, glucose, high density lipoprotein-cholesterol, LDH, urea and uric acid. In multivariate analysis, only VO2max (p=0.042) and baseline α-amylase (p=0.021) remained significant predictors of running performance. The combination of these two variables predicted 71% of variance in running performance. The baseline concentration of serum α-amylase was positively correlated with variation of serum glucose during the trial (r=0.345; p=0.025) and negatively with capillary blood lactate at the end of the run (r=-0.352; p=0.021). We showed that the baseline serum α-amylase concentration significantly and independently predicts distance running performance in recreational runners.

Emotional Intelligence and Personality Traits as Predictors of Occupational Therapy students' Practice Education Performance: A Cross-Sectional Study.

PubMed

Brown, Ted; Williams, Brett; Etherington, Jamie

2016-12-01

This study investigated whether occupational therapy students' emotional intelligence and personality traits are predictive of specific aspects of their fieldwork performance. A total of 114 second and third year undergraduate occupational therapy students (86.6% response rate) completed the Genos Emotional Intelligence Inventory (Genos EI) and the Ten-Item Personality Inventory (TIPI). Fieldwork performance scores were obtained from the Student Practice Evaluation Form Revised (SPEF-R). Linear regressions were completed with the SPEF-R domains being the dependent variables and the Genos EI and TIPI factors being the independent variables. Regression analysis results revealed that the Genos EI subscales of Emotional Management of Others (EMO), Emotional Awareness of Others (EAO), Emotional Expression (EEX) and Emotional Reasoning (ERE) were significant predictors of various domains of students' fieldwork performance. EAO and ERE were significant predictors of students' Communication Skills accounting for 4.6% of its variance. EMO, EAO, EEX and ERE were significant predictors of students' Documentation Skills explaining 6.8% of its variance. EMO was a significant predictor of students' Professional Behaviour accounting for 3.2% of its variance. No TIPI factors were found to be significant predictors of the SPEF-R domains. Occupational therapy students' emotional intelligence was a significant predictor of components of their fieldwork performance while students' personality traits were not. The convenience sampling approach used, small sample size recruited and potential issue of social desirability of the self-reported Genos EI and TIPI data are acknowledged as study limitations. It is recommended that other studies be completed to investigate if any other relevant constructs or factors are predictive of occupational therapy students' fieldwork performance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Individual differences in working memory capacity determine the effects of oculomotor task load on concurrent word recall performance.

PubMed

Lee, Eun-Ju; Kwon, Gusang; Lee, Aekyoung; Ghajar, Jamshid; Suh, Minah

2011-07-05

In this study, the interaction between individual differences in working memory capacity, which were assessed by the Korean version of the California Verbal Learning Test (K-CVLT), and the effects of oculomotor task load on word recall performance are examined in a dual-task experiment. We hypothesized that varying levels of oculomotor task load should result in different demands on cognitive resources. The verbal working memory task used in this study involved a brief exposure to seven words to be remembered, followed by a 30-second delay during which the subject carried out an oculomotor task. Then, memory performance was assessed by having the subjects recall as many words as possible. Forty healthy normal subjects with no vision-related problems carried out four separate dual-tasks over four consecutive days of participation, wherein word recall performances were tested under unpredictable random SPEM (smooth pursuit eye movement), predictive SPEM, fixation, and eyes-closed conditions. The word recall performance of subjects with low K-CVLT scores was significantly enhanced under predictive SPEM conditions as opposed to the fixation and eyes-closed conditions, but performance was reduced under the random SPEM condition, thus reflecting an inverted-U relationship between the oculomotor task load and word recall performance. Subjects with high K-CVLT scores evidenced steady word recall performances, regardless of the type of oculomotor task performed. The concurrent oculomotor performance measured by velocity error did not differ significantly among the K-CVLT groups. However, the high-scoring subjects evidenced smaller phase errors under predictive SPEM conditions than did the low-scoring subjects; this suggests that different resource allocation strategies may be adopted, depending on individuals' working memory capacity. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

PubMed

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2015-02-01

To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences. Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort. Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions. None. We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models. Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

PubMed

Torres, César Iván

2014-06-01

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.

PubMed Central

Kayaalp, M.; Cooper, G. F.; Clermont, G.

2000-01-01

OBJECTIVE: This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge. DESIGN: This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves. RESULTS: The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model. CONCLUSION: Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone. PMID:11079917

Does teacher evaluation based on student performance predict motivation, well-being, and ill-being?

PubMed

Cuevas, Ricardo; Ntoumanis, Nikos; Fernandez-Bustos, Juan G; Bartholomew, Kimberley

2018-06-01

This study tests an explanatory model based on self-determination theory, which posits that pressure experienced by teachers when they are evaluated based on their students' academic performance will differentially predict teacher adaptive and maladaptive motivation, well-being, and ill-being. A total of 360 Spanish physical education teachers completed a multi-scale inventory. We found support for a structural equation model that showed that perceived pressure predicted teacher autonomous motivation negatively, predicted amotivation positively, and was unrelated to controlled motivation. In addition, autonomous motivation predicted vitality positively and exhaustion negatively, whereas controlled motivation and amotivation predicted vitality negatively and exhaustion positively. Amotivation significantly mediated the relation between pressure and vitality and between pressure and exhaustion. The results underline the potential negative impact of pressure felt by teachers due to this type of evaluation on teacher motivation and psychological health. Copyright © 2018 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Genomic Prediction Accounting for Genotype by Environment Interaction Offers an Effective Framework for Breeding Simultaneously for Adaptation to an Abiotic Stress and Performance Under Normal Cropping Conditions in Rice.

PubMed

Ben Hassen, Manel; Bartholomé, Jérôme; Valè, Giampiero; Cao, Tuong-Vi; Ahmadi, Nourollah

2018-05-09

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6% to 4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed. Copyright © 2018, G3: Genes, Genomes, Genetics.

Psychological factors that predict reaction to abortion.

PubMed

Moseley, D T; Follingstad, D R; Harley, H; Heckel, R V

1981-04-01

Investigated demographic and psychological factors related to positive or negative reactions to legal abortions performed during the first trimester of pregnancy in 62 females in an urban southern community. Results suggest that the social context and the degree of support from a series of significant persons rather than demographic variables were most predictive of a positive reaction.

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach

PubMed Central

Kneifel, Joshua; Webb, David

2016-01-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF. PMID:27956756

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

PubMed

Kneifel, Joshua; Webb, David

2016-09-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF.

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

PubMed

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

2016-10-05

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

More than just the mean: moving to a dynamic view of performance-based compensation.

PubMed

Barnes, Christopher M; Reb, Jochen; Ang, Dionysius

2012-05-01

Compensation decisions have important consequences for employees and organizations and affect factors such as retention, motivation, and recruitment. Past research has primarily focused on mean performance as a predictor of compensation, promoting the implicit assumption that alternative aspects of dynamic performance are not relevant. To address this gap in the literature, we examined the influence of dynamic performance characteristics on compensation decisions in the National Basketball Association (NBA). We predicted that, in addition to performance mean, performance trend and variability would also affect compensation decisions. Results revealed that performance mean and trend, but not variability, were significantly and positively related to changes in compensation levels of NBA players. Moreover, trend (but not mean or variability) predicted compensation when controlling for future performance, suggesting that organizations overweighted trend in their compensation decisions. Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

The relation between cognitive and motor performance and their relevance for children's transition to school: a latent variable approach.

PubMed

Roebers, Claudia M; Röthlisberger, Marianne; Neuenschwander, Regula; Cimeli, Patrizia; Michel, Eva; Jäger, Katja

2014-02-01

Both theoretically and empirically there is a continuous interest in understanding the specific relation between cognitive and motor development in childhood. In the present longitudinal study including three measurement points, this relation was targeted. At the beginning of the study, the participating children were 5-6-year-olds. By assessing participants' fine motor skills, their executive functioning, and their non-verbal intelligence, their cross-sectional and cross-lagged interrelations were examined. Additionally, performance in these three areas was used to predict early school achievement (in terms of mathematics, reading, and spelling) at the end of participants' first grade. Correlational analyses and structural equation modeling revealed that fine motor skills, non-verbal intelligence and executive functioning were significantly interrelated. Both fine motor skills and intelligence had significant links to later school achievement. However, when executive functioning was additionally included into the prediction of early academic achievement, fine motor skills and non-verbal intelligence were no longer significantly associated with later school performance suggesting that executive functioning plays an important role for the motor-cognitive performance link. Copyright © 2013 Elsevier B.V. All rights reserved.

Trait impulsivity predicts D-KEFS tower test performance in university students.

PubMed

Lyvers, Michael; Basch, Vanessa; Duff, Helen; Edwards, Mark S

2015-01-01

The present study examined a widely used self-report index of trait impulsiveness in relation to performance on a well-known neuropsychological executive function test in 70 university undergraduate students (50 women, 20 men) aged 18 to 24 years old. Participants completed the Barratt Impulsiveness Scale (BIS-11) and the Frontal Systems Behavior Scale (FrSBe), after which they performed the Tower Test of the Delis-Kaplan Executive Function System. Hierarchical linear regression showed that after controlling for gender, current alcohol consumption, age at onset of weekly alcohol use, and FrSBe scores, BIS-11 significantly predicted Tower Test Achievement scores, β = -.44, p < .01. The results indicate that self-reported impulsiveness is associated with poorer executive cognitive performance even in a sample likely to be characterized by relatively high general cognitive functioning (i.e., university students). The results also support the role of inhibition as a key aspect of executive task performance. Elevated scores on the BIS-11 and FrSBe are known to be linked to risky drinking in young adults as confirmed in this sample; however, only BIS-11 predicted Tower Test performance.

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

PubMed Central

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

2014-01-01

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

Developing Quantum Chemical and Polyparameter Models for Predicting Environmentally Significant Parameters for New Munition Compounds

DTIC Science & Technology

2017-05-31

Allen Se. TASK NUMBER Stanley I. Sandler Sf. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADORESS(ES) 8. PERFORMING ORGANIZATION ...226 6.2.2.2 Soil Organic Carbon–Water pp–LFER ....................... 227 6.2.3 Experimental Data...

Leadership Styles and Organizational Performance: A Predictive Analysis

ERIC Educational Resources Information Center

Kieu, Hung Q.

2010-01-01

Leadership is critically important because it affects the health of the organization. Research has found that leadership is one of the most significant contributors to organizational performance. Expanding and replicating previous research, and focusing on the specific telecommunications sector, this study used multiple correlation and regression…

Does the MCAT predict medical school and PGY-1 performance?

PubMed

Saguil, Aaron; Dong, Ting; Gingerich, Robert J; Swygert, Kimberly; LaRochelle, Jeffrey S; Artino, Anthony R; Cruess, David F; Durning, Steven J

2015-04-01

The Medical College Admissions Test (MCAT) is a high-stakes test required for entry to most U. S. medical schools; admissions committees use this test to predict future accomplishment. Although there is evidence that the MCAT predicts success on multiple choice-based assessments, there is little information on whether the MCAT predicts clinical-based assessments of undergraduate and graduate medical education performance. This study looked at associations between the MCAT and medical school grade point average (GPA), Medical Licensing Examination (USMLE) scores, observed patient care encounters, and residency performance assessments. This study used data collected as part of the Long-Term Career Outcome Study to determine associations between MCAT scores, USMLE Step 1, Step 2 clinical knowledge and clinical skill, and Step 3 scores, Objective Structured Clinical Examination performance, medical school GPA, and PGY-1 program director (PD) assessment of physician performance for students graduating 2010 and 2011. MCAT data were available for all students, and the PGY PD evaluation response rate was 86.2% (N = 340). All permutations of MCAT scores (first, last, highest, average) were weakly associated with GPA, Step 2 clinical knowledge scores, and Step 3 scores. MCAT scores were weakly to moderately associated with Step 1 scores. MCAT scores were not significantly associated with Step 2 clinical skills Integrated Clinical Encounter and Communication and Interpersonal Skills subscores, Objective Structured Clinical Examination performance or PGY-1 PD evaluations. MCAT scores were weakly to moderately associated with assessments that rely on multiple choice testing. The association is somewhat stronger for assessments occurring earlier in medical school, such as USMLE Step 1. The MCAT was not able to predict assessments relying on direct clinical observation, nor was it able to predict PD assessment of PGY-1 performance. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

1.5-Tesla Multiparametric-Magnetic Resonance Imaging for the detection of clinically significant prostate cancer.

PubMed

Popita, Cristian; Popita, Anca Raluca; Sitar-Taut, Adela; Petrut, Bogdan; Fetica, Bogdan; Coman, Ioan

2017-01-01

Multiparametric-magnetic resonance imaging (mp-MRI) is the main imaging modality used for prostate cancer detection. The aim of this study is to evaluate the diagnostic performance of mp-MRI at 1.5-Tesla (1.5-T) for the detection of clinically significant prostate cancer. In this ethical board approved prospective study, 39 patients with suspected prostate cancer were included. Patients with a history of positive prostate biopsy and patients treated for prostate cancer were excluded. All patients were examined at 1.5-T MRI, before standard transrectal ultrasonography-guided biopsy. The overall sensitivity, specificity, positive predictive value and negative predictive value for mp-MRI were 100%, 73.68%, 80% and 100%, respectively. Our results showed that 1.5 T mp-MRI has a high sensitivity for detection of clinically significant prostate cancer and high negative predictive value in order to rule out significant disease.

Testing the Predictive Validity of the Hendrich II Fall Risk Model.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae

2018-03-01

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

Predicted effect of dynamic load on pitting fatigue life for low-contact-ratio spur gears

NASA Technical Reports Server (NTRS)

Lewicki, David G.

1986-01-01

How dynamic load affects the surface pitting fatigue life of external spur gears was predicted by using the NASA computer program TELSGE. Parametric studies were performed over a range of various gear parameters modeling low-contact-ratio involute spur gears. In general, gear life predictions based on dynamic loads differed significantly from those based on static loads, with the predictions being strongly influenced by the maximum dynamic load during contact. Gear mesh operating speed strongly affected predicted dynamic load and life. Meshes operating at a resonant speed or one-half the resonant speed had significantly shorter lives. Dynamic life factors for gear surface pitting fatigue were developed on the basis of the parametric studies. In general, meshes with higher contact ratios had higher dynamic life factors than meshes with lower contact ratios. A design chart was developed for hand calculations of dynamic life factors.

Long-term bleeding risk prediction in 'real world' patients with atrial fibrillation: Comparison of the HAS-BLED and ABC-Bleeding risk scores. The Murcia Atrial Fibrillation Project.

PubMed

Esteve-Pastor, María Asunción; Rivera-Caravaca, José Miguel; Roldan, Vanessa; Vicente, Vicente; Valdés, Mariano; Marín, Francisco; Lip, Gregory Y H

2017-10-05

Risk scores in patients with atrial fibrillation (AF) based on clinical factors alone generally have only modest predictive value for predicting high risk patients that sustain events. Biomarkers might be an attractive prognostic tool to improve bleeding risk prediction. The new ABC-Bleeding score performed better than HAS-BLED score in a clinical trial cohort but has not been externally validated. The aim of this study was to analyze the predictive performance of the ABC-Bleeding score compared to HAS-BLED score in an independent "real-world" anticoagulated AF patients with long-term follow-up. We enrolled 1,120 patients stable on vitamin K antagonist treatment. The HAS-BLED and ABC-Bleeding scores were quantified. Predictive values were compared by c-indexes, IDI, NRI, as well as decision curve analysis (DCA). Median HAS-BLED score was 2 (IQR 2-3) and median ABC-Bleeding was 16.5 (IQR 14.3-18.6). After 6.5 years of follow-up, 207 (2.84 %/year) patients had major bleeding events, of which 65 (0.89 %/year) had intracranial haemorrhage (ICH) and 85 (1.17 %/year) had gastrointestinal bleeding events (GIB). The c-index of HAS-BLED was significantly higher than ABC-Bleeding for major bleeding (0.583 vs 0.518; p=0.025), GIB (0.596 vs 0.519; p=0.017) and for the composite of ICH-GIB (0.593 vs 0.527; p=0.030). NRI showed a significant negative reclassification for major bleeding and for the composite of ICH-GIB with the ABC-Bleeding score compared to HAS-BLED. Using DCAs, the use of HAS-BLED score gave an approximate net benefit of 4 % over the ABC-Bleeding score. In conclusion, in the first "real-world" validation of the ABC-Bleeding score, HAS-BLED performed significantly better than the ABC-Bleeding score in predicting major bleeding, GIB and the composite of GIB and ICH.

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

Multi-material Preforming of Structural Composites

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

Norris, Robert E.; Eberle, Cliff C.; Pastore, Christopher M.

2015-05-01

Fiber-reinforced composites offer significant weight reduction potential, with glass fiber composites already widely adopted. Carbon fiber composites deliver the greatest performance benefits, but their high cost has inhibited widespread adoption. This project demonstrates that hybrid carbon-glass solutions can realize most of the benefits of carbon fiber composites at much lower cost. ORNL and Owens Corning Reinforcements along with program participants at the ORISE collaborated to demonstrate methods for produce hybrid composites along with techniques to predict performance and economic tradeoffs. These predictions were then verified in testing coupons and more complex demonstration articles.

Predictive importance of anthropometric and training data in recreational male Ironman triathletes and marathon runners: comment on the study by Gianoli, et al. (2012).

PubMed

Burtscher, Martin; Gatterer, Hannes

2013-04-01

Anthropometric and training data have been reported as statistically significant predictors of race performance in endurance events. However, it is well established that physiological characteristics, i.e., maximal oxygen uptake (VO2max), the use of a high percentage of VO2max during sustained exercise, and work efficiency are predominant predictors of performance in those events. Thus, the essential issue is whether the anthropometric and training data give additional predictive power beyond these other measures.

Predictive factors for complications in children with esophageal atresia and tracheoesophageal fistula.

PubMed

Shah, R; Varjavandi, V; Krishnan, U

2015-04-01

The objective of this study was to describe the incidence of complications in children with esophageal atresia (EA) with or without tracheoesophageal fistula (TEF) at a tertiary pediatric hospital and to identify predictive factors for their occurrence. A retrospective chart review of 110 patients born in or transferred to Sydney Children's Hospital with EA/TEF between January 1999 and December 2010 was done. Univariate and multivariate regression analyses were performed to identify predictive factors for the occurrence of complications in these children. From univariate analysis, early esophageal stricture formation was more likely in children with 'long-gap' EA (odds ratio [OR] = 16.32). Patients with early strictures were more likely to develop chest infections (OR = 3.33). Patients with severe tracheomalacia were more likely to experience 'cyanotic/dying' (OR = 180) and undergo aortopexy (OR = 549). Patients who had gastroesophageal reflux disease were significantly more likely to require fundoplication (OR = 10.83) and undergo aortopexy (OR = 6.417). From multivariate analysis, 'long-gap' EA was a significant predictive factor for late esophageal stricture formation (P = 0.007) and for gastrostomy insertion (P = 0.001). Reflux was a significant predictive factor for requiring fundoplication (P = 0.007) and gastrostomy (P = 0.002). Gastrostomy insertion (P = 0.000) was a significant predictive factor for undergoing fundoplication. Having a prior fundoplication (P = 0.001) was a significant predictive factor for undergoing a subsequent aortopexy. Predictive factors for the occurrence of complications post EA/TEF repair were identified in this large single centre pediatric study. © 2014 International Society for Diseases of the Esophagus.

Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

PubMed

Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

2015-02-01

The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

PubMed

Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

2018-05-17

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Relationships between academic performance of medical students and their workplace performance as junior doctors.

PubMed

Carr, Sandra E; Celenza, Antonio; Puddey, Ian B; Lake, Fiona

2014-07-30

Little recent published evidence explores the relationship between academic performance in medical school and performance as a junior doctor. Although many forms of assessment are used to demonstrate a medical student's knowledge or competence, these measures may not reliably predict performance in clinical practice following graduation. This descriptive cohort study explores the relationship between academic performance of medical students and workplace performance as junior doctors, including the influence of age, gender, ethnicity, clinical attachment, assessment type and summary score measures (grade point average) on performance in the workplace as measured by the Junior Doctor Assessment Tool. There were two hundred participants. There were significant correlations between performance as a Junior Doctor (combined overall score) and the grade point average (r = 0.229, P = 0.002), the score from the Year 6 Emergency Medicine attachment (r = 0.361, P < 0.001) and the Written Examination in Year 6 (r = 0.178, P = 0.014). There was no significant effect of any individual method of assessment in medical school, gender or ethnicity on the overall combined score of performance of the junior doctor. Performance on integrated assessments from medical school is correlated to performance as a practicing physician as measured by the Junior Doctor Assessment Tool. These findings support the value of combining undergraduate assessment scores to assess competence and predict future performance.

Can formative quizzes predict or improve summative exam performance?*

PubMed Central

Zhang, Niu; Henderson, Charles N.R.

2015-01-01

Objective Despite wide use, the value of formative exams remains unclear. We evaluated the possible benefits of formative assessments in a physical examination course at our chiropractic college. Methods Three hypotheses were examined: (1) Receiving formative quizzes (FQs) will increase summative exam (SX) scores, (2) writing FQ questions will further increase SE scores, and (3) FQs can predict SX scores. Hypotheses were tested across three separate iterations of the class. Results The SX scores for the control group (Class 3) were significantly less than those of Classes 1 and 2, but writing quiz questions and taking FQs (Class 1) did not produce significantly higher SX scores than only taking FQs (Class 2). The FQ scores were significant predictors of SX scores, accounting for 52% of the SX score. Sex, age, academic degrees, and ethnicity were not significant copredictors. Conclusion Our results support the assertion that FQs can improve written SX performance, but students producing quiz questions didn't further increase SX scores. We concluded that nonthreatening FQs may be used to enhance student learning and suggest that they also may serve to identify students who, without additional remediation, will perform poorly on subsequent summative written exams. PMID:25517737

Neuropsychological test performance and prediction of functional capacities among Spanish-speaking and English-speaking patients with dementia.

PubMed

Loewenstein, D A; Rubert, M P; Argüelles, T; Duara, R

1995-03-01

Neuropsychological measures have been widely used by clinicians to assist them in making judgments regarding a cognitively impaired patient's ability to independently perform important activities of daily living. However, important questions have been raised concerning the degree to which neuropsychological instruments can predict a broad array of specific functional capacities required in the home environment. In the present study, we examined 127 English-speaking and 56 Spanish-speaking patients with Alzheimer's disease (AD) and determined the extent to which various neuropsychological measures and demographic variables were predictive of performance on functional measures administered within the clinical setting. Among English-speaking AD patients, Block Design and Digit-Span of the WAIS-R, as well as tests of language were among the strongest predictors of functional performance. For Spanish-speakers, Block Design, The Mini-Mental State Evaluation (MMSE) and Digit Span had the optimal predictive power. When stepwise regression was conducted on the entire sample of 183 subjects, ethnicity emerged as a statistically significant predictor variable on one of the seven functional tests (writing a check). Despite the predictive power of several of the neuropsychological measures for both groups, most of the variability in objective functional performance could not be explained in our regression models. As a result, it would appear prudent to include functional measures as part of a comprehensive neuropsychological evaluation for dementia.

Comparison of the performances of copeptin and multiple biomarkers in long-term prognosis of severe traumatic brain injury.

PubMed

Zhang, Zu-Yong; Zhang, Li-Xin; Dong, Xiao-Qiao; Yu, Wen-Hua; Du, Quan; Yang, Ding-Bo; Shen, Yong-Feng; Wang, Hao; Zhu, Qiang; Che, Zhi-Hao; Liu, Qun-Jie; Jiang, Li; Du, Yuan-Feng

2014-10-01

Enhanced blood levels of copeptin correlate with poor clinical outcomes after acute critical illness. This study aimed to compare the prognostic performances of plasma concentrations of copeptin and other biomarkers like myelin basic protein, glial fibrillary astrocyte protein, S100B, neuron-specific enolase, phosphorylated axonal neurofilament subunit H, Tau and ubiquitin carboxyl-terminal hydrolase L1 in severe traumatic brain injury. We recruited 102 healthy controls and 102 acute patients with severe traumatic brain injury. Plasma concentrations of these biomarkers were determined using enzyme-linked immunosorbent assay. Their prognostic predictive performances of 6-month mortality and unfavorable outcome (Glasgow Outcome Scale score of 1-3) were compared. Plasma concentrations of these biomarkers were statistically significantly higher in all patients than in healthy controls, in non-survivors than in survivors and in patients with unfavorable outcome than with favorable outcome. Areas under receiver operating characteristic curves of plasma concentrations of these biomarkers were similar to those of Glasgow Coma Scale score for prognostic prediction. Except plasma copeptin concentration, other biomarkers concentrations in plasma did not statistically significantly improve prognostic predictive value of Glasgow Coma Scale score. Copeptin levels may be a useful tool to predict long-term clinical outcomes after severe traumatic brain injury and have a potential to assist clinicians. Copyright © 2014 Elsevier Inc. All rights reserved.

