Feasibility and Efficacy of Brief Computerized Training to Improve Emotion Recognition in Premanifest and Early-Symptomatic Huntington's Disease.

PubMed

Kempnich, Clare L; Wong, Dana; Georgiou-Karistianis, Nellie; Stout, Julie C

2017-04-01

Deficits in the recognition of negative emotions emerge before clinical diagnosis in Huntington's disease (HD). To address emotion recognition deficits, which have been shown in schizophrenia to be improved by computerized training, we conducted a study of the feasibility and efficacy of computerized training of emotion recognition in HD. We randomly assigned 22 individuals with premanifest or early symptomatic HD to the training or control group. The training group used a self-guided online training program, MicroExpression Training Tool (METT), twice weekly for 4 weeks. All participants completed measures of emotion recognition at baseline and post-training time-points. Participants in the training group also completed training adherence measures. Participants in the training group completed seven of the eight sessions on average. Results showed a significant group by time interaction, indicating that METT training was associated with improved accuracy in emotion recognition. Although sample size was small, our study demonstrates that emotion recognition remediation using the METT is feasible in terms of training adherence. The evidence also suggests METT may be effective in premanifest or early-symptomatic HD, opening up a potential new avenue for intervention. Further study with a larger sample size is needed to replicate these findings, and to characterize the durability and generalizability of these improvements, and their impact on functional outcomes in HD. (JINS, 2017, 23, 314-321).

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

PubMed

Ozçift, Akin

2011-05-01

Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

An Organization's Economic Return on Training Investment.

ERIC Educational Resources Information Center

Pucel, David J.; Lyau, Nyan-Myau

A study examined the relationship between investment in training and labor productivity in a sample of 237 large and medium-size Taiwanese firms producing auto parts. Of the 162 firms (68.4%) that returned usable questionnaires, 142 (59.9%) had training programs and 131 (55.3%) provided full cost data. The data were analyzed by multiple regression…

Performance of a Line Loss Correction Method for Gas Turbine Emission Measurements

NASA Astrophysics Data System (ADS)

Hagen, D. E.; Whitefield, P. D.; Lobo, P.

2015-12-01

International concern for the environmental impact of jet engine exhaust emissions in the atmosphere has led to increased attention on gas turbine engine emission testing. The Society of Automotive Engineers Aircraft Exhaust Emissions Measurement Committee (E-31) has published an Aerospace Information Report (AIR) 6241 detailing the sampling system for the measurement of non-volatile particulate matter from aircraft engines, and is developing an Aerospace Recommended Practice (ARP) for methodology and system specification. The Missouri University of Science and Technology (MST) Center for Excellence for Aerospace Particulate Emissions Reduction Research has led numerous jet engine exhaust sampling campaigns to characterize emissions at different locations in the expanding exhaust plume. Particle loss, due to various mechanisms, occurs in the sampling train that transports the exhaust sample from the engine exit plane to the measurement instruments. To account for the losses, both the size dependent penetration functions and the size distribution of the emitted particles need to be known. However in the proposed ARP, particle number and mass are measured, but size is not. Here we present a methodology to generate number and mass correction factors for line loss, without using direct size measurement. A lognormal size distribution is used to represent the exhaust aerosol at the engine exit plane and is defined by the measured number and mass at the downstream end of the sample train. The performance of this line loss correction is compared to corrections based on direct size measurements using data taken by MST during numerous engine test campaigns. The experimental uncertainty in these correction factors is estimated. Average differences between the line loss correction method and size based corrections are found to be on the order of 10% for number and 2.5% for mass.

Training of Existing Workers: Issues, Incentives and Models

ERIC Educational Resources Information Center

Mawer, Giselle; Jackson, Elaine

2005-01-01

This report presents issues associated with incentives for training existing workers in small to medium-sized firms, identified through a small sample of case studies from the retail, manufacturing, and building and construction industries. While the majority of employers recognise workforce skill levels are fundamental to the success of the…

Feasibility of a Humor Training to Promote Humor and Decrease Stress in a Subclinical Sample: A Single-Arm Pilot Study

PubMed Central

Tagalidou, Nektaria; Loderer, Viola; Distlberger, Eva; Laireiter, Anton-Rupert

2018-01-01

The present study investigates the feasibility of a humor training for a subclinical sample suffering from increased stress, depressiveness, or anxiety. Based on diagnostic interviews, 35 people were invited to participate in a 7-week humor training. Evaluation measures were filled in prior training, after training, and at a 1-month follow-up including humor related outcomes (coping humor and cheerfulness) and mental health-related outcomes (perceived stress, depressiveness, anxiety, and well-being). Outcomes were analyzed using repeated-measures ANOVAs. Within-group comparisons of intention-to-treat analysis showed main effects of time with large effect sizes on all outcomes. Post hoc tests showed medium to large effect sizes on all outcomes from pre to post and results remained stable until follow-up. Satisfaction with the training was high, attrition rate low (17.1%), and participants would highly recommend the training. Summarizing the results, the pilot study showed promising effects for people suffering from subclinical symptoms. All outcomes were positively influenced and showed stability over time. Humor trainings could be integrated more into mental health care as an innovative program to reduce stress whilst promoting also positive emotions. However, as this study was a single-arm pilot study, further research (including also randomized controlled trials) is still needed to evaluate the effects more profoundly. PMID:29740368

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Local health department epidemiologic capacity: a stratified cross-sectional assessment describing the quantity, education, training, and perceived competencies of epidemiologic staff.

PubMed

O'Keefe, Kaitlin A; Shafir, Shira C; Shoaf, Kimberley I

2013-01-01

Local health departments (LHDs) must have sufficient numbers of staff functioning in an epidemiologic role with proper education, training, and skills to protect the health of communities they serve. This pilot study was designed to describe the composition, training, and competency level of LHD staff and examine the hypothesis that potential disparities exist between LHDs serving different sized populations. Cross-sectional surveys were conducted with directors and epidemiologic staff from a sample of 100 LHDs serving jurisdictions of varied sizes. Questionnaires included inquiries regarding staff composition, education, training, and measures of competency modeled on previously conducted studies by the Council of State and Territorial Epidemiologists. Number of epidemiologic staff, academic degree distribution, epidemiologic training, and both director and staff confidence in task competencies were calculated for each LHD size strata. Disparities in measurements were observed in LHDs serving different sized populations. LHDs serving small populations reported a smaller average number of epidemiologic staff than those serving larger jurisdictions. As size of population served increased, percentages of staff and directors holding bachelors' and masters' degrees increased, while those holding RN degrees decreased. A higher degree of perceived competency of staff in most task categories was reported in LHDs serving larger populations. LHDs serving smaller populations reported fewer epidemiologic staff, therefore might benefit from additional resources. Differences observed in staff education, training, and competencies suggest that enhanced epidemiologic training might be particularly needed in LHDs serving smaller populations. RESULTS can be used as a baseline for future research aimed at identifying areas where training and personnel resources might be particularly needed to increase the capabilities of LHDs.

Effect of exercise training on walking mobility in multiple sclerosis: a meta-analysis.

PubMed

Snook, Erin M; Motl, Robert W

2009-02-01

The study used meta-analytic procedures to examine the overall effect of exercise training interventions on walking mobility among individuals with multiple sclerosis. A search was conducted for published exercise training studies from 1960 to November 2007 using MEDLINE, PsychINFO, CINAHL, and Current Contents Plus. Studies were selected if they measured walking mobility, using instruments identified as acceptable walking mobility constructs and outcome measures for individuals with neurologic disorders, before and after an intervention that included exercise training. Forty-two published articles were located and reviewed, and 22 provided enough data to compute effect sizes expressed as Cohen's d. Sixty-six effect sizes were retrieved from the 22 publications with 600 multiple sclerosis participants and yielded a weighted mean effect size of g = 0.19 (95% confidence interval, 0.09-0.28). There were larger effects associated with supervised exercise training ( g = 0.32), exercise programs that were less than 3 months in duration (g = 0.28), and mixed samples of relapsing-remitting and progressive multiple sclerosis (g = 0.52). The cumulative evidence supports that exercise training is associated with a small improvement in walking mobility among individuals with multiple sclerosis.

ENHANCEMENT OF LEARNING ON SAMPLE SIZE CALCULATION WITH A SMARTPHONE APPLICATION: A CLUSTER-RANDOMIZED CONTROLLED TRIAL.

PubMed

Ngamjarus, Chetta; Chongsuvivatwong, Virasakdi; McNeil, Edward; Holling, Heinz

2017-01-01

Sample size determination usually is taught based on theory and is difficult to understand. Using a smartphone application to teach sample size calculation ought to be more attractive to students than using lectures only. This study compared levels of understanding of sample size calculations for research studies between participants attending a lecture only versus lecture combined with using a smartphone application to calculate sample sizes, to explore factors affecting level of post-test score after training sample size calculation, and to investigate participants’ attitude toward a sample size application. A cluster-randomized controlled trial involving a number of health institutes in Thailand was carried out from October 2014 to March 2015. A total of 673 professional participants were enrolled and randomly allocated to one of two groups, namely, 341 participants in 10 workshops to control group and 332 participants in 9 workshops to intervention group. Lectures on sample size calculation were given in the control group, while lectures using a smartphone application were supplied to the test group. Participants in the intervention group had better learning of sample size calculation (2.7 points out of maximnum 10 points, 95% CI: 24 - 2.9) than the participants in the control group (1.6 points, 95% CI: 1.4 - 1.8). Participants doing research projects had a higher post-test score than those who did not have a plan to conduct research projects (0.9 point, 95% CI: 0.5 - 1.4). The majority of the participants had a positive attitude towards the use of smartphone application for learning sample size calculation.

The Effect of Multiprofessional Simulation-Based Obstetric Team Training on Patient-Reported Quality of Care: A Pilot Study.

PubMed

Truijens, Sophie E M; Banga, Franyke R; Fransen, Annemarie F; Pop, Victor J M; van Runnard Heimel, Pieter J; Oei, S Guid

2015-08-01

This study aimed to explore whether multiprofessional simulation-based obstetric team training improves patient-reported quality of care during pregnancy and childbirth. Multiprofessional teams from a large obstetric collaborative network in the Netherlands were trained in teamwork skills using the principles of crew resource management. Patient-reported quality of care was measured with the validated Pregnancy and Childbirth Questionnaire (PCQ) at 6 weeks postpartum. Before the training, 76 postpartum women (sample I) completed the questionnaire 6 weeks postpartum. Three months after the training, another sample of 68 postpartum women (sample II) completed the questionnaire. In sample II (after the training), the mean (SD) score of 108.9 (10.9) on the PCQ questionnaire was significantly higher than the score of 103.5 (11.6) in sample I (before training) (t = 2.75, P = 0.007). The effect size of the increase in PCQ total score was 0.5. Moreover, the subscales "personal treatment during pregnancy" and "educational information" showed a significant increase after the team training (P < 0.001). Items with the largest increase in mean scores included communication between health care professionals, clear leadership, involvement in planning, and better provision of information. Despite the methodological restrictions of a pilot study, the preliminary results indicate that multiprofessional simulation-based obstetric team training seems to improve patient-reported quality of care. The possibility that this improvement relates to the training is supported by the fact that the items with the largest increase are about the principles of crew resource management, used in the training.

A Field Study of Performance Among Embarked Infantry Personnel Exposed to Waterborne Motion

DTIC Science & Technology

2012-09-01

was designed with four groups with 16 participants per group to accommodate the calculated sample size and the maximum seating capacity of the...25  A.  APPROACH TO THE EXPERIMENTAL DESIGN .................................25  B.  VARIABLES...39  viii 1.  Design of the Training Period ...........................................................39  2.  Training Period

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

NASA Technical Reports Server (NTRS)

Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

2012-01-01

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to the phenology, solar-view geometry, and atmospheric condition etc. factors but not actual landcover difference. Finally, we will compare the classification results from screened and unscreened training samples to assess the improvement achieved by cleaning up the training samples. Keywords:

Social Awareness and Action Training (SAAT)

DTIC Science & Technology

2015-06-01

scheduled for September, 2013, and the one -year follow-in June, 2014. o Preliminary analyses of the pretest - posttest data from Fort Sill and JBLM...training session ( pretest , Time 1) and immediately after the last training session ( posttest , Time 2). The sample size was estimated based on an expected...reverse worded items. As noted in Figure 1, data from 20 soldiers on the pretest or posttest (11 from the SRT, 9 from the CAT) were judged to be of

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

PubMed Central

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

2016-01-01

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

Machine Learning for Big Data: A Study to Understand Limits at Scale

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

Sukumar, Sreenivas R.; Del-Castillo-Negrete, Carlos Emilio

This report aims to empirically understand the limits of machine learning when applied to Big Data. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny, evaluation and application for gleaning insights from the data than ever before. Much is expected from algorithms without understanding their limitations at scale while dealing with massive datasets. In that context, we pose and address the following questions How does a machine learning algorithm perform on measuresmore » such as accuracy and execution time with increasing sample size and feature dimensionality? Does training with more samples guarantee better accuracy? How many features to compute for a given problem? Do more features guarantee better accuracy? Do efforts to derive and calculate more features and train on larger samples worth the effort? As problems become more complex and traditional binary classification algorithms are replaced with multi-task, multi-class categorization algorithms do parallel learners perform better? What happens to the accuracy of the learning algorithm when trained to categorize multiple classes within the same feature space? Towards finding answers to these questions, we describe the design of an empirical study and present the results. We conclude with the following observations (i) accuracy of the learning algorithm increases with increasing sample size but saturates at a point, beyond which more samples do not contribute to better accuracy/learning, (ii) the richness of the feature space dictates performance - both accuracy and training time, (iii) increased dimensionality often reflected in better performance (higher accuracy in spite of longer training times) but the improvements are not commensurate the efforts for feature computation and training and (iv) accuracy of the learning algorithms drop significantly with multi-class learners training on the same feature matrix and (v) learning algorithms perform well when categories in labeled data are independent (i.e., no relationship or hierarchy exists among categories).« less

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Alternatives to Three-Mode Factor Analysis: A Case Study with Data Evaluating Perceived Barriers to Medical School Training.

ERIC Educational Resources Information Center

Thomson, William A.; And Others

While educational researchers frequently collect data from a sample of individuals on a sample of variables, they sometimes collect data involving samples of: (1) subjects; (2) variables; and (3) occasions of measurement. A multistage procedure for analyzing such three-mode data is presented, focusing on effect sizes and graphic confidence…

Fostering Early Numerical Skills at School Start in Children at Risk for Mathematical Achievement Problems: A Small Sample Size Training Study

ERIC Educational Resources Information Center

Hasselhorn, Marcus; Linke-Hasselhorn, Kathrin

2013-01-01

Eight six-year old German children with development disabilities regarding such number competencies as have been demonstrated to be among the most relevant precursor skills for the acquisition of elementary mathematics received intensive training with the program "Mengen, zählen, Zahlen" ["quantities, counting, numbers"] (MZZ,…

Assessment of Leadership Training of Head Teachers and Secondary School Performance in Mubende District, Uganda

ERIC Educational Resources Information Center

Benson, Kayiwa

2011-01-01

The purpose of the study was to establish the relationship between leadership training of head teachers and school performance in secondary schools in Mubende district, Uganda. Descriptive-correlational research design was used. Six schools out of 32 were selected and the sample size of head teachers, teachers and students leaders was 287 out of…

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Air Force Human Resources Laboratory Annual Report - Fiscal Year 1982.