Relationships between testosterone levels and cognition in patients with Alzheimer disease and nondemented elderly men.

PubMed

Seidl, Jennifer N Travis; Massman, Paul J

2015-03-01

Previous research suggests that low levels of testosterone may be associated with the development of Alzheimer disease (AD), as well as poorer performance on certain neuropsychological tests and increased risk of depression. This study utilized data from 61 nondemented older men and 68 men with probable AD. Testosterone levels did not differ between the groups. Regression analyses in men with AD revealed that testosterone levels did not significantly predict performance on neuropsychological tests or a measure of depression. Among controls, testosterone levels predicted estimated premorbid verbal IQ and performance on a verbal fluency test. Findings suggest that testosterone is not associated with most neuropsychological test performances in patients with AD. © The Author(s) 2014.

Hyperdeactivation of the Default Mode Network in People With Schizophrenia When Focusing Attention in Space.

PubMed

Hahn, Britta; Harvey, Alexander N; Gold, James M; Fischer, Bernard A; Keller, William R; Ross, Thomas J; Stein, Elliot A

2016-09-01

When studying selective attention in people with schizophrenia (PSZ), a counterintuitive but replicated finding has been that PSZ display larger performance benefits than healthy control subjects (HCS) by cues that predicts the location of a target stimulus relative to non-predictive cues. Possible explanations are that PSZ hyperfocus attention in response to predictive cues, or that an inability to maintain a broad attentional window impairs performance when the cue is non-predictive. Over-recruitment of regions involved in top-down focusing of spatial attention in response to predictive cues would support the former possibility, and an inappropriate recruitment of these regions in response to non-predictive cues the latter. We probed regions of the dorsal attention network while PSZ (N = 20) and HCS (N = 20) performed a visuospatial attention task. A central cue either predicted at which of 4 peripheral locations a target signal would appear, or it gave no information about the target location. As observed previously, PSZ displayed a larger reaction time difference between predictive and non-predictive cue trials than HCS. Activity in frontoparietal and occipital regions was greater for predictive than non-predictive cues. This effect was almost identical between PSZ and HCS. There was no sign of over-recruitment when the cue was predictive, or of inappropriate recruitment when the cue was non-predictive. However, PSZ differed from HCS in their cue-dependent deactivation of the default mode network. Unexpectedly, PSZ displayed significantly greater deactivation than HCS in predictive cue trials, which may reflect a tendency to expend more processing resources when focusing attention in space. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

PubMed Central

Ming, Wei; Xiong, Tao

2014-01-01

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. PMID:24723814

Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease.

PubMed

Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe

2015-10-01

Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

Prediction models for successful external cephalic version: a systematic review.

PubMed

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Word Memory Test Predicts Recovery in Claimants With Work-Related Head Injury.

PubMed

Colangelo, Annette; Abada, Abigail; Haws, Calvin; Park, Joanne; Niemeläinen, Riikka; Gross, Douglas P

2016-05-01

To investigate the predictive validity of the Word Memory Test (WMT), a verbal memory neuropsychological test developed as a performance validity measure to assess memory, effort, and performance consistency. Cohort study with 1-year follow-up. Workers' compensation rehabilitation facility. Participants included workers' compensation claimants with work-related head injury (N=188; mean age, 44y; 161 men [85.6%]). Not applicable. Outcome measures for determining predictive validity included days to suspension of wage replacement benefits during the 1-year follow-up and work status at discharge in claimants undergoing rehabilitation. Analysis included multivariable Cox and logistic regression. Better WMT performance was significantly but weakly correlated with younger age (r=-.30), documented brain abnormality (r=.28), and loss of consciousness at the time of injury (r=.25). Claimants with documented brain abnormalities on diagnostic imaging scans performed better (∼9%) on the WMT than those without brain abnormalities. The WMT predicted days receiving benefits (adjusted hazard ratio, 1.13; 95% confidence interval, 1.04-1.24) and work status outcome at program discharge (adjusted odds ratio, 1.62; 95% confidence interval, 1.13-2.34). Our results provide evidence for the predictive validity of the WMT in workers' compensation claimants. Younger claimants and those with more severe brain injuries performed better on the WMT. It may be that financial incentives or other factors related to the compensation claim affected the performance. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The validity of physical aggression in predicting adolescent academic performance.

PubMed

Loveland, James M; Lounsbury, John W; Welsh, Deborah; Buboltz, Walter C

2007-03-01

Aggression has a long history in academic research as both a criterion and a predictor variable and it is well documented that aggression is related to a variety of poor academic outcomes such as: lowered academic performance, absenteeism and lower graduation rates. However, recent research has implicated physical aggression as being predictive of lower academic performance. The purpose of this study was to examine the role of the 'Big Five' personality traits of agreeableness, openness to experience, conscientiousness, neuroticism and extraversion and physical aggression in predicting the grade point averages (GPA) of adolescent students and to investigate whether or not there were differences in these relationships between male and female students. A sample of 992 students in grades 9 to 12 from a high school in south-eastern USA as part of a larger study examining the students' preparation for entry into the workforce. The study was correlational in nature: students completed a personality inventory developed by the second author with the GPA information supplied by the school. Results indicated that physical aggression accounts for 16% of variance in GPA and it adds 7% to the prediction of GPA beyond the Big Five. The Big Five traits added only 1.5% to the prediction of GPA after controlling for physical aggression. Interestingly, a significantly larger amount of variance in GPA was predicted by physical aggression for females than for males. Aggression accounts for significantly more variance in the GPA of females than for males, even when controlling for the Big Five personality factors. Future research should examine the differences in the expression of aggression in males and females, as well as how this is affecting interactions between peers and between students and their teachers.

Role of post-mapping computed tomography in virtual-assisted lung mapping.

PubMed

Sato, Masaaki; Nagayama, Kazuhiro; Kuwano, Hideki; Nitadori, Jun-Ichi; Anraku, Masaki; Nakajima, Jun

2017-02-01

Background Virtual-assisted lung mapping is a novel bronchoscopic preoperative lung marking technique in which virtual bronchoscopy is used to predict the locations of multiple dye markings. Post-mapping computed tomography is performed to confirm the locations of the actual markings. This study aimed to examine the accuracy of marking locations predicted by virtual bronchoscopy and elucidate the role of post-mapping computed tomography. Methods Automated and manual virtual bronchoscopy was used to predict marking locations. After bronchoscopic dye marking under local anesthesia, computed tomography was performed to confirm the actual marking locations before surgery. Discrepancies between marking locations predicted by the different methods and the actual markings were examined on computed tomography images. Forty-three markings in 11 patients were analyzed. Results The average difference between the predicted and actual marking locations was 30 mm. There was no significant difference between the latest version of the automated virtual bronchoscopy system (30.7 ± 17.2 mm) and manual virtual bronchoscopy (29.8 ± 19.1 mm). The difference was significantly greater in the upper vs. lower lobes (37.1 ± 20.1 vs. 23.0 ± 6.8 mm, for automated virtual bronchoscopy; p < 0.01). Despite this discrepancy, all targeted lesions were successfully resected using 3-dimensional image guidance based on post-mapping computed tomography reflecting the actual marking locations. Conclusions Markings predicted by virtual bronchoscopy were dislocated from the actual markings by an average of 3 cm. However, surgery was accurately performed using post-mapping computed tomography guidance, demonstrating the indispensable role of post-mapping computed tomography in virtual-assisted lung mapping.

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.

The multiple mini-interview for emergency medicine resident selection.

PubMed

Hopson, Laura R; Burkhardt, John C; Stansfield, R Brent; Vohra, Taher; Turner-Lawrence, Danielle; Losman, Eve D

2014-04-01

The Multiple Mini-Interview (MMI) uses multiple, short-structured contacts to evaluate communication and professionalism. It predicts medical school success better than the traditional interview and application. Its acceptability and utility in emergency medicine (EM) residency selection are unknown. We theorized that participants would judge the MMI equal to a traditional unstructured interview and it would provide new information for candidate assessment. Seventy-one interns from 3 programs in the first month of training completed an eight-station MMI focused on EM topics. Pre- and post-surveys assessed reactions. MMI scores were compared with application data. EM grades correlated with MMI performance (F[1, 66] = 4.18; p < 0.05) with honors students having higher scores. Higher third-year clerkship grades were associated with higher MMI performance, although this was not statistically significant. MMI performance did not correlate with match desirability and did not predict most other components of an application. There was a correlation between lower MMI scores and lower global ranking on the Standardized Letter of Recommendation. Participants preferred a traditional interview (mean difference = 1.36; p < 0.01). A mixed format (traditional interview and MMI) was preferred over a MMI alone (mean difference = 1.1; p < 0.01). MMI performance did not significantly correlate with preference for the MMI. Although the MMI alone was viewed less favorably than a traditional interview, participants were receptive to a mixed-methods interview. The MMI does correlate with performance on the EM clerkship and therefore can measure important abilities for EM success. Future work will determine whether MMI performance predicts residency performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Performance of medical students on a virtual reality simulator for knee arthroscopy: an analysis of learning curves and predictors of performance.

PubMed

Rahm, Stefan; Wieser, Karl; Wicki, Ilhui; Holenstein, Livia; Fucentese, Sandro F; Gerber, Christian

2016-03-25

Ethical concerns for surgical training on patients, limited working hours with fewer cases per trainee and the potential to better select talented persons for arthroscopic surgery raise the interest in simulator training for arthroscopic surgery. It was the purpose of this study to analyze learning curves of novices using a knee arthroscopy simulator and to correlate their performance with potentially predictive factors. Twenty medical students completed visuospatial tests and were then subjected to a simulator training program of eight 30 min sessions. Their test results were quantitatively correlated with their simulator performance at initiation, during and at the end of the program. The mean arthroscopic performance score (z-score in points) at the eight test sessions were 1. -35 (range, -126 to -5) points, 2. -16 (range, -30 to -2), 3. -11 (range, -35 to 4), 4. -3 (range, -16 to 5), 5. -2 (range, -28 to 7), 6. 1 (range, -18 to 8), 7. 2 (range, -9 to 8), 8. 2 (range, -4 to 7). Scores improved significantly from sessions 1 to 2 (p = 0.001), 2 to 3 (p = 0.052) and 3 to 4 (p = 0.001) but not thereafter. None of the investigated parameters predicted performance or development of arthroscopic performance. Novices improve significantly within four 30 min test virtual arthroscopy knee simulator training but not thereafter within the setting studied. No factors, predicting talent or speed and magnitude of improvement of skills could be identified.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

The influence of peer affiliation and student activities on adolescent drug involvement.

PubMed

Jenkins, J E

1996-01-01

This study examined the importance of students' academic performance level and extracurricular activities as predictors of drug involvement relative to peer influence. Social development theory provided the theoretical rational for the study. Data were obtained from 2,229 randomly selected students in the eighth, tenth, and twelfth grades from seventeen school districts in northeastern Ohio. At all three grade levels, involvement in extracurricular activities and academic level were significantly correlated with students' gateway and hard drug use. Consistent with prior research, the strongest correlate of gateway and hard drug use across all grade levels was affiliation with drug-using friends. Having a job after school was marginally related to self-reported gateway drug use at grade level ten. Multiple regression analysis revealed that extracurricular involvement and academic performance level make small, but unique contributions to the prediction of adolescents' gateway drug use beyond affiliation with drug-using peers at all three grade levels. The findings of this study suggest that students' academic performance and extracurricular involvements are significantly related to adolescent gateway and hard drug use, but have less predictive significance relative to peer relationships.

Understanding the Impact of School Factors on School Counselor Burnout: A Mixed-Methods Study

ERIC Educational Resources Information Center

Bardhoshi, Gerta; Schweinle, Amy; Duncan, Kelly

2014-01-01

This mixed-methods study investigated the relationship between burnout and performing noncounseling duties among a national sample of professional school counselors, while identifying school factors that could attenuate this relationship. Results of regression analyses indicate that performing noncounseling duties significantly predicted burnout…

Meditation in Higher Education: Does It Enhance Cognition?

ERIC Educational Resources Information Center

Helber, Casey; Zook, Nancy A.; Immergut, Matthew

2012-01-01

We predicted that students in a sociology course that included contemplative practices (i.e., mindfulness meditation) would show an increase in performance on higher level cognitive abilities (executive functions) over the semester compared to a control group of students. Change in executive functions performance was not significantly different…

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players

PubMed Central

Janot, Jeffrey M.; Beltz, Nicholas M.; Dalleck, Lance D.

2015-01-01

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key points The 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed. In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity. Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams. PMID:26336338

Change in organizational justice and job performance in Japanese employees: A prospective cohort study.

PubMed

Nakagawa, Yuko; Inoue, Akiomi; Kawakami, Norito; Tsuno, Kanami; Tomioka, Kimiko; Nakanishi, Mayuko; Mafune, Kosuke; Hiro, Hisanori

2015-01-01

The aim of the present study was to investigate the association of one-year change in organizational justice (i.e., procedural justice and interactional justice) with job performance in Japanese employees. This study surveyed 425 men and 683 women from a manufacturing company in Japan. Self-administered questionnaires, including the Organizational Justice Questionnaire (OJQ), the World Health Organization Health and Work Performance Questionnaire (WHO-HPQ) and the scales on demographic characteristics, were administered at baseline (August 2009). At one-year follow-up (August 2010), the OJQ and WHO-HPQ were used again to assess organizational justice and job performance. The change in organizational justice was measured by dichotomizing each OJQ subscale score by median at baseline and follow-up, and the participants were classified into four groups (i.e., stable low, adverse change, favorable change and stable high). Analysis of covariance (ANCOVA) was employed. After adjusting for demographic and occupational characteristics and job performance at baseline, the groups classified based on the change in procedural justice differed significantly in job performance at follow-up (ANCOVA: F [3, 1097]=4.35, p<0.01). Multiple comparisons revealed that the stable high procedural justice group had significantly higher job performance at follow-up compared with the stable low procedural justice group. The groups classified based on change in interactional justice did not differ significantly in job performance at follow-up (p>0.05). The present findings suggest that keeping the level of procedural justice high predicts higher levels of job performance, whereas the psychosocial factor of interactional justice is not so important for predicting job performance.

Genetic and developmental factors in spontaneous selective attention: a study of normal twins.

PubMed

Myles-Worsley, M; Coon, H

1997-08-08

The Spontaneous Selective Attention Task (SSAT) is a visual word identification task designed to measure the type of selective attention that occurs spontaneously when there are multiple stimuli, all potentially relevant, and insufficient time to process each of them fully. These are conditions which are common in everyday life. SSAT performance is measured by word identification accuracy, first under a baseline divided attention condition with no predictability, then under a selective attention condition with partial predictability introduced via word repetition. Accuracy to identify novel words in the upper location which becomes partially predictable (P words) vs. the lower location which remains non-predictable (N words) can be used to calculate a baseline performance index and a P/N ratio measure of selective attention. The SSAT has been shown to identify an attentional abnormality that may be useful in the development of an attentional endophenotype for family-genetic studies of schizophrenia. This study examined age and genetic effects on SSAT performance in normal children in order to evaluate whether the SSAT has the potential to qualify as a candidate endophenotype for schizophrenia in studies of at-risk children. A total of 59 monozygotic twin pairs and 33 same-sex dizygotic twin pairs ranging from 10 to 18 years of age were tested on the SSAT, a Continuous Performance Test. (CPT), a Span of Apprehension Test (SPAN) and a full-scale IQ test. Baseline performance on the SSAT, which was correlated with verbal IQ and SPAN performance, improved with age but showed no significant heritability. The P/N selectivity ratio was stable over the 10-18-year age range, was not significantly correlated with IQ, CPT, or SPAN performance, and its heritability was estimated to be 0.41. These findings suggest that the P/N selectivity ratio measured by the SSAT may be useful as a vulnerability marker in studies of children born into families segregating schizophrenia.

Prediction of missing links and reconstruction of complex networks

NASA Astrophysics Data System (ADS)

Zhang, Cheng-Jun; Zeng, An

2016-04-01

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

The value of the UK Clinical Aptitude Test in predicting pre-clinical performance: a prospective cohort study at Nottingham Medical School.

PubMed

Yates, Janet; James, David

2010-07-28

The UK Clinical Aptitude Test (UKCAT) was introduced in 2006 as an additional tool for the selection of medical students. It tests mental ability in four distinct domains (Quantitative Reasoning, Verbal Reasoning, Abstract Reasoning, and Decision Analysis), and the results are available to students and admissions panels in advance of the selection process. As yet the predictive validity of the test against course performance is largely unknown.The study objective was to determine whether UKCAT scores predict performance during the first two years of the 5-year undergraduate medical course at Nottingham. We studied a single cohort of students, who entered Nottingham Medical School in October 2007 and had taken the UKCAT. We used linear regression analysis to identify independent predictors of marks for different parts of the 2-year preclinical course. Data were available for 204/260 (78%) of the entry cohort. The UKCAT total score had little predictive value. Quantitative Reasoning was a significant independent predictor of course marks in Theme A ('The Cell'), (p = 0.005), and Verbal Reasoning predicted Theme C ('The Community') (p < 0.001), but otherwise the effects were slight or non-existent. This limited study from a single entry cohort at one medical school suggests that the predictive value of the UKCAT, particularly the total score, is low. Section scores may predict success in specific types of course assessment.The ultimate test of validity will not be available for some years, when current cohorts of students graduate. However, if this test of mental ability does not predict preclinical performance, it is arguably less likely to predict the outcome in the clinical years. Further research from medical schools with different types of curriculum and assessment is needed, with longitudinal studies throughout the course.

Memory shaped by age stereotypes over time.

PubMed

Levy, Becca R; Zonderman, Alan B; Slade, Martin D; Ferrucci, Luigi

2012-07-01

Previous studies showed that negative self-stereotypes detrimentally affect the cognitive performance of marginalized group members; however, these findings were confined to short-term experiments. In the present study, we considered whether stereotypes predicted memory over time, which had not been previously examined. We also considered whether self-relevance increased the influence of stereotypes on memory over time. Multiple waves of memory performance were analyzed using individual growth models. The sample consisted of 395 participants in the Baltimore Longitudinal Study of Aging. Those with more negative age stereotypes demonstrated significantly worse memory performance over 38 years than those with less negative age stereotypes, after adjusting for relevant covariates. The decline in memory performance for those aged 60 and above was 30.2% greater for the more negative age stereotype group than for the less negative age stereotype group. Also, the impact of age stereotypes on memory was significantly greater among those for whom the age stereotypes were self-relevant. This study shows that the adverse influence of negative self-stereotypes on cognitive performance is not limited to a short-term laboratory effect. Rather, the findings demonstrate, for the first time, that stereotypes also predict memory performance over an extended period in the community.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Thickness dependences of solar cell performance

NASA Technical Reports Server (NTRS)

Sah, C. T.

1982-01-01

The significance of including factors such as the base resistivity loss for solar cells thicker than 100 microns and emitter and BSF layer recombination for thin cells in predicting the fill factor and efficiency of solar cells is demonstrated analytically. A model for a solar cell is devised with the inclusion of the dopant impurity concentration profile, variation of the electron and hole mobility with dopant concentration, the concentration and thermal capture and emission rates of the recombination center, device temperature, the AM1 spectra and the Si absorption coefficient. Device equations were solved by means of the transmission line technique. The analytical results were compared with those of low-level theory for cell performance. Significant differences in predictions of the fill factor resulted, and inaccuracies in the low-level approximations are discussed.

Predicting falls in older adults using the four square step test.

PubMed

Cleary, Kimberly; Skornyakov, Elena

2017-10-01

The Four Square Step Test (FSST) is a performance-based balance tool involving stepping over four single-point canes placed on the floor in a cross configuration. The purpose of this study was to evaluate properties of the FSST in older adults who lived independently. Forty-five community dwelling older adults provided fall history and completed the FSST, Berg Balance Scale (BBS), Timed Up and Go (TUG), and Tinetti in random order. Future falls were recorded for 12 months following testing. The FSST accurately distinguished between non-fallers and multiple fallers, and the 15-second threshold score accurately distinguished multiple fallers from non-multiple fallers based on fall history. The FSST predicted future falls, and performance on the FSST was significantly correlated with performance on the BBS, TUG, and Tinetti. However, the test is not appropriate for older adults who use walkers. Overall, the FSST is a valid yet underutilized measure of balance performance and fall prediction tool that physical therapists should consider using in ambulatory community dwelling older adults.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Passive cyclic pitch control for horizontal axis wind turbines

NASA Technical Reports Server (NTRS)

Bottrell, G. W.

1981-01-01

A flexible rotor concept, called the balanced pitch rotor, is described. The system provides passive adjustment of cyclic pitch in response to unbalanced pitching moments across the rotor disk. Various applications are described and performance predictions are made for wind shear and cross wind operating conditions. Comparisons with the teetered hub are made and significant cost savings are predicted.

Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in Rainbow Trout: Insights on genotyping methods and genomic prediction models

USDA-ARS?s Scientific Manuscript database

Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic br...

Comparison of predictive equations for resting metabolic rate in obese psychiatric patients taking olanzapine.

PubMed

Skouroliakou, Maria; Giannopoulou, Ifigenia; Kostara, Christina; Vasilopoulou, Melanie

2009-02-01

The prediction of resting metabolic rate (RMR) is important to determine the energy expenditure of obese patients with severe mental illnesses (SMIs). However, there is lack of research concerning the most accurate RMR predictive equations. The purpose of this study was to compare the validity of four RMR equations on patients with SMIs taking olanzapine. One hundred twenty-eight obese (body mass index >30 kg/m(2)) patients with SMIs (41 men and 87 women) treated with olanzapine were tested from 2005 to 2008. Measurements of anthropometric parameters (height, weight, body mass index, waist circumference) and body composition (using the BodPod) were performed at the beginning of the study. RMR was measured using indirect calorimetry. Comparisons between measured and estimated RMRs from four equations (Harris-Benedict adjusted and current body weights, Schofield, and Mifflin-St. Jeor) were performed using Pearson's correlation coefficient and Bland-Altman analysis. Significant correlations were found between the measured and predicted RMRs with all four equations (P < 0.001), with the Mifflin-St. Jeor equation demonstrating the strongest correlation in men and women (r = 0.712, P < 0.001). In men and women, the Bland-Altman analysis revealed no significant bias in the RMR prediction using the Harris-Benedict adjusted body weight and the Mifflin equations (P > 0.05). However, in men and women, the Harris-Benedict current body weight and the Schofield equations showed significant overestimation error in the RMR prediction (P < 0.001). When estimating RMR in men and women with SMIs taking olanzapine, the Mifflin-St. Jeor and Harris-Benedict adjusted body weight equations appear to be the most appropriate for clinical use.

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.

New test of the dynamic theory of neutron diffraction by a moving grating

NASA Astrophysics Data System (ADS)

Zakharov, Maxim; Frank, Alexander; Kulin, German; Goryunov, Semyon

2018-04-01

Recently, multiwave dynamical theory of neutron diffraction by a moving grating was developed. The theory predicts that at a certain height of the grating profile a significant suppression of the zero-order diffraction may occur. The experiment to confirm predictions of this theory was performed. The resulting diffracted UCNs spectra were measured using time-of-flight Fourier diffractometer. The experimental data were compared with the results of numerical simulation and were found in a good agreement with theoretical predictions.