DTIC Science & Technology

1983-06-01

test are used a clearer understanding of the impact of sample to assess literacy skills . The use of AFRAT size and curtailment on calibration accuracy...training within determine the feasibility of using newly devised Specialized Undergraduate Pilot Training. tests of psychomotor skills , information...individual skill underscored by unacceptable levels of literacy deficiencies. Empirical job requirements and among recent military enlistees. Plans are

Nearest neighbor density ratio estimation for large-scale applications in astronomy

NASA Astrophysics Data System (ADS)

Kremer, J.; Gieseke, F.; Steenstrup Pedersen, K.; Igel, C.

2015-09-01

In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art.

Visual search attentional bias modification reduced social phobia in adolescents.

PubMed

De Voogd, E L; Wiers, R W; Prins, P J M; Salemink, E

2014-06-01

An attentional bias for negative information plays an important role in the development and maintenance of (social) anxiety and depression, which are highly prevalent in adolescence. Attention Bias Modification (ABM) might be an interesting tool in the prevention of emotional disorders. The current study investigated whether visual search ABM might affect attentional bias and emotional functioning in adolescents. A visual search task was used as a training paradigm; participants (n = 16 adolescents, aged 13-16) had to repeatedly identify the only smiling face in a 4 × 4 matrix of negative emotional faces, while participants in the control condition (n = 16) were randomly allocated to one of three placebo training versions. An assessment version of the task was developed to directly test whether attentional bias changed due to the training. Self-reported anxiety and depressive symptoms and self-esteem were measured pre- and post-training. After two sessions of training, the ABM group showed a significant decrease in attentional bias for negative information and self-reported social phobia, while the control group did not. There were no effects of training on depressive mood or self-esteem. No correlation between attentional bias and social phobia was found, which raises questions about the validity of the attentional bias assessment task. Also, the small sample size precludes strong conclusions. Visual search ABM might be beneficial in changing attentional bias and social phobia in adolescents, but further research with larger sample sizes and longer follow-up is needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

NASA Astrophysics Data System (ADS)

Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

2013-03-01

Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

How to Train an Injured Brain? A Pilot Feasibility Study of Home-Based Computerized Cognitive Training.

PubMed

Verhelst, Helena; Vander Linden, Catharine; Vingerhoets, Guy; Caeyenberghs, Karen

2017-02-01

Computerized cognitive training programs have previously shown to be effective in improving cognitive abilities in patients suffering from traumatic brain injury (TBI). These studies often focused on a single cognitive function or required expensive hardware, making it difficult to be used in a home-based environment. This pilot feasibility study aimed to evaluate the feasibility of a newly developed, home-based, computerized cognitive training program for adolescents who suffered from TBI. Additionally, feasibility of study design, procedures, and measurements were examined. Case series, longitudinal, pilot, feasibility intervention study with one baseline and two follow-up assessments. Nine feasibility outcome measures and criteria for success were defined, including accessibility, training motivation/user experience, technical smoothness, training compliance, participation willingness, participation rates, loss to follow-up, assessment timescale, and assessment procedures. Five adolescent patients (four boys, mean age = 16 years 7 months, standard deviation = 9 months) with moderate to severe TBI in the chronic stage were recruited and received 8 weeks of cognitive training with BrainGames. Effect sizes (Cohen's d) were calculated to determine possible training-related effects. The new cognitive training intervention, BrainGames, and study design and procedures proved to be feasible; all nine feasibility outcome criteria were met during this pilot feasibility study. Estimates of effect sizes showed small to very large effects on cognitive measures and questionnaires, which were retained after 6 months. Our pilot study shows that a longitudinal intervention study comprising our novel, computerized cognitive training program and two follow-up assessments is feasible in adolescents suffering from TBI in the chronic stage. Future studies with larger sample sizes will evaluate training-related effects on cognitive functions and underlying brain structures.

Increasing complexity of clinical research in gastroenterology: implications for the training of clinician-scientists.

PubMed

Scott, Frank I; McConnell, Ryan A; Lewis, Matthew E; Lewis, James D

2012-04-01

Significant advances have been made in clinical and epidemiologic research methods over the past 30 years. We sought to demonstrate the impact of these advances on published gastroenterology research from 1980 to 2010. Twenty original clinical articles were randomly selected from each of three journals from 1980, 1990, 2000, and 2010. Each article was assessed for topic, whether the outcome was clinical or physiologic, study design, sample size, number of authors and centers collaborating, reporting of various statistical methods, and external funding. From 1980 to 2010, there was a significant increase in analytic studies, clinical outcomes, number of authors per article, multicenter collaboration, sample size, and external funding. There was increased reporting of P values, confidence intervals, and power calculations, and increased use of large multicenter databases, multivariate analyses, and bioinformatics. The complexity of clinical gastroenterology and hepatology research has increased dramatically, highlighting the need for advanced training of clinical investigators.

Effectiveness of training in organizations: a meta-analysis of design and evaluation features.

PubMed

Arthur, Winfred; Bennett, Winston; Edens, Pamela S; Bell, Suzanne T

2003-04-01

The authors used meta-analytic procedures to examine the relationship between specified training design and evaluation features and the effectiveness of training in organizations. Results of the meta-analysis revealed training effectiveness sample-weighted mean ds of 0.60 (k = 15, N = 936) for reaction criteria, 0.63 (k = 234, N = 15,014) for learning criteria, 0.62 (k = 122, N = 15,627) for behavioral criteria, and 0.62 (k = 26, N = 1,748) for results criteria. These results suggest a medium to large effect size for organizational training. In addition, the training method used, the skill or task characteristic trained, and the choice of evaluation criteria were related to the effectiveness of training programs. Limitations of the study along with suggestions for future research are discussed.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Many Molecular Properties from One Kernel in Chemical Space

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

Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole

We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varying size, as well as similarity measures sufficiently diffuse in chemical space to sample over all training molecules. Corresponding molecular reference properties provided, they enable the instantaneous generation of ML models which can systematically be improved through the addition of more data. This idea is exemplified for single kernel based modeling of internal energy, enthalpy, free energy, heat capacity, polarizability, electronic spread, zero-point vibrational energy, energies of frontier orbitals, HOMOLUMO gap, and the highest fundamental vibrational wavenumber. Modelsmore » of these properties are trained and tested using 112 kilo organic molecules of similar size. Resulting models are discussed as well as the kernels’ use for generating and using other property models.« less

Method and system for laser-based formation of micro-shapes in surfaces of optical elements

DOEpatents

Bass, Isaac Louis; Guss, Gabriel Mark

2013-03-05

A method of forming a surface feature extending into a sample includes providing a laser operable to emit an output beam and modulating the output beam to form a pulse train having a plurality of pulses. The method also includes a) directing the pulse train along an optical path intersecting an exposed portion of the sample at a position i and b) focusing a first portion of the plurality of pulses to impinge on the sample at the position i. Each of the plurality of pulses is characterized by a spot size at the sample. The method further includes c) ablating at least a portion of the sample at the position i to form a portion of the surface feature and d) incrementing counter i. The method includes e) repeating steps a) through d) to form the surface feature. The sample is free of a rim surrounding the surface feature.

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations.

PubMed

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández Del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS 25 ), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants ( n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning.

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations

PubMed Central

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G.

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS25), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants (n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning. PMID:29740271

Improved Optics For Quasi-Elastic Light Scattering

NASA Technical Reports Server (NTRS)

Cheung, Harry Michael

1995-01-01

Improved optical train devised for use in light-scattering measurements of quasi-elastic light scattering (QELS) and laser spectroscopy. Measurements performed on solutions, microemulsions, micellular solutions, and colloidal dispersions. Simultaneous measurements of total intensity and fluctuations in total intensity of light scattered from sample at various angles provides data used, in conjunction with diffusion coefficients, to compute sizes of particles in sample.

A System Approach to Navy Medical Education and Training. Appendix 18. Radiation Technician.

DTIC Science & Technology

1974-08-31

attrition was forecast to approximate twenty percent, final sample and sub-sample sizes were adjusted accordingly. Stratified random sampling... HYPERTENSIVE INTRAVENOUS PYELOGRAMS 2 ITAKE RENAL LOOPOGRAMI I 3 ITAKE CIXU, I.Eo CONSTANT INFUSION 4 10 RENAL SPLIT FUNCTION TEST, E.G. STAMEY 5...ITAKE PORTAL FILM OF AREA BEING TREATED WITH COBALT 32 [INFORM DOCTOR OF UNEXPECTED X-RAY FINDINGS 33 IREAD X-RAY FILMS FOR TECHNICAL ADEQUACY 34

Multicategory nets of single-layer perceptrons: complexity and sample-size issues.

PubMed

Raudys, Sarunas; Kybartas, Rimantas; Zavadskas, Edmundas Kazimieras

2010-05-01

The standard cost function of multicategory single-layer perceptrons (SLPs) does not minimize the classification error rate. In order to reduce classification error, it is necessary to: 1) refuse the traditional cost function, 2) obtain near to optimal pairwise linear classifiers by specially organized SLP training and optimal stopping, and 3) fuse their decisions properly. To obtain better classification in unbalanced training set situations, we introduce the unbalance correcting term. It was found that fusion based on the Kulback-Leibler (K-L) distance and the Wu-Lin-Weng (WLW) method result in approximately the same performance in situations where sample sizes are relatively small. The explanation for this observation is by theoretically known verity that an excessive minimization of inexact criteria becomes harmful at times. Comprehensive comparative investigations of six real-world pattern recognition (PR) problems demonstrated that employment of SLP-based pairwise classifiers is comparable and as often as not outperforming the linear support vector (SV) classifiers in moderate dimensional situations. The colored noise injection used to design pseudovalidation sets proves to be a powerful tool for facilitating finite sample problems in moderate-dimensional PR tasks.

An empirical identification and categorisation of training best practices for ERP implementation projects

NASA Astrophysics Data System (ADS)

Esteves, Jose Manuel

2014-11-01

Although training is one of the most cited critical success factors in Enterprise Resource Planning (ERP) systems implementations, few empirical studies have attempted to examine the characteristics of management of the training process within ERP implementation projects. Based on the data gathered from a sample of 158 respondents across four stakeholder groups involved in ERP implementation projects, and using a mixed method design, we have assembled a derived set of training best practices. Results suggest that the categorised list of ERP training best practices can be used to better understand training activities in ERP implementation projects. Furthermore, the results reveal that the company size and location have an impact on the relevance of training best practices. This empirical study also highlights the need to investigate the role of informal workplace trainers in ERP training activities.

Interpretation of correlations in clinical research.

PubMed

Hung, Man; Bounsanga, Jerry; Voss, Maren Wright

2017-11-01

Critically analyzing research is a key skill in evidence-based practice and requires knowledge of research methods, results interpretation, and applications, all of which rely on a foundation based in statistics. Evidence-based practice makes high demands on trained medical professionals to interpret an ever-expanding array of research evidence. As clinical training emphasizes medical care rather than statistics, it is useful to review the basics of statistical methods and what they mean for interpreting clinical studies. We reviewed the basic concepts of correlational associations, violations of normality, unobserved variable bias, sample size, and alpha inflation. The foundations of causal inference were discussed and sound statistical analyses were examined. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias. Recent published studies in the medical field provide evidence of causal assertion overreach drawn from correlational findings. The findings present a primer on the assumptions and nature of correlational methods of analysis and urge clinicians to exercise appropriate caution as they critically analyze the evidence before them and evaluate evidence that supports practice. Critically analyzing new evidence requires statistical knowledge in addition to clinical knowledge. Studies can overstate relationships, expressing causal assertions when only correlational evidence is available. Failure to account for the effect of sample size in the analyses tends to overstate the importance of predictive variables. It is important not to overemphasize the statistical significance without consideration of effect size and whether differences could be considered clinically meaningful.

Consistency of Pilot Trainee Cognitive Ability, Personality, and Training Performance in Undergraduate Pilot Training

DTIC Science & Technology

2013-09-09

multivariate correction method (Lawley, 1943) was used for all scores except the MAB FSIQ which used the univariate ( Thorndike , 1949) method. FSIQ... Thorndike , R. L. (1949). Personnel selection. NY: Wiley. Tupes, E. C., & Christal, R. C. (1961). Recurrent personality factors based on trait ratings... Thorndike , 1949). aThe correlations for 1995 were not corrected due to the small sample size (N = 17). *p< .05 Consistency of Pilot Attributes

Effect of low-intensity resistance training with heat stress on the HSP72, anabolic hormones, muscle size, and strength in elderly women.

PubMed

Yoon, Sung Jin; Lee, Moon Jin; Lee, Hyo Min; Lee, Jin Seok

2017-10-01

Several recent studies have reported that heat stress stimulates the activation of heat shock protein 72 (HSP72), leading to an increase in muscle synthesis. Some studies suggested that low-intensity resistance training combined with heat stress could improve muscle size and strength. This study aimed to identify the effect of low-intensity resistance training with heat stress over 12 weeks on the HSP72, anabolic hormones, muscle size, and strength in elderly women. The subjects were physically healthy women of 65-75 years, who were randomly assigned to one of three groups: a low-intensity resistance training with heating sheet group (HRT group, n = 8), a moderate-intensity resistance training (RT group, n = 6), and a heating sheet group (HEAT group, n = 7). Computed tomography scans, 1-repetition maximum (1RM), and blood samples were taken pre- and post-training. The HSP72 did not vary significantly between the different groups and times. The IGF-1 and 1RM had significantly increased in all three groups after the training (respectively, p < 0.05). Moreover, the cross-sectional area (CSA) of the quadriceps showed a significantly greater increase in the HRT group than in the HEAT group (p < 0.05). We found that low-intensity training with heat stress stimulated the anabolic hormones of elderly women, improving their muscle strength and hypertrophy. We believe that low-intensity training with heat stress is an effective way to prevent muscle atrophy and to improve muscle strength in elderly women.

A comparative analysis of support vector machines and extreme learning machines.

PubMed

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal

NASA Astrophysics Data System (ADS)

Rana, Parvez; Vauhkonen, Jari; Junttila, Virpi; Hou, Zhengyang; Gautam, Basanta; Cawkwell, Fiona; Tokola, Timo

2017-12-01

Large-diameter trees (taking DBH > 30 cm to define large trees) dominate the dynamics, function and structure of a forest ecosystem. The aim here was to employ sparse airborne laser scanning (ALS) data with a mean point density of 0.8 m-2 and the non-parametric k-most similar neighbour (k-MSN) to predict tree diameter at breast height (DBH) distributions in a subtropical forest in southern Nepal. The specific objectives were: (1) to evaluate the accuracy of the large-tree fraction of the diameter distribution; and (2) to assess the effect of the number of training areas (sample size, n) on the accuracy of the predicted tree diameter distribution. Comparison of the predicted distributions with empirical ones indicated that the large tree diameter distribution can be derived in a mixed species forest with a RMSE% of 66% and a bias% of -1.33%. It was also feasible to downsize the sample size without losing the interpretability capacity of the model. For large-diameter trees, even a reduction of half of the training plots (n = 250), giving a marginal increase in the RMSE% (1.12-1.97%) was reported compared with the original training plots (n = 500). To be consistent with these outcomes, the sample areas should capture the entire range of spatial and feature variability in order to reduce the occurrence of error.

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations

PubMed Central

Wright, Mark H.; Tung, Chih-Wei; Zhao, Keyan; Reynolds, Andy; McCouch, Susan R.; Bustamante, Carlos D.