To transfer or not to transfer? Kinematics and laterality quotient predict interlimb transfer of motor learning

PubMed Central

Lefumat, Hannah Z.; Vercher, Jean-Louis; Miall, R. Chris; Cole, Jonathan; Buloup, Frank; Bringoux, Lionel; Bourdin, Christophe

2015-01-01

Humans can remarkably adapt their motor behavior to novel environmental conditions, yet it remains unclear which factors enable us to transfer what we have learned with one limb to the other. Here we tested the hypothesis that interlimb transfer of sensorimotor adaptation is determined by environmental conditions but also by individual characteristics. We specifically examined the adaptation of unconstrained reaching movements to a novel Coriolis, velocity-dependent force field. Right-handed subjects sat at the center of a rotating platform and performed forward reaching movements with the upper limb toward flashed visual targets in prerotation, per-rotation (i.e., adaptation), and postrotation tests. Here only the dominant arm was used during adaptation and interlimb transfer was assessed by comparing performance of the nondominant arm before and after dominant-arm adaptation. Vision and no-vision conditions did not significantly influence interlimb transfer of trajectory adaptation, which on average was significant but limited. We uncovered a substantial heterogeneity of interlimb transfer across subjects and found that interlimb transfer can be qualitatively and quantitatively predicted for each healthy young individual. A classifier showed that in our study, interlimb transfer could be predicted based on the subject's task performance, most notably motor variability during learning, and his or her laterality quotient. Positive correlations suggested that variability of motor performance and lateralization of arm movement control facilitate interlimb transfer. We further show that these individual characteristics can predict the presence and the magnitude of interlimb transfer of left-handers. Overall, this study suggests that individual characteristics shape the way the nervous system can generalize motor learning. PMID:26334018

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-05-14

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

A Portable Platform for Evaluation of Visual Performance in Glaucoma Patients

PubMed Central

Rosen, Peter N.; Boer, Erwin R.; Gracitelli, Carolina P. B.; Abe, Ricardo Y.; Diniz-Filho, Alberto; Marvasti, Amir H.; Medeiros, Felipe A.

2015-01-01

Purpose To propose a new tablet-enabled test for evaluation of visual performance in glaucoma, the PERformance CEntered Portable Test (PERCEPT), and to evaluate its ability to predict history of falls and motor vehicle crashes. Design Cross-sectional study. Methods The study involved 71 patients with glaucomatous visual field defects on standard automated perimetry (SAP) and 59 control subjects. The PERCEPT was based on the concept of increasing visual task difficulty to improve detection of central visual field losses in glaucoma patients. Subjects had to perform a foveal 8-alternative-forced-choice orientation discrimination task, while detecting a simultaneously presented peripheral stimulus within a limited presentation time. Subjects also underwent testing with the Useful Field of View (UFOV) divided attention test. The ability to predict history of motor vehicle crashes and falls was investigated by odds ratios and incident-rate ratios, respectively. Results When adjusted for age, only the PERCEPT processing speed parameter showed significantly larger values in glaucoma compared to controls (difference: 243ms; P<0.001). PERCEPT results had a stronger association with history of motor vehicle crashes and falls than UFOV. Each 1 standard deviation increase in PERCEPT processing speed was associated with an odds ratio of 2.69 (P = 0.003) for predicting history of motor vehicle crashes and with an incident-rate ratio of 1.95 (P = 0.003) for predicting history of falls. Conclusion A portable platform for testing visual function was able to detect functional deficits in glaucoma, and its results were significantly associated with history of involvement in motor vehicle crashes and history of falls. PMID:26445501

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

Using predicated execution to improve the performance of a dynamically scheduled machine with speculative execution

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

Chang, P.Y.; Hao, E.; Patt, Y.

Conditional branches incur a severe performance penalty in wide-issue, deeply pipelined processors. Speculative execution and predicated execution are two mechanisms that have been proposed for reducing this penalty. Speculative execution can completely eliminate the penalty associated with a particular branch, but requires accurate branch prediction to be effective. Predicated execution does not require accurate branch prediction to eliminate the branch penalty, but is not applicable to all branches and can increase the latencies within the program. This paper examines the performance benefit of using both mechanisms to reduce the branch execution penalty. Predicated execution is used to handle the hard-to-protectmore » branches and speculative execution is used to handle the remaining branches. The hard-to-predict branches within the program are determined by profiling. We show that this approach can significantly reduce the branch execution penalty suffered by wide-issue processors.« less

Ability of the D-15 panel tests and HRR pseudoisochromatic plates to predict performance in naming VDT colors.

PubMed

Ramaswamy, Shankaran; Hovis, Jeffery K

2004-01-01

Color codes in VDT displays often contain sets of colors that are confusing to individuals with color-vision deficiencies. The purpose of this study is to determine whether individuals with color-vision deficiencies (color defectives) can perform as well as individuals without color-vision deficiencies (color normals) on a colored VDT display used in the railway industry and to determine whether clinical color-vision tests can predict their performance. Of the 52 color defectives, 58% failed the VDT test. The kappa coefficients of agreement for the Farnsworth D-15, Adams desaturated D-15, and Richmond 3rd Edition HRR PIC diagnostic plates were significantly greater than chance. In particular, the D-15 tests have a high probability of predicting who fails the practical test. However, all three tests had an unacceptably high false-negative rate (9.5-35%); so that a practical test is still needed.

Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage.

PubMed

Zheng, Jun; Yu, Zhiyuan; Guo, Rui; Li, Hao; You, Chao; Ma, Lu

2018-04-27

Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH. A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias. Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias. This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high. Copyright © 2018 Elsevier Inc. All rights reserved.

A large-scale evaluation of computational protein function prediction

PubMed Central

Radivojac, Predrag; Clark, Wyatt T; Ronnen Oron, Tal; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kassner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Böhm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

2013-01-01

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools. PMID:23353650

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies

PubMed Central

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-01-01

Background The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study Aim To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. Design and setting A three-part longitudinal predictive validity study of selection into training for UK general practice. Method In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Results Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. Conclusion In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered. PMID:24267856

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies.

PubMed

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-11-01

The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. A three-part longitudinal predictive validity study of selection into training for UK general practice. In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered.

The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

PubMed

Lindahl, Jonas; Danell, Rickard

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch.

PubMed

Madai, Vince Istvan; Wood, Carla N; Galinovic, Ivana; Grittner, Ulrike; Piper, Sophie K; Revankar, Gajanan S; Martin, Steve Z; Zaro-Weber, Olivier; Moeller-Hartmann, Walter; von Samson-Himmelstjerna, Federico C; Heiss, Wolf-Dieter; Ebinger, Martin; Fiebach, Jochen B; Sobesky, Jan

2016-01-01

With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset <12 h were included. The DWI lesion was segmented and overlaid on ADC and FLAIR images. rSI mean and SD, were calculated as follows: (mean ROI value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction models. © 2016 S. Karger AG, Basel.

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.

Flight Stability and Control and Performance Results from the Linear Aerospike SR-71 Experiment (LASRE)

NASA Technical Reports Server (NTRS)

Moes, Timothy R.; Cobleigh, Brent R.; Cox, Timothy H.; Conners, Timothy R.; Iliff, Kenneth W.; Powers, Bruce G.

1998-01-01

The Linear Aerospike SR-71 Experiment (LASRE) is presently being conducted to test a 20-percent-scale version of the Linear Aerospike rocket engine. This rocket engine has been chosen to power the X-33 Single Stage to Orbit Technology Demonstrator Vehicle. The rocket engine was integrated into a lifting body configuration and mounted to the upper surface of an SR-71 aircraft. This paper presents stability and control results and performance results from the envelope expansion flight tests of the LASRE configuration up to Mach 1.8 and compares the results with wind tunnel predictions. Longitudinal stability and elevator control effectiveness were well-predicted from wind tunnel tests. Zero-lift pitching moment was mispredicted transonically. Directional stability, dihedral stability, and rudder effectiveness were overpredicted. The SR-71 handling qualities were never significantly impacted as a result of the missed predictions. Performance results confirmed the large amount of wind-tunnel-predicted transonic drag for the LASRE configuration. This drag increase made the performance of the vehicle so poor that acceleration through transonic Mach numbers could not be achieved on a hot day without depleting the available fuel.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia.

PubMed

Gurrera, R J; Moye, J; Karel, M J; Azar, A R; Armesto, J C

2006-05-09

To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool--Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (beta) profiles were unique for each ability. Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness.

Keep your eyes on the ball: smooth pursuit eye movements enhance prediction of visual motion.

PubMed

Spering, Miriam; Schütz, Alexander C; Braun, Doris I; Gegenfurtner, Karl R

2011-04-01

Success of motor behavior often depends on the ability to predict the path of moving objects. Here we asked whether tracking a visual object with smooth pursuit eye movements helps to predict its motion direction. We developed a paradigm, "eye soccer," in which observers had to either track or fixate a visual target (ball) and judge whether it would have hit or missed a stationary vertical line segment (goal). Ball and goal were presented briefly for 100-500 ms and disappeared from the screen together before the perceptual judgment was prompted. In pursuit conditions, the ball moved towards the goal; in fixation conditions, the goal moved towards the stationary ball, resulting in similar retinal stimulation during pursuit and fixation. We also tested the condition in which the goal was fixated and the ball moved. Motion direction prediction was significantly better in pursuit than in fixation trials, regardless of whether ball or goal served as fixation target. In both fixation and pursuit trials, prediction performance was better when eye movements were accurate. Performance also increased with shorter ball-goal distance and longer presentation duration. A longer trajectory did not affect performance. During pursuit, an efference copy signal might provide additional motion information, leading to the advantage in motion prediction.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

PubMed Central

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-01-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

NASA Astrophysics Data System (ADS)

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-05-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

Predictive value of cognition for different domains of outcome in recent-onset schizophrenia.

PubMed

Holthausen, Esther A E; Wiersma, Durk; Cahn, Wiepke; Kahn, René S; Dingemans, Peter M; Schene, Aart H; van den Bosch, Robert J

2007-01-15

The aim of this study was to see whether and how cognition predicts outcome in recent-onset schizophrenia in a large range of domains such as course of illness, self-care, interpersonal functioning, vocational functioning and need for care. At inclusion, 115 recent-onset patients were tested on a cognitive battery and 103 patients participated in the follow-up 2 years after inclusion. Differences in outcome between cognitively normal and cognitively impaired patients were also analysed. Cognitive measures at inclusion did not predict number of relapses, activities of daily living and interpersonal functioning. Time in psychosis or in full remission, as well as need for care, were partly predicted by specific cognitive measures. Although statistically significant, the predictive value of cognition with regard to clinical outcome was limited. There was a significant difference between patients with and without cognitive deficits in competitive employment status and vocational functioning. The predictive value of cognition for different social outcome domains varies. It seems that cognition most strongly predicts work performance, where having a cognitive deficit, regardless of the nature of the deficit, acts as a rate-limiting factor.

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

PubMed Central

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

2011-01-01

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

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

The relationship between phonological memory, receptive vocabulary, and fast mapping in young children with specific language impairment.

PubMed

Gray, Shelley

2006-10-01

This study assessed the fast mapping performance of children with specific language impairment (SLI) across the preschool to kindergarten age span in relation to their phonological memory and vocabulary development. Fifty-three children diagnosed with SLI and 53 children with normal language (NL) matched for age and gender (30 three-year-olds, 18 four-year-olds, 28 five-year-olds, and 30 six-year-olds) participated. Children's phonological memory was assessed using nonword repetition and digit span tasks. Receptive vocabulary was assessed using the Peabody Picture Vocabulary Test-III. Children learned the names for 8 objects during 2 fast mapping tasks. Overall, the NL group demonstrated significantly better performance on phonological memory and vocabulary measures across the age span; however, performance on the fast mapping task differed significantly only at age 5. Phonological memory and existing receptive vocabulary did not predict fast mapping ability. The phonological memory skills of preschoolers with NL and SLI followed a similar developmental pattern, but the SLI group consistently scored significantly lower than the NL group. Overall, the NL group showed significantly better receptive vocabulary, with evidence that between-group differences increased at age 6. Neither short-term phonological memory nor receptive vocabulary predicted fast mapping comprehension or production performance, even though both have been shown to correlate with later stages of word learning.

Blind predictions of protein interfaces by docking calculations in CAPRI.

PubMed

Lensink, Marc F; Wodak, Shoshana J

2010-11-15

Reliable prediction of the amino acid residues involved in protein-protein interfaces can provide valuable insight into protein function, and inform mutagenesis studies, and drug design applications. A fast-growing number of methods are being proposed for predicting protein interfaces, using structural information, energetic criteria, or sequence conservation or by integrating multiple criteria and approaches. Overall however, their performance remains limited, especially when applied to nonobligate protein complexes, where the individual components are also stable on their own. Here, we evaluate interface predictions derived from protein-protein docking calculations. To this end we measure the overlap between the interfaces in models of protein complexes submitted by 76 participants in CAPRI (Critical Assessment of Predicted Interactions) and those of 46 observed interfaces in 20 CAPRI targets corresponding to nonobligate complexes. Our evaluation considers multiple models for each target interface, submitted by different participants, using a variety of docking methods. Although this results in a substantial variability in the prediction performance across participants and targets, clear trends emerge. Docking methods that perform best in our evaluation predict interfaces with average recall and precision levels of about 60%, for a small majority (60%) of the analyzed interfaces. These levels are significantly higher than those obtained for nonobligate complexes by most extant interface prediction methods. We find furthermore that a sizable fraction (24%) of the interfaces in models ranked as incorrect in the CAPRI assessment are actually correctly predicted (recall and precision ≥50%), and that these models contribute to 70% of the correct docking-based interface predictions overall. Our analysis proves that docking methods are much more successful in identifying interfaces than in predicting complexes, and suggests that these methods have an excellent potential of addressing the interface prediction challenge. © 2010 Wiley-Liss, Inc.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.

PubMed

Li, Bian; Mendenhall, Jeffrey L; Kroncke, Brett M; Taylor, Keenan C; Huang, Hui; Smith, Derek K; Vanoye, Carlos G; Blume, Jeffrey D; George, Alfred L; Sanders, Charles R; Meiler, Jens

2017-10-01

An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools. © 2017 American Heart Association, Inc.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

PubMed

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ 2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates.

PubMed

Helbling, Damian E; Johnson, David R; Lee, Tae Kwon; Scheidegger, Andreas; Fenner, Kathrin

2015-03-01

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific micropollutants. This work is an important step towards developing tools to predict biotransformation rates in WWTPs based on taxonomic composition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Re-examining Prostate-specific Antigen (PSA) Density: Defining the Optimal PSA Range and Patients for Using PSA Density to Predict Prostate Cancer Using Extended Template Biopsy.

PubMed

Jue, Joshua S; Barboza, Marcelo Panizzutti; Prakash, Nachiketh S; Venkatramani, Vivek; Sinha, Varsha R; Pavan, Nicola; Nahar, Bruno; Kanabur, Pratik; Ahdoot, Michael; Dong, Yan; Satyanarayana, Ramgopal; Parekh, Dipen J; Punnen, Sanoj

2017-07-01

To compare the predictive accuracy of prostate-specific antigen (PSA) density vs PSA across different PSA ranges and by prior biopsy status in a prospective cohort undergoing prostate biopsy. Men from a prospective trial underwent an extended template biopsy to evaluate for prostate cancer at 26 sites throughout the United States. The area under the receiver operating curve assessed the predictive accuracy of PSA density vs PSA across 3 PSA ranges (<4 ng/mL, 4-10 ng/mL, >10 ng/mL). We also investigated the effect of varying the PSA density cutoffs on the detection of cancer and assessed the performance of PSA density vs PSA in men with or without a prior negative biopsy. Among 1290 patients, 585 (45%) and 284 (22%) men had prostate cancer and significant prostate cancer, respectively. PSA density performed better than PSA in detecting any prostate cancer within a PSA of 4-10 ng/mL (area under the receiver operating characteristic curve [AUC]: 0.70 vs 0.53, P < .0001) and within a PSA >10 mg/mL (AUC: 0.84 vs 0.65, P < .0001). PSA density was significantly more predictive than PSA in detecting any prostate cancer in men without (AUC: 0.73 vs 0.67, P < .0001) and with (AUC: 0.69 vs 0.55, P < .0001) a previous biopsy; however, the incremental difference in AUC was higher among men with a previous negative biopsy. Similar inferences were seen for significant cancer across all analyses. As PSA increases, PSA density becomes a better marker for predicting prostate cancer compared with PSA alone. Additionally, PSA density performed better than PSA in men with a prior negative biopsy. Copyright © 2017 Elsevier Inc. All rights reserved.

A whole blood gene expression-based signature for smoking status

PubMed Central

2012-01-01

Background Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. Methods Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. Results Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). Conclusion We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression. PMID:23210427

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Wu, Yirong; Abbey, Craig K.; Liu, Jie; Ong, Irene; Peissig, Peggy; Onitilo, Adedayo A.; Fan, Jun; Yuan, Ming; Burnside, Elizabeth S.

2016-03-01

Technology advances in genome-wide association studies (GWAS) has engendered optimism that we have entered a new age of precision medicine, in which the risk of breast cancer can be predicted on the basis of a person's genetic variants. The goal of this study is to evaluate the discriminatory power of common genetic variants in breast cancer risk estimation. We conducted a retrospective case-control study drawing from an existing personalized medicine data repository. We collected variables that predict breast cancer risk: 153 high-frequency/low-penetrance genetic variants, reflecting the state-of-the-art GWAS on breast cancer, mammography descriptors and BI-RADS assessment categories in the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We trained and tested naïve Bayes models by using these predictive variables. We generated ROC curves and used the area under the ROC curve (AUC) to quantify predictive performance. We found that genetic variants achieved comparable predictive performance to BI-RADS assessment categories in terms of AUC (0.650 vs. 0.659, p-value = 0.742), but significantly lower predictive performance than the combination of BI-RADS assessment categories and mammography descriptors (0.650 vs. 0.751, p-value < 0.001). A better understanding of relative predictive capability of genetic variants and mammography data may benefit clinicians and patients to make appropriate decisions about breast cancer screening, prevention, and treatment in the era of precision medicine.

Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients.

PubMed

Pourhoseingholi, Mohamad Amin; Kheirian, Sedigheh; Zali, Mohammad Reza

2017-12-01

Colorectal cancer (CRC) is one of the most common malignancies and cause of cancer mortality worldwide. Given the importance of predicting the survival of CRC patients and the growing use of data mining methods, this study aims to compare the performance of models for predicting 5-year survival of CRC patients using variety of basic and ensemble data mining methods. The CRC dataset from The Shahid Beheshti University of Medical Sciences Research Center for Gastroenterology and Liver Diseases were used for prediction and comparative study of the base and ensemble data mining techniques. Feature selection methods were used to select predictor attributes for classification. The WEKA toolkit and MedCalc software were respectively utilized for creating and comparing the models. The obtained results showed that the predictive performance of developed models was altogether high (all greater than 90%). Overall, the performance of ensemble models was higher than that of basic classifiers and the best result achieved by ensemble voting model in terms of area under the ROC curve (AUC= 0.96). AUC Comparison of models showed that the ensemble voting method significantly outperformed all models except for two methods of Random Forest (RF) and Bayesian Network (BN) considered the overlapping 95% confidence intervals. This result may indicate high predictive power of these two methods along with ensemble voting for predicting 5-year survival of CRC patients.

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

PubMed

Kiral-Kornek, Isabell; Roy, Subhrajit; Nurse, Ewan; Mashford, Benjamin; Karoly, Philippa; Carroll, Thomas; Payne, Daniel; Saha, Susmita; Baldassano, Steven; O'Brien, Terence; Grayden, David; Cook, Mark; Freestone, Dean; Harrer, Stefan

2018-01-01

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases.

PubMed

Liang, He-Yue; Huang, Ya-Qin; Yang, Zhao-Xia; Ying-Ding; Zeng, Meng-Su; Rao, Sheng-Xiang

2016-07-01

To determine if magnetic resonance imaging (MRI) histogram analyses can help predict response to chemotherapy in patients with colorectal hepatic metastases by using response evaluation criteria in solid tumours (RECIST1.1) as the reference standard. Standard MRI including diffusion-weighted imaging (b=0, 500 s/mm(2)) was performed before chemotherapy in 53 patients with colorectal hepatic metastases. Histograms were performed for apparent diffusion coefficient (ADC) maps, arterial, and portal venous phase images; thereafter, mean, percentiles (1st, 10th, 50th, 90th, 99th), skewness, kurtosis, and variance were generated. Quantitative histogram parameters were compared between responders (partial and complete response, n=15) and non-responders (progressive and stable disease, n=38). Receiver operator characteristics (ROC) analyses were further analyzed for the significant parameters. The mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, 99th percentile of the ADC maps were significantly lower in responding group than that in non-responding group (p=0.000-0.002) with area under the ROC curve (AUCs) of 0.76-0.82. The histogram parameters of arterial and portal venous phase showed no significant difference (p>0.05) between the two groups. Histogram-derived parameters for ADC maps seem to be a promising tool for predicting response to chemotherapy in patients with colorectal hepatic metastases. • ADC histogram analyses can potentially predict chemotherapy response in colorectal liver metastases. • Lower histogram-derived parameters (mean, percentiles) for ADC tend to have good response. • MR enhancement histogram analyses are not reliable to predict response.

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

The PAPAS index: a novel index for the prediction of hepatitis C-related fibrosis.

PubMed

Ozel, Banu D; Poyrazoğlu, Orhan K; Karaman, Ahmet; Karaman, Hatice; Altinkaya, Engin; Sevinç, Eylem; Zararsiz, Gökmen

2015-08-01

Several noninvasive tests have been developed to determine the degree of hepatic fibrosis in patients with chronic hepatitis C (CHC) without performing liver biopsy. This study aimed to determine the performance of the PAPAS (Platelet/Age/Phosphatase/AFP/AST) index in patients with CHC for the prediction of significant fibrosis and cirrhosis and to compare it with other noninvasive tests. To date, no study has evaluated the application of the PAPAS index in CHC-associated liver fibrosis. This retrospective study included 137 consecutive patients with CHC who had undergone a percutaneous liver biopsy before treatment. The aspartate aminotransferase/platelet ratio (APRI), aspartate aminotransferase/alanine transaminase ratio (AAR), age-platelet index (API), FIB4, cirrhosis discriminate score (CDS), the Göteborg University cirrhosis index (GUCI), and PAPAS were calculated and compared with the diagnostic accuracies of all fibrosis indices between the groups F0-F2 (no-mild fibrosis) versus F3-F6 (significant fibrosis) and F0-F4 (no cirrhosis) versus F5-F6 (cirrhosis). To predict significant fibrosis, the area under curve (95% confidence interval) for FIB4 was 0.727 followed by GUCI (0.721), PAPAS≈APRI≈CDS (0.716), and API (0.68). To predict cirrhosis, the area under curve (95% confidence interval) for FIB4 was calculated to be 0.735, followed by GUCI (0.723), PAPAS≈APRI≈CDS≈(0.71), and API (0.66). No statistically significant difference was observed among these predictors to exclude both significant fibrosis and cirrhosis (P>0.05). The diagnostic capability of the PAPAS index has moderate efficiency and was not superior to other fibrosis markers for the identification of fibrosis in CHC patients. There is a need for more comprehensive prospective studies to help determine the diagnostic value of PAPAS for liver fibrosis.

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

PubMed

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting Pilot Performance in Off-Nominal Conditions: A Meta-Analysis and Model Validation

NASA Technical Reports Server (NTRS)

Wickens, C.D.; Hooey, B.L.; Gore, B.F.; Sebok, A.; Koenecke, C.; Salud, E.

2009-01-01

Pilot response to off-nominal (very rare) events represents a critical component to understanding the safety of next generation airspace technology and procedures. We describe a meta-analysis designed to integrate the existing data regarding pilot accuracy of detecting rare, unexpected events such as runway incursions in realistic flight simulations. Thirty-five studies were identified and pilot responses were categorized by expectancy, event location, and whether the pilot was flying with a highway-in-the-sky display. All three dichotomies produced large, significant effects on event miss rate. A model of human attention and noticing, N-SEEV, was then used to predict event noticing performance as a function of event salience and expectancy, and retinal eccentricity. Eccentricity is predicted from steady state scanning by the SEEV model of attention allocation. The model was used to predict miss rates for the expectancy, location and highway-in-the-sky (HITS) effects identified in the meta-analysis. The correlation between model-predicted results and data from the meta-analysis was 0.72.

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

PubMed

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

2012-06-01

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

Competence with Fractions Predicts Gains in Mathematics Achievement

PubMed Central

Bailey, Drew H.; Hoard, Mary K.; Nugent, Lara; Geary, David C.

2012-01-01

Competence with fractions predicts later mathematics achievement, but the co-developmental pattern between fractions knowledge and mathematics achievement is not well understood. We assessed this co-development through examination of the cross-lagged relation between a measure of conceptual knowledge of fractions and mathematics achievement in sixth and seventh grade (n = 212). The cross-lagged effects indicated that performance on the sixth grade fractions concepts measure predicted one year gains in mathematics achievement (β = .14, p<.01), controlling for the central executive component of working memory and intelligence, but sixth grade mathematics achievement did not predict gains on the fractions concepts measure (β = .03, p>.50). In a follow-up assessment, we demonstrated that measures of fluency with computational fractions significantly predicted seventh grade mathematics achievement above and beyond the influence of fluency in computational whole number arithmetic, performance on number fluency and number line tasks, and central executive span and intelligence. Results provide empirical support for the hypothesis that competence with fractions underlies, in part, subsequent gains in mathematics achievement. PMID:22832199

Staying on the job: The relationship between work performance and cognition in individuals diagnosed with multiple sclerosis.

PubMed

Baughman, Brandon C; Basso, Michael R; Sinclair, Robert R; Combs, Dennis R; Roper, Brad L

2015-01-01

People with multiple sclerosis (MS) are apt to become unemployed as the disease progresses, and most research implies that this is due to diminishing mobility. Some studies have shown that presence of cognitive impairment also predicts employment status. Yet, no studies have examined how neuropsychological factors predict vocational performance among individuals with MS who remain employed. We assessed employer- and self-rated work performance, mobility status, and neuropsychological function in a sample of 44 individuals diagnosed with MS. Results suggest that cognitive impairment is common in these employed individuals, despite largely intact mobility status. Moreover, a significant interaction emerged, such that cognitively impaired individuals' work performance was rated more poorly by supervisors. In contrast, self-ratings of work performance were higher in cognitively impaired than in unimpaired participants. These novel findings suggest that cognitive impairment may influence work performance, even in patients whose physical disability status is relatively intact.

Towards psychologically adaptive brain-computer interfaces

NASA Astrophysics Data System (ADS)

Myrden, A.; Chau, T.

2016-12-01

Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K.; Keyser, D. A.; Mccumber, M. C.

1983-01-01

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

NASA Astrophysics Data System (ADS)

Yao, Bing; Yang, Hui

2016-12-01

This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

Technology Solutions Case Study: Predicting Envelope Leakage in Attached Dwellings

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

None

2013-11-01

The most common method of measuring air leakage is to perform single (or solo) blower door pressurization and/or depressurization test. In detached housing, the single blower door test measures leakage to the outside. In attached housing, however, this “solo” test method measures both air leakage to the outside and air leakage between adjacent units through common surfaces. In an attempt to create a simplified tool for predicting leakage to the outside, Building America team Consortium for Advanced Residential Buildings (CARB) performed a preliminary statistical analysis on blower door test results from 112 attached dwelling units in four apartment complexes. Althoughmore » the subject data set is limited in size and variety, the preliminary analyses suggest significant predictors are present and support the development of a predictive model. Further data collection is underway to create a more robust prediction tool for use across different construction types, climate zones, and unit configurations.« less

Prenatal prediction of postnatal large-for-date neonates using a simplified method at MR imaging: comparison with conventional 2D ultrasound estimates.

PubMed

Kadji, Caroline; Cannie, Mieke M; De Angelis, Ricardo; Camus, Margaux; Klass, Magdalena; Fellas, Stéphanie; Cecotti, Vera; Dütemeyer, Vivien; Jani, Jacques C

2017-05-15

To evaluate the performance of a simple method of estimating fetal weight (EFW) using MR imaging as compared with 2D US in the prediction of large-for-date neonates. Written informed consent was obtained for this EC-approved study. Between March 2011 and May 2016, 2 groups of women with singleton pregnancies were evaluated: women that underwent US-EFW and MR-EFW within 48 h before delivery and those undergoing these evaluations between 35 + 0 weeks and 37 + 6 weeks of gestation. US-EFW was based on Hadlock et al. and MR-EFW on the formula described by Backer et al. Planimetric measurement of the fetal body volume (FBV) needed for MR-EFW was performed using a semi-automated method and the time required for measurement was noted. Our outcome measure was performance in prediction of large-for-date neonates by MR imaging versus US-EFW using receiver-operating characteristic (ROC) curves. 270 women were included in the first part of the study with 48 newborns (17.8%) of birthweight ≥90 th centile and 30 (11.1%) ≥95 th centile. Eighty-three women were included in the second part with 9 newborns (10.8%) of birthweight ≥95 th centile. The median time needed for FBV planimetric measurements in all 353 fetuses was 3.5 (range; 1.5-5.5) min. The area under the ROC curve for prediction of postnatal large-for-date neonates by prenatal MR imaging performed within 48 h before delivery was significantly better than by US (difference between the AUROC = 0.085, P < 0.001; standard error = 0.020 for birthweight ≥90 th centile and 0.036, P = 0.01; standard error = 0.014 for birthweight ≥95 th centile). Similarly, MR-EFW was better than US-EFW, with both performed remote from delivery, in predicting birthweight ≥ 95 th centile (difference between the AUROC = 0.077, P = 0.045; standard error = 0.039). MR planimetry using our purpose-designed semi-automated method is not time-consuming. MR-EFW performed immediately prior to delivery or remote from delivery predicts large-for-date neonates significantly better than US-EFW. This article is protected by copyright. All rights reserved.

Can Multiple Mini-Interviews Predict Academic Performance of Dental Students? A Two-Year Follow-Up.

PubMed

Alaki, Sumer M; Yamany, Ibrahim A; Shinawi, Lana A; Hassan, Mona H A; Tekian, Ara

2016-11-01

Prior research has shown that students' previous grade point average (GPA) is the best predictor for future academic success. However, it can only partly predict the variability in dental school performance. The aim of this study was to assess the predictive value of multiple mini-interviews (MMI) as an admission criterion by comparing them with the academic performance of dental students over a two-year period. All incoming undergraduate dental students at the King Abdulaziz University Faculty of Dentistry (KAUFD) during academic year 2013-14 were invited to participate in MMI. Students rotated through six objective structured clinical exam (OSCE)-like stations for 30 minutes total and were interviewed by two trained faculty interviewers at each station. The stations were focused on noncognitive skills thought to be essential to academic performance at KAUFD. The academic performance of these students was then followed for two years and linked to their MMI scores. A total of 146 students (71 males and 75 females) participated in an interview (response rate=92.9%). Most students scored in the acceptable range at each MMI station. Students' total MMI score, ambitions, and motives were significant predictors of GPA during the two years of follow-up (p<0.038 and p<0.001, respectively). In this study, MMI was found to be able to predict future academic performance of undergraduate dental students.

Performance of finned thermal capacitors. Ph.D. Thesis - Texas Univ., Austin

NASA Technical Reports Server (NTRS)

Humphries, W. R.

1974-01-01

The performance of typical thermal capacitors, both in earth and orbital environments, was investigated. Techniques which were used to make predictions of thermal behavior in a one-g earth environment are outlined. Orbital performance parameters are qualitatively discussed, and those effects expected to be important under zero-g conditions are outlined. A summary of thermal capacitor applications are documentated, along with significant problem areas and current configurations. An experimental program was conducted to determine typical one-g performance, and the physical significance of these data is discussed in detail. Numerical techniques were employed to allow comparison between analytical and experimental data.

MCAT Verbal Reasoning score: less predictive of medical school performance for English language learners.

PubMed

Winegarden, Babbi; Glaser, Dale; Schwartz, Alan; Kelly, Carolyn

2012-09-01

Medical College Admission Test (MCAT) scores are widely used as part of the decision-making process for selecting candidates for admission to medical school. Applicants who learned English as a second language may be at a disadvantage when taking tests in their non-native language. Preliminary research found significant differences between English language learners (ELLs), applicants who learned English after the age of 11 years, and non-ELL examinees on the Verbal Reasoning (VR) sub-test of the MCAT. The purpose of this study was to determine if relationships between VR sub-test scores and measures of medical school performance differed between ELL and non-ELL students. Scores on the MCAT VR sub-test and student performance outcomes (grades, examination scores, and markers of distinction and difficulty) were extracted from University of California San Diego School of Medicine admissions files and the Association of American Medical Colleges database for 924 students who matriculated in 1998-2005 (graduation years 2002-2009). Regression models were fitted to determine whether MCAT VR sub-test scores predicted medical school performance similarly for ELLs and non-ELLs. For several outcomes, including pre-clerkship grades, academic distinction, US Medical Licensing Examination Step 2 Clinical Knowledge scores and two clerkship shelf examinations, ELL status significantly affects the ability of the VR score to predict performance. Higher correlations between VR score and medical school performance emerged for non-ELL students than for ELL students for each of these outcomes. The MCAT VR score should be used with discretion when assessing ELL applicants for admission to medical school. © Blackwell Publishing Ltd 2012.

Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus.

PubMed

Pena, Michelle J; Heinzel, Andreas; Rossing, Peter; Parving, Hans-Henrik; Dallmann, Guido; Rossing, Kasper; Andersen, Steen; Mayer, Bernd; Heerspink, Hiddo J L

2016-07-05

Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

Predictive performance of three practical approaches for grapefruit juice-induced 2-fold or greater increases in AUC of concomitantly administered drugs.

PubMed

Takahashi, M; Onozawa, S; Ogawa, R; Uesawa, Y; Echizen, H

2015-02-01

Clinical pharmacists have a challenging task when answering patients' question about whether they can take specific drugs with grapefruit juice (GFJ) without risk of drug interaction. To identify the most practicable method for predicting clinically relevant changes in plasma concentrations of orally administered drugs caused by the ingestion of GFJ, we compared the predictive performance of three methods using data obtained from the literature. We undertook a systematic search of drug interactions associated with GFJ using MEDLINE and the Metabolism & Transport Drug Interaction Database (DIDB version 4.0). We considered an elevation of the area under the plasma concentration-time curve (AUC) of 2 or greater relative to the control value [AUC ratio (AUCR) ≥ 2.0] as a clinically significant interaction. The data from 74 drugs (194 data sets) were analysed. When the reported information of CYP3A involvement in the metabolism of a drug of interest was adopted as a predictive criterion for GFJ-drug interaction, the performance assessed by positive predictive value (PPV) was low (0.26), but that assessed by negative predictive value (NPV) and sensitivity was high (1.00 for both). When the reported oral bioavailability of ≤ 0.1 was used as a criterion, the PPV improved to 0.50 with an acceptable NPV of 0.81, but sensitivity was reduced to 0.21. When the reported AUCR was ≥ 10 after co-administration of a typical CYP3A inhibitor, the corresponding values were 0.64, 0.79 and 0.19, respectively. We consider that an oral bioavailability of ≤ 0.1 or an AUCR of ≥ 10 caused by a CYP3A inhibitor of a drug of interest may be a practical prediction criterion for avoiding significant interactions with GFJ. Information about the involvement of CYP3A in their metabolism should also be taken into account for drugs with narrow therapeutic ranges. © 2014 John Wiley & Sons Ltd.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Job Characteristics, Work Involvement, and Job Performance of Public Servants

ERIC Educational Resources Information Center

Johari, Johanim; Yahya, Khulida Kirana

2016-01-01

Purpose: The primary purpose of this study is to assess the predicting role of job characteristics on job performance. Dimensions in the job characteristics construct are skill variety, task identity, task significance, autonomy and feedback. Further, work involvement is tested as a mediator in the hypothesized link. Design/methodology/approach: A…

The Importance of Intrinsic Motivation for High and Low Ability Readers' Reading Comprehension Performance

ERIC Educational Resources Information Center

Logan, Sarah; Medford, Emma; Hughes, Naomi

2011-01-01

The study examined how cognitive and motivational factors predicted reading skill and whether intrinsic reading motivation would explain significantly more variance in low ability readers' reading performance. One hundred and eleven children (aged 9-11) completed assessments of reading comprehension skill, verbal IQ, decoding skill and intrinsic…

Sequential-Simultaneous Analysis of Japanese Children's Performance on the Japanese McCarthy.

ERIC Educational Resources Information Center

Ishikuma, Toshinori; And Others

This study explored the hypothesis that Japanese children perform significantly better on simultaneous processing than on sequential processing. The Kaufman Assessment Battery for Children (K-ABC) served as the criterion of the two types of mental processing. Regression equations to predict Sequential and Simultaneous processing from McCarthy…

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

PubMed

Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

2018-01-01

Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling and Prediction of Spark-ignition Engine Power Performance Using Incremental Least Squares Support Vector Machines

NASA Astrophysics Data System (ADS)

Wong, Pak-kin; Vong, Chi-man; Wong, Hang-cheong; Li, Ke

2010-05-01

Modern automotive spark-ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS-SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS-SVM is also proposed in this paper, so the power performance models built from the incremental LS-SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS-SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

Sensory modality, temperament, and the development of sustained attention: a vigilance study in children and adults.

PubMed

Curtindale, Lori; Laurie-Rose, Cynthia; Bennett-Murphy, Laura; Hull, Sarah

2007-05-01

Applying optimal stimulation theory, the present study explored the development of sustained attention as a dynamic process. It examined the interaction of modality and temperament over time in children and adults. Second-grade children and college-aged adults performed auditory and visual vigilance tasks. Using the Carey temperament questionnaires (S. C. McDevitt & W. B. Carey, 1995), the authors classified participants according to temperament composites of reactivity and task orientation. In a preliminary study, tasks were equated across age and modality using d' matching procedures. In the main experiment, 48 children and 48 adults performed these calibrated tasks. The auditory task proved more difficult for both children and adults. Intermodal relations changed with age: Performance across modality was significantly correlated for children but not for adults. Although temperament did not significantly predict performance in adults, it did for children. The temperament effects observed in children--specifically in those with the composite of reactivity--occurred in connection with the auditory task and in a manner consistent with theoretical predictions derived from optimal stimulation theory. Copyright (c) 2007 APA, all rights reserved.

Performance of a steel spar wind turbine blade on the Mod-0 100 kW experimental wind turbine

NASA Technical Reports Server (NTRS)

Keith, T. G., Jr.; Sullivan, T. L.; Viterna, L. A.

1980-01-01

The performance and loading of a large wind rotor, 38.4 m in diameter and composed of two low-cost steel spar blades were examined. Two blades were fabricated at Lewis Research Center and successfully operated on the Mod-0 wind turbine at Plum Brook. The blades were operated on a tower on which the natural bending frequency were altered by placing the tower on a leaf-spring apparatus. It was found that neither blade performance nor loading were affected significantly by this tower softening technique. Rotor performance exceeded prediction while blade loads were found to be in reasonable agreement with those predicted. Seventy-five hours of operation over a five month period resulted in no deterioration in the blade.

Prediction of Balance Compensation After Vestibular Schwannoma Surgery.

PubMed

Parietti-Winkler, Cécile; Lion, Alexis; Frère, Julien; Perrin, Philippe P; Beurton, Renaud; Gauchard, Gérome C

2016-06-01

Background Balance compensation after vestibular schwannoma (VS) surgery is under the influence of specific preoperative patient and tumor characteristics. Objective To prospectively identify potential prognostic factors for balance recovery, we compared the respective influence of these preoperative characteristics on balance compensation after VS surgery. Methods In 50 patients scheduled for VS surgical ablation, we measured postural control before surgery (BS), 8 (AS8) days after, and 90 (AS90) days after surgery. Based on factors found previously in the literature, we evaluated age, body mass index and preoperative physical activity (PA), tumor grade, vestibular status, and preference for visual cues to control balance as potential prognostic factors using stepwise multiple regression models. Results An asymmetric vestibular function was the sole significant explanatory factor for impaired balance performance BS, whereas the preoperative PA alone significantly contributed to higher performance at AS8. An evaluation of patients' balance recovery over time showed that PA and vestibular status were the 2 significant predictive factors for short-term postural compensation (BS to AS8), whereas none of these preoperative factors was significantly predictive for medium-term postoperative postural recovery (AS8 to AS90). Conclusions We identified specific preoperative patient and vestibular function characteristics that may predict postoperative balance recovery after VS surgery. Better preoperative characterization of these factors in each patient could inform more personalized presurgical and postsurgical management, leading to a better, more rapid balance recovery, earlier return to normal daily activities and work, improved quality of life, and reduced medical and societal costs. © The Author(s) 2015.

3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity.

PubMed

Kawabata, Takeshi; Nakamura, Haruki

2014-07-28

A protein-bound conformation of a target molecule can be predicted by aligning the target molecule on the reference molecule obtained from the 3D structure of the compound-protein complex. This strategy is called "similarity-based docking". For this purpose, we develop the flexible alignment program fkcombu, which aligns the target molecule based on atomic correspondences with the reference molecule. The correspondences are obtained by the maximum common substructure (MCS) of 2D chemical structures, using our program kcombu. The prediction performance was evaluated using many target-reference pairs of superimposed ligand 3D structures on the same protein in the PDB, with different ranges of chemical similarity. The details of atomic correspondence largely affected the prediction success. We found that topologically constrained disconnected MCS (TD-MCS) with the simple element-based atomic classification provides the best prediction. The crashing potential energy with the receptor protein improved the performance. We also found that the RMSD between the predicted and correct target conformations significantly correlates with the chemical similarities between target-reference molecules. Generally speaking, if the reference and target compounds have more than 70% chemical similarity, then the average RMSD of 3D conformations is <2.0 Å. We compared the performance with a rigid-body molecular alignment program based on volume-overlap scores (ShaEP). Our MCS-based flexible alignment program performed better than the rigid-body alignment program, especially when the target and reference molecules were sufficiently similar.

Closed loop models for analyzing the effects of simulator characteristics. [digital simulation of human operators

NASA Technical Reports Server (NTRS)

Baron, S.; Muralidharan, R.; Kleinman, D. L.

1978-01-01

The optimal control model of the human operator is used to develop closed loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed loop multirate simulation in which the optimal control model simulates the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

PubMed Central

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

PubMed

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Link prediction in multiplex online social networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Link prediction in multiplex online social networks.

PubMed

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

The influence of critical thinking skills on performance and progression in a pre-registration nursing program.

PubMed

Pitt, Victoria; Powis, David; Levett-Jones, Tracy; Hunter, Sharyn

2015-01-01

The importance of developing critical thinking skills in preregistration nursing students is recognized worldwide. Yet, there has been limited exploration of how students' critical thinking skill scores on entry to pre-registration nursing education influence their academic and clinical performance and progression. The aim of this study was to: i) describe entry and exit critical thinking scores of nursing students enrolled in a three year bachelor of nursing program in Australia in comparison to norm scores; ii) explore entry critical thinking scores in relation to demographic characteristics, students' performance and progression. This longitudinal correlational study used the Health Sciences Reasoning Test (HSRT) to measure critical thinking skills in a sample (n=134) of students, at entry and exit (three years later). A one sample t-test was used to determine if differences existed between matched student critical thinking scores between entry and exit points. Academic performance, clinical performance and progression data were collected and correlations with entry critical thinking scores were examined. There was a significant relationship between critical thinking scores, academic performance and students' risk of failing, especially in the first semester of study. Critical thinking scores were predictive of program completion within three years. The increase in critical thinking scores from entry to exit was significant for the 28 students measured. In comparison to norm scores, entry level critical thinking scores were significantly lower, but exit scores were comparable. Critical thinking scores had no significant relationship to clinical performance. Entry critical thinking scores significantly correlate to academic performance and predict students risk of course failure and ability to complete a nursing degree in three years. Students' critical thinking scores are an important determinant of their success and as such can inform curriculum development and selection strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predictors of professional behaviour and academic outcomes in a UK medical school: A longitudinal cohort study.

PubMed

Adam, Jane; Bore, Miles; Childs, Roy; Dunn, Jason; Mckendree, Jean; Munro, Don; Powis, David

2015-01-01

Over the past 70 years, there has been a recurring debate in the literature and in the popular press about how best to select medical students. This implies that we are still not getting it right: either some students are unsuited to medicine or the graduating doctors are considered unsatisfactory, or both. To determine whether particular variables at the point of selection might distinguish those more likely to become satisfactory professional doctors, by following a complete intake cohort of students throughout medical school and analysing all the data used for the students' selection, their performance on a range of other potential selection tests, academic and clinical assessments throughout their studies, and records of professional behaviour covering the entire five years of the course. A longitudinal database captured the following anonymised information for every student (n = 146) admitted in 2007 to the Hull York Medical School (HYMS) in the UK: demographic data (age, sex, citizenship); performance in each component of the selection procedure; performance in some other possible selection instruments (cognitive and non-cognitive psychometric tests); professional behaviour in tutorials and in other clinical settings; academic performance, clinical and communication skills at summative assessments throughout; professional behaviour lapses monitored routinely as part of the fitness-to-practise procedures. Correlations were sought between predictor variables and criterion variables chosen to demonstrate the full range of course outcomes from failure to complete the course to graduation with honours, and to reveal clinical and professional strengths and weaknesses. Student demography was found to be an important predictor of outcomes, with females, younger students and British citizens performing better overall. The selection variable "HYMS academic score", based on prior academic performance, was a significant predictor of components of Year 4 written and Year 5 clinical examinations. Some cognitive subtest scores from the UK Clinical Aptitude Test (UKCAT) and the UKCAT total score were also significant predictors of the same components, and a unique predictor of the Year 5 written examination. A number of the non-cognitive tests were significant independent predictors of Years 4 and 5 clinical performance, and of lapses in professional behaviour. First- and second-year tutor ratings were significant predictors of all outcomes, both desirable and undesirable. Performance in Years 1 and 2 written exams did not predict performance in Year 4 but did generally predict Year 5 written and clinical performance. Measures of a range of relevant selection attributes and personal qualities can predict intermediate and end of course achievements in academic, clinical and professional behaviour domains. In this study HYMS academic score, some UKCAT subtest scores and the total UKCAT score, and some non-cognitive tests completed at the outset of studies, together predicted outcomes most comprehensively. Tutor evaluation of students early in the course also identified the more and less successful students in the three domains of academic, clinical and professional performance. These results may be helpful in informing the future development of selection tools.

Intraindividual Variability in Executive Functions but Not Speed of Processing or Conflict Resolution Predicts Performance Differences in Gait Speed in Older Adults

PubMed Central

Mahoney, Jeannette; Verghese, Joe

2014-01-01

Background. The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Methods. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19–38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = −.606; 95% CI = −1.11 to −.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = −.901; 95% CI = −1.557 to −.245). Conclusion. Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. PMID:24285744

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

PubMed

Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

2012-12-01

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Do subjective measures of attention and memory predict actual performance? Metacognition in older couples.

PubMed

Volz-Sidiropoulou, Eftychia; Gauggel, Siegfried

2012-06-01

Older individuals who recognize their cognitive difficulties are more likely to adjust their everyday life to their actual cognitive functioning, particularly when they are able to estimate their abilities accurately. We assessed self- and spouse-ratings of memory and attention difficulties in everyday life of healthy, older individuals and compared them with the respective test performance. Eighty-four older individuals (women's age, M = 67.4 years, SD = 5.2; men's age, M = 68.5 years, SD = 4.9) completed both the self and the spouse versions of the Attention Deficit Questionnaire and the Everyday Memory Questionnaire and completed two neuropsychological tests. Using the residual score approach, subjective metacognitive measures of memory and attention were created and compared with actual test performance. Significant associations between subjective and objective scores were found only for men and only for episodic memory measures. Men who underreported memory difficulties performed more poorly; men who overreported memory difficulties performed better. Men's recognition performance was best predicted by subjective measures (R² = .25), followed by delayed recall (R² = .14) and forgetting rate (R² = .13). The results indicate gender-specific differences in metacognitive accuracy and predictive validity of subjective ratings. PsycINFO Database Record (c) 2012 APA, all rights reserved

Analysis and Design of Rotors at Ultra-Low Reynolds Numbers

NASA Technical Reports Server (NTRS)

Kunz, Peter J.; Strawn, Roger C.

2003-01-01

Design tools have been developed for ultra-low Reynolds number rotors, combining enhanced actuator-ring / blade-element theory with airfoil section data based on two-dimensional Navier-Stokes calculations. This performance prediction method is coupled with an optimizer for both design and analysis applications. Performance predictions from these tools have been compared with three-dimensional Navier Stokes analyses and experimental data for a 2.5 cm diameter rotor with chord Reynolds numbers below 10,000. Comparisons among the analyses and experimental data show reasonable agreement both in the global thrust and power required, but the spanwise distributions of these quantities exhibit significant deviations. The study also reveals that three-dimensional and rotational effects significantly change local airfoil section performance. The magnitude of this issue, unique to this operating regime, may limit the applicability of blade-element type methods for detailed rotor design at ultra-low Reynolds numbers, but these methods are still useful for evaluating concept feasibility and rapidly generating initial designs for further analysis and optimization using more advanced tools.