2010-01-01

Motivation: The development of new high-throughput genotyping products requires a significant investment in testing and training samples to evaluate and optimize the product before it can be used reliably on new samples. One reason for this is current methods for automated calling of genotypes are based on clustering approaches which require a large number of samples to be analyzed simultaneously, or an extensive training dataset to seed clusters. In systems where inbred samples are of primary interest, current clustering approaches perform poorly due to the inability to clearly identify a heterozygote cluster. Results: As part of the development of two custom single nucleotide polymorphism genotyping products for Oryza sativa (domestic rice), we have developed a new genotype calling algorithm called ‘ALCHEMY’ based on statistical modeling of the raw intensity data rather than modelless clustering. A novel feature of the model is the ability to estimate and incorporate inbreeding information on a per sample basis allowing accurate genotyping of both inbred and heterozygous samples even when analyzed simultaneously. Since clustering is not used explicitly, ALCHEMY performs well on small sample sizes with accuracy exceeding 99% with as few as 18 samples. Availability: ALCHEMY is available for both commercial and academic use free of charge and distributed under the GNU General Public License at http://alchemy.sourceforge.net/ Contact: mhw6@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20926420

Community-based group exercise for persons with Parkinson disease: a randomized controlled trial.

PubMed

Combs, Stephanie A; Diehl, M Dyer; Chrzastowski, Casey; Didrick, Nora; McCoin, Brittany; Mox, Nicholas; Staples, William H; Wayman, Jessica

2013-01-01

The purpose of this study was to compare group boxing training to traditional group exercise on function and quality of life in persons with Parkinson disease (PD). A convenience sample of adults with PD (n = 31) were randomly assigned to boxing training or traditional exercise for 24-36 sessions, each lasting 90 minutes, over 12 weeks. Boxing training included: stretching, boxing (e.g. lateral foot work, punching bags), resistance exercises, and aerobic training. Traditional exercise included: stretching, resistance exercises, aerobic training, and balance activities. Participants were tested before and after completion of training on balance, balance confidence, mobility, gait velocity, gait endurance, and quality of life. The traditional exercise group demonstrated significantly greater gains in balance confidence than the boxing group (p < 0.025). Only the boxing group demonstrated significant improvements in gait velocity and endurance over time with a medium between-group effect size for the gait endurance (d = 0.65). Both groups demonstrated significant improvements with the balance, mobility, and quality of life with large within-group effect sizes (d ≥ 0.80). While groups significantly differed in balance confidence after training, both groups demonstrated improvements in most outcome measures. Supporting options for long-term community-based group exercise for persons with PD will be an important future consideration for rehabilitation professionals.

Heavy resistance training and peri-exercise ingestion of a multi-ingredient ergogenic nutritional supplement in males: effects on body composition, muscle performance and markers of muscle protein synthesis.

PubMed

Spillane, Mike; Schwarz, Neil; Willoughby, Darryn S

2014-12-01

This study determined the effects of heavy resistance training and peri-exercise ergogenic multi-ingredient nutritional supplement ingestion on blood and skeletal markers of muscle protein synthesis (MPS), body composition, and muscle performance. Twenty-four college-age males were randomly assigned to either a multi-ingredient SizeOn Maximum Performance (SIZE) or protein/carbohydrate/creatine (PCC) comparator supplement group in a double-blind fashion. Body composition and muscle performance were assessed, and venous blood samples and muscle biopsies were obtained before and after 6 weeks of resistance training and supplementation. Data were analyzed by 2-way ANOVA (p ≤ 0.05). Total body mass, body water, and fat mass were not differentially affected (p > 0.05). However, fat-free mass was significantly increased in both groups in response to resistance training (p = 0.037). Lower-body muscle strength (p = 0.029) and endurance (p = 0.027) were significantly increased with resistance training, but not supplementation (p > 0.05). Serum insulin, IGF-1, GH, and cortisol were not differentially affected (p > 0.05). Muscle creatine content was significantly increased in both groups from supplementation (p = 0.044). Total muscle protein (p = 0.038), MHC 1 (p = 0.041), MHC 2A, (p = 0.029), total IRS- (p = 0.041), and total Akt (p = 0.011) were increased from resistance training, but not supplementation. In response to heavy resistance training when compared to PCC, the peri-exercise ingestion of SIZE did not preferentially improve body composition, muscle performance, and markers indicative of MPS. Key pointsIn response to 42 days of heavy resistance training and either SizeOn Maximum Performance or protein/carbohydrate/creatine supplementation, similar increases in muscle mass and strength in both groups occurred; however, the increases were not different between supplement groups.The supplementation of SizeOn Maximum Performance had no preferential effect on augmenting serum insulin, IGF-1, and GH, or in decreasing cortisol.While resistance training was effective in increasing total creatine content in skeletal muscle, myofibrillar protein, and the content of total IRS-1 and Akt, it was not preferentially due to SizeOn Maximum Performance supplementation.At the daily dose of 50 g, SizeOn Maximum Performance supplementation for 42 days combined with resistance training does not increases muscle mass and strength due to its ability to elevate serum hormones and growth factors or in its ability to augment skeletal muscle signaling pathway markers indicative of muscle protein synthesis when compared to an equivalent daily dose of protein/carbohydrate/creatine.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

A Meta-Analysis of Treatments for Panic Disorder.

ERIC Educational Resources Information Center

Clum, George A.; And Others

1993-01-01

Used metanalysis to compare effectiveness of psychological and pharmacological treatments for panic disorder. Percentage of agoraphobic subjects in sample and duration of illness were unrelated to effect size (ES). Psychological coping strategies involving relaxation training, cognitive restructuring, and exposure yielded most consistent ESs;…

Ultrasound detection of simulated intra-ocular foreign bodies by minimally trained personnel.

PubMed

Sargsyan, Ashot E; Dulchavsky, Alexandria G; Adams, James; Melton, Shannon; Hamilton, Douglas R; Dulchavsky, Scott A

2008-01-01

To test the ability of non-expert ultrasound operators of divergent backgrounds to detect the presence, size, location, and composition of foreign bodies in an ocular model. High school students (N = 10) and NASA astronauts (N = 4) completed a brief ultrasound training session which focused on basic ultrasound principles and the detection of foreign bodies. The operators used portable ultrasound devices to detect foreign objects of varying location, size (0.5-2 mm), and material (glass, plastic, metal) in a gelatinous ocular model. Operator findings were compared to known foreign object parameters and ultrasound experts (N = 2) to determine accuracy across and between groups. Ultrasound had high sensitivity (astronauts 85%, students 87%, and experts 100%) and specificity (astronauts 81%, students 83%, and experts 95%) for the detection of foreign bodies. All user groups were able to accurately detect the presence of foreign bodies in this model (astronauts 84%, students 81%, and experts 97%). Astronaut and student sensitivity results for material (64% vs. 48%), size (60% vs. 46%), and position (77% vs. 64%) were not statistically different. Experts' results for material (85%), size (90%), and position (98%) were higher; however, the small sample size precluded statistical conclusions. Ultrasound can be used by operators with varying training to detect the presence, location, and composition of intraocular foreign bodies with high sensitivity, specificity, and accuracy.

Efficacy of attention bias modification using threat and appetitive stimuli: a meta-analytic review.

PubMed

Beard, Courtney; Sawyer, Alice T; Hofmann, Stefan G

2012-12-01

Attention bias modification (ABM) protocols aim to modify attentional biases underlying many forms of pathology. Our objective was to conduct an effect size analysis of ABM across a wide range of samples and psychological problems. We conducted a literature search using PubMed, PsycInfo, and author searches to identify randomized studies that examined the effects of ABM on attention and subjective experiences. We identified 37 studies (41 experiments) totaling 2,135 participants who were randomized to training toward neutral, positive, threat, or appetitive stimuli or to a control condition. The effect size estimate for changes in attentional bias was large for the neutral versus threat comparisons (g=1.06), neutral versus appetitive (g=1.41), and neutral versus control comparisons (g=0.80), and small for positive versus control (g=0.24). The effects of ABM on attention bias were moderated by stimulus type (words vs. pictures) and sample characteristics (healthy vs. high symptomatology). Effect sizes of ABM on subjective experiences ranged from 0.03 to 0.60 for postchallenge outcomes, -0.31 to 0.51 for posttreatment, and were moderated by number of training sessions, stimulus type, and stimulus orientation (top/bottom vs. left/right). Fail-safe N calculations suggested that the effect size estimates were robust for the training effects on attentional biases, but not for the effect on subjective experiences. ABM studies using threat stimuli produced significant effects on attention bias across comparison conditions, whereas appetitive stimuli produced changes in attention only when comparing appetitive versus neutral conditions. ABM has a moderate and robust effect on attention bias when using threat stimuli. Further studies are needed to determine whether these effects are also robust when using appetitive stimuli and for affecting subjective experiences. Copyright © 2012. Published by Elsevier Ltd.

Why did persons invited to train in cardiopulmonary resuscitation not do so?

PubMed

Lejeune, P O; Delooz, H H

1987-03-01

All citizens (N = 22066) aged 16 to 65 of a medium-sized Belgian town were personally invited to CPR training sessions held in their neighbourhood. 1152 responded by attending a training session. Those who did not so respond were surveyed (random sample N = 600) for reasons of their not coming. The sample fitted well with census data for gender, age and suburb location but not for job, because retired persons and women at home were overrepresented. 123 persons did not want to answer the questions. 116 persons said they were already trained in CPR, 276 said they would accept on a future occasion and 82 said they would not. Three persons did not answer this question. There was no discrimination for job, gender and suburb location between those who did not accept a future training opportunity, nor was the existence of a heart patient among relatives. The older the person, the less inclined was that person to participate in CPR training (age effect chi 2 = 17 X 17, d.f. = 9, P less than 0.05). The 276 who accepted future training, chose their workplace (221) and/or their social meeting place (club etc.) as the place where this future training should be held. We suggest that CPR training is well accepted and that the training opportunities should be given at places of work and social gatherings.

Function approximation and documentation of sampling data using artificial neural networks.

PubMed

Zhang, Wenjun; Barrion, Albert

2006-11-01

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field. Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors. BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.

Study design requirements for RNA sequencing-based breast cancer diagnostics.

PubMed

Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

The effect of exercise training on cutaneous microvascular reactivity: A systematic review and meta-analysis.

PubMed

Lanting, Sean M; Johnson, Nathan A; Baker, Michael K; Caterson, Ian D; Chuter, Vivienne H

2017-02-01

This study aimed to review the efficacy of exercise training for improving cutaneous microvascular reactivity in response to local stimulus in human adults. Systematic review with meta-analysis. A systematic search of Medline, Cinahl, AMED, Web of Science, Scopus, and Embase was conducted up to June 2015. Included studies were controlled trials assessing the effect of an exercise training intervention on cutaneous microvascular reactivity as instigated by local stimulus such as local heating, iontophoresis and post-occlusive reactive hyperaemia. Studies where the control was only measured at baseline or which included participants with vasospastic disorders were excluded. Two authors independently reviewed and selected relevant controlled trials and extracted data. Quality was assessed using the Downs and Black checklist. Seven trials were included, with six showing a benefit of exercise training but only two reaching statistical significance with effect size ranging from -0.14 to 1.03. The meta-analysis revealed that aerobic exercise had a moderate statistically significant effect on improving cutaneous microvascular reactivity (effect size (ES)=0.43, 95% CI: 0.08-0.78, p=0.015). Individual studies employing an exercise training intervention have tended to have small sample sizes and hence lacked sufficient power to detect clinically meaningful benefits to cutaneous microvascular reactivity. Pooled analysis revealed a clear benefit of exercise training on improving cutaneous microvascular reactivity in older and previously inactive adult cohorts. Exercise training may provide a cost-effective option for improving cutaneous microvascular reactivity in adults and may be of benefit to those with cardiovascular disease and metabolic disorders such as diabetes. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Using Opinions and Knowledge to Identify Natural Groups of Gambling Employees.

PubMed

Gray, Heather M; Tom, Matthew A; LaPlante, Debi A; Shaffer, Howard J

2015-12-01

Gaming industry employees are at higher risk than the general population for health conditions including gambling disorder. Responsible gambling training programs, which train employees about gambling and gambling-related problems, might be a point of intervention. However, such programs tend to use a "one-size-fits-all" approach rather than multiple tiers of instruction. We surveyed employees of one Las Vegas casino (n = 217) and one online gambling operator (n = 178) regarding their gambling-related knowledge and opinions prior to responsible gambling training, to examine the presence of natural knowledge groups among recently hired employees. Using k-means cluster analysis, we observed four natural groups within the Las Vegas casino sample and two natural groups within the online operator sample. We describe these natural groups in terms of opinion/knowledge differences as well as distributions of demographic/occupational characteristics. Gender and language spoken at home were correlates of cluster group membership among the sample of Las Vegas casino employees, but we did not identify demographic or occupational correlates of cluster group membership among the online gambling operator employees. Gambling operators should develop more sophisticated training programs that include instruction that targets different natural knowledge groups.

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

Responsiveness of outcome measures for upper limb prosthetic rehabilitation.

PubMed

Resnik, Linda; Borgia, Matthew

2016-02-01

There is limited research on responsiveness of prosthetic rehabilitation outcome measures. To examine responsiveness of the Box and Block test, Jebsen-Taylor Hand Function tests, Upper Extremity Functional Scale, University of New Brunswick skill and spontaneity tests, Activity Measure for Upper Limb Amputation, and the Patient-Specific Functional Scale. This was a quasi-experimental study with repeated measurements in a convenience sample of upper limb amputees. Measures were collected before, during, and after training with the DEKA Arm. Largest effect sizes were observed for Patient-Specific Functional Scale (effect size: 1.59, confidence interval: 1.00, 2.14), Activity Measure for Upper Limb Amputation (effect size: 1.33, confidence interval: 0.73, 1.90), and University of New Brunswick skill test (effect size: 1.18, confidence interval: 0.61, 1.73). Other measures that were responsive to change were Box and Block test, Jebsen-Taylor Hand Function light and heavy can tests, and University of New Brunswick spontaneity test. Responsiveness and pattern of responsiveness varied by prosthetic level. The Box and Block test, Jebsen-Taylor Hand Function light and heavy can tests, University of New Brunswick skill and spontaneity tests, Activities Measure for Upper Limb Amputation, and the Patient-Specific Functional Scale were responsive to change during prosthetic training. These findings have implications for choice of measures for research and practice and inform clinicians about the amount of training necessary to maximize outcomes with the DEKA Arm. Findings on responsiveness of outcome measures have implications for the choice of measures for clinical trials and practice. Findings regarding the responsiveness to change over the course of training can inform clinicians about the amount of training that may be necessary to maximize specific outcomes with the DEKA Arm. © The International Society for Prosthetics and Orthotics 2014.

Survey of Employers.

ERIC Educational Resources Information Center

European Social Fund, Dublin (Ireland).

A study examined attitudes of Irish employers toward vocational training (VT) activities, state agencies responsible for administering VT, and the skills that employees would need in the future. Of a sample of 500 firms that were selected as being representative from the standpoints of size, sector, location, and form of ownership, 219 were…

Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track

DTIC Science & Technology

2015-11-20

Department of Computer Science and Software Engineering, Laval University 2 Department of Software Engineering, Lakehead University Abstract—In this...Linear Regression and Lambda Mart perform poorly in this case, be- cause the size of the training data per user is small (less than 50 samples). On the

Headspace concentrations of explosive vapors in containers designed for canine testing and training: theory, experiment, and canine trials.

PubMed

Lotspeich, Erica; Kitts, Kelley; Goodpaster, John

2012-07-10

It is a common misconception that the amount of explosive is the chief contributor to the quantity of vapor that is available to trained canines. In fact, this quantity (known as odor availability) depends not only on the amount of explosive material, but also the container volume, explosive vapor pressure and temperature. In order to better understand odor availability, headspace experiments were conducted and the results were compared to theory. The vapor-phase concentrations of three liquid explosives (nitromethane, nitroethane and nitropropane) were predicted using the Ideal Gas Law for containers of various volumes that are in use for canine testing. These predictions were verified through experiments that varied the amount of sample, the container size, and the temperature. These results demonstrated that the amount of sample that is needed to saturate different sized containers is small, predictable and agrees well with theory. In general, and as expected, once the headspace of a container is saturated, any subsequent increase in sample volume will not result in the release of more vapors. The ability of canines to recognize and alert to differing amounts of nitromethane has also been studied. In particular, it was found that the response of trained canines is independent of the amount of nitromethane present, provided it is a sufficient quantity to saturate the container in which it is held. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Morpho-z: improving photometric redshifts with galaxy morphology

NASA Astrophysics Data System (ADS)

Soo, John Y. H.; Moraes, Bruno; Joachimi, Benjamin; Hartley, William; Lahav, Ofer; Charbonnier, Aldée; Makler, Martín; Pereira, Maria E. S.; Comparat, Johan; Erben, Thomas; Leauthaud, Alexie; Shan, Huanyuan; Van Waerbeke, Ludovic

2018-04-01

We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the changes in the scatter and outlier fraction of photometric redshifts when galaxy size, ellipticity, Sérsic index, and surface brightness are included in training on galaxy samples from the SDSS and the CFHT Stripe-82 Survey (CS82). We show that by adding galaxy morphological parameters to full ugriz photometry, only mild improvements are obtained, while the gains are substantial in cases where fewer passbands are available. For instance, the combination of grz photometry and morphological parameters almost fully recovers the metrics of 5-band photometric redshifts. We demonstrate that with morphology it is possible to determine useful redshift distribution N(z) of galaxy samples without any colour information. We also find that the inclusion of quasar redshifts and associated object sizes in training improves the quality of photometric redshift catalogues, compensating for the lack of a good star-galaxy separator. We further show that morphological information can mitigate biases and scatter due to bad photometry. As an application, we derive both point estimates and posterior distributions of redshifts for the official CS82 catalogue, training on morphology and SDSS Stripe-82 ugriz bands when available. Our redshifts yield a 68th percentile error of 0.058(1 + z), and a outlier fraction of 5.2 per cent. We further include a deep extension trained on morphology and single i-band CS82 photometry.

Public health financial management needs: report of a national survey.

PubMed

Costich, Julia F; Honoré, Peggy A; Scutchfield, F Douglas

2009-01-01

The work reported here builds on the identification of public health financial management practice competencies by a national expert panel. The next logical step was to provide a validity check for the competencies and identify priority areas for educational programming. We developed a survey for local public health finance officers based on the public health finance competencies and field tested it with a convenience sample of officials. We asked respondents to indicate the importance of each competency area and the need for training to improve performance; we also requested information regarding respondent education, jurisdiction size, and additional comments. Our local agency survey sample drew on the respondent list from the National Association of County and City Health Officials 2005 local health department survey, stratified by agency size and limited to jurisdiction populations of 25,000 to 1,000,000. Identifying appropriate respondents was a major challenge. The survey was fielded electronically, yielding 112 responses from 30 states. The areas identified as most important and needing most additional training were knowledge of budget activities, financial data interpretation and communication, and ability to assess and correct the organization's financial status. The majority of respondents had some postbaccalaureate education. Many provided additional comments and recommendations. Health department finance officers demonstrated a high level of general agreement regarding the importance of finance competencies in public health and the need for training. The findings point to a critical need for additional training opportunities that are accessible, cost-effective, and targeted to individual needs.

Multi-domain computerized cognitive training program improves performance of bookkeeping tasks: a matched-sampling active-controlled trial.

PubMed

Lampit, Amit; Ebster, Claus; Valenzuela, Michael

2014-01-01

Cognitive skills are important predictors of job performance, but the extent to which computerized cognitive training (CCT) can improve job performance in healthy adults is unclear. We report, for the first time, that a CCT program aimed at attention, memory, reasoning and visuo-spatial abilities can enhance productivity in healthy younger adults on bookkeeping tasks with high relevance to real-world job performance. 44 business students (77.3% female, mean age 21.4 ± 2.6 years) were assigned to either (a) 20 h of CCT, or (b) 20 h of computerized arithmetic training (active control) by a matched sampling procedure. Both interventions were conducted over a period of 6 weeks, 3-4 1-h sessions per week. Transfer of skills to performance on a 60-min paper-based bookkeeping task was measured at three time points-baseline, after 10 h and after 20 h of training. Repeated measures ANOVA found a significant Group X Time effect on productivity (F = 7.033, df = 1.745; 73.273, p = 0.003) with a significant interaction at both the 10-h (Relative Cohen's effect size = 0.38, p = 0.014) and 20-h time points (Relative Cohen's effect size = 0.40, p = 0.003). No significant effects were found on accuracy or on Conners' Continuous Performance Test, a measure of sustained attention. The results are discussed in reference to previous findings on the relationship between brain plasticity and job performance. Generalization of results requires further study.

Twelve Weeks of Plyometric Training Improves Motor Performance of 7- to 9-Year-Old Boys Who Were Overweight/Obese: A Randomized Controlled Intervention.

PubMed

Nobre, Gabriela G; de Almeida, Marcelus B; Nobre, Isabele G; Dos Santos, Fernanda K; Brinco, Raphael A; Arruda-Lima, Thalison R; de-Vasconcelos, Kenya L; de-Lima, Jociellen G; Borba-Neto, Manoel E; Damasceno-Rodrigues, Emmanuel M; Santos-Silva, Steve M; Leandro, Carol G; Moura-Dos-Santos, Marcos A

2017-08-01

Nobre, GG, de Almeida, MB, Nobre, IG, dos Santos, FK, Brinco, RA, Arruda-Lima, TR, de-Vasconcelos, KL, de-Lima, JG, Borba-Neto, ME, Damasceno-Rodrigues, EM, Santos-Silva, SM, Leandro, CG, and Moura-dos-Santos, MA. Twelve weeks of plyometric training improves motor performance of 7- to 9-year-old boys who were overweight/obese: a randomized controlled intervention. J Strength Cond Res 31(8): 2091-2099, 2017-The prevalence of childhood overweight/obesity has increased, and physical training at school may to be effective to combat this scenario. We analyzed the effects of a protocol of plyometric training on body composition and motor performance of boys who were overweight/obese aged 7-9 years. The sample was randomly assigned into 2 groups: plyometric training group (T, n = 40) and control group (C, n = 19). Training consisted of 20 min·d (twice a week, during 12 weeks) of lower extremity plyometric exercise. Health-related physical fitness was measured by handgrip strength, standing long jump (SLJ), curl-ups, sit and reach, square test, running speed, and mile run test. Gross motor coordination was evaluated by means of the Körperkoordinations-test für Kinder (KTK) tests. Baseline and postintervention differences were investigated, and effect size was estimated through Cohen's d coefficient. Both groups showed increased body weight, height, and sitting height after intervention with a negligible effect size. Only T group showed increased fat-free mass (p = 0.011) compared with baseline values with small effect size. Plyometric training improved handgrip strength (d = 0.23), sit and reach (d = 0.18), curl-ups (d = 0.39), SLJ (d = 0.80), agility (d = 0.48), and time in the mile run test (d = 0.38). For gross motor coordination results, T group showed better performance in all tests after plyometric training with moderate/large effect size. Thus, 12 weeks of PT improved health-related physical fitness components and motor coordination acquisition of 7- to 9-year-old boys who were overweight/obese.

Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case.

PubMed

Bolin, Jocelyn Holden; Finch, W Holmes

2014-01-01

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

Effects of an explicit problem-solving skills training program using a metacomponential approach for outpatients with acquired brain injury.

PubMed

Fong, Kenneth N K; Howie, Dorothy R

2009-01-01

We investigated the effects of an explicit problem-solving skills training program using a metacomponential approach with 33 outpatients with moderate acquired brain injury, in the Hong Kong context. We compared an experimental training intervention with this explicit problem-solving approach, which taught metacomponential strategies, with a conventional cognitive training approach that did not have this explicit metacognitive training. We found significant advantages for the experimental group on the Metacomponential Interview measure in association with the explicit metacomponential training, but transfer to the real-life problem-solving measures was not evidenced in statistically significant findings. Small sample size, limited time of intervention, and some limitations with these tools may have been contributing factors to these results. The training program was demonstrated to have a significantly greater effect than the conventional training approach on metacomponential functioning and the component of problem representation. However, these benefits were not transferable to real-life situations.

Reducing Individual Variation for fMRI Studies in Children by Minimizing Template Related Errors

PubMed Central

Weng, Jian; Dong, Shanshan; He, Hongjian; Chen, Feiyan; Peng, Xiaogang

2015-01-01

Spatial normalization is an essential process for group comparisons in functional MRI studies. In practice, there is a risk of normalization errors particularly in studies involving children, seniors or diseased populations and in regions with high individual variation. One way to minimize normalization errors is to create a study-specific template based on a large sample size. However, studies with a large sample size are not always feasible, particularly for children studies. The performance of templates with a small sample size has not been evaluated in fMRI studies in children. In the current study, this issue was encountered in a working memory task with 29 children in two groups. We compared the performance of different templates: a study-specific template created by the experimental population, a Chinese children template and the widely used adult MNI template. We observed distinct differences in the right orbitofrontal region among the three templates in between-group comparisons. The study-specific template and the Chinese children template were more sensitive for the detection of between-group differences in the orbitofrontal cortex than the MNI template. Proper templates could effectively reduce individual variation. Further analysis revealed a correlation between the BOLD contrast size and the norm index of the affine transformation matrix, i.e., the SFN, which characterizes the difference between a template and a native image and differs significantly across subjects. Thereby, we proposed and tested another method to reduce individual variation that included the SFN as a covariate in group-wise statistics. This correction exhibits outstanding performance in enhancing detection power in group-level tests. A training effect of abacus-based mental calculation was also demonstrated, with significantly elevated activation in the right orbitofrontal region that correlated with behavioral response time across subjects in the trained group. PMID:26207985

Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks.

PubMed

Vázquez-Baeza, Yoshiki; Hyde, Embriette R; Suchodolski, Jan S; Knight, Rob

2016-10-03

Inflammatory bowel disease (IBD) is an autoimmune condition that is difficult to diagnose, and animal models of this disease have questionable human relevance 1 . Here, we show that the dysbiosis network underlying IBD in dogs differs from that in humans, with some bacteria such as Fusobacterium switching roles between the two species (as Bacteroides fragilis switches roles between humans and mice) 2 . For example, a dysbiosis index trained on humans fails when applied to dogs, but a dog-specific dysbiosis index achieves high correlations with the overall dog microbial community diversity patterns. In addition, a random forest classifier trained on dog-specific samples achieves high discriminatory power, even when using stool samples rather than the mucosal biopsies required for high discriminatory power in humans 2 . These relationships were not detected in previously published dog IBD data sets due to their limited sample size and statistical power 3 . Taken together, these results reveal the need to train host-specific dysbiosis networks and point the way towards a generalized understanding of IBD across different mammalian models.

How Broad Liberal Arts Training Produces Phd Economists: Carleton's Story

ERIC Educational Resources Information Center

Bourne, Jenny; Grawe, Nathan D.

2015-01-01

Several recent studies point to strong performance in economics PhD programs of graduates from liberal arts colleges. While every undergraduate program is unique and the likelihood of selection bias combines with small sample sizes to caution against drawing strong conclusions, the authors reflect on their experience at Carleton College to…

An IRT Analysis of Preservice Teacher Self-Efficacy in Technology Integration

ERIC Educational Resources Information Center

Browne, Jeremy

2011-01-01

The need for rigorously developed measures of preservice teacher traits regarding technology integration training has been acknowledged (Kay 2006), but such instruments are still extremely rare. The Technology Integration Confidence Scale (TICS) represents one such measure, but past analyses of its functioning have been limited by sample size and…

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

NASA Astrophysics Data System (ADS)

Cofré, Rodrigo; Maldonado, Cesar

2018-01-01

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

The influence of deliberate practice on musical achievement: a meta-analysis.

PubMed

Platz, Friedrich; Kopiez, Reinhard; Lehmann, Andreas C; Wolf, Anna

2014-01-01

Deliberate practice (DP) is a task-specific structured training activity that plays a key role in understanding skill acquisition and explaining individual differences in expert performance. Relevant activities that qualify as DP have to be identified in every domain. For example, for training in classical music, solitary practice is a typical training activity during skill acquisition. To date, no meta-analysis on the quantifiable effect size of deliberate practice on attained performance in music has been conducted. Yet the identification of a quantifiable effect size could be relevant for the current discussion on the role of various factors on individual difference in musical achievement. Furthermore, a research synthesis might enable new computational approaches to musical development. Here we present the first meta-analysis on the role of deliberate practice in the domain of musical performance. A final sample size of 13 studies (total N = 788) was carefully extracted to satisfy the following criteria: reported durations of task-specific accumulated practice as predictor variables and objectively assessed musical achievement as the target variable. We identified an aggregated effect size of r c = 0.61; 95% CI [0.54, 0.67] for the relationship between task-relevant practice (which by definition includes DP) and musical achievement. Our results corroborate the central role of long-term (deliberate) practice for explaining expert performance in music.

Children's emotion understanding: A meta-analysis of training studies.

PubMed

Sprung, Manuel; Münch, Hannah M; Harris, Paul L; Ebesutani, Chad; Hofmann, Stefan G

2015-09-01

In the course of development, children show increased insight and understanding of emotions-both of their own emotions and those of others. However, little is known about the efficacy of training programs aimed at improving children's understanding of emotion. To conduct an effect size analysis of trainings aimed at three aspects of emotion understanding: external aspects (i.e., the recognition of emotional expressions, understanding external causes of emotion, understanding the influence of reminders on present emotions); mental aspects (i.e., understanding desire-based emotions, understanding belief-based emotions, understanding hidden emotions); and reflective aspects (i.e., understanding the regulation of an emotion, understanding mixed emotions, understanding moral emotions). A literature search was conducted using PubMed, PsycInfo, the Cochrane Library, and manual searches. The search identified 19 studies or experiments including a total of 749 children with an average age of 86 months ( S.D .=30.71) from seven different countries. Emotion understanding training procedures are effective for improving external (Hedge's g = 0.62), mental (Hedge's g = 0.31), and reflective (Hedge's g = 0.64) aspects of emotion understanding. These effect sizes were robust and generally unrelated to the number and lengths of training sessions, length of the training period, year of publication, and sample type. However, training setting and social setting moderated the effect of emotion understanding training on the understanding of external aspects of emotion. For the length of training session and social setting, we observed significant moderator effects of training on reflective aspects of emotion. Emotion understanding training may be a promising tool for both preventive intervention and the psychotherapeutic process. However, more well-controlled studies are needed.

Children’s emotion understanding: A meta-analysis of training studies

PubMed Central

Sprung, Manuel; Münch, Hannah M.; Harris, Paul L.; Ebesutani, Chad; Hofmann, Stefan G.