Fast H.264/AVC FRExt intra coding using belief propagation.

PubMed

Milani, Simone

2011-01-01

In the H.264/AVC FRExt coder, the coding performance of Intra coding significantly overcomes the previous still image coding standards, like JPEG2000, thanks to a massive use of spatial prediction. Unfortunately, the adoption of an extensive set of predictors induces a significant increase of the computational complexity required by the rate-distortion optimization routine. The paper presents a complexity reduction strategy that aims at reducing the computational load of the Intra coding with a small loss in the compression performance. The proposed algorithm relies on selecting a reduced set of prediction modes according to their probabilities, which are estimated adopting a belief-propagation procedure. Experimental results show that the proposed method permits saving up to 60 % of the coding time required by an exhaustive rate-distortion optimization method with a negligible loss in performance. Moreover, it permits an accurate control of the computational complexity unlike other methods where the computational complexity depends upon the coded sequence.

Sustained attention failures are primarily due to sustained cognitive load not task monotony.

PubMed

Head, James; Helton, William S

2014-11-01

We conducted two studies using a modified sustained attention to response task (SART) to investigate the developmental process of SART performance and the role of cognitive load on performance when the speed-accuracy trade-off is controlled experimentally. In study 1, 23 participants completed the modified SART (target stimuli location was not predictable) and a subjective thought content questionnaire 4 times over the span of 4 weeks. As predicted, the influence of speed-accuracy trade-off was significantly mitigated on the modified SART by having target stimuli occur in unpredictable locations. In study 2, 21 of the 23 participants completed an abridged version of the modified SART with a verbal free-recall memory task. Participants performed significantly worse when completing the verbal memory task and SART concurrently. Overall, the results support a resource theory perspective with concern to errors being a result of limited mental resources and not simply mindlessness per se. Copyright © 2014. Published by Elsevier B.V.

Predicting performance of junior doctors: Association of workplace based assessment with demographic characteristics, emotional intelligence, selection scores, and undergraduate academic performance.

PubMed

Carr, Sandra E; Celenza, Antonio; Mercer, Annette M; Lake, Fiona; Puddey, Ian B

2018-01-21

Predicting workplace performance of junior doctors from before entry or during medical school is difficult and has limited available evidence. This study explored the association between selected predictor variables and workplace based performance in junior doctors during their first postgraduate year. Two cohorts of medical students (n = 200) from one university in Western Australia participated in the longitudinal study. Pearson correlation coefficients and multivariate analyses utilizing linear regression were used to assess the relationships between performance on the Junior Doctor Assessment Tool (JDAT) and its sub-components with demographic characteristics, selection scores for medical school entry, emotional intelligence, and undergraduate academic performance. Grade Point Average (GPA) at the completion of undergraduate studies had the most significant association with better performance on the overall JDAT and each subscale. Increased age was a negative predictor for junior doctor performance on the Clinical management subscale and understanding emotion was a predictor for the JDAT Communication subscale. Secondary school performance measured by Tertiary Entry Rank on entry to medical school score predicted GPA but not junior doctor performance. The GPA as a composite measure of ability and performance in medical school is associated with junior doctor assessment scores. Using this variable to identify students at risk of difficulty could assist planning for appropriate supervision, support, and training for medical graduates transitioning to the workplace.

Developing operator capacity estimates for supervisory control of autonomous vehicles.

PubMed

Cummings, M L; Guerlain, Stephanie

2007-02-01

This study examined operators' capacity to successfully reallocate highly autonomous in-flight missiles to time-sensitive targets while performing secondary tasks of varying complexity. Regardless of the level of autonomy for unmanned systems, humans will be necessarily involved in the mission planning, higher level operation, and contingency interventions, otherwise known as human supervisory control. As a result, more research is needed that addresses the impact of dynamic decision support systems that support rapid planning and replanning in time-pressured scenarios, particularly on operator workload. A dual screen simulation that allows a single operator the ability to monitor and control 8, 12, or 16 missiles through high level replanning was tested on 42 U.S. Navy personnel. The most significant finding was that when attempting to control 16 missiles, participants' performance on three separate objective performance metrics and their situation awareness were significantly degraded. These results mirror studies of air traffic control that demonstrate a similar decline in performance for controllers managing 17 aircraft as compared with those managing only 10 to 11 aircraft. Moreover, the results suggest that a 70% utilization (percentage busy time) score is a valid threshold for predicting significant performance decay and could be a generalizable metric that can aid in manning predictions. This research is relevant to human supervisory control of networked military and commercial unmanned vehicles in the air, on the ground, and on and under the water.

Validity of Predicting Left Ventricular End Systolic Pressure Changes Following An Acute Bout of Exercise

PubMed Central

Kappus, Rebecca M.; Ranadive, Sushant M.; Yan, Huimin; Lane, Abbi D.; Cook, Marc D.; Hall, Grenita; Harvey, I. Shevon; Wilund, Kenneth R.; Woods, Jeffrey A.; Fernhall, Bo

2012-01-01

Objective Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance. LV ESP is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Design We compared measured LV ESP versus LV ESP values from the prediction equations at rest, 15 minutes and 30 minutes following peak aerobic exercise in 60 participants. Methods LV ESP was obtained by applanation tonometry at rest, 15 minutes post and 30 minutes post peak cycle exercise. Results Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). Conclusions These data indicate that the two prediction equations commonly used did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. PMID:22721862

Validity of predicting left ventricular end systolic pressure changes following an acute bout of exercise.

PubMed

Kappus, Rebecca M; Ranadive, Sushant M; Yan, Huimin; Lane, Abbi D; Cook, Marc D; Hall, Grenita; Harvey, I Shevon; Wilund, Kenneth R; Woods, Jeffrey A; Fernhall, Bo

2013-01-01

Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance and is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Measured LV ESP vs. LV ESP values from the prediction equations were compared at rest, 15 min and 30 min following peak aerobic exercise in 60 participants. LV ESP was obtained by applanation tonometry at rest, 15 min post and 30 min post peak cycle exercise. Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). The two commonly used prediction equations did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young, healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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

Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics.

PubMed

Sammour, T; Cohen, L; Karunatillake, A I; Lewis, M; Lawrence, M J; Hunter, A; Moore, J W; Thomas, M L

2017-11-01

Recently published data support the use of a web-based risk calculator ( www.anastomoticleak.com ) for the prediction of anastomotic leak after colectomy. The aim of this study was to externally validate this calculator on a larger dataset. Consecutive adult patients undergoing elective or emergency colectomy for colon cancer at a single institution over a 9-year period were identified using the Binational Colorectal Cancer Audit database. Patients with a rectosigmoid cancer, an R2 resection, or a diverting ostomy were excluded. The primary outcome was anastomotic leak within 90 days as defined by previously published criteria. Area under receiver operating characteristic curve (AUROC) was derived and compared with that of the American College of Surgeons National Surgical Quality Improvement Program ® (ACS NSQIP) calculator and the colon leakage score (CLS) calculator for left colectomy. Commercially available artificial intelligence-based analytics software was used to further interrogate the prediction algorithm. A total of 626 patients were identified. Four hundred and fifty-six patients met the inclusion criteria, and 402 had complete data available for all the calculator variables (126 had a left colectomy). Laparoscopic surgery was performed in 39.6% and emergency surgery in 14.7%. The anastomotic leak rate was 7.2%, with 31.0% requiring reoperation. The anastomoticleak.com calculator was significantly predictive of leak and performed better than the ACS NSQIP calculator (AUROC 0.73 vs 0.58) and the CLS calculator (AUROC 0.96 vs 0.80) for left colectomy. Artificial intelligence-predictive analysis supported these findings and identified an improved prediction model. The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.

Fourier transform wavefront control with adaptive prediction of the atmosphere.

PubMed

Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre

2007-09-01

Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.

The role of early fine and gross motor development on later motor and cognitive ability.

PubMed

Piek, Jan P; Dawson, Lisa; Smith, Leigh M; Gasson, Natalie

2008-10-01

The aim of this study was to determine whether information obtained from measures of motor performance taken from birth to 4 years of age predicted motor and cognitive performance of children once they reached school age. Participants included 33 children aged from 6 years to 11 years and 6 months who had been assessed at ages 4 months to 4 years using the ages and stages questionnaires (ASQ: [Squires, J. K., Potter, L., & Bricker, D. (1995). The ages and stages questionnaire users guide. Baltimore: Brookes]). These scores were used to obtain trajectory information consisting of the age of asymptote, maximum or minimum score, and the variance of ASQ scores. At school age, both motor and cognitive ability were assessed using the McCarron Assessment of Neuromuscular Development (MAND: [McCarron, L. (1997). McCarron assessment of neuromuscular development: Fine and gross motor abilities (revised ed.). Dallas, TX: Common Market Press.]), and the Wechsler Intelligence Scale for Children-Version IV (WISC-IV: [Wechsler, D. (2004). WISC-IV integrated technical and interpretive manual. San Antonio, Texas: Harcourt Assessment]). In contrast to previous research, results demonstrated that, although socio-economic status (SES) predicted fine motor performance and three of four cognitive domains at school age, gestational age was not a significant predictor of later development. This may have been due to the low-risk nature of the sample. After controlling for SES, fine motor trajectory information did not account for a significant proportion of the variance in school aged fine motor performance or cognitive performance. The ASQ gross motor trajectory set of predictors accounted for a significant proportion of the variance for cognitive performance once SES was controlled for. Further analysis showed a significant predictive relationship for gross motor trajectory information and the subtests of working memory and processing speed. These results provide evidence for detecting children at risk of developmental delays or disorders with a parent report questionnaire prior to school age. The findings also add to recent investigations into the relationship between early motor development and later cognitive function, and support the need for ongoing research into a potential etiological relationship.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.

Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

PubMed

Gracitelli, Carolina P B; Tatham, Andrew J; Boer, Erwin R; Abe, Ricardo Y; Diniz-Filho, Alberto; Rosen, Peter N; Medeiros, Felipe A

2015-01-01

To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma. Prospective observational cohort study. 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years. All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records. Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors. Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18). Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.Above results are discussed in a peer-review paper just being accepted for publication on Climate Dynamics (Alessandri et al., 2017; doi:10.1007/s00382-017-3766-y).

Predicting Esophagitis After Chemoradiation Therapy for Non-Small Cell Lung Cancer: An Individual Patient Data Meta-Analysis

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

Palma, David A., E-mail: david.palma@uwo.ca; Senan, Suresh; Oberije, Cary

Purpose: Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE. Methods and Materials: After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade ≥2 and grade ≥3) weremore » assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model. Results: The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c > .60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade ≥2 and grade ≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60 <0.07%), intermediate (V60 0.07% to 16.99%), and high (V60 ≥17%). With use of the validation set, the predictive model performed inferiorly for the grade ≥2 endpoint (c = .58) but performed well for the grade ≥3 endpoint (c = .66). Conclusions: Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.« less

A brief peripheral motion contrast threshold test predicts older drivers' hazardous behaviors in simulated driving.

PubMed

Henderson, Steven; Woods-Fry, Heather; Collin, Charles A; Gagnon, Sylvain; Voloaca, Misha; Grant, John; Rosenthal, Ted; Allen, Wade

2015-05-01

Our research group has previously demonstrated that the peripheral motion contrast threshold (PMCT) test predicts older drivers' self-report accident risk, as well as simulated driving performance. However, the PMCT is too lengthy to be a part of a battery of tests to assess fitness to drive. Therefore, we have developed a new version of this test, which takes under two minutes to administer. We assessed the motion contrast thresholds of 24 younger drivers (19-32) and 25 older drivers (65-83) with both the PMCT-10min and the PMCT-2min test and investigated if thresholds were associated with measures of simulated driving performance. Younger participants had significantly lower motion contrast thresholds than older participants and there were no significant correlations between younger participants' thresholds and any measures of driving performance. The PMCT-10min and the PMCT-2min thresholds of older drivers' predicted simulated crash risk, as well as the minimum distance of approach to all hazards. This suggests that our tests of motion processing can help predict the risk of collision or near collision in older drivers. Thresholds were also correlated with the total lane deviation time, suggesting a deficiency in processing of peripheral flow and delayed detection of adjacent cars. The PMCT-2min is an improved version of a previously validated test, and it has the potential to help assess older drivers' fitness to drive. Copyright © 2015 Elsevier Ltd. All rights reserved.

Performance Evaluation of Five Different Disseminated Intravascular Coagulation (DIC) Diagnostic Criteria for Predicting Mortality in Patients with Complicated Sepsis.

PubMed

Ha, Sang Ook; Park, Sang Hyuk; Hong, Sang Bum; Jang, Seongsoo

2016-11-01

Disseminated intravascular coagulation (DIC) is a major complication in sepsis patients. We compared the performance of five DIC diagnostic criteria, focusing on the prediction of mortality. One hundred patients with severe sepsis or septic shock admitted to intensive care unit (ICU) were enrolled. Routine DIC laboratory tests were performed over the first 4 days after admission. The overall ICU and 28-day mortality in DIC patients diagnosed from five criteria (International Society on Thrombosis and Haemostasis [ISTH], the Japanese Association for Acute Medicine [JAAM], the revised JAAM [R-JAAM], the Japanese Ministry of Health and Welfare [JMHW] and the Korean Society on Thrombosis and Hemostasis [KSTH]) were compared. Both KSTH and JMHW criteria showed superior performance than ISTH, JAAM and R-JAAM criteria in the prediction of overall ICU mortality in DIC patients (odds ratio 3.828 and 5.181, P = 0.018 and 0.006, 95% confidence interval 1.256-11.667 and 1.622-16.554, respectively) when applied at day 1 after admission, and survival analysis demonstrated significant prognostic impact of KSTH and JMHW criteria on the prediction of 28-day mortality (P = 0.007 and 0.049, respectively) when applied at day 1 after admission. In conclusion, both KSTH and JMHW criteria would be more useful than other three criteria in predicting prognosis in DIC patients with severe sepsis or septic shock.

Self-reported assistive technology outcomes and personal characteristics in college students with less-apparent disabilities.

PubMed

Malcolm, Matt P; Roll, Marla C

2017-11-20

The impact of assistive technology (AT) services for college students with less-apparent disabilities is under-reported. Using the Canadian Occupational Performance Measure (COPM), we assessed student Performance and Satisfaction ratings of common academic tasks at the start and end of a semester during which 105 student-clients with less-apparent disabilities received AT services. We examined if COPM scores related to personal characteristics of gender, class-level (e.g., Sophomore), and STEM education; if personal characteristics predicted a student's follow-through with an AT service referral (n=231); and if personal characteristics and initial COPM scores predicted dropout from AT services (n=187). COPM ratings significantly increased in all academic tasks (p<.001). Gender predicted initial Satisfaction (male ratings > female ratings; p=.01), and Performance changes (females were more likely to have a service-meaningful change; p=.02). Higher class-level predicted better follow-through with a referral for AT services (p=.006). Increasing class-level (p=.05) and higher initial studying (p<.006) and reading (p<.029) ratings predicted a lower likelihood for dropout. These findings demonstrate that college students with less-apparent disabilities experience substantial improvements in their self-ratings of academic performance and satisfaction following AT services. Gender, class-level, and initial self-perceived reading and studying abilities may influence if and how the student participates with AT services.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Husbands’ and Wives’ Physical Activity and Depressive Symptoms: Longitudinal Findings from the Cardiovascular Health Study

PubMed Central

Monin, Joan K.; Levy, Becca; Chen, Baibing; Fried, Terri; Stahl, Sarah T.; Schulz, Richard; Doyle, Margaret; Kershaw, Trace

2015-01-01

Background When examining older adults’ health behaviors and psychological health it is important to consider the social context. Purpose To examine in older adult marriages whether each spouse’s physical activity predicted changes in their own (actor effects) and their partner’s (partner effects) depressive symptoms. Gender differences were also examined. Method Each spouse within 1,260 married couples (at baseline) in the Cardiovascular Health Study completed self-report measures at wave 1 (1989–1990), wave 3 (1992–1993), and wave 7 (1996–1997). Dyadic path analyses were performed. Results Husbands’ physical activity significantly predicted own decreased depressive symptoms (actor effect). For both spouses, own physical activity did not significantly predict the spouse’s depressive symptoms (partner effects). However, husbands’ physical activity and depressive symptoms predicted wives’ physical activity and depressive symptoms (partner effects), respectively. Depressive symptoms did not predict physical activity. Conclusion Findings suggest that husbands’ physical activity is particularly influential for older married couples’ psychological health. PMID:25868508

1.5-Tesla Multiparametric-Magnetic Resonance Imaging for the detection of clinically significant prostate cancer

PubMed Central

POPITA, CRISTIAN; POPITA, ANCA RALUCA; SITAR-TAUT, ADELA; PETRUT, BOGDAN; FETICA, BOGDAN; COMAN, IOAN

2017-01-01

Background and aim Multiparametric-magnetic resonance imaging (mp-MRI) is the main imaging modality used for prostate cancer detection. The aim of this study is to evaluate the diagnostic performance of mp-MRI at 1.5-Tesla (1.5-T) for the detection of clinically significant prostate cancer. Methods In this ethical board approved prospective study, 39 patients with suspected prostate cancer were included. Patients with a history of positive prostate biopsy and patients treated for prostate cancer were excluded. All patients were examined at 1.5-T MRI, before standard transrectal ultrasonography–guided biopsy. Results The overall sensitivity, specificity, positive predictive value and negative predictive value for mp-MRI were 100%, 73.68%, 80% and 100%, respectively. Conclusion Our results showed that 1.5 T mp-MRI has a high sensitivity for detection of clinically significant prostate cancer and high negative predictive value in order to rule out significant disease. PMID:28246496

Genomic selection of agronomic traits in hybrid rice using an NCII population.

PubMed

Xu, Yang; Wang, Xin; Ding, Xiaowen; Zheng, Xingfei; Yang, Zefeng; Xu, Chenwu; Hu, Zhongli

2018-05-10

Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.

Differential diagnosis of cardiovascular diseases and T-wave alternans

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2016-04-01

T wave alternans (TWA) is the variation of the T-wave in electrocardiogram that is observed between periodic beats. TWA is one of the important precursors used to diagnose sudden cardiac death (SCD). Several clinical studies have tried to determine the significance of using TWA analysis to detect abnormalities that may lead to Ventricular Arrhythmias, as well as establish metrics to perform risk stratification for cardiovascular patients with prior cardiac episodes. The statistical significance of TWA in predicting ventricular arrhythmias has been established in patients across several diagnoses. Studies have also shown the significance of the predictive value of TWA analysis in post myocardial infarction patients, risk of SCD, congestive heart failure, ischemic cardiomyopathy, and Chagas disease.

Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

NASA Technical Reports Server (NTRS)

Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

2015-01-01

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

Predicting introductory programming performance: A multi-institutional multivariate study

NASA Astrophysics Data System (ADS)

Bergin, Susan; Reilly, Ronan

2006-12-01

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

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Do Maximal Roller Skiing Speed and Double Poling Performance Predict Youth Cross-Country Skiing Performance?

PubMed Central

Stöggl, Roland; Müller, Erich; Stöggl, Thomas

2017-01-01

The aims of the current study were to analyze whether specific roller skiing tests and cycle length are determinants of youth cross-country (XC) skiing performance, and to evaluate sex specific differences by applying non-invasive diagnostics. Forty-nine young XC skiers (33 boys; 13.8 ± 0.6 yrs and 16 girls; 13.4 ± 0.9 yrs) performed roller skiing tests consisting of both shorter (50 m) and longer durations (575 m). Test results were correlated with on snow XC skiing performance (PXC) based on 3 skating and 3 classical distance competitions (3 to 6 km). The main findings of the current study were: 1) Anthropometrics and maturity status were related to boys’, but not to girls’ PXC; 2) Significant moderate to acceptable correlations between girls’ and boys’ short duration maximal roller skiing speed (double poling, V2 skating, leg skating) and PXC were found; 3) Boys’ PXC was best predicted by double poling test performance on flat and uphill, while girls’ performance was mainly predicted by uphill double poling test performance; 4) When controlling for maturity offset, boys’ PXC was still highly associated with the roller skiing tests. The use of simple non-invasive roller skiing tests for determination of PXC represents practicable support for ski clubs, schools or skiing federations in the guidance and evaluation of young talent. Key points Double poling tests on flat and uphill terrain and short duration maximal speed tests were the highest cross-country skiing predicting factors in girls and boys. Only in the boys there was an effect of maturation on the performance outcomes, pointing out that girls seem to be almost fully matured at the age of 13 in contrast to the boys. Roller skiing tests over short distance (50-m) and longer distance 225 m and 350 m are stable and valid measures and suitable for performance prediction in youth cross-country skiers. PMID:28912656

Histopathology of the tissue adhering to the multiple tine expandable electrodes used for radiofrequency ablation of hepatocellular carcinoma predicts local recurrence.

PubMed

Ishikawa, Toru; Kubota, Tomoyuki; Abe, Hiroyuki; Nagashima, Aiko; Hirose, Kanae; Togashi, Tadayuki; Seki, Keiichi; Honma, Terasu; Yoshida, Toshiaki; Kamimura, Tomoteru; Nemoto, Takeo; Takeda, Keiko; Ishihara, Noriko

2012-01-01

To assess the ability to predict the local recurrence of hepatocellular carcinoma by analyzing tissues adhering to the radiofrequency ablation probe after complete ablation. From May 2002 to March 2011, tissue specimens adhering to the radiofrequency ablation probe from 284 radiofrequency ablation sessions performed for hepatocellular carcinomas ≤3 cm in size were analyzed. The specimens were classified as either viable tumor tissue or complete necrosis, and the local recurrence rates were calculated using the Kaplan-Meier method. From the tumors ≤3 cm in size, viable tissue was present in 6 (2.1%) of 284 specimens, and the local recurrence rates after 1 and 2 years of follow-up were 6.7% and 11.2%, respectively. Local recurrence developed significantly earlier in the viable tissue group. The recurrence rate was not significantly different based on whether transcatheter arterial chemoembolization was performed. The histopathology of the tissue adhering to the radiofrequency ablation probes used for hepatocellular carcinoma treatment can predict local recurrence. Additional aggressive treatment for patients with viable tissue can therefore improve the overall survival.

Prognostic value of DNA repair based stratification of hepatocellular carcinoma

PubMed Central

Lin, Zhuo; Xu, Shi-Hao; Wang, Hai-Qing; Cai, Yi-Jing; Ying, Li; Song, Mei; Wang, Yu-Qun; Du, Shan-Jie; Shi, Ke-Qing; Zhou, Meng-Tao

2016-01-01

Aberrant activation of DNA repair is frequently associated with tumor progression and response to therapy in hepatocellular carcinoma (HCC). Bioinformatics analyses of HCC data in the Cancer Genome Atlas (TCGA) were performed to define DNA repair based molecular classification that could predict the prognosis of patients with HCC. Furthermore, we tested its predictive performance in 120 independent cases. Four molecular subgroups were identified on the basis of coordinate DNA repair cluster (CDRC) comprising 15 genes in TCGA dataset. Increasing expression of CDRC genes were significantly associated with TP53 mutation. High CDRC was significantly correlated with advanced tumor grades, advanced pathological stage and increased vascular invasion rate. Multivariate Cox regression analysis indicated that the molecular subgrouping was an independent prognostic parameter for both overall survival (p = 0.004, hazard ratio (HR): 2.989) and tumor-free survival (p = 0.049, HR: 3.366) in TCGA dataset. Similar results were also obtained by analyzing the independent cohort. These data suggest that distinct dysregulation of DNA repair constituents based molecular classes in HCC would be useful for predicting prognosis and designing clinical trials for targeted therapy. PMID:27174663

Risk-adjusted performance evaluation in three academic thoracic surgery units using the Eurolung risk models.