2015-01-01

BACKGROUND In the course of development, children show increased insight and understanding of emotions—both of their own emotions and those of others. However, little is known about the efficacy of training programs aimed at improving children’s understanding of emotion. OBJECTIVES To conduct an effect size analysis of trainings aimed at three aspects of emotion understanding: external aspects (i.e., the recognition of emotional expressions, understanding external causes of emotion, understanding the influence of reminders on present emotions); mental aspects (i.e., understanding desire-based emotions, understanding belief-based emotions, understanding hidden emotions); and reflective aspects (i.e., understanding the regulation of an emotion, understanding mixed emotions, understanding moral emotions). DATA SOURCES A literature search was conducted using PubMed, PsycInfo, the Cochrane Library, and manual searches. REVIEW METHODS The search identified 19 studies or experiments including a total of 749 children with an average age of 86 months (S.D.=30.71) from seven different countries. RESULTS Emotion understanding training procedures are effective for improving external (Hedge’s g = 0.62), mental (Hedge’s g = 0.31), and reflective (Hedge’s g = 0.64) aspects of emotion understanding. These effect sizes were robust and generally unrelated to the number and lengths of training sessions, length of the training period, year of publication, and sample type. However, training setting and social setting moderated the effect of emotion understanding training on the understanding of external aspects of emotion. For the length of training session and social setting, we observed significant moderator effects of training on reflective aspects of emotion. CONCLUSION Emotion understanding training may be a promising tool for both preventive intervention and the psychotherapeutic process. However, more well-controlled studies are needed. PMID:26405369

Geriatric dentistry education and context in a selection of countries in 5 continents.

PubMed

Marchini, Leonardo; Ettinger, Ronald; Chen, Xi; Kossioni, Anastassia; Tan, Haiping; Tada, Sayaka; Ikebe, Kazunori; Dosumu, Elizabeth Bosede; Oginni, Fadekemi O; Akeredolu, Patricia Adetokunbo; Butali, Azeez; Donnelly, Leeann; Brondani, Mario; Fritzsch, Bernd; Adeola, Henry A

2018-05-01

To summarize and discuss how geriatric dentistry has been addressed in dental schools of different countries regarding to (1) teaching students at the predoctoral level; (2) advanced training, and (3) research. A convenience sample of faculty members from a selection of high, upper-middle and lower-middle income countries were recruited to complete the survey. The survey had 5 open-ended main topics, and asked about (1) the size of their elderly population, (2) general information about dental education; (3) the number of dental schools teaching geriatric dentistry, and their teaching methods; (4) advanced training in geriatric dentistry; (5) scholarship/research in geriatric dentistry. (1) There is great variation in the size of elderly population; (2) duration of training and content of dental education curriculum varies; (3) geriatric dentistry has not been established as a standalone course in dental schools in the majority of the countries, (4) most countries, with the exception of Japan, lack adequate number of dentists trained in geriatric dentistry as well as training programs, and (5) geriatric dentistry-related research has increased in recent years in scope and content, although the majority of these papers are not in English. © 2018 Special Care Dentistry Association and Wiley Periodicals, Inc.

Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

PubMed

Guye, Sabrina; von Bastian, Claudia C

2017-12-01

The question of whether working memory training leads to generalized improvements in untrained cognitive abilities is a longstanding and heatedly debated one. Previous research provides mostly ambiguous evidence regarding the presence or absence of transfer effects in older adults. Thus, to draw decisive conclusions regarding the effectiveness of working memory training interventions, methodologically sound studies with larger sample sizes are needed. In this study, we investigated whether or not a computer-based working memory training intervention induced near and far transfer in a large sample of 142 healthy older adults (65 to 80 years). Therefore, we randomly assigned participants to either the experimental group, which completed 25 sessions of adaptive, process-based working memory training, or to the active, adaptive visual search control group. Bayesian linear mixed-effects models were used to estimate performance improvements on the level of abilities, using multiple indicator tasks for near (working memory) and far transfer (fluid intelligence, shifting, and inhibition). Our data provided consistent evidence supporting the absence of near transfer to untrained working memory tasks and the absence of far transfer effects to all of the assessed abilities. Our results suggest that working memory training is not an effective way to improve general cognitive functioning in old age. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Balance Training Does Not Alter Reliance on Visual Information during Static Stance in Those with Chronic Ankle Instability: A Systematic Review with Meta-Analysis.

PubMed

Song, Kyeongtak; Rhodes, Evan; Wikstrom, Erik A

2018-04-01

Visual, vestibular, and somatosensory systems contribute to postural control. Chronic ankle instability (CAI) patients have been observed to have a reduced ability to dynamically shift their reliance among sources of sensory information and rely more heavily on visual information during a single-limb stance relative to uninjured controls. Balance training is proven to improve postural control but there is a lack of evidence regarding the ability of balance training programs to alter the reliance on visual information in CAI patients. Our objective was to determine if balance training alters the reliance on visual information during static stance in CAI patients. The PubMed, CINAHL, and SPORTDiscus databases were searched from their earliest available date to October 2017 using a combination of keywords. Study inclusion criteria consisted of (1) using participants with CAI; (2) use of a balance training intervention; and (3) calculation of an objective measure of static postural control during single-limb stance with eyes open and eyes closed. Sample sizes, means, and standard deviations of single-leg balance measures for eyes-open and eyes-closed testing conditions before and after balance training were extracted from the included studies. Eyes-open to eyes-closed effect sizes [Hedges' g and 95% confidence intervals (CI)] before and after balance training were calculated, and between-study variability for heterogeneity and potential risks of publication bias were examined. Six studies were identified. The overall eyes-open to eyes-closed effect size difference between pre- and post-intervention assessments was not significant (Hedges' g effect size = 0.151, 95% CI = - 0.151 to 0.453, p = 0.26). This result indicates that the utilization of visual information in individuals with CAI during the single-leg balance is not altered after balance training. Low heterogeneity (Q(5) = 2.96, p = 0.71, I 2  = 0%) of the included studies and no publication bias were found. On the basis of our systematic review with meta-analysis, it appears that traditional balance training protocols do not alter the reliance on visual information used by CAI patients during a single-leg stance.

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

PubMed

Choi, Yoonha; Liu, Tiffany Ting; Pankratz, Daniel G; Colby, Thomas V; Barth, Neil M; Lynch, David A; Walsh, P Sean; Raghu, Ganesh; Kennedy, Giulia C; Huang, Jing

2018-05-09

We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

Repeated Low-Level Blast Exposure: A Descriptive Human Subjects Study.

PubMed

Carr, Walter; Stone, James R; Walilko, Tim; Young, Lee Ann; Snook, Tianlu Li; Paggi, Michelle E; Tsao, Jack W; Jankosky, Christopher J; Parish, Robert V; Ahlers, Stephen T

2016-05-01

The relationship between repeated exposure to blast overpressure and neurological function was examined in the context of breacher training at the U.S. Marine Corps Weapons Training Battalion Dynamic Entry School. During this training, Students are taught to apply explosive charges to achieve rapid ingress into secured buildings. For this study, both Students and Instructors participated in neurobehavioral testing, blood toxin screening, vestibular/auditory testing, and neuroimaging. Volunteers wore instrumentation during training to allow correlation of human response measurements and blast overpressure exposure. The key findings of this study were from high-memory demand tasks and were limited to the Instructors. Specific tests showing blast-related mean differences were California Verbal Learning Test II, Automated Neuropsychological Assessment Metrics subtests (Match-to-Sample, Code Substitution Delayed), and Delayed Matching-to-Sample 10-second delay condition. Importantly, apparent deficits were paralleled with functional magnetic resonance imaging using the n-back task. The findings of this study are suggestive, but not conclusive, owing to small sample size and effect. The observed changes yield descriptive evidence for potential neurological alterations in the subset of individuals with occupational history of repetitive blast exposure. This is the first study to integrate subject instrumentation for measurement of individual blast pressure exposure, neurocognitive testing, and neuroimaging. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

A Sequential Monte Carlo Approach for Streamflow Forecasting

NASA Astrophysics Data System (ADS)

Hsu, K.; Sorooshian, S.

2008-12-01

As alternatives to traditional physically-based models, Artificial Neural Network (ANN) models offer some advantages with respect to the flexibility of not requiring the precise quantitative mechanism of the process and the ability to train themselves from the data directly. In this study, an ANN model was used to generate one-day-ahead streamflow forecasts from the precipitation input over a catchment. Meanwhile, the ANN model parameters were trained using a Sequential Monte Carlo (SMC) approach, namely Regularized Particle Filter (RPF). The SMC approaches are known for their capabilities in tracking the states and parameters of a nonlinear dynamic process based on the Baye's rule and the proposed effective sampling and resampling strategies. In this study, five years of daily rainfall and streamflow measurement were used for model training. Variable sample sizes of RPF, from 200 to 2000, were tested. The results show that, after 1000 RPF samples, the simulation statistics, in terms of correlation coefficient, root mean square error, and bias, were stabilized. It is also shown that the forecasted daily flows fit the observations very well, with the correlation coefficient of higher than 0.95. The results of RPF simulations were also compared with those from the popular back-propagation ANN training approach. The pros and cons of using SMC approach and the traditional back-propagation approach will be discussed.

The Efficacy of Stuttering Measurement Training: Evaluating Two Training Programs

PubMed Central

Bainbridge, Lauren A.; Stavros, Candace; Ebrahimian, Mineh; Wang, Yuedong

2015-01-01

Purpose Two stuttering measurement training programs currently used for training clinicians were evaluated for their efficacy in improving the accuracy of total stuttering event counting. Method Four groups, each with 12 randomly allocated participants, completed a pretest–posttest design training study. They were evaluated by their counts of stuttering events on eight 3-min audiovisual speech samples from adults and children who stutter. Stuttering judgment training involved use of either the Stuttering Measurement System (SMS), Stuttering Measurement Assessment and Training (SMAAT) programs, or no training. To test for the reliability of any training effect, SMS training was repeated with the 4th group. Results Both SMS-trained groups produced approximately 34% improvement, significantly better than no training or the SMAAT program. The SMAAT program produced a mixed result. Conclusions The SMS program was shown to produce a “medium” effect size improvement in the accuracy of stuttering event counts, and this improvement was almost perfectly replicated in a 2nd group. Half of the SMAAT judges produced a 36% improvement in accuracy, but the other half showed no improvement. Additional studies are needed to demonstrate the durability of the reported improvements, but these positive effects justify the importance of stuttering measurement training. PMID:25629956

The efficacy of stuttering measurement training: evaluating two training programs.

PubMed

Bainbridge, Lauren A; Stavros, Candace; Ebrahimian, Mineh; Wang, Yuedong; Ingham, Roger J

2015-04-01

Two stuttering measurement training programs currently used for training clinicians were evaluated for their efficacy in improving the accuracy of total stuttering event counting. Four groups, each with 12 randomly allocated participants, completed a pretest-posttest design training study. They were evaluated by their counts of stuttering events on eight 3-min audiovisual speech samples from adults and children who stutter. Stuttering judgment training involved use of either the Stuttering Measurement System (SMS), Stuttering Measurement Assessment and Training (SMAAT) programs, or no training. To test for the reliability of any training effect, SMS training was repeated with the 4th group. Both SMS-trained groups produced approximately 34% improvement, significantly better than no training or the SMAAT program. The SMAAT program produced a mixed result. The SMS program was shown to produce a "medium" effect size improvement in the accuracy of stuttering event counts, and this improvement was almost perfectly replicated in a 2nd group. Half of the SMAAT judges produced a 36% improvement in accuracy, but the other half showed no improvement. Additional studies are needed to demonstrate the durability of the reported improvements, but these positive effects justify the importance of stuttering measurement training.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

Neural Network Emulation of Reionization Simulations

NASA Astrophysics Data System (ADS)

Schmit, Claude J.; Pritchard, Jonathan R.

2018-05-01

Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

Experimental evaluation of coal conversion solid waste residuals. Progress report, August 1-October 31, 1979

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

Neufeld, R. D.; Bern, J.; Erdogan, H.

1979-11-15

Activities are underway to investigate basic phenomena that would assist demonstration and commercial sized coal conversion facilities in the environmentally acceptable disposal of process solid waste residuals. The approach taken is to consider only those residuals coming from the conversion technology itself, i.e. from gasification, liquefaction, and hot-clean-up steps as well as residuals from the wastewater treatment train. Residuals from the coal mining and coal grinding steps will not be considered in detail since those materials are being handled in some manner in the private sector. Laboratory evalations have been conducted on solid waste samples of fly ash from anmore » existing Capman gasifier. ASTM-A and EPA-EP leaching procedures have been completed on sieved size fractions of the above wastes. Data indicate that smaller size fractions pose greater contamination potential than do larger size particles with a transition zone occurring at particle sizes of about 0.05 inches in diameter. Ames testing of such residuals is reported. Similar studies are under way with samples of H-Coal solid waste residuals.« less

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

Multi-domain computerized cognitive training program improves performance of bookkeeping tasks: a matched-sampling active-controlled trial

PubMed Central

Lampit, Amit; Ebster, Claus; Valenzuela, Michael

2014-01-01

Cognitive skills are important predictors of job performance, but the extent to which computerized cognitive training (CCT) can improve job performance in healthy adults is unclear. We report, for the first time, that a CCT program aimed at attention, memory, reasoning and visuo-spatial abilities can enhance productivity in healthy younger adults on bookkeeping tasks with high relevance to real-world job performance. 44 business students (77.3% female, mean age 21.4 ± 2.6 years) were assigned to either (a) 20 h of CCT, or (b) 20 h of computerized arithmetic training (active control) by a matched sampling procedure. Both interventions were conducted over a period of 6 weeks, 3–4 1-h sessions per week. Transfer of skills to performance on a 60-min paper-based bookkeeping task was measured at three time points—baseline, after 10 h and after 20 h of training. Repeated measures ANOVA found a significant Group X Time effect on productivity (F = 7.033, df = 1.745; 73.273, p = 0.003) with a significant interaction at both the 10-h (Relative Cohen's effect size = 0.38, p = 0.014) and 20-h time points (Relative Cohen's effect size = 0.40, p = 0.003). No significant effects were found on accuracy or on Conners' Continuous Performance Test, a measure of sustained attention. The results are discussed in reference to previous findings on the relationship between brain plasticity and job performance. Generalization of results requires further study. PMID:25120510

Barriers to Application of E-Learning in Training Activities of SMEs

ERIC Educational Resources Information Center

Anderson, Randy J.; Wielicki, Tomasz; Anderson, Lydia E.

2010-01-01

This paper reports on the on-going study of Small and Mid-Size Enterprises (SMEs) in the Central California concerning their use of Information and Communication Technology (ICT). This research project analyzed data from a sample of 161 SMEs. Specifically, this part of the study is investigating the major barriers to applications of e-learning…

Object Classification With Joint Projection and Low-Rank Dictionary Learning.

PubMed

Foroughi, Homa; Ray, Nilanjan; Hong Zhang

2018-02-01

For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.

Applying deep neural networks to HEP job classification

NASA Astrophysics Data System (ADS)

Wang, L.; Shi, J.; Yan, X.

2015-12-01

The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision.

A randomized controlled trial to evaluate the feasibility of the Wii Fit for improving walking in older adults with lower limb amputation.

PubMed

Imam, Bita; Miller, William C; Finlayson, Heather; Eng, Janice J; Jarus, Tal

2017-01-01

To assess the feasibility of Wii.n.Walk for improving walking capacity in older adults with lower limb amputation. A parallel, evaluator-blind randomized controlled feasibility trial. Community-living. Individuals who were ⩾50 years old with a unilateral lower limb amputation. Wii.n.Walk consisted of Wii Fit training, 3x/week (40 minute sessions), for 4 weeks. Training started in the clinic in groups of 3 and graduated to unsupervised home training. Control group were trained using cognitive games. Feasibility indicators: trial process (recruitment, retention, participants' perceived benefit from the Wii.n.Walk intervention measured by exit questionnaire), resources (adherence), management (participant processing, blinding), and treatment (adverse event, and Cohen's d effect size and variance). Primary clinical outcome: walking capacity measured using the 2 Minute Walk Test at baseline, end of treatment, and 3-week retention. Of 28 randomized participants, 24 completed the trial (12/arm). Median (range) age was 62.0 (50-78) years. Mean (SD) score for perceived benefit from the Wii.n.Walk intervention was 38.9/45 (6.8). Adherence was 83.4%. The effect sizes for the 2 Minute Walk Test were 0.5 (end of treatment) and 0.6 (3-week retention) based on intention to treat with imputed data; and 0.9 (end of treatment) and 1.2 (3-week retention) based on per protocol analysis. The required sample size for a future larger RCT was deemed to be 72 (36 per arm). The results suggested the feasibility of the Wii.n.Walk with a medium effect size for improving walking capacity. Future larger randomized controlled trials investigating efficacy are warranted.

Illumination estimation via thin-plate spline interpolation.

PubMed

Shi, Lilong; Xiong, Weihua; Funt, Brian

2011-05-01

Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.

The evolution of body size and shape in the human career

PubMed Central

Grabowski, Mark; Hatala, Kevin G.; Richmond, Brian G.

2016-01-01

Body size is a fundamental biological property of organisms, and documenting body size variation in hominin evolution is an important goal of palaeoanthropology. Estimating body mass appears deceptively simple but is laden with theoretical and pragmatic assumptions about best predictors and the most appropriate reference samples. Modern human training samples with known masses are arguably the ‘best’ for estimating size in early bipedal hominins such as the australopiths and all members of the genus Homo, but it is not clear if they are the most appropriate priors for reconstructing the size of the earliest putative hominins such as Orrorin and Ardipithecus. The trajectory of body size evolution in the early part of the human career is reviewed here and found to be complex and nonlinear. Australopith body size varies enormously across both space and time. The pre-erectus early Homo fossil record from Africa is poor and dominated by relatively small-bodied individuals, implying that the emergence of the genus Homo is probably not linked to an increase in body size or unprecedented increases in size variation. Body size differences alone cannot explain the observed variation in hominin body shape, especially when examined in the context of small fossil hominins and pygmy modern humans. This article is part of the themed issue ‘Major transitions in human evolution’. PMID:27298459

The evolution of body size and shape in the human career.

PubMed

Jungers, William L; Grabowski, Mark; Hatala, Kevin G; Richmond, Brian G

2016-07-05

Body size is a fundamental biological property of organisms, and documenting body size variation in hominin evolution is an important goal of palaeoanthropology. Estimating body mass appears deceptively simple but is laden with theoretical and pragmatic assumptions about best predictors and the most appropriate reference samples. Modern human training samples with known masses are arguably the 'best' for estimating size in early bipedal hominins such as the australopiths and all members of the genus Homo, but it is not clear if they are the most appropriate priors for reconstructing the size of the earliest putative hominins such as Orrorin and Ardipithecus The trajectory of body size evolution in the early part of the human career is reviewed here and found to be complex and nonlinear. Australopith body size varies enormously across both space and time. The pre-erectus early Homo fossil record from Africa is poor and dominated by relatively small-bodied individuals, implying that the emergence of the genus Homo is probably not linked to an increase in body size or unprecedented increases in size variation. Body size differences alone cannot explain the observed variation in hominin body shape, especially when examined in the context of small fossil hominins and pygmy modern humans.This article is part of the themed issue 'Major transitions in human evolution'. © 2016 The Author(s).

Cycle training induces muscle hypertrophy and strength gain: strategies and mechanisms.

PubMed

Ozaki, Hayao; Loenneke, J P; Thiebaud, R S; Abe, T

2015-03-01

Cycle training is widely performed as a major part of any exercise program seeking to improve aerobic capacity and cardiovascular health. However, the effect of cycle training on muscle size and strength gain still requires further insight, even though it is known that professional cyclists display larger muscle size compared to controls. Therefore, the purpose of this review is to discuss the effects of cycle training on muscle size and strength of the lower extremity and the possible mechanisms for increasing muscle size with cycle training. It is plausible that cycle training requires a longer period to significantly increase muscle size compared to typical resistance training due to a much slower hypertrophy rate. Cycle training induces muscle hypertrophy similarly between young and older age groups, while strength gain seems to favor older adults, which suggests that the probability for improving in muscle quality appears to be higher in older adults compared to young adults. For young adults, higher-intensity intermittent cycling may be required to achieve strength gains. It also appears that muscle hypertrophy induced by cycle training results from the positive changes in muscle protein net balance.

The generalization ability of online SVM classification based on Markov sampling.

PubMed

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Resistance versus Balance Training to Improve Postural Control in Parkinson's Disease: A Randomized Rater Blinded Controlled Study

PubMed Central

Schlenstedt, Christian; Paschen, Steffen; Kruse, Annika; Raethjen, Jan; Weisser, Burkhard; Deuschl, Günther

2015-01-01

Background Reduced muscle strength is an independent risk factor for falls and related to postural instability in individuals with Parkinson’s disease. The ability of resistance training to improve postural control still remains unclear. Objective To compare resistance training with balance training to improve postural control in people with Parkinson’s disease. Methods 40 patients with idiopathic Parkinson’s disease (Hoehn&Yahr: 2.5–3.0) were randomly assigned into resistance or balance training (2x/week for 7 weeks). Assessments were performed at baseline, 8- and 12-weeks follow-up: primary outcome: Fullerton Advanced Balance (FAB) scale; secondary outcomes: center of mass analysis during surface perturbations, Timed-up-and-go-test, Unified Parkinson’s Disease Rating Scale, Clinical Global Impression, gait analysis, maximal isometric leg strength, PDQ-39, Beck Depression Inventory. Clinical tests were videotaped and analysed by a second rater, blind to group allocation and assessment time. Results 32 participants (resistance training: n = 17, balance training: n = 15; 8 drop-outs) were analyzed at 8-weeks follow-up. No significant difference was found in the FAB scale when comparing the effects of the two training types (p = 0.14; effect size (Cohen’s d) = -0.59). Participants from the resistance training group, but not from the balance training group significantly improved on the FAB scale (resistance training: +2.4 points, Cohen’s d = -0.46; balance training: +0.3 points, Cohen’s d = -0.08). Within the resistance training group, improvements of the FAB scale were significantly correlated with improvements of rate of force development and stride time variability. No significant differences were found in the secondary outcome measures when comparing the training effects of both training types. Conclusions The difference between resistance and balance training to improve postural control in people with Parkinson’s disease was small and not significant with this sample size. There was weak evidence that freely coordinated resistance training might be more effective than balance training. Our results indicate a relationship between the enhancement of rate of force development and the improvement of postural control. Trial Registration ClinicalTrials.gov ID: NCT02253563 PMID:26501562

Do genetic variations alter the effects of exercise training on cardiovascular disease and can we identify the candidate variants now or in the future?

PubMed

Hagberg, James M

2011-09-01

Cardiovascular disease (CVD) and CVD risk factors are highly heritable, and numerous lines of evidence indicate they have a strong genetic basis. While there is nothing known about the interactive effects of genetics and exercise training on CVD itself, there is at least some literature addressing their interactive effect on CVD risk factors. There is some evidence indicating that CVD risk factor responses to exercise training are also heritable and, thus, may have a genetic basis. While roughly 100 studies have reported significant effects of genetic variants on CVD risk factor responses to exercise training, no definitive conclusions can be generated at the present time, because of the lack of consistent and replicated results and the small sample sizes evident in most studies. There is some evidence supporting "possible" candidate genes that may affect these responses to exercise training: APO E and CETP for plasma lipoprotein-lipid profiles; eNOS, ACE, EDN1, and GNB3 for blood pressure; PPARG for type 2 diabetes phenotypes; and FTO and BAR genes for obesity-related phenotypes. However, while genotyping technologies and statistical methods are advancing rapidly, the primary limitation in this field is the need to generate what in terms of exercise intervention studies would be almost incomprehensible sample sizes. Most recent diabetes, obesity, and blood pressure genetic studies have utilized populations of 10,000-250,000 subjects, which result in the necessary statistical power to detect the magnitude of effects that would probably be expected for the impact of an individual gene on CVD risk factor responses to exercise training. Thus at this time it is difficult to see how this field will advance in the future to the point where robust, consistent, and replicated data are available to address these issues. However, the results of recent large-scale genomewide association studies for baseline CVD risk factors may drive future hypothesis-driven exercise training intervention studies in smaller populations addressing the impact of specific genetic variants on well-defined physiological phenotypes.

Machine Learning for Education: Learning to Teach

DTIC Science & Technology

2016-12-01

such as commercial aviation, healthcare, and military operations. In the context of military applications, serious gaming – the training warfighters...problems. Playing these games not only allowed the warfighter to discover and learn new tactics, techniques, and procedures, but also allowed the...collecting information across relevant sample sizes have motivated a data-driven, game - based simulation approach. For example, industry and academia alike

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

Effects of Wearable Sensor-Based Balance and Gait Training on Balance, Gait, and Functional Performance in Healthy and Patient Populations: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

PubMed

Gordt, Katharina; Gerhardy, Thomas; Najafi, Bijan; Schwenk, Michael

2018-01-01

Wearable sensors (WS) can accurately measure body motion and provide interactive feedback for supporting motor learning. This review aims to summarize current evidence for the effectiveness of WS training for improving balance, gait and functional performance. A systematic literature search was performed in PubMed, Cochrane, Web of Science, and CINAHL. Randomized controlled trials (RCTs) using a WS exercise program were included. Study quality was examined by the PEDro scale. Meta-analyses were conducted to estimate the effects of WS balance training on the most frequently reported outcome parameters. Eight RCTs were included (Parkinson n = 2, stroke n = 1, Parkinson/stroke n = 1, peripheral neuropathy n = 2, frail older adults n = 1, healthy older adults n = 1). The sample size ranged from n = 20 to 40. Three types of training paradigms were used: (1) static steady-state balance training, (2) dynamic steady-state balance training, which includes gait training, and (3) proactive balance training. RCTs either used one type of training paradigm (type 2: n = 1, type 3: n = 3) or combined different types of training paradigms within their intervention (type 1 and 2: n = 2; all types: n = 2). The meta-analyses revealed significant overall effects of WS training on static steady-state balance outcomes including mediolateral (eyes open: Hedges' g = 0.82, CI: 0.43-1.21; eyes closed: g = 0.57, CI: 0.14-0.99) and anterior-posterior sway (eyes open: g = 0.55, CI: 0.01-1.10; eyes closed: g = 0.44, CI: 0.02-0.86). No effects on habitual gait speed were found in the meta-analysis (g = -0.19, CI: -0.68 to 0.29). Two RCTs reported significant improvements for selected gait variables including single support time, and fast gait speed. One study identified effects on proactive balance (Alternate Step Test), but no effects were found for the Timed Up and Go test and the Berg Balance Scale. Two studies reported positive results on feasibility and usability. Only one study was performed in an unsupervised setting. This review provides evidence for a positive effect of WS training on static steady-state balance in studies with usual care controls and studies with conventional balance training controls. Specific gait parameters and proactive balance measures may also be improved by WS training, yet limited evidence is available. Heterogeneous training paradigms, small sample sizes, and short intervention durations limit the validity of our findings. Larger studies are required for estimating the true potential of WS technology. © 2017 S. Karger AG, Basel.

Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data

USGS Publications Warehouse

Liang, Lu; Chen, Yanlei; Hawbaker, Todd J.; Zhu, Zhi-Liang; Gong, Peng

2014-01-01

Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.

To Go or Not to Go: A Proof of Concept Study Testing Food-Specific Inhibition Training for Women with Eating and Weight Disorders.

PubMed

Turton, Robert; Nazar, Bruno P; Burgess, Emilee E; Lawrence, Natalia S; Cardi, Valentina; Treasure, Janet; Hirsch, Colette R

2018-01-01

Inefficient food-specific inhibitory control is a potential mechanism that underlies binge eating in bulimia nervosa and binge eating disorder. Go/no-go training tools have been developed to increase inhibitory control over eating impulses. Using a within-subjects design, this study examined whether one session of food-specific go/no-go training, versus general inhibitory control training, modifies eating behaviour. The primary outcome measure was food consumption on a taste test following each training session. Women with bulimia nervosa and binge eating disorder had small non-significant reductions in high-calorie food consumption on the taste test following the food-specific compared with the general training. There were no effects on eating disorder symptomatic behaviour (i.e. binge eating/purging) in the 24 h post-training. The training task was found to be acceptable by the clinical groups. More research is needed with larger sample sizes to determine the effectiveness of this training approach for clinical populations. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

The effect of peer-group size on the delivery of feedback in basic life support refresher training: a cluster randomized controlled trial.

PubMed

Cho, Youngsuk; Je, Sangmo; Yoon, Yoo Sang; Roh, Hye Rin; Chang, Chulho; Kang, Hyunggoo; Lim, Taeho

2016-07-04

Students are largely providing feedback to one another when instructor facilitates peer feedback rather than teaching in group training. The number of students in a group affect the learning of students in the group training. We aimed to investigate whether a larger group size increases students' test scores on a post-training test with peer feedback facilitated by instructor after video-guided basic life support (BLS) refresher training. Students' one-rescuer adult BLS skills were assessed by a 2-min checklist-based test 1 year after the initial training. A cluster randomized controlled trial was conducted to evaluate the effect of student number in a group on BLS refresher training. Participants included 115 final-year medical students undergoing their emergency medicine clerkship. The median number of students was 8 in the large groups and 4 in the standard group. The primary outcome was to examine group differences in post-training test scores after video-guided BLS training. Secondary outcomes included the feedback time, number of feedback topics, and results of end-of-training evaluation questionnaires. Scores on the post-training test increased over three consecutive tests with instructor-led peer feedback, but not differ between large and standard groups. The feedback time was longer and number of feedback topics generated by students were higher in standard groups compared to large groups on the first and second tests. The end-of-training questionnaire revealed that the students in large groups preferred the smaller group size compared to their actual group size. In this BLS refresher training, the instructor-led group feedback increased the test score after tutorial video-guided BLS learning, irrespective of the group size. A smaller group size allowed more participations in peer feedback.

Methods employed for chest radiograph interpretation education for radiographers: A systematic review of the literature.

PubMed

McLaughlin, L; McConnell, J; McFadden, S; Bond, R; Hughes, C

2017-11-01

This systematic review aimed to determine the strength of evidence available in the literature on the effect of training to develop the skills required by radiographers to interpret plain radiography chest images. Thirteen articles feature within the review. Sample size varied from one reporting radiographer to 148 radiography students/experienced radiographers. The quality of the articles achieved a mean score of 7.5/10, indicating the evidence is strong and the quality of studies in this field is high. Investigative approaches included audit of participants' performance in clinical practice post formal training, evaluation of informal training and the impact of short feedback sessions on performance. All studies demonstrated positive attributions on user performance. Using a combination of training techniques can help maximise learning and accommodate those with different preferred learning types. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Randomized control trial of computer-based training targeting alertness in older adults: the ALERT trial protocol.