PubMed

Pompili, Cecilia; Shargall, Yaron; Decaluwe, Herbert; Moons, Johnny; Chari, Madhu; Brunelli, Alessandro

2018-01-03

The objective of this study was to evaluate the performance of 3 thoracic surgery centres using the Eurolung risk models for morbidity and mortality. This was a retrospective analysis performed on data collected from 3 academic centres (2014-2016). Seven hundred and twenty-one patients in Centre 1, 857 patients in Centre 2 and 433 patients in Centre 3 who underwent anatomical lung resections were analysed. The Eurolung1 and Eurolung2 models were used to predict risk-adjusted cardiopulmonary morbidity and 30-day mortality rates. Observed and risk-adjusted outcomes were compared within each centre. The observed morbidity of Centre 1 was in line with the predicted morbidity (observed 21.1% vs predicted 22.7%, P = 0.31). Centre 2 performed better than expected (observed morbidity 20.2% vs predicted 26.7%, P < 0.001), whereas the observed morbidity of Centre 3 was higher than the predicted morbidity (observed 41.1% vs predicted 24.3%, P < 0.001). Centre 1 had higher observed mortality when compared with the predicted mortality (3.6% vs 2.1%, P = 0.005), whereas Centre 2 had an observed mortality rate significantly lower than the predicted mortality rate (1.2% vs 2.5%, P = 0.013). Centre 3 had an observed mortality rate in line with the predicted mortality rate (observed 1.4% vs predicted 2.4%, P = 0.17). The observed mortality rates in the patients with major complications were 30.8% in Centre 1 (versus predicted mortality rate 3.8%, P < 0.001), 8.2% in Centre 2 (versus predicted mortality rate 4.1%, P = 0.030) and 9.0% in Centre 3 (versus predicted mortality rate 3.5%, P = 0.014). The Eurolung models were successfully used as risk-adjusting instruments to internally audit the outcomes of 3 different centres, showing their applicability for future quality improvement initiatives. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

PubMed

García-Jacas, César R; Contreras-Torres, Ernesto; Marrero-Ponce, Yovani; Pupo-Meriño, Mario; Barigye, Stephen J; Cabrera-Leyva, Lisset

2016-01-01

Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.

PREDICTIVE MEASURES OF A RESIDENT'S PERFORMANCE ON WRITTEN ORTHOPAEDIC BOARD SCORES

PubMed Central

Dyrstad, Bradley W; Pope, David; Milbrandt, Joseph C; Beck, Ryan T; Weinhoeft, Anita L.; Idusuyi, Osaretin B

2011-01-01

Objective Residency programs are continually attempting to predict the performance of both current and potential residents. Previous studies have supported the use of USMLE Steps 1 and 2 as predictors of Orthopaedic In-Training Examination (OITE) and eventual American Board of Orthopaedic Surgery success, while others show no significant correlation. A strong performance on OITE examinations does correlate with strong residency performance, and some believe OITE scores are good predictors of future written board success. The current study was designed to examine potential differences in resident assessment measures and their predictive value for written boards. Design/Methods A retrospective review of resident performance data was performed for the past 10 years. Personalized information was removed by the residency coordinator. USMLE Step 1, USMLE Step 2, Orthopaedic In-Training Examination (from first to fifth years of training), and written orthopaedic specialty board scores were collected. Subsequently, the residents were separated into two groups, those scoring above the 35th percentile on written boards and those scoring below. Data were analyzed using correlation and regression analyses to compare and contrast the scores across all tests. Results A significant difference was seen between the groups in regard to USMLE scores for both Step 1 and 2. Also, a significant difference was found between OITE scores for both the second and fifth years. Positive correlations were found for USMLE Step 1, Step 2, OITE 2 and OITE 5 when compared to performance on written boards. One resident initially failed written boards, but passed on the second attempt This resident consistently scored in the 20th and 30th percentiles on the in-training examinations. Conclusions USMLE Step 1 and 2 scores along with OITE scores are helpful in gauging an orthopaedic resident’s performance on written boards. Lower USMLE scores along with consistently low OITE scores likely identify residents at risk of failing their written boards. Close monitoring of the annual OITE scores is recommended and may be useful to identify struggling residents. Future work involving multiple institutions is warranted and would ensure applicability of our findings to other orthopedic residency programs. PMID:22096449

Audiometric Predictions Using SFOAE and Middle-Ear Measurements

PubMed Central

Ellison, John C.; Keefe, Douglas H.

2006-01-01

Objective The goals of the study are to determine how well stimulus-frequency otoacoustic emissions (SFOAEs) identify hearing loss, classify hearing loss as mild or moderate-severe, and correlate with pure-tone thresholds in a population of adults with normal middle-ear function. Other goals are to determine if middle-ear function as assessed by wideband acoustic transfer function (ATF) measurements in the ear canal account for the variability in normal thresholds, and if the inclusion of ATFs improves the ability of SFOAEs to identify hearing loss and predict pure-tone thresholds. Design The total suppressed SFOAE signal and its corresponding noise were recorded in 85 ears (22 normal ears and 63 ears with sensorineural hearing loss) at octave frequencies from 0.5 – 8 kHz using a nonlinear residual method. SFOAEs were recorded a second time in three impaired ears to assess repeatability. Ambient-pressure ATFs were obtained in all but one of these 85 ears, and were also obtained from an additional 31 normal-hearing subjects in whom SFOAE data were not obtained. Pure-tone air-and bone-conduction thresholds and 226-Hz tympanograms were obtained on all subjects. Normal tympanometry and the absence of air-bone gaps were used to screen subjects for normal middle-ear function. Clinical decision theory was used to assess the performance of SFOAE and ATF predictors in classifying ears as normal or impaired, and linear regression analysis was used to test the ability of SFOAE and ATF variables to predict the air-conduction audiogram. Results The ability of SFOAEs to classify ears as normal or hearing impaired was significant at all test frequencies. The ability of SFOAEs to classify impaired ears as either mild or moderate-severe was significant at test frequencies from 0.5 to 4 kHz. SFOAEs were present in cases of severe hearing loss. SFOAEs were also significantly correlated with air-conduction thresholds from 0.5 to 8 kHz. The best performance occurred using the SFOAE signal-to-noise ratio (S/N) as the predictor, and the overall best performance was at 2 kHz. The SFOAE S/N measures were repeatable to within 3.5 dB in impaired ears. The ATF measures explained up to 25% of the variance in the normal audiogram; however, ATF measures did not improve SFOAEs predictors of hearing loss except at 4 kHz. Conclusions In common with other OAE types, SFOAEs are capable of identifying the presence of hearing loss. In particular, SFOAEs performed better than distortion-product and click-evoked OAEs in predicting auditory status at 0.5 kHz; SFOAE performance was similar to that of other OAE types at higher frequencies except for a slight performance reduction at 4 kHz. Because SFOAEs were detected in ears with mild to severe cases of hearing loss they may also provide an estimate of the classification of hearing loss. Although SFOAEs were significantly correlated with hearing threshold, they do not appear to have clinical utility in predicting a specific behavioral threshold. Information on middle-ear status as assessed by ATF measures offered minimal improvement in SFOAE predictions of auditory status in a population of normal and impaired ears with normal middle-ear function. However, ATF variables did explain a significant fraction of the variability in the audiograms of normal ears, suggesting that audiometric thresholds in normal ears are partially constrained by middle-ear function as assessed by ATF tests. PMID:16230898

To transfer or not to transfer? Kinematics and laterality quotient predict interlimb transfer of motor learning.

PubMed

Lefumat, Hannah Z; Vercher, Jean-Louis; Miall, R Chris; Cole, Jonathan; Buloup, Frank; Bringoux, Lionel; Bourdin, Christophe; Sarlegna, Fabrice R

2015-11-01

Humans can remarkably adapt their motor behavior to novel environmental conditions, yet it remains unclear which factors enable us to transfer what we have learned with one limb to the other. Here we tested the hypothesis that interlimb transfer of sensorimotor adaptation is determined by environmental conditions but also by individual characteristics. We specifically examined the adaptation of unconstrained reaching movements to a novel Coriolis, velocity-dependent force field. Right-handed subjects sat at the center of a rotating platform and performed forward reaching movements with the upper limb toward flashed visual targets in prerotation, per-rotation (i.e., adaptation), and postrotation tests. Here only the dominant arm was used during adaptation and interlimb transfer was assessed by comparing performance of the nondominant arm before and after dominant-arm adaptation. Vision and no-vision conditions did not significantly influence interlimb transfer of trajectory adaptation, which on average was significant but limited. We uncovered a substantial heterogeneity of interlimb transfer across subjects and found that interlimb transfer can be qualitatively and quantitatively predicted for each healthy young individual. A classifier showed that in our study, interlimb transfer could be predicted based on the subject's task performance, most notably motor variability during learning, and his or her laterality quotient. Positive correlations suggested that variability of motor performance and lateralization of arm movement control facilitate interlimb transfer. We further show that these individual characteristics can predict the presence and the magnitude of interlimb transfer of left-handers. Overall, this study suggests that individual characteristics shape the way the nervous system can generalize motor learning. Copyright © 2015 the American Physiological Society.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

PubMed

Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

2018-04-01

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.

Using the STOPBANG questionnaire and other pre-test probability tools to predict OSA in younger, thinner patients referred to a sleep medicine clinic.

PubMed

McMahon, Michael J; Sheikh, Karen L; Andrada, Teotimo F; Holley, Aaron B

2017-12-01

The STOPBANG questionnaire is used to predict the presence of obstructive sleep apnea (OSA). We sought to assess the performance of the STOPBANG questionnaire in younger, thinner patients referred to a sleep medicine clinic. We applied the STOPBANG questionnaire to patients referred for level I polysomnography (PSG) at our sleep center. We calculated likelihood ratios and area under the receiver operator characteristic (AUROC) curve and performed sensitivity analyses. We performed our analysis on 338 patients referred for PSG. Only 17.2% (n = 58) were above age 50 years, and 30.5 and 6.8% had a BMI above 30 and 35 years, respectively. The mean apnea-hypopnea index (AHI) was 12.9 ± 16.4 and 63.9% had an AHI ≥5. The STOPBANG (threshold ≥3) identified 83.1% of patients as high risk for an AHI ≥5, and sensitivity, specificity, positive (PPV), and negative predictive values (NPV) were 83.8, 18.0, 64.4, and 38.0%, respectively. Positive and negative likelihood ratios were poor at 1.02-1.11 and 0.55-0.90, respectively, across AHI thresholds (AHI ≥5, AHI ≥15 and AHI ≥30), and AUROCs were 0.52 (AHI ≥5) and 0.56 (AHI ≥15). Sensitivity analyses adjusting for insomnia, combat deployment, traumatic brain injury, post-traumatic stress disorder, clinically significant OSA (ESS >10 and/or co-morbid disease), and obesity did not significantly alter STOPBANG performance. In a younger, thinner population with predominantly mild-to-moderate OSA, the STOPBANG Score does not accurately predict the presence of obstructive sleep apnea.

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Felice, Matteo De; Catalano, Franco; Lee, June-Yi; Wang, Bin; Lee, Doo Young; Yoo, Jin-Ho; Weisheimer, Antije

2018-04-01

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.

Noninvasive scoring system for significant inflammation related to chronic hepatitis B

NASA Astrophysics Data System (ADS)

Hong, Mei-Zhu; Ye, Linglong; Jin, Li-Xin; Ren, Yan-Dan; Yu, Xiao-Fang; Liu, Xiao-Bin; Zhang, Ru-Mian; Fang, Kuangnan; Pan, Jin-Shui

2017-03-01

Although a liver stiffness measurement-based model can precisely predict significant intrahepatic inflammation, transient elastography is not commonly available in a primary care center. Additionally, high body mass index and bilirubinemia have notable effects on the accuracy of transient elastography. The present study aimed to create a noninvasive scoring system for the prediction of intrahepatic inflammatory activity related to chronic hepatitis B, without the aid of transient elastography. A total of 396 patients with chronic hepatitis B were enrolled in the present study. Liver biopsies were performed, liver histology was scored using the Scheuer scoring system, and serum markers and liver function were investigated. Inflammatory activity scoring models were constructed for both hepatitis B envelope antigen (+) and hepatitis B envelope antigen (-) patients. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 86.00%, 84.80%, 62.32%, 95.39%, and 0.9219, respectively, in the hepatitis B envelope antigen (+) group and 91.89%, 89.86%, 70.83%, 97.64%, and 0.9691, respectively, in the hepatitis B envelope antigen (-) group. Significant inflammation related to chronic hepatitis B can be predicted with satisfactory accuracy by using our logistic regression-based scoring system.

Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?

NASA Astrophysics Data System (ADS)

Rayner, Millicent; Harkness, Elaine F.; Foden, Philip; Wilson, Mary; Gadde, Soujanya; Beetles, Ursula; Lim, Yit Y.; Jain, Anil; Bundred, Sally; Barr, Nicky; Evans, D. Gareth; Howell, Anthony; Maxwell, Anthony; Astley, Susan M.

2018-03-01

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

Impact of the Occlusion Duration on the Performance of J-CTO Score in Predicting Failure of Percutaneous Coronary Intervention for Chronic Total Occlusion.

PubMed

de Castro-Filho, Antonio; Lamas, Edgar Stroppa; Meneguz-Moreno, Rafael A; Staico, Rodolfo; Siqueira, Dimytri; Costa, Ricardo A; Braga, Sergio N; Costa, J Ribamar; Chamié, Daniel; Abizaid, Alexandre

2017-06-01

The present study examined the association between Multicenter CTO Registry in Japan (J-CTO) score in predicting failure of percutaneous coronary intervention (PCI) correlating with the estimated duration of chronic total occlusion (CTO). The J-CTO score does not incorporate estimated duration of the occlusion. This was an observational retrospective study that involved all consecutive procedures performed at a single tertiary-care cardiology center between January 2009 and December 2014. A total of 174 patients, median age 59.5 years (interquartile range [IQR], 53-65 years), undergoing CTO-PCI were included. The median estimated occlusion duration was 7.5 months (IQR, 4.0-12.0 months). The lesions were classified as easy (score = 0), intermediate (score = 1), difficult (score = 2), and very difficult (score ≥3) in 51.1%, 33.9%, 9.2%, and 5.7% of the patients, respectively. Failure rate significantly increased with higher J-CTO score (7.9%, 20.3%, 50.0%, and 70.0% in groups with J-CTO scores of 0, 1, 2, and ≥3, respectively; P<.001). There was no significant difference in success rate according to estimated duration of occlusion (P=.63). Indeed, J-CTO score predicted failure of CTO-PCI independently of the estimated occlusion duration (P=.24). Areas under receiver-operating characteristic curves were computed and it was observed that for each occlusion time period, the discriminatory capacity of the J-CTO score in predicting CTO-PCI failure was good, with a C-statistic >0.70. The estimated duration of occlusion had no influence on the J-CTO score performance in predicting failure of PCI in CTO lesions. The probability of failure was mainly determined by grade of lesion complexity.

Predicting success on the certification examinations of the American Board of Anesthesiology.

PubMed

McClintock, Joseph C; Gravlee, Glenn P

2010-01-01

Currently, residency programs lack objective predictors for passing the sequenced American Board of Anesthesiology (ABA) certification examinations on the first attempt. Our hypothesis was that performance on the ABA/American Society of Anesthesiologists In-Training Examination (ITE) and other variables can predict combined success on the ABA Part 1 and Part 2 examinations. The authors studied 2,458 subjects who took the ITE immediately after completing the first year of clinical anesthesia training and took the ABA Part 1 examination for primary certification immediately after completing residency training 2 yr later. ITE scores and other variables were used to predict which residents would complete the certification process (passing the ABA Part 1 and Part 2 examinations) in the shortest possible time after graduation. ITE scores alone accounted for most of the explained variation in the desired outcome of certification in the shortest possible time. In addition, almost half of the observed variation and most of the explained variance in ABA Part 1 scores was accounted for by ITE scores. A combined model using ITE scores, residency program accreditation cycle length, country of medical school, and gender best predicted which residents would complete the certification examinations in the shortest possible time. The principal implication of this study is that higher ABA/ American Society of Anesthesiologists ITE scores taken at the end of the first clinical anesthesia year serve as a significant and moderately strong predictor of high performance on the ABA Part 1 (written) examination, and a significant predictor of success in completing both the Part 1 and Part 2 examinations within the calendar year after the year of graduation from residency. Future studies may identify other predictors, and it would be helpful to identify factors that predict clinical performance as well.

Brain Regional Blood Flow and Working Memory Performance Predict Change in Blood Pressure Over 2 Years.

PubMed

Jennings, J Richard; Heim, Alicia F; Sheu, Lei K; Muldoon, Matthew F; Ryan, Christopher; Gach, H Michael; Schirda, Claudiu; Gianaros, Peter J

2017-12-01

Hypertension is a presumptive risk factor for premature cognitive decline. However, lowering blood pressure (BP) does not uniformly reverse cognitive decline, suggesting that high BP per se may not cause cognitive decline. We hypothesized that essential hypertension has initial effects on the brain that, over time, manifest as cognitive dysfunction in conjunction with both brain vascular abnormalities and systemic BP elevation. Accordingly, we tested whether neuropsychological function and brain blood flow responses to cognitive challenges among prehypertensive individuals would predict subsequent progression of BP. Midlife adults (n=154; mean age, 49; 45% men) with prehypertensive BP underwent neuropsychological testing and assessment of regional cerebral blood flow (rCBF) response to cognitive challenges. Neuropsychological performance measures were derived for verbal and logical memory (memory), executive function, working memory, mental efficiency, and attention. A pseudo-continuous arterial spin labeling magnetic resonance imaging sequence compared rCBF responses with control and active phases of cognitive challenges. Brain areas previously associated with BP were grouped into composites for frontoparietal, frontostriatal, and insular-subcortical rCBF areas. Multiple regression models tested whether BP after 2 years was predicted by initial BP, initial neuropsychological scores, and initial rCBF responses to cognitive challenge. The neuropsychological composite of working memory (standardized beta, -0.276; se=0.116; P =0.02) and the frontostriatal rCBF response to cognitive challenge (standardized beta, 0.234; se=0.108; P =0.03) significantly predicted follow-up BP. Initial BP failed to significantly predict subsequent cognitive performance or rCBF. Changes in brain function may precede or co-occur with progression of BP toward hypertensive levels in midlife. © 2017 American Heart Association, Inc.

The role of anthropometric, performance and psychological attributes in predicting selection into an elite development programme in older adolescent rugby league players.

PubMed

Tredrea, Matthew; Dascombe, Ben; Sanctuary, Colin E; Scanlan, Aaron Terrence

2017-10-01

This study aimed to identify attributes that discriminate selected from non-selected players and predict selection into a rugby league development programme in older adolescent players. Anthropometric, performance and psychological attributes were measured in under-16 (N = 100) and under-18 (N = 60) rugby league players trialling for selection into a development programme with a professional Australian club. Sprint times (P < 0.001), predicted VO 2max (P = 0.002) and push-ups 1   min (P = 0.004) were superior in selected under-16 players, and sprint times (P ≤ 0.045), push-ups 1   min (P < 0.001) and chin-ups 1   min (P = 0.013) were superior in selected under-18 players. Further, 10-m sprint (β = -7.706, standard error [SE] = 2.412), VO 2max (β = 0.168, SE = 0.052) and body mass (β = 0.071, SE = 0.023) significantly predicted selection (R 2  = 0.339) in under-16 players, while push-ups 1   min (β = 0.564, SE = 0.250), 10-m sprint (β = -68.477, SE = 28.107), body mass (β = 0.360, SE = 0.155) and chronological age (β = -3.577, SE = 1.720) significantly predicted selection (R 2  = 0.894) in under-18 players. These findings emphasise the importance of performance attributes in junior rugby league and indicate talent identification test batteries should be age-specific in older adolescent players.

Relationship of cognitive and perceptual abilities to functional independence in adults who have had a stroke.

PubMed

Brown, Ted; Mapleston, Jennifer; Nairn, Allison; Molloy, Andrew

2013-03-01

Most individuals who have had a stroke present with some degree of residual cognitive and/or perceptual impairment. Occupational therapists often utilize standardized cognitive and perceptual assessments with clients to establish a baseline of skill performance as well as to inform goal setting and intervention planning. Being able to predict the functional independence of individuals who have had a stroke based on cognitive and perceptual impairments would assist with appropriate discharge planning and follow-up resource allocation. The study objective was to investigate the ability of the Developmental Test of Visual Perception - Adolescents and Adults (DTVP-A) and the Neurobehavioural Cognitive Status Exam (Cognistat) to predict the functional performance as measured by the Barthel Index of individuals who have had a stroke. Data was collected using the DTVP-A, Cognistat and the Barthal Index from 32 adults recovering from stroke. Two standard multiple regression models were used to determine predictive variables of the functional independence dependent variable. Both the Cognistat and DTVP-A had a statistically significant ability to predict functional performance (as measured by the Barthel Index) accounting for 64.4% and 27.9% of each regression model, respectively. Two Cognistat subscales (Comprehension [beta = 0.48; p < 0.001)] and Repetition [beta = 0.45; p < 0.004]) and one DTVP-A subscale (Copying [beta = 0.46; p < 0.014]) made statistically significant contributions to the regression models as independent variables. On the basis of the regression model findings, it appears that DTVP-A's Copying and the Cognistat's Comprehension and Repetition subscales are useful in predicting the functional independence (as measured by the Barthel Index) in those individuals who have had a stroke. Given the fundamental importance that cognition and perception has for one's ability to function independently, further investigation is warranted to determine other predictors of functional performance of individuals with a stroke. Copyright © 2012 John Wiley & Sons, Ltd.

Validation of the Manchester scoring system for predicting BRCA1/2 mutations in 9,390 families suspected of having hereditary breast and ovarian cancer.

PubMed

Kast, Karin; Schmutzler, Rita K; Rhiem, Kerstin; Kiechle, Marion; Fischer, Christine; Niederacher, Dieter; Arnold, Norbert; Grimm, Tiemo; Speiser, Dorothee; Schlegelberger, Brigitte; Varga, Dominic; Horvath, Judit; Beer, Marit; Briest, Susanne; Meindl, Alfons; Engel, Christoph

2014-11-15

The Manchester scoring system (MSS) allows the calculation of the probability for the presence of mutations in BRCA1 or BRCA2 genes in families suspected of having hereditary breast and ovarian cancer. In 9,390 families, we determined the predictive performance of the MSS without (MSS-2004) and with (MSS-2009) consideration of pathology parameters. Moreover, we validated a recalibrated version of the MSS-2009 (MSS-recal). Families were included in the registry of the German Consortium for Hereditary Breast and Ovarian Cancer, using defined clinical criteria. Receiver operating characteristics (ROC) analysis was used to determine the predictive performance. The recalibrated model was developed using logistic regression analysis and tested using an independent random validation sample. The area under the ROC curves regarding a mutation in any of the two BRCA genes was 0.77 (95%CI 0.75-0.79) for MSS-2004, 0.80 (95%CI 0.78-0.82) for MSS-2009, and 0.82 (95%CI 0.80-0.83) for MSS-recal. Sensitivity at the 10% mutation probability cutoff was similar for all three models (MSS-2004 92.2%, MSS-2009 92.2%, and MSS-recal 90.3%), but specificity of MSS-recal (46.0%) was considerably higher than that of MSS-2004 (25.4%) and MSS-2009 (32.3%). In the MSS-recal model, almost all predictors of the original MSS were significantly predictive. However, the score values of some predictors, for example, high grade triple negative breast cancers, differed considerably from the originally proposed score values. The original MSS performed well in our sample of high risk families. The use of pathological parameters increased the predictive performance significantly. Recalibration improved the specificity considerably without losing much sensitivity. © 2014 UICC.

Enrollment Management in Medical School Admissions: A Novel Evidence-Based Approach at One Institution.

PubMed

Burkhardt, John C; DesJardins, Stephen L; Teener, Carol A; Gay, Steven E; Santen, Sally A

2016-11-01

In higher education, enrollment management has been developed to accurately predict the likelihood of enrollment of admitted students. This allows evidence to dictate numbers of interviews scheduled, offers of admission, and financial aid package distribution. The applicability of enrollment management techniques for use in medical education was tested through creation of a predictive enrollment model at the University of Michigan Medical School (U-M). U-M and American Medical College Application Service data (2006-2014) were combined to create a database including applicant demographics, academic application scores, institutional financial aid offer, and choice of school attended. Binomial logistic regression and multinomial logistic regression models were estimated in order to study factors related to enrollment at the local institution versus elsewhere and to groupings of competing peer institutions. A predictive analytic "dashboard" was created for practical use. Both models were significant at P < .001 and had similar predictive performance. In the binomial model female, underrepresented minority students, grade point average, Medical College Admission Test score, admissions committee desirability score, and most individual financial aid offers were significant (P < .05). The significant covariates were similar in the multinomial model (excluding female) and provided separate likelihoods of students enrolling at different institutional types. An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.