PubMed

VanVleet, Thomas; Voss, Michelle; Dabit, Sawsan; Mitko, Alex; DeGutis, Joseph

2018-05-03

Healthy aging is associated with a decline in multiple functional domains including perception, attention, short and long-term memory, reasoning, decision-making, as well as cognitive and motor control functions; all of which are significantly modulated by an individual's level of alertness. The control of alertness also significantly declines with age and contributes to increased lapses of attention in everyday life, ranging from minor memory slips to a lack of vigilance and increased risk of falls or motor-vehicle accidents. Several experimental behavioral therapies designed to remediate age-related cognitive decline have been developed, but differ widely in content, method and dose. Preliminary studies demonstrate that Tonic and Phasic Alertness Training (TAPAT) can improve executive functions in older adults and may be a useful adjunct treatment to enhance benefits gained in other clinically validated treatments. The purpose of the current trial (referred to as the Attention training for Learning Enhancement and Resilience Trial or ALERT) is to compare TAPAT to an active control training condition, include a larger sample of patients, and assess both cognitive and functional outcomes. We will employ a multi-site, longitudinal, blinded randomized controlled trial (RCT) design with a target sample of 120 patients with age-related cognitive decline. Patients will be asked to complete 36 training sessions remotely (30 min/day, 5 days a week, over 3 months) of either the experimental TAPAT training program or an active control computer games condition. Patients will be assessed on a battery of cognitive and functional outcomes at four time points, including: a) immediately before training, b) halfway through training, c) within forty-eight hours post completion of total training, and d) after a three-month no-contact period post completion of total training, to assess the longevity of potential training effects. The strengths of this protocol are that it tests an innovative, in-home administered treatment that targets a fundamental deficit in adults with age-related cognitive decline; employs highly sensitive computer-based assessments of cognition as well as functional abilities, and incorporates a large sample size in an RCT design. ClinicalTrials.gov identifier: NCT02416401.

Increasing Complexity of Clinical Research in Gastroenterology: Implications for Training Clinician-Scientists

PubMed Central

Scott, Frank I.; McConnell, Ryan A.; Lewis, Matthew E.; Lewis, James D.

2014-01-01

Background Significant advances have been made in clinical and epidemiologic research methods over the past 30 years. We sought to demonstrate the impact of these advances on published research in gastroenterology from 1980 to 2010. Methods Three journals (Gastroenterology, Gut, and American Journal of Gastroenterology) were selected for evaluation given their continuous publication during the study period. Twenty original clinical articles were randomly selected from each journal from 1980, 1990, 2000, and 2010. Each article was assessed for topic studied, whether the outcome was clinical or physiologic, study design, sample size, number of authors and centers collaborating, and reporting of statistical methods such as sample size calculations, p-values, confidence intervals, and advanced techniques such as bioinformatics or multivariate modeling. Research support with external funding was also recorded. Results A total of 240 articles were included in the study. From 1980 to 2010, there was a significant increase in analytic studies (p<0.001), clinical outcomes (p=0.003), median number of authors per article (p<0.001), multicenter collaboration (p<0.001), sample size (p<0.001), and external funding (p<0.001)). There was significantly increased reporting of p-values (p=0.01), confidence intervals (p<0.001), and power calculations (p<0.001). There was also increased utilization of large multicenter databases (p=0.001), multivariate analyses (p<0.001), and bioinformatics techniques (p=0.001). Conclusions There has been a dramatic increase in complexity in clinical research related to gastroenterology and hepatology over the last three decades. This increase highlights the need for advanced training of clinical investigators to conduct future research. PMID:22475957

Robot-assisted upper extremity rehabilitation for cervical spinal cord injuries: a systematic scoping review.

PubMed

Singh, Hardeep; Unger, Janelle; Zariffa, José; Pakosh, Maureen; Jaglal, Susan; Craven, B Catharine; Musselman, Kristin E

2018-01-15

Abstact Purpose: To provide an overview of the feasibility and outcomes of robotic-assisted upper extremity training for individuals with cervical spinal cord injury (SCI), and to identify gaps in current research and articulate future research directions. A systematic search was conducted using Medline, Embase, PsycINFO, CCTR, CDSR, CINAHL and PubMed on June 7, 2017. Search terms included 3 themes: (1) robotics; (2) SCI; (3) upper extremity. Studies using robots for upper extremity rehabilitation among individuals with cervical SCI were included. Identified articles were independently reviewed by two researchers and compared to pre-specified criteria. Disagreements regarding article inclusion were resolved through discussion. The modified Downs and Black checklist was used to assess article quality. Participant characteristics, study and intervention details, training outcomes, robot features, study limitations and recommendations for future studies were abstracted from included articles. Twelve articles (one randomized clinical trial, six case series, five case studies) met the inclusion criteria. Five robots were exoskeletons and three were end-effectors. Sample sizes ranged from 1 to 17 subjects. Articles had variable quality, with quality scores ranging from 8 to 20. Studies had a low internal validity primarily from lack of blinding or a control group. Individuals with mild-moderate impairments showed the greatest improvements on body structure/function and performance-level measures. This review is limited by the small number of articles, low-sample sizes and the diversity of devices and their associated training protocols, and outcome measures. Preliminary evidence suggests robot-assisted interventions are safe, feasible and can reduce active assistance provided by therapists. Implications for rehabilitation Robot-assisted upper extremity training for individuals with cervical spinal cord injury is safe, feasible and can reduce hands-on assistance provided by therapists. Future research in robotics rehabilitation with individuals with spinal cord injury is needed to determine the optimal device and training protocol as well as effectiveness.

Visual search by chimpanzees (Pan): assessment of controlling relations.

PubMed Central

Tomonaga, M

1995-01-01

Three experimentally sophisticated chimpanzees (Pan), Akira, Chloe, and Ai, were trained on visual search performance using a modified multiple-alternative matching-to-sample task in which a sample stimulus was followed by the search display containing one target identical to the sample and several uniform distractors (i.e., negative comparison stimuli were identical to each other). After they acquired this task, they were tested for transfer of visual search performance to trials in which the sample was not followed by the uniform search display (odd-item search). Akira showed positive transfer of visual search performance to odd-item search even when the display size (the number of stimulus items in the search display) was small, whereas Chloe and Ai showed a transfer only when the display size was large. Chloe and Ai used some nonrelational cues such as perceptual isolation of the target among uniform distractors (so-called pop-out). In addition to the odd-item search test, various types of probe trials were presented to clarify the controlling relations in multiple-alternative matching to sample. Akira showed a decrement of accuracy as a function of the display size when the search display was nonuniform (i.e., each "distractor" stimulus was not the same), whereas Chloe and Ai showed perfect performance. Furthermore, when the sample was identical to the uniform distractors in the search display, Chloe and Ai never selected an odd-item target, but Akira selected it when the display size was large. These results indicated that Akira's behavior was controlled mainly by relational cues of target-distractor oddity, whereas an identity relation between the sample and the target strongly controlled the performance of Chloe and Ai. PMID:7714449

Beneficial effects of polyethylene packages containing micrometer-sized silver particles on the quality and shelf life of dried barberry (Berberis vulgaris).

PubMed

Motlagh, N Valipoor; Mosavian, M T Hamed; Mortazavi, S A; Tamizi, A

2012-01-01

In this research, the effects of low-density polyethylene (LDPE) packages containing micrometer-sized silver particles (LDPE-Ag) on microbial and sensory factors of dried barberry were investigated in comparison with the pure LDPE packages. LDPE-Ag packages with 1% and 2% concentrations of silver particles statistically caused a decrease in the microbial growth of barberry, especially in the case of mold and total bacteria count, compared with the pure LDPE packages. The taste, aroma, appearance, and total acceptance were evaluated by trained panelists using the 9-point hedonic scale. This test showed improvement of all these factors in the samples related to packages containing 1% and 2% concentrations of silver particles in comparison with other samples. Low-density polyethylene package containing micrometer-sized silver particles had beneficial effects on the sensory and microbial quality of barberry when compared with normal packing material. © 2011 Institute of Food Technologists®

Aircrew Sizing Survey 2011

DTIC Science & Technology

2014-10-01

Resource JSF Joint Strike Fighter JPATS Joint Primary Aircraft Training System USMC United States Marine Corps USAF United States Air Force LIST...Surface Anthropometry Resource (CAESAR) was developed for the Joint Strike Fighter (JSF) program. The ACSS was intended to replace the JSF-CAESAR...an aircrew sample was made in 2003 by Hudson et al. They extracted a subset, named JSF CAESAR (Joint Strike Fighter), from the Civilian American and

Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

PubMed Central

2010-01-01

Background To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. Methods The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. Results In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00) relative to private non-franchises. Service use was significantly associated with training (P = 0.00), franchise affiliation (P = 0.01), providers' years of family planning experience (P = 0.02) and the number of trained staff working at government owned clinics (P = 0.00). In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00). Conclusions These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services. PMID:21062460

Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

PubMed

Qureshi, Asma M

2010-11-09

To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00) relative to private non-franchises. Service use was significantly associated with training (P = 0.00), franchise affiliation (P = 0.01), providers' years of family planning experience (P = 0.02) and the number of trained staff working at government owned clinics (P = 0.00). In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00). These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services.

Improving communication and practical skills in working with inpatients who self-harm: a pre-test/post-test study of the effects of a training programme

PubMed Central

2014-01-01

Background Differing perspectives of self-harm may result in a struggle between patients and treatment staff. As a consequence, both sides have difficulty communicating effectively about the underlying problems and feelings surrounding self-harm. Between 2009 and 2011, a programme was developed and implemented to train mental health care staff (nurses, social workers, psychologists, psychiatrists, and occupational therapists) in how to communicate effectively with and care for patients who self-harm. An art exhibition focusing on self-harm supported the programme. Lay experts in self-harm, i.e. people who currently harm themselves, or who have harmed themselves in the past and have the skills to disseminate their knowledge and experience, played an important role throughout the programme. Methods Paired sample t-tests were conducted to measure the effects of the training programme using the Attitude Towards Deliberate Self-Harm Questionnaire, the Self-Perceived Efficacy in Dealing with Self-Harm Questionnaire, and the Patient Contact Questionnaire. Effect sizes were calculated using r. Participants evaluated the training programme with the help of a survey. The questionnaires used in the survey were analysed descriptively. Results Of the 281 persons who followed the training programme, 178 completed the questionnaires. The results show a significant increase in the total scores of the three questionnaires, with large to moderate effect sizes. Respondents were positive about the training, especially about the role of the lay expert. Conclusion A specialised training programme in how to care for patients who self-harm can result in a more positive attitude towards self-harm patients, an improved self-efficacy in caring for patients who self-harm, and a greater closeness with the patients. The deployment of lay experts is essential here. PMID:24592861

Improving communication and practical skills in working with inpatients who self-harm: a pre-test/post-test study of the effects of a training programme.

PubMed

Kool, Nienke; van Meijel, Berno; Koekkoek, Bauke; van der Bijl, Jaap; Kerkhof, Ad

2014-03-04

Differing perspectives of self-harm may result in a struggle between patients and treatment staff. As a consequence, both sides have difficulty communicating effectively about the underlying problems and feelings surrounding self-harm. Between 2009 and 2011, a programme was developed and implemented to train mental health care staff (nurses, social workers, psychologists, psychiatrists, and occupational therapists) in how to communicate effectively with and care for patients who self-harm. An art exhibition focusing on self-harm supported the programme. Lay experts in self-harm, i.e. people who currently harm themselves, or who have harmed themselves in the past and have the skills to disseminate their knowledge and experience, played an important role throughout the programme. Paired sample t-tests were conducted to measure the effects of the training programme using the Attitude Towards Deliberate Self-Harm Questionnaire, the Self-Perceived Efficacy in Dealing with Self-Harm Questionnaire, and the Patient Contact Questionnaire. Effect sizes were calculated using r. Participants evaluated the training programme with the help of a survey. The questionnaires used in the survey were analysed descriptively. Of the 281 persons who followed the training programme, 178 completed the questionnaires. The results show a significant increase in the total scores of the three questionnaires, with large to moderate effect sizes. Respondents were positive about the training, especially about the role of the lay expert. A specialised training programme in how to care for patients who self-harm can result in a more positive attitude towards self-harm patients, an improved self-efficacy in caring for patients who self-harm, and a greater closeness with the patients. The deployment of lay experts is essential here.

Training-induced neuroplasticity in young children.

PubMed

Schlaug, Gottfried; Forgeard, Marie; Zhu, Lin; Norton, Andrea; Norton, Andrew; Winner, Ellen

2009-07-01

As the main interhemispheric fiber tract, the corpus callosum (CC) is of particular importance for musicians who simultaneously engage parts of both hemispheres to process and play music. Professional musicians who began music training before the age of 7 years have larger anterior CC areas than do nonmusicians, which suggests that plasticity due to music training may occur in the CC during early childhood. However, no study has yet demonstrated that the increased CC area found in musicians is due to music training rather than to preexisting differences. We tested the hypothesis that approximately 29 months of instrumental music training would cause a significant increase in the size of particular subareas of the CC known to have fibers that connect motor-related areas of both hemispheres. On the basis of total weekly practice time, a sample of 31 children aged 5-7 was divided into three groups: high-practicing, low-practicing, and controls. No CC size differences were seen at base line, but differences emerged after an average of 29 months of observation in the high-practicing group in the anterior midbody of the CC (which connects premotor and supplementary motor areas of the two hemispheres). Total weekly music exposure predicted degree of change in this subregion of the CC as well as improvement on a motor-sequencing task. Our results show that it is intense musical experience/practice, not preexisting differences, that is responsible for the larger anterior CC area found in professional adult musicians.

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks.

PubMed

Lavado Contador, J F; Maneta, M; Schnabel, S

2006-10-01

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.

Functional polymorphisms associated with human muscle size and strength.

PubMed

Thompson, Paul D; Moyna, Niall; Seip, Richard; Price, Thomas; Clarkson, Priscilla; Angelopoulos, Theodore; Gordon, Paul; Pescatello, Linda; Visich, Paul; Zoeller, Robert; Devaney, Joseph M; Gordish, Heather; Bilbie, Stephen; Hoffman, Eric P

2004-07-01

Skeletal muscle is critically important to human performance and health, but little is known of the genetic factors influencing muscle size, strength, and its response to exercise training. The Functional single nucleotide polymorphisms (SNP) Associated with Muscle Size and Strength, or FAMuSS, Study is a multicenter, NIH-funded program to examine the influence of gene polymorphisms on skeletal muscle size and strength before and after resistance exercise training. One thousand men and women, age 18 - 40 yr, will train their nondominant arm for 12 wk. Skeletal muscle size (magnetic resonance imaging) and isometric and dynamic strength will be measured before and after training. Individuals whose baseline values or response to training deviate > or = 1.5 SD will be defined as outliers and examined for genetic variants. Initially candidate genes previously associated with muscle performance will be examined, but the study will ultimately attempt to identify genes associated with muscle performance. FAMuSS should help identify genetic factors associated with muscle performance and the response to exercise training. Such insight should contribute to our ability to predict the individual response to exercise training but may also contribute to understanding better muscle physiology, to identifying individuals who are susceptible to muscle loss with environmental challenge, and to developing pharmacologic agents capable of preserving muscle size and function.

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Portfolio of automated trading systems: complexity and learning set size issues.

PubMed

Raudys, Sarunas

2013-03-01

In this paper, we consider using profit/loss histories of multiple automated trading systems (ATSs) as N input variables in portfolio management. By means of multivariate statistical analysis and simulation studies, we analyze the influences of sample size (L) and input dimensionality on the accuracy of determining the portfolio weights. We find that degradation in portfolio performance due to inexact estimation of N means and N(N - 1)/2 correlations is proportional to N/L; however, estimation of N variances does not worsen the result. To reduce unhelpful sample size/dimensionality effects, we perform a clustering of N time series and split them into a small number of blocks. Each block is composed of mutually correlated ATSs. It generates an expert trading agent based on a nontrainable 1/N portfolio rule. To increase the diversity of the expert agents, we use training sets of different lengths for clustering. In the output of the portfolio management system, the regularized mean-variance framework-based fusion agent is developed in each walk-forward step of an out-of-sample portfolio validation experiment. Experiments with the real financial data (2003-2012) confirm the effectiveness of the suggested approach.

Factors affecting initial training success of blood glucose testing in captive chimpanzees (Pan troglodytes).

PubMed

Reamer, Lisa A; Haller, Rachel L; Thiele, Erica J; Freeman, Hani D; Lambeth, Susan P; Schapiro, Steven J

2014-01-01

Type 2 diabetes can be a problem for captive chimpanzees. Accurate blood glucose (BG) readings are necessary to monitor and treat this disease. Thus, obtaining voluntary samples from primates through positive reinforcement training (PRT) is critical. The current study assessed the voluntary participation of 123 chimpanzees in BG sampling and investigated factors that may contribute to individual success. All subjects participate in regular PRT sessions as part of a comprehensive behavioral management program. Basic steps involved in obtaining BG values include: voluntarily presenting a finger/toe; allowing digit disinfection; holding for the lancet device; and allowing blood collection onto a glucometer test strip for analysis. We recorded the level of participation (none, partial, or complete) when each chimpanzee was first asked to perform the testing procedure. Nearly 30% of subjects allowed the entire procedure in one session, without any prior specific training for the target behavior. Factors that affected this initial successful BG testing included sex, personality (chimpanzees rated higher on the factor "openness" were more likely to participate with BG testing), and past training performance for "present-for-injection" (chimpanzees that presented for their most recent anesthetic injection were more likely to participate). Neither age, rearing history, time since most recent anesthetic event nor social group size significantly affected initial training success. These results have important implications for captive management and training program success, underlining individual differences in training aptitude and the need for developing individual management plans in order to provide optimal care and treatment for diabetic chimpanzees in captivity. © 2014 Wiley Periodicals, Inc.

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China.

PubMed

Tang, Yunwei; Jing, Linhai; Li, Hui; Liu, Qingjie; Yan, Qi; Li, Xiuxia

2016-11-22

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k -nearest neighbor ( k -NN) method produced the greatest accuracy. A geostatistically-weighted k -NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer's and user's accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.

Topological Analysis and Gaussian Decision Tree: Effective Representation and Classification of Biosignals of Small Sample Size.

PubMed

Zhang, Zhifei; Song, Yang; Cui, Haochen; Wu, Jayne; Schwartz, Fernando; Qi, Hairong

2017-09-01

Bucking the trend of big data, in microdevice engineering, small sample size is common, especially when the device is still at the proof-of-concept stage. The small sample size, small interclass variation, and large intraclass variation, have brought biosignal analysis new challenges. Novel representation and classification approaches need to be developed to effectively recognize targets of interests with the absence of a large training set. Moving away from the traditional signal analysis in the spatiotemporal domain, we exploit the biosignal representation in the topological domain that would reveal the intrinsic structure of point clouds generated from the biosignal. Additionally, we propose a Gaussian-based decision tree (GDT), which can efficiently classify the biosignals even when the sample size is extremely small. This study is motivated by the application of mastitis detection using low-voltage alternating current electrokinetics (ACEK) where five categories of bisignals need to be recognized with only two samples in each class. Experimental results demonstrate the robustness of the topological features as well as the advantage of GDT over some conventional classifiers in handling small dataset. Our method reduces the voltage of ACEK to a safe level and still yields high-fidelity results with a short assay time. This paper makes two distinctive contributions to the field of biosignal analysis, including performing signal processing in the topological domain and handling extremely small dataset. Currently, there have been no related works that can efficiently tackle the dilemma between avoiding electrochemical reaction and accelerating assay process using ACEK.

Disaster response team FAST skills training with a portable ultrasound simulator compared to traditional training: pilot study.

PubMed

Paddock, Michael T; Bailitz, John; Horowitz, Russ; Khishfe, Basem; Cosby, Karen; Sergel, Michelle J

2015-03-01

Pre-hospital focused assessment with sonography in trauma (FAST) has been effectively used to improve patient care in multiple mass casualty events throughout the world. Although requisite FAST knowledge may now be learned remotely by disaster response team members, traditional live instructor and model hands-on FAST skills training remains logistically challenging. The objective of this pilot study was to compare the effectiveness of a novel portable ultrasound (US) simulator with traditional FAST skills training for a deployed mixed provider disaster response team. We randomized participants into one of three training groups stratified by provider role: Group A. Traditional Skills Training, Group B. US Simulator Skills Training, and Group C. Traditional Skills Training Plus US Simulator Skills Training. After skills training, we measured participants' FAST image acquisition and interpretation skills using a standardized direct observation tool (SDOT) with healthy models and review of FAST patient images. Pre- and post-course US and FAST knowledge were also assessed using a previously validated multiple-choice evaluation. We used the ANOVA procedure to determine the statistical significance of differences between the means of each group's skills scores. Paired sample t-tests were used to determine the statistical significance of pre- and post-course mean knowledge scores within groups. We enrolled 36 participants, 12 randomized to each training group. Randomization resulted in similar distribution of participants between training groups with respect to provider role, age, sex, and prior US training. For the FAST SDOT image acquisition and interpretation mean skills scores, there was no statistically significant difference between training groups. For US and FAST mean knowledge scores, there was a statistically significant improvement between pre- and post-course scores within each group, but again there was not a statistically significant difference between training groups. This pilot study of a deployed mixed-provider disaster response team suggests that a novel portable US simulator may provide equivalent skills training in comparison to traditional live instructor and model training. Further studies with a larger sample size and other measures of short- and long-term clinical performance are warranted.

Changes in myonuclear domain size do not precede muscle hypertrophy during prolonged resistance-type exercise training.

PubMed

Snijders, T; Smeets, J S J; van Kranenburg, J; Kies, A K; van Loon, L J C; Verdijk, L B

2016-02-01

Muscle fibre hypertrophy is accompanied by an increase in myonuclear number, an increase in myonuclear domain size or both. It has been suggested that increases in myonuclear domain size precede myonuclear accretion and subsequent muscle fibre hypertrophy during prolonged exercise training. In this study, we assessed the changes in muscle fibre size, myonuclear and satellite cell content throughout 12 weeks of resistance-type exercise training in young men. Twenty-two young men (23 ± 1 year) were assigned to a progressive, 12-weeks resistance-type exercise training programme (3 sessions per week). Muscle biopsies from the vastus lateralis muscle were taken before and after 2, 4, 8 and 12 weeks of exercise training. Muscle fibre size, myonuclear content, myonuclear domain size and satellite cell content were assessed by immunohistochemistry. Type I and type II muscle fibre size increased gradually throughout the 12 weeks of training (type I: 18 ± 5%, type II: 41 ± 6%, P < 0.01). Myonuclear content increased significantly over time in both the type I (P < 0.01) and type II (P < 0.001) muscle fibres. No changes in type I and type II myonuclear domain size were observed at any time point throughout the intervention. Satellite cell content increased significantly over time in both type I and type II muscle fibres (P < 0.001). Increases in myonuclear domain size do not appear to drive myonuclear accretion and muscle fibre hypertrophy during prolonged resistance-type exercise training in vivo in humans. © 2015 Scandinavian Physiological Society. Published by John Wiley & Sons Ltd.

Interventions to improve mental health nurses' skills, attitudes, and knowledge related to people with a diagnosis of borderline personality disorder: Systematic review.

PubMed

Dickens, Geoffrey L; Hallett, Nutmeg; Lamont, Emma

2016-04-01

There is some evidence that mental health nurses have poor attitudes towards people with a diagnosis of borderline personality disorder and that this might impact negatively on the development of helpful therapeutic relationships. We aimed to collate the current evidence about interventions that have been devised to improve the responses of mental health nurses towards this group of people. Systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses statement. Comprehensive terms were used to search CINAHL, PsycINFO, Medline, Biomedical Reference Collection: Comprehensive, Web of Science, ASSIA, Cochrane Library, EMBASE, ProQuest [including Dissertations/Theses], and Google Scholar for relevant studies. Included studies were those that described an intervention whose aim was to improve attitudes towards, knowledge about or responses to people with a diagnosis of borderline personality disorder. The sample described had to include mental health nurses. Information about study characteristics, intervention content and mode of delivery was extracted. Study quality was assessed, and effect sizes of interventions and potential moderators of those interventions were extracted and converted to Cohen's d to aid comparison. The search strategy yielded a total of eight studies, half of which were judged to be methodologically weak with the remaining four studies judged to be of moderate quality. Only one study employed a control group. The largest effect sizes were found for changes related to cognitive attitudes including knowledge; smaller effect sizes were found in relation to changes in affective outcomes. Self-reported behavioural change in the form of increased use of components of Dialectical Behaviour Therapy following training in this treatment was associated with moderate effect sizes. The largest effect sizes were found among those with poorer baseline attitudes and without previous training about borderline personality disorder. There is a dearth of high quality evidence about the attitudes of mental health nurses towards people with a diagnosis of borderline personality disorder. This is an important gap since nurses hold the poorest attitudes of professional disciplines involved in the care of this group. Further work is needed to ascertain the most effective elements of training programmes; this should involve trials of interventions in samples that are compared against adequately matched control groups. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effectiveness of gait training using an electromechanical gait trainer, with and without functional electric stimulation, in subacute stroke: a randomized controlled trial.

PubMed

Tong, Raymond K; Ng, Maple F; Li, Leonard S

2006-10-01

To compare the therapeutic effects of conventional gait training (CGT), gait training using an electromechanical gait trainer (EGT), and gait training using an electromechanical gait trainer with functional electric stimulation (EGT-FES) in people with subacute stroke. Nonblinded randomized controlled trial. Rehabilitation hospital for adults. Fifty patients were recruited within 6 weeks after stroke onset; 46 of these completed the 4-week training period. Participants were randomly assigned to 1 of 3 gait intervention groups: CGT, EGT, or EGT-FES. The experimental intervention was a 20-minute session per day, 5 days a week (weekdays) for 4 weeks. In addition, all participants received their 40-minute sessions of regular physical therapy every weekday as part of their treatment by the hospital. Five-meter walking speed test, Elderly Mobility Scale (EMS), Berg Balance Scale, Functional Ambulatory Category (FAC), Motricity Index leg subscale, FIM instrument score, and Barthel Index. The EGT and EGT-FES groups had statistically significantly more improvement than the CGT group in the 5-m walking speed test (CGT vs EGT, P=.011; CGT vs EGT-FES, P=.001), Motricity Index (CGT vs EGT-FES, P=.011), EMS (CGT vs EGT, P=.006; CGT vs EGT-FES, P=.009), and FAC (CGT vs EGT, P=.005; CGT vs EGT-FES, P=.002) after the 4 weeks of training. No statistically significant differences were found between the EGT and EGT-FES groups in all outcome measures. In this sample with subacute stroke, participants who trained on the electromechanical gait trainer with body-weight support, with or without FES, had a faster gait, better mobility, and improvement in functional ambulation than participants who underwent conventional gait training. Future studies with assessor blinding and larger sample sizes are warranted.

Comparison of machine learned approaches for thyroid nodule characterization from shear wave elastography images

NASA Astrophysics Data System (ADS)

Pereira, Carina; Dighe, Manjiri; Alessio, Adam M.

2018-02-01

Various Computer Aided Diagnosis (CAD) systems have been developed that characterize thyroid nodules using the features extracted from the B-mode ultrasound images and Shear Wave Elastography images (SWE). These features, however, are not perfect predictors of malignancy. In other domains, deep learning techniques such as Convolutional Neural Networks (CNNs) have outperformed conventional feature extraction based machine learning approaches. In general, fully trained CNNs require substantial volumes of data, motivating several efforts to use transfer learning with pre-trained CNNs. In this context, we sought to compare the performance of conventional feature extraction, fully trained CNNs, and transfer learning based, pre-trained CNNs for the detection of thyroid malignancy from ultrasound images. We compared these approaches applied to a data set of 964 B-mode and SWE images from 165 patients. The data were divided into 80% training/validation and 20% testing data. The highest accuracies achieved on the testing data for the conventional feature extraction, fully trained CNN, and pre-trained CNN were 0.80, 0.75, and 0.83 respectively. In this application, classification using a pre-trained network yielded the best performance, potentially due to the relatively limited sample size and sub-optimal architecture for the fully trained CNN.

Computerized Cognitive Training with Older Adults: A Systematic Review

PubMed Central

Kueider, Alexandra M.; Parisi, Jeanine M.; Gross, Alden L.; Rebok, George W.

2012-01-01

A systematic review to examine the efficacy of computer-based cognitive interventions for cognitively healthy older adults was conducted. Studies were included if they met the following criteria: average sample age of at least 55 years at time of training; participants did not have Alzheimer’s disease or mild cognitive impairment; and the study measured cognitive outcomes as a result of training. Theoretical articles, review articles, and book chapters that did not include original data were excluded. We identified 151 studies published between 1984 and 2011, of which 38 met inclusion criteria and were further classified into three groups by the type of computerized program used: classic cognitive training tasks, neuropsychological software, and video games. Reported pre-post training effect sizes for intervention groups ranged from 0.06 to 6.32 for classic cognitive training interventions, 0.19 to 7.14 for neuropsychological software interventions, and 0.09 to 1.70 for video game interventions. Most studies reported older adults did not need to be technologically savvy in order to successfully complete or benefit from training. Overall, findings are comparable or better than those from reviews of more traditional, paper-and-pencil cognitive training approaches suggesting that computerized training is an effective, less labor intensive alternative. PMID:22792378

Resistance Training Effects on Metabolic Function Among Youth: A Systematic Review.

PubMed

Bea, Jennifer W; Blew, Robert M; Howe, Carol; Hetherington-Rauth, Megan; Going, Scott B

2017-08-01

This systematic review evaluates the relationship between resistance training and metabolic function in youth. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, and ClinicalTrials. gov were searched for articles that (1): studied children (2); included resistance training (3); were randomized interventions; and (4) reported markers of metabolic function. The selected studies were analyzed using the Cochrane Risk-of-Bias Tool. Thirteen articles met inclusion criteria. Mean age ranged from 12.2 to 16.9 years, but most were limited to high school (n = 11) and overweight/obese (n = 12). Sample sizes (n = 22-304), session duration (40-60min), and intervention length (8-52 wks) varied. Exercise frequency was typically 2-3 d/wk. Resistance training was metabolically beneficial compared with control or resistance plus aerobic training in 5 studies overall and 3 out of the 4 studies with the fewest threats to bias (p ≤ .05); each was accompanied by beneficial changes in body composition, but only one study adjusted for change in body composition. Limited evidence suggests that resistance training may positively affect metabolic parameters in youth. Well-controlled resistance training interventions of varying doses are needed to definitively determine whether resistance training can mitigate metabolic dysfunction in youth and whether training benefits on metabolic parameters are independent of body composition changes.

An open trial of a comprehensive anger treatment program on an outpatient sample.

PubMed

Fuller, J Ryan; Digiuseppe, Raymond; O'Leary, Siobhan; Fountain, Tina; Lang, Colleen

2010-07-01

This pilot study was designed to investigate the efficacy of a cognitive behavioral treatment for anger. Twelve (5 men and 7 women) outpatient adults completed 2-hour group sessions for 16 sessions. Participants were diagnosed with 29 Axis I and 34 Axis II disorders with high rates of comorbidity. Empirically supported techniques of skills training, cognitive restructuring, and relaxation were utilized. In this protocol, cognitive restructuring emphasized the use of the ABC model to understand anger episodes and the Rational Emotive Behavior Therapy (REBT) techniques of disputing irrational beliefs and rehearsing rational coping statements, but additional cognitive techniques were used, e.g. self-instructional training (SIT). Skills training included problem-solving and assertiveness. Relaxation training was paced respiration. Motivational interviewing, imaginal exposure with coping, and relapse prevention were also included. Significant improvements were found from pre- to post-treatment on the following measures: the Trait Anger Scale of the State-Trait Anger Expression Inventory-II; and Anger Disorder