The significance of eosinophils in predicting the severity of acute ischemic stroke

PubMed Central

Wang, Jun; Ma, Li; Lin, Tao; Li, Shi-Jing; Chen, Lei-Lei; Wang, De-Zhao

2017-01-01

Background Previous studies have shown that tumor-associated tissue eosinophilia have a role in various types of solid tumors. However, the relationship between eosinophil and acute ischemic stroke (AIS) is unclear. We aimed to investigate the diagnostic significance of eosinophil in AIS patients. Methods This study included 300 AIS patients without hypereosinophilic syndrome (HES). The hematologic indices were collected from each patient, including white blood count, eosinophil count, eosinophil percentage, neutrophil count, red blood count, and platelet. The severity of AIS was estimated by national institute of health stroke scale (NIHSS). Logistic regression analyses were performed to confirm the biomarkers for NIHSS and in-hospital non-death among the cases. Moreover, receiver-operating characteristics (ROC) analyses were used to investigate the clinical performances of eosinophils and NIHSS in prediction of non-death. Results The admission NIHSS (P<0.001) and BMI (P<0.001) were predictors to the non-death of the patients. There was a significant correlation between eosinophil counts or eosinophil percentage and NIHSS score (r= -0.451, P < 0.001; r= -0.617, P<0.001, Spearson Correlation). ROC analysis showed that eosinophil counts and eosinophil percentage could predict non-death of the patients in-hospital, with the areas under the curves (AUC) of 0.791 and 0.867, respectively. Conclusions Our study revealed a relationship between eosinophil and NIHSS score in the patients with AIS. Eosinophils might have certain value for predicting the severity of AIS. PMID:29262636

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Using the temporal self-regulation theory to examine the influence of environmental cues on maintaining a healthy lifestyle.

PubMed

Booker, Liesel; Mullan, Barbara

2013-11-01

The aim of the current study is to explore the predictive utility of the temporal self-regulation theory (TST) for maintaining a healthy lifestyle (Hall & Fong, 2007, Health Psychology Review, 1, 6). According to TST, the influence of intention, self-regulation, and behavioural prepotency differs depending on the environmental context in which the behaviour is performed. This study examined the influence of perceptions about the supportiveness of the environmental context on TST-related factors. Temporal self-regulation theory was tested using a prospective design with a 1-week follow-up. One hundred and fifty-two undergraduates were administered three executive functioning tasks and an online questionnaire regarding their intentions to maintain a healthy lifestyle, environmental responsiveness, and previous behaviour. One week later, they completed a follow-up questionnaire. Participants who were supported by the environment were significantly more likely to maintain a healthy lifestyle than those distracted by the environment. Behavioural prepotency was significantly predictive of behaviour performance for 'supported' participants. Behavioural prepotency, planning, and response inhibition were significantly predictive of 'unsupported' participants' behaviour. These findings provided preliminary support for the use of TST for the prediction of healthy lifestyle behaviour. Importantly, this study provided support for the contention that the influence of TST-related factors would vary according to the perceived supportiveness of the environment. These findings suggest that environmental responsiveness may be an important determinant to close the intention-behaviour gap for maintaining a healthy lifestyle. © 2012 The British Psychological Society.

Diatomic gasdynamic lasers.

NASA Technical Reports Server (NTRS)

Mckenzie, R. L.

1972-01-01

Predictions from a numerical model of the vibrational relaxation of anharmonic diatomic oscillators in supersonic expansions are used to show the extent to which the small anharmonicity of gases like CO can cause significant overpopulations of upper vibrational states. When mixtures of CO and N2 are considered, radiative gain on many of the vibration-rotation transitions of CO is predicted. Experiments are described that qualitatively verify the predictions by demonstrating laser oscillation in CO-N2 expansions. The resulting CO-N2 gasdynamic laser displays performance characteristics that equal or exceed those of similar CO2 lasers.

Diatomic gasdynamic lasers

NASA Technical Reports Server (NTRS)

Mckenzie, R. L.

1971-01-01

Predictions from a numerical model of the vibrational relaxation of anharmonic diatomic oscillators in supersonic expansions are used to show the extent to which the small anharmonicity of gases like CO can cause significant overpopulations of upper vibrational states. When mixtures of CO and N2 are considered, radiative gain on many of the vibration-rotation transitions of CO is predicted. Experiments are described that qualitatively verify the predictions by demonstrating laser oscillation in CO-N2 expansions. The resulting CO-N2 gasdynamic laser displays performance characteristics that equal or exceed those of similar CO2 lasers.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Development of a Higher Fidelity Model for the Cascade Distillation Subsystem (CDS)

NASA Technical Reports Server (NTRS)

Perry, Bruce; Anderson, Molly

2014-01-01

Significant improvements have been made to the ACM model of the CDS, enabling accurate predictions of dynamic operations with fewer assumptions. The model has been utilized to predict how CDS performance would be impacted by changing operating parameters, revealing performance trade-offs and possibilities for improvement. CDS efficiency is driven by the THP coefficient of performance, which in turn is dependent on heat transfer within the system. Based on the remaining limitations of the simulation, priorities for further model development include: center dot Relaxing the assumption of total condensation center dot Incorporating dynamic simulation capability for the buildup of dissolved inert gasses in condensers center dot Examining CDS operation with more complex feeds center dot Extending heat transfer analysis to all surfaces

Towards Principled Experimental Study of Autonomous Mobile Robots

NASA Technical Reports Server (NTRS)

Gat, Erann

1995-01-01

We review the current state of research in autonomous mobile robots and conclude that there is an inadequate basis for predicting the reliability and behavior of robots operating in unengineered environments. We present a new approach to the study of autonomous mobile robot performance based on formal statistical analysis of independently reproducible experiments conducted on real robots. Simulators serve as models rather than experimental surrogates. We demonstrate three new results: 1) Two commonly used performance metrics (time and distance) are not as well correlated as is often tacitly assumed. 2) The probability distributions of these performance metrics are exponential rather than normal, and 3) a modular, object-oriented simulation accurately predicts the behavior of the real robot in a statistically significant manner.

Early childrearing practices and their relationship to academic performance in Mexican American children.

PubMed

Arevalo, Amanda; Kolobe, Thubi H A; Arnold, Sandra; DeGrace, Beth

2014-01-01

To examine whether parenting behaviors and childrearing practices in the first 3 years of life among Mexican American (MA) families predict children's academic performance at school age. Thirty-six children were assessed using the Parent Behavior Checklist, Nursing Child Assessment Teaching Scale, Home Observation for Measurement of the Environment Inventory, and Bayley Scales of Infant Development II. Academic performance was measured with the Illinois Standards Achievement Test during third grade. Correlation between parents' developmental expectations, nurturing behaviors, discipline, and academic performance were statistically significant (P < .05). Developmental expectations and discipline strategies predicted 30% of the variance in the Illinois Standards Achievement Test of reading. The results of this study suggest that early developmental expectations that MA parents have for their children, and the nurturing and discipline behaviors they engage in, are related to how well the children perform on academic tests at school age.

Recent Progress Towards Predicting Aircraft Ground Handling Performance

NASA Technical Reports Server (NTRS)

Yager, T. J.; White, E. J.

1981-01-01

The significant progress which has been achieved in development of aircraft ground handling simulation capability is reviewed and additional improvements in software modeling identified. The problem associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior is discussed and efforts to improve this complex model, and hence simulator fidelity, are described. Aircraft braking performance data obtained on several wet runway surfaces is compared to ground vehicle friction measurements and, by use of empirically derived methods, good agreement between actual and estimated aircraft braking friction from ground vehilce data is shown. The performance of a relatively new friction measuring device, the friction tester, showed great promise in providing data applicable to aircraft friction performance. Additional research efforts to improve methods of predicting tire friction performance are discussed including use of an instrumented tire test vehicle to expand the tire friction data bank and a study of surface texture measurement techniques.

The Relative Utility of Three English Language Dominance Measures in Predicting the Neuropsychological Performance of HIV+ Bilingual Latino/a Adults

PubMed Central

Miranda, Caitlin; Rentería, Miguel Arce; Fuentes, Armando; Coulehan, Kelly; Arentoft, Alyssa; Byrd, Desiree; Rosario, Ana; Monzones, Jennifer; Morgello, Susan; Mindt, Monica Rivera

2016-01-01

Objective Given the disproportionate impact of neurologic disorders such as HIV on racial/ethnic minorities, neuropsychologists are increasingly evaluating individuals of diverse linguistic backgrounds. This study compares the utility of two brief and one comprehensive language measure to account for variation in English neuropsychological performance within a bilingual population. Method Sixty-two HIV+ English/Spanish bilingual Latino adults completed three language measures in English and Spanish: Self-Reported Language Ability; Verbal Fluency (FAS/PMR); and the Woodcock Munoz Language Survey-Revised (WMLS-R). All participants also completed an English language neuropsychological (NP) battery. Results It was hypothesized that the comprehensive English/Spanish WMLS-R language dominance index (LDI) would be significantly correlated with NP performance, as well as the best predictor of NP performance over and above the two brief language measures. Contrary to our hypothesis, the WMLS-R LDI was not significantly correlated to NP performance, whereas the easily administered Verbal Fluency and Self-Report LDIs were each correlated with global NP performance and multiple NP domains. After accounting for Verbal Fluency and Self-Report LDI in a multivariate regression predicting NP performance, the WMLS-R LDI did not provide a unique contribution to the model. Conclusions These findings suggest that the more comprehensive WMLS-R does not improve understanding of the effects of language on NP performance in an HIV+ bilingual Latino population. PMID:26934820

Memory Shaped by Age Stereotypes over Time

PubMed Central

Zonderman, Alan B.; Slade, Martin D.; Ferrucci, Luigi

2012-01-01

Objectives. Previous studies showed that negative self-stereotypes detrimentally affect the cognitive performance of marginalized group members; however, these findings were confined to short-term experiments. In the present study, we considered whether stereotypes predicted memory over time, which had not been previously examined. We also considered whether self-relevance increased the influence of stereotypes on memory over time. Method. Multiple waves of memory performance were analyzed using individual growth models. The sample consisted of 395 participants in the Baltimore Longitudinal Study of Aging. Results. Those with more negative age stereotypes demonstrated significantly worse memory performance over 38 years than those with less negative age stereotypes, after adjusting for relevant covariates. The decline in memory performance for those aged 60 and above was 30.2% greater for the more negative age stereotype group than for the less negative age stereotype group. Also, the impact of age stereotypes on memory was significantly greater among those for whom the age stereotypes were self-relevant. Discussion. This study shows that the adverse influence of negative self-stereotypes on cognitive performance is not limited to a short-term laboratory effect. Rather, the findings demonstrate, for the first time, that stereotypes also predict memory performance over an extended period in the community. PMID:22056832

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians.

PubMed

Hosking, Diane E; Nettelbeck, Ted; Wilson, Carlene; Danthiir, Vanessa

2014-07-28

Dietary intake is a modifiable exposure that may have an impact on cognitive outcomes in older age. The long-term aetiology of cognitive decline and dementia, however, suggests that the relevance of dietary intake extends across the lifetime. In the present study, we tested whether retrospective dietary patterns from the life periods of childhood, early adulthood, adulthood and middle age predicted cognitive performance in a cognitively healthy sample of 352 older Australian adults >65 years. Participants completed the Lifetime Diet Questionnaire and a battery of cognitive tests designed to comprehensively assess multiple cognitive domains. In separate regression models, lifetime dietary patterns were the predictors of cognitive factor scores representing ten constructs derived by confirmatory factor analysis of the cognitive test battery. All regression models were progressively adjusted for the potential confounders of current diet, age, sex, years of education, English as native language, smoking history, income level, apoE ɛ4 status, physical activity, other past dietary patterns and health-related variables. In the adjusted models, lifetime dietary patterns predicted cognitive performance in this sample of older adults. In models additionally adjusted for intake from the other life periods and mechanistic health-related variables, dietary patterns from the childhood period alone reached significance. Higher consumption of the 'coffee and high-sugar, high-fat extras' pattern predicted poorer performance on simple/choice reaction time, working memory, retrieval fluency, short-term memory and reasoning. The 'vegetable and non-processed' pattern negatively predicted simple/choice reaction time, and the 'traditional Australian' pattern positively predicted perceptual speed and retrieval fluency. Identifying early-life dietary antecedents of older-age cognitive performance contributes to formulating strategies for delaying or preventing cognitive decline.

Predicting Performance in Practical Writing: Factors That Influence Success in the Introductory Business Communication Course.

ERIC Educational Resources Information Center

Siegel, Gerald

A study attempted to develop and test a questionnaire that could combine various sorts of demographic information to identify strong or weak students and forecast their course performance. The study determined if a significant relationship existed between students' personal and academic profiles and their final course grades in an introductory…

Relationship between college success and employer competency ratings for graduates of a baccalaureate nursing program.

PubMed

Bolin, S E; Hogle, E L

1984-01-01

This expost facto correlational study sought to determine which measures of academic success in one class of BSN graduates predicted their competence as employees one year after graduation, as judged by their employers. The relationship between pre-entrance test scores, clinical experience grades, GPA, State Board Test Pool examination scores, and employer competency ratings were also determined. In keeping with the literature in fields other than nursing, the findings suggest that there may be little relationship between academic performance in a nursing program and subsequent job performance as a nurse, even though verbal ability may be predictive of success in school. While significant positive correlations were found between pre-entrance test data and final grade point averages, as well as pre-entrance test scores and State Board Test Pool examination scores, there was little evidence that pre-entrance test scores were predictive of nursing abilities. Isolated correlations were found between the clinical components of some nursing courses and specific nursing abilities. Using multiple regression analysis, no clinical course grade was found to be a significant predictor of the mean employer competency rating. Significant predictors were found for only four of the individual nursing abilities, with the clinical component of Leadership in Nursing being the most frequent and best predictor.

Prediction of Kinematic and Kinetic Performance in a Drop Vertical Jump with Individual Anthropometric Factors in Adolescent Female Athletes: Implications for Cadaveric Investigations

PubMed Central

Bates, Nathaniel A.; Myer, Gregory D.; Hewett, Timothy E.

2014-01-01

Anterior cruciate ligament injuries are common, expensive to repair, and often debilitate athletic careers. Robotic manipulators have evaluated knee ligament biomechanics in cadaveric specimens, but face limitations such as accounting for variation in bony geometry between specimens that may influence dynamic motion pathways. This study examined individual anthropometric measures for significant linear relationships with in vivo kinematic and kinetic performance and determined their implications for robotic studies. Anthropometrics and 3D motion during a 31 cm drop vertical jump task were collected in high school female basketball players. Anthropometric measures demonstrated differential statistical significance in linear regression models relative to kinematic variables (P-range < 0.01-0.95). However, none of the anthropometric relationships accounted for clinical variance or provided substantive univariate accuracy needed for clinical prediction algorithms (r2 < 0.20). Mass and BMI demonstrated models that were significant (P < 0.05) and predictive (r2 > 0.20) relative to peak flexion moment, peak adduction moment, flexion moment range, abduction moment range, and internal rotation moment range. The current findings indicate that anthropometric measures are less associated with kinematics than with kinetics. Relative to the robotic manipulation of cadaveric limbs, the results do not support the need to normalize kinematic rotations relative to specimen dimensions. PMID:25266933

Endoscopic assessment of airway function as a predictor of racing performance in Thoroughbred yearlings: 427 cases (1997-2000).

PubMed

Stick, J A; Peloso, J G; Morehead, J P; Lloyd, J; Eberhart, S; Padungtod, P; Derksen, F J

2001-10-01

To compare endoscopic findings of the upper portion of the respiratory tract in Thoroughbred yearlings with their subsequent race records to determine whether subjective assessment of airway function may be used as a predictor of future racing performance. Retrospective study. 427 Thoroughbred yearlings. Endoscopic examination findings were obtained from the medical records and the videoendoscopic repository of the Keeneland 1996 September yearling sales. Racing records were requested for the yearlings through the end of their 4-year-old racing season (1997-2000). Twenty-nine measures of racing performance were correlated with endoscopic findings. Subjective arytenoid cartilage movement grades were determined, using a 4-point grading scale (grade 1 = symmetrical synchronous abduction of the arytenoid cartilages; grade 4 = no substantial movement of the left arytenoid cartilage). Of the 427 Thoroughbred yearlings included in this study, 364 established race records, and 63 did not. Opinions regarding suitability for purchase, meeting conditions of the sale, and the presence of epiglottic abnormalities had no significant association with racing performance. Arytenoid cartilage movement grades were significantly associated with many of the dependent variables. However, palatine abnormalities were not predictive of inferior racing performance. Thoroughbred yearlings with grade-1 and -2 arytenoid cartilage movements had significantly better racing performance as adults, compared with yearlings with grade-3 arytenoid cartilage movements. In contrast, epiglottic and palatine abnormalities were not predictive of inferior racing performance. Therefore, evaluation of laryngeal function, but not epiglottic or palatine abnormalities, using the 4-point grading system, should be the major factor in developing recommendations for prospective buyers.

Anthropometry as a predictor of bench press performance done at different loads.

PubMed

Caruso, John F; Taylor, Skyler T; Lutz, Brant M; Olson, Nathan M; Mason, Melissa L; Borgsmiller, Jake A; Riner, Rebekah D

2012-09-01

The purpose of our study was to examine the ability of anthropometric variables (body mass, total arm length, biacromial width) to predict bench press performance at both maximal and submaximal loads. Our methods required 36 men to visit our laboratory and submit to anthropometric measurements, followed by lifting as much weight as possible in good form one time (1 repetition maximum, 1RM) in the exercise. They made 3 more visits in which they performed 4 sets of bench presses to volitional failure at 1 of 3 (40, 55, or 75% 1RM) submaximal loads. An accelerometer (Myotest Inc., Royal Oak MI) measured peak force, velocity, and power after each submaximal load set. With stepwise multivariate regression, our 3 anthropometric variables attempted to explain significant amounts of variance for 13 bench press performance indices. For criterion measures that reached significance, separate Pearson product moment correlation coefficients further assessed if the strength of association each anthropometric variable had with the criterion was also significant. Our analyses showed that anthropometry explained significant amounts (p < 0.05) of variance for 8 criterion measures. It was concluded that body mass had strong univariate correlations with 1RM and force-related measures, total arm length was moderately associated with 1RM and criterion variables at the lightest load, whereas biacromial width had an inverse relationship with the peak number of repetitions performed per set at the 2 lighter loads. Practical applications suggest results may help coaches and practitioners identify anthropometric features that may best predict various measures of bench press prowess in athletes.

Tier One Performance Screen Initial Operational Test and Evaluation: Early Results

DTIC Science & Technology

2011-04-01

Requirement: In addition to educational, physical , and moral screens, the U.S. Army relies on a composite score from the Armed Services Vocational Aptitude...analyses suggest that the individual TAPAS scales significantly predict a number of criteria of interest. Most notably, the Physical Conditioning scale...predicted Soldiers’ self-reported Army Physical Fitness Test (APFT) scores, number of restarts in training, adjustment to Army life, and 3-month

Evaluating the performance of a new model for predicting the growth of Clostridium perfringens in cooked, uncured meat and poultry products under isothermal, heating, and dynamically cooling conditions

USDA-ARS?s Scientific Manuscript database

Clostridium perfringens Type A is a significant public health threat and may germinate, outgrow, and multiply during cooling of cooked meats. This study evaluates a new C. perfringens growth model in IPMP Dynamic Prediction using the same criteria and cooling data in Mohr and others (2015), but inc...

The prediction of nozzle performance and heat transfer in hydrogen/oxygen rocket engines with transpiration cooling, film cooling, and high area ratios

NASA Technical Reports Server (NTRS)

Kacynski, Kenneth J.; Hoffman, Joe D.

1993-01-01

An advanced engineering computational model has been developed to aid in the analysis and design of hydrogen/oxygen chemical rocket engines. The complete multi-species, chemically reacting and diffusing Navier-Stokes equations are modelled, finite difference approach that is tailored to be conservative in an axisymmetric coordinate system for both the inviscid and viscous terms. Demonstration cases are presented for a 1030:1 area ratio nozzle, a 25 lbf film cooled nozzle, and transpiration cooled plug-and-spool rocket engine. The results indicate that the thrust coefficient predictions of the 1030:1 nozzle and the film cooled nozzle are within 0.2 to 0.5 percent, respectively, of experimental measurements when all of the chemical reaction and diffusion terms are considered. Further, the model's predictions agree very well with the heat transfer measurements made in all of the nozzle test cases. The Soret thermal diffusion term is demonstrated to have a significant effect on the predicted mass fraction of hydrogen along the wall of the nozzle in both the laminar flow 1030:1 nozzle and the turbulent plug-and-spool rocket engine analysis cases performed. Further, the Soret term was shown to represent a significant fraction of the diffusion fluxes occurring in the transpiration cooled rocket engine.

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Deficits in Attention and Visual Processing but not Global Cognition Predict Simulated Driving Errors in Drivers Diagnosed With Mild Alzheimer's Disease.

PubMed

Yamin, Stephanie; Stinchcombe, Arne; Gagnon, Sylvain

2016-06-01

This study sought to predict driving performance of drivers with Alzheimer's disease (AD) using measures of attention, visual processing, and global cognition. Simulated driving performance of individuals with mild AD (n = 20) was contrasted with performance of a group of healthy controls (n = 21). Performance on measures of global cognitive function and specific tests of attention and visual processing were examined in relation to simulated driving performance. Strong associations were observed between measures of attention, notably the Test of Everyday Attention (sustained attention; r = -.651, P = .002) and the Useful Field of View (r = .563, P = .010), and driving performance among drivers with mild AD. The Visual Object and Space Perception Test-object was significantly correlated with the occurrence of crashes (r = .652, P = .002). Tests of global cognition did not correlate with simulated driving outcomes. The results suggest that professionals exercise caution when extrapolating driving performance based on global cognitive indicators. © The Author(s) 2015.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Organizational work-family resources as predictors of job performance and attitudes: the process of work-family conflict and enrichment.

PubMed

Odle-Dusseau, Heather N; Britt, Thomas W; Greene-Shortridge, Tiffany M

2012-01-01

The goal of the current study was to test a model where organizational resources (aimed at managing work and family responsibilities) predict job attitudes and supervisor ratings of performance through the mechanisms of work-family conflict and work-family enrichment. Employees (n = 174) at a large metropolitan hospital were surveyed at two time periods regarding perceptions of family supportive supervisor behaviors (FSSB), family supportive organizational perceptions (FSOP), bidirectional work-family conflict, bidirectional work-family enrichment, and job attitudes. Supervisors were also asked to provide performance ratings at Time 2. Results revealed FSSB at Time 1 predicted job satisfaction, organizational commitment and intention to leave, as well as supervisor ratings of performance, at Time 2. In addition, both work-family enrichment and family-work enrichment were found to mediate relationships between FSSB and various organizational outcomes, while work-family conflict was not a significant mediator. Results support further testing of supervisor behaviors specific to family support, as well models that include bidirectional work-family enrichment as the mechanism by which work-family resources predict employee and organizational outcomes.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia

PubMed Central

Gurrera, R.J.; Moye, J.; Karel, M.J.; Azar, A.R.; Armesto, J.C.

2016-01-01

Objective To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. Methods The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool—Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Results Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (β) profiles were unique for each ability. Conclusions Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness. PMID:16682669

The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer.

PubMed

Thompson, J E; van Leeuwen, P J; Moses, D; Shnier, R; Brenner, P; Delprado, W; Pulbrook, M; Böhm, M; Haynes, A M; Hayen, A; Stricker, P D

2016-05-01

We assess the accuracy of multiparametric magnetic resonance imaging for significant prostate cancer detection before diagnostic biopsy in men with an abnormal prostate specific antigen/digital rectal examination. A total of 388 men underwent multiparametric magnetic resonance imaging, including T2-weighted, diffusion weighted and dynamic contrast enhanced imaging before biopsy. Two radiologists used PI-RADS to allocate a score of 1 to 5 for suspicion of significant prostate cancer (Gleason 7 with more than 5% grade 4). PI-RADS 3 to 5 was considered positive. Transperineal template guided mapping biopsy of 18 regions (median 30 cores) was performed with additional manually directed cores from magnetic resonance imaging positive regions. The anatomical location, size and grade of individual cancer areas in the biopsy regions (18) as the primary outcome and in prostatectomy specimens (117) as the secondary outcome were correlated to the magnetic resonance imaging positive regions. Of the 388 men who were enrolled in the study 344 were analyzed. Multiparametric magnetic resonance imaging was positive in 77.0% of patients, 62.5% had prostate cancer and 41.6% had significant prostate cancer. The detection of significant prostate cancer by multiparametric magnetic resonance imaging had a sensitivity of 96%, specificity of 36%, negative predictive value of 92% and positive predictive value of 52%. Adding PI-RADS to the multivariate model, including prostate specific antigen, digital rectal examination, prostate volume and age, improved the AUC from 0.776 to 0.879 (p <0.001). Anatomical concordance analysis showed a low mismatch between the magnetic resonance imaging positive regions and biopsy positive regions (4 [2.9%]), and the significant prostate cancer area in the radical prostatectomy specimen (3 [3.3%]). In men with an abnormal prostate specific antigen/digital rectal examination, multiparametric magnetic resonance imaging detected significant prostate cancer with an excellent negative predictive value and moderate positive predictive value. The use of multiparametric magnetic resonance imaging to diagnose significant prostate cancer may result in a substantial number of unnecessary biopsies while missing a minimum of significant prostate cancers. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

PubMed

Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines. Copyright © 2017 Elsevier B.V. All rights reserved.

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

USGS Publications Warehouse

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

2015-01-01

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

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

PubMed

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

Responsiveness of the Countermovement Jump and Handgrip Strength to an Incremental Running Test in Endurance Athletes: Influence of Sex

PubMed Central

García-Pinillos, Felipe; Delgado-Floody, Pedro; Martínez-Salazar, Cristian; Latorre-Román, Pedro Á.

2018-01-01

Abstract The present study analyzed the acute effects of an incremental running test on countermovement jump (CMJ) and handgrip strength performance in endurance athletes, considering the effect of post-exercise recovery time and sex. Thirty-three recreationally trained long-distance runners, 20 men and 13 women, participated voluntarily in this study. The participants performed the Léger test, moreover, the CMJ and handgrip strength tests were carried out before and after the running test and during different stages of recovery (at the 1st min of recovery (posttest1), 5th min of recovery (posttest2), and 10th min of recovery (posttest3)). Two-way analysis of variance revealed a significant improvement in the CMJ (pre-posttest1, p = 0.001) and handgrip strength (pre-posttest2, p = 0.017) during recovery time. The Pearson’s Chi-2 test showed no significant relationship (p ≥ 0.05) between sex and post-activation potentiation (PAP). A linear regression analysis pointed to heart rate recovery as a predictive factor of CMJ improvement (PAP). In conclusion, despite significant fatigue reached during the Léger test, the long-distance runners did not experience an impaired CMJ and handgrip strength performance, either men or women, achieving an improvement (PAP) in posttest conditions. The results obtained showed no significant relationship between sex and PAP. Moreover, significant effect of recovery after running at high intensity on CMJ performance and handgrip strength was found. Finally, the data suggest that PAP condition can be predicted by heart rate recovery in endurance runners. PMID:29599872

Respiratory muscle weakness and respiratory muscle training in severely disabled multiple sclerosis patients.

PubMed

Gosselink, R; Kovacs, L; Ketelaer, P; Carton, H; Decramer, M

2000-06-01

To evaluate the contribution of respiratory muscle weakness (part 1) and respiratory muscle training (part 2) to pulmonary function, cough efficacy, and functional status in patients with advanced multiple sclerosis (MS). Survey (part 1) and randomized controlled trial (part 2). Rehabilitation center for MS. Twenty-eight bedridden or wheelchair-bound MS patients (part 1); 18 patients were randomly assigned to a training group (n = 9) or a control group (n = 9) (part 2). The training group (part 2) performed three series of 15 contractions against an expiratory resistance (60% maximum expiratory pressure [PEmax]) two times a day, whereas the control group performed breathing exercises to enhance maximal inspirations. Forced vital capacity (FVC), inspiratory, and expiratory muscle strength (PImax and PEmax), neck flexion force (NFF), cough efficacy by means of the Pulmonary Index (PI), and functional status by means of the Extended Disability Status Scale (EDSS). Part 1 revealed a significantly reduced FVC (43% +/- 26% predicted), PEmax (18% +/- 8% predicted), and PImax (27% +/- 11% predicted), whereas NFF was only mildly reduced (93% +/- 26% predicted). The PI (median score, 10) and EDSS (median score, 8.5) were severely reduced. PEmax was significantly correlated to FVC, EDSS, and PI (r = .77, -.79, and -.47, respectively). In stepwise multiple regression analysis. PEmax was the only factor contributing to the explained variance in FVC (R2 = .60), whereas body weight (R2 = .41) was the only factor for the PI. In part 2, changes in PImax and PEmax tended to be higher in the training group (p = .06 and p = .07, respectively). The PI was significantly improved after 3 months of training compared with the control group (p < .05). After 6 months, the PI remained significantly better in the training group. Expiratory muscle strength was significantly reduced and related to FVC, cough efficacy, and functional status. Expiratory muscle training tended to enhance inspiratory and expiratory muscle strength. In addition, subjectively and objectively rated cough efficacy improved significantly and lasted for 3 months after training cessation.

A Multiscale Virtual Fabrication and Lattice Modeling Approach for the Fatigue Performance Prediction of Asphalt Concrete

NASA Astrophysics Data System (ADS)

Dehghan Banadaki, Arash

Predicting the ultimate performance of asphalt concrete under realistic loading conditions is the main key to developing better-performing materials, designing long-lasting pavements, and performing reliable lifecycle analysis for pavements. The fatigue performance of asphalt concrete depends on the mechanical properties of the constituent materials, namely asphalt binder and aggregate. This dependent link between performance and mechanical properties is extremely complex, and experimental techniques often are used to try to characterize the performance of hot mix asphalt. However, given the seemingly uncountable number of mixture designs and loading conditions, it is simply not economical to try to understand and characterize the material behavior solely by experimentation. It is well known that analytical and computational modeling methods can be combined with experimental techniques to reduce the costs associated with understanding and characterizing the mechanical behavior of the constituent materials. This study aims to develop a multiscale micromechanical lattice-based model to predict cracking in asphalt concrete using component material properties. The proposed algorithm, while capturing different phenomena for different scales, also minimizes the need for laboratory experiments. The developed methodology builds on a previously developed lattice model and the viscoelastic continuum damage model to link the component material properties to the mixture fatigue performance. The resulting lattice model is applied to predict the dynamic modulus mastercurves for different scales. A framework for capturing the so-called structuralization effects is introduced that significantly improves the accuracy of the modulus prediction. Furthermore, air voids are added to the model to help capture this important micromechanical feature that affects the fatigue performance of asphalt concrete as well as the modulus value. The effects of rate dependency are captured by implementing the viscoelastic fracture criterion. In the end, an efficient cyclic loading framework is developed to evaluate the damage accumulation in the material that is caused by long-sustained cyclic loads.

The predictive validity of three versions of the MCAT in relation to performance in medical school, residency, and licensing examinations: a longitudinal study of 36 classes of Jefferson Medical College.

PubMed

Callahan, Clara A; Hojat, Mohammadreza; Veloski, Jon; Erdmann, James B; Gonnella, Joseph S

2010-06-01

The Medical College Admission Test (MCAT) has undergone several revisions for content and validity since its inception. With another comprehensive review pending, this study examines changes in the predictive validity of the MCAT's three recent versions. Study participants were 7,859 matriculants in 36 classes entering Jefferson Medical College between 1970 and 2005; 1,728 took the pre-1978 version of the MCAT; 3,032 took the 1978-1991 version, and 3,099 took the post-1991 version. MCAT subtest scores were the predictors, and performance in medical school, attrition, scores on the medical licensing examinations, and ratings of clinical competence in the first year of residency were the criterion measures. No significant improvement in validity coefficients was observed for performance in medical school or residency. Validity coefficients for all three versions of the MCAT in predicting Part I/Step 1 remained stable (in the mid-0.40s, P < .01). A systematic decline was observed in the validity coefficients of the MCAT versions in predicting Part II/Step 2. It started at 0.47 for the pre-1978 version, decreased to between 0.42 and 0.40 for the 1978-1991 versions, and to 0.37 for the post-1991 version. Validity coefficients for the MCAT versions in predicting Part III/Step 3 remained near 0.30. These were generally larger for women than men. Although the findings support the short- and long-term predictive validity of the MCAT, opportunities to strengthen it remain. Subsequent revisions should increase the test's ability to predict performance on United States Medical Licensing Examination Step 2 and must minimize the differential validity for gender.

Predictive performance of four frailty measures in an older Australian population

PubMed Central

Widagdo, Imaina S.; Pratt, Nicole; Russell, Mary; Roughead, Elizabeth E.

2015-01-01

Background: there are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined. Objective: to examine the predictive performance of four internationally validated frailty measures in an older Australian population. Methods: a retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined. Results: prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4–2.8), SFP: 3.6 (1.5–8.8), FI: 3.4 (2.7–4.3) and PFS: 2.3 (1.8–2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2–77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59–0.70. Conclusions: being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied. PMID:26504118

Sprinting performance on the Woodway Curve 3.0 is related to muscle architecture.

PubMed

Mangine, Gerald T; Fukuda, David H; Townsend, Jeremy R; Wells, Adam J; Gonzalez, Adam M; Jajtner, Adam R; Bohner, Jonathan D; LaMonica, Michael; Hoffman, Jay R; Fragala, Maren S; Stout, Jeffrey R

2015-01-01

To determine if unilateral measures of muscle architecture in the rectus femoris (RF) and vastus lateralis (VL) were related to (and predictive of) sprinting speed and unilateral (and bilateral) force (FRC) and power (POW) during a 30 s maximal sprint on the Woodway Curve 3.0 non-motorized treadmill. Twenty-eight healthy, physically active men (n = 14) and women (n = 14) (age = 22.9 ± 2.4 years; body mass = 77.1 ± 16.2 kg; height = 171.6 ± 11.2 cm; body-fa t = 19.4 ± 8.1%) completed one familiarization and one 30-s maximal sprint on the TM to obtain maximal sprinting speed, POW and FRC. Muscle thickness (MT), cross-sectional area (CSA) and echo intensity (ECHO) of the RF and VL in the dominant (DOM; determined by unilateral sprinting power) and non-dominant (ND) legs were measured via ultrasound. Pearson correlations indicated several significant (p < 0.05) relationships between sprinting performance [POW (peak, DOM and ND), FRC (peak, DOM, ND) and sprinting time] and muscle architecture. Stepwise regression indicated that POW(DOM) was predictive of ipsilateral RF (MT and CSA) and VL (CSA and ECHO), while POW(ND) was predictive of ipsilateral RF (MT and CSA) and VL (CSA); sprinting power/force asymmetry was not predictive of architecture asymmetry. Sprinting time was best predicted by peak power and peak force, though muscle quality (ECHO) and the bilateral percent difference in VL (CSA) were strong architectural predictors. Muscle architecture is related to (and predictive of) TM sprinting performance, while unilateral POW is predictive of ipsilateral architecture. However, the extent to which architecture and other factors (i.e. neuromuscular control and sprinting technique) affect TM performance remains unknown.

Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method

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

Yan, Shiju; Qian, Wei; Guan, Yubao

2016-06-15

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initiallymore » computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716 ± 0.071 and 0.642 ± 0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859 ± 0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy.« less

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-02-24

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved. This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed. Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers' convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

Who succeeds at dental school? Factors predicting students' academic performance in a dental school in republic of Korea.

PubMed

Ihm, Jung-Joon; Lee, Gene; Kim, Kack-Kyun; Jang, Ki-Taeg; Jin, Bo-Hyoung

2013-12-01

The purpose of this study was to examine what cognitive and non-cognitive factors were responsible for predicting the academic performance of dental students in a dental school in the Republic of Korea. This school is one of those in Korea that now require applicants to have a bachelor's degree. In terms of cognitive factors, students' undergraduate grade point average (GPA) and Dental Education Eligibility Test (DEET) scores were used, while surveys were conducted to evaluate four non-cognitive measures: locus of control, self-esteem, self-directed learning, and interpersonal skills. A total of 353 students matriculating at Seoul National University School of Dentistry in 2005, 2006, 2007, and 2008 consented to the collection of records and completed the surveys. The main finding was that applicants who scored higher on internal locus of control and self-efficacy were more likely to be academically successful dental students. Self-directed learning was significantly associated with students ranked in the top 50 percent in cumulative GPA. However, students' interpersonal skills were negatively related to their academic performance. In particular, students' lack of achievement could be predicted by monitoring their first-year GPA. Therefore, the identification of those factors to predict dental school performance has implications for the dental curriculum and effective pedagogy in dental education.

Integrating machine learning to achieve an automatic parameter prediction for practical continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Liu, Weiqi; Huang, Peng; Peng, Jinye; Fan, Jianping; Zeng, Guihua

2018-02-01

For supporting practical quantum key distribution (QKD), it is critical to stabilize the physical parameters of signals, e.g., the intensity, phase, and polarization of the laser signals, so that such QKD systems can achieve better performance and practical security. In this paper, an approach is developed by integrating a support vector regression (SVR) model to optimize the performance and practical security of the QKD system. First, a SVR model is learned to precisely predict the time-along evolutions of the physical parameters of signals. Second, such predicted time-along evolutions are employed as feedback to control the QKD system for achieving the optimal performance and practical security. Finally, our proposed approach is exemplified by using the intensity evolution of laser light and a local oscillator pulse in the Gaussian modulated coherent state QKD system. Our experimental results have demonstrated three significant benefits of our SVR-based approach: (1) it can allow the QKD system to achieve optimal performance and practical security, (2) it does not require any additional resources and any real-time monitoring module to support automatic prediction of the time-along evolutions of the physical parameters of signals, and (3) it is applicable to any measurable physical parameter of signals in the practical QKD system.

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

NASA Astrophysics Data System (ADS)

Balasubramanian, A.; Shamsuddin, R.; Prabhakaran, B.; Sawant, A.

2017-03-01

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%) (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient look-ahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors.

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

PubMed Central

Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

2017-01-01

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5–91.4%); (ii) the predictive modeling yields lowest accuracies (50–60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96–0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient lookahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors. PMID:28075331

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts.

PubMed

Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

2017-03-07

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%); (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient look-ahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors.

Early math and reading achievement are associated with the error positivity.

PubMed

Kim, Matthew H; Grammer, Jennie K; Marulis, Loren M; Carrasco, Melisa; Morrison, Frederick J; Gehring, William J

2016-12-01

Executive functioning (EF) and motivation are associated with academic achievement and error-related ERPs. The present study explores whether early academic skills predict variability in the error-related negativity (ERN) and error positivity (Pe). Data from 113 three- to seven-year-old children in a Go/No-Go task revealed that stronger early reading and math skills predicted a larger Pe. Closer examination revealed that this relation was quadratic and significant for children performing at or near grade level, but not significant for above-average achievers. Early academics did not predict the ERN. These findings suggest that the Pe - which reflects individual differences in motivational processes as well as attention - may be associated with early academic achievement. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Parent Expectations Mediate Outcomes for Young Adults with Autism Spectrum Disorder.

PubMed

Kirby, Anne V

2016-05-01

Understanding the complex relationships among factors that may predict the outcomes of young adults with autism spectrum disorder (ASD) is of utmost importance given the increasing population undergoing and anticipating the transition to adulthood. With a sample of youth with ASD (n = 1170) from the National Longitudinal Transition Study-2, structural equation modeling techniques were used to test parent expectations as a mediator of young adult outcomes (i.e., employment, residential independence, social participation) in a longitudinal analysis. The mediation hypothesis was confirmed; family background and functional performance variables significantly predicted parent expectations which significantly predicted outcomes. These findings add context to previous studies examining the role of parent expectations on young adult outcomes and inform directions for family-centered interventions and future research.

Dorsolateral Prefrontal Cortex GABA Concentration in Humans Predicts Working Memory Load Processing Capacity.

PubMed

Yoon, Jong H; Grandelis, Anthony; Maddock, Richard J

2016-11-16

The discovery of neural mechanisms of working memory (WM) would significantly enhance our understanding of complex human behaviors and guide treatment development for WM-related impairments found in neuropsychiatric conditions and aging. Although the dorsolateral prefrontal cortex (DLPFC) has long been considered critical for WM, we still know little about the neural elements and pathways within the DLPFC that support WM in humans. In this study, we tested whether an individual's DLPFC gamma-aminobutryic acid (GABA) content predicts individual differences in WM task performance using a novel behavioral approach. Twenty-three healthy adults completed a task that measured the unique contribution of major WM components (memory load, maintenance, and distraction resistance) to performance. This was done to address the possibility that components have differing GABA dependencies and the failure to parse WM into components would lead to missing true associations with GABA. The subjects then had their DLPFC GABA content measured by single-voxel proton magnetic spectroscopy. We found that individuals with lower DLPFC GABA showed greater performance degradation with higher load, accounting for 31% of variance, p (corrected) = 0.015. This relationship was component, neurochemical, and brain region specific. DLPFC GABA content did not predict performance sensitivity to other components tested; DLPFC glutamate + glutamine and visual cortical GABA content did not predict load sensitivity. These results confirm the involvement of DLPFC GABA in WM load processing in humans and implicate factors controlling DLPFC GABA content in the neural mechanisms of WM and its impairments. This study demonstrated for the first time that the amount of gamma-aminobutryic acid (GABA), the major inhibitory neurotransmitter of the brain, in an individual's prefrontal cortex predicts working memory (WM) task performance. Given that WM is required for many of the most characteristic cognitive and behavioral capabilities in humans, this finding could have a significant impact on our understanding of the neural basis of complex human behavior. Furthermore, this finding suggests that efforts to preserve or increase brain GABA levels could be fruitful in remediating WM-related deficits associated with neuropsychiatric conditions. Copyright © 2016 the authors 0270-6474/16/3611788-07$15.00/0.

A simplified approach to predict performance degradation of a solid oxide fuel cell anode

NASA Astrophysics Data System (ADS)

Khan, Muhammad Zubair; Mehran, Muhammad Taqi; Song, Rak-Hyun; Lee, Jong-Won; Lee, Seung-Bok; Lim, Tak-Hyoung

2018-07-01

The agglomeration of nickel (Ni) particles in a Ni-cermet anode is a significant degradation phenomenon for solid oxide fuel cells (SOFCs). This work aims to predict the performance degradation of SOFCs due to Ni grain growth by using a simplified approach. Accelerated aging of Ni-scandia stabilized zirconia (SSZ) as an SOFC anode is carried out at 900 °C and subsequent microstructural evolution is investigated every 100 h up to 1000 h using scanning electron microscopy (SEM). The resulting morphological changes are quantified using a two-dimensional image analysis technique that yields the particle size, phase proportion, and triple phase boundary (TPB) point distribution. The electrochemical properties of an anode-supported SOFC are characterized using electrochemical impedance spectroscopy (EIS). The changes of particle size and TPB length in the anode as a function of time are in excellent agreement with the power-law coarsening model. This model is further combined with an electrochemical model to predict the changes in the anode polarization resistance. The predicted polarization resistances are in good agreement with the experimentally obtained values. This model for prediction of anode lifetime provides deep insight into the time-dependent Ni agglomeration behavior and its impact on the electrochemical performance degradation of the SOFC anode.

Physical fitness is predictive for a decline in the ability to perform instrumental activities of daily living in older adults with intellectual disabilities: Results of the HA-ID study.

PubMed

Oppewal, Alyt; Hilgenkamp, Thessa I M; van Wijck, Ruud; Schoufour, Josje D; Evenhuis, Heleen M

2015-01-01

The ability to perform instrumental activities of daily living (IADL) is important for one's level of independence. A high incidence of limitations in IADL is seen in older adults with intellectual disabilities (ID), which is an important determinant for the amount of support one needs. The aim of this study was to assess the predictive value of physical fitness for the ability to perform IADL, over a 3-year follow-up period, in 601 older adults with ID. At baseline, an extensive physical fitness assessment was performed. In addition, professional caregivers completed the Lawton IADL scale, both at baseline and at follow-up. The average ability to perform IADL declined significantly over the 3-year follow-up period. A decline in the ability to perform IADL was seen in 44.3% of the participants. The percentage of participants being completely independent in IADL declined from 2.7% to 1.3%. Manual dexterity, balance, comfortable and fast gait speed, muscular endurance, and cardiorespiratory fitness were significant predictors for a decline in IADL after correcting for baseline IADL and personal characteristics (age, gender, level of ID, and Down syndrome). This can be interpreted as representing the predictive validity of the physical tests for a decline in IADL. This study shows that even though older adults with ID experience dependency on others due to cognitive limitations, physical fitness also is an important aspect for IADL, which stresses the importance of using physical fitness tests and physical fitness enhancing programs in the care for older adults with ID. Copyright © 2015 Elsevier Ltd. All rights reserved.

Transient Elastography is Superior to FIB-4 in Assessing the Risk of Hepatocellular Carcinoma in Patients With Chronic Hepatitis B

PubMed Central

Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Song, Kijun; Han, Kwang-Hyub

2016-01-01

Abstract Liver stiffness (LS), assessed using transient elastography (TE), and (FIB-4) can both estimate the risk of developing hepatocellular carcinoma (HCC). We compared prognostic performances of LS and FIB-4 to predict HCC development in patients with chronic hepatitis B (CHB). Data from 1308 patients with CHB, who underwent TE, were retrospectively analyzed. FIB-4 was calculated for all patients. The cumulative rate of HCC development was assessed using Kaplan–Meier curves. The predictive performances of LS and FIB-4 were evaluated using time-dependent receiver-operating characteristic (ROC) curves. The mean age (883 men) was 50 years. During follow-up (median 6.1 years), 119 patients developed HCC. The areas under the ROC curves (AUROCs) predicting HCC risk at 3, 5, and 7 years were consistently greater for LS than for FIB-4 (0.791–0.807 vs 0.691–0.725; all P < 0.05). Similarly, when the respective AUROCs for LS and FIB-4 at every time point during the 7-year follow-up were plotted, LS also showed consistently better performance than FIB-4 after 1 year of enrollment. The combined use of LS and FIB-4 significantly enhanced the prognostic performance compared with the use of FIB-4 alone (P < 0.05), but the performance of the combined scores was statistically similar to that of LS alone (P > 0.05). LS showed significantly better performance than FIB-4 in assessing the risk of HCC development, and the combined use of LS and FIB-4 did not provide additional benefit compared with the use of LS alone. Hence, LS assessed using TE might be helpful for optimizing HCC surveillance strategies. PMID:27196449

Transient Elastography is Superior to FIB-4 in Assessing the Risk of Hepatocellular Carcinoma in Patients With Chronic Hepatitis B.

PubMed

Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Song, Kijun; Han, Kwang-Hyub

2016-05-01

Liver stiffness (LS), assessed using transient elastography (TE), and (FIB-4) can both estimate the risk of developing hepatocellular carcinoma (HCC). We compared prognostic performances of LS and FIB-4 to predict HCC development in patients with chronic hepatitis B (CHB).Data from 1308 patients with CHB, who underwent TE, were retrospectively analyzed. FIB-4 was calculated for all patients. The cumulative rate of HCC development was assessed using Kaplan-Meier curves. The predictive performances of LS and FIB-4 were evaluated using time-dependent receiver-operating characteristic (ROC) curves.The mean age (883 men) was 50 years. During follow-up (median 6.1 years), 119 patients developed HCC. The areas under the ROC curves (AUROCs) predicting HCC risk at 3, 5, and 7 years were consistently greater for LS than for FIB-4 (0.791-0.807 vs 0.691-0.725; all P < 0.05). Similarly, when the respective AUROCs for LS and FIB-4 at every time point during the 7-year follow-up were plotted, LS also showed consistently better performance than FIB-4 after 1 year of enrollment. The combined use of LS and FIB-4 significantly enhanced the prognostic performance compared with the use of FIB-4 alone (P < 0.05), but the performance of the combined scores was statistically similar to that of LS alone (P > 0.05).LS showed significantly better performance than FIB-4 in assessing the risk of HCC development, and the combined use of LS and FIB-4 did not provide additional benefit compared with the use of LS alone. Hence, LS assessed using TE might be helpful for optimizing HCC surveillance strategies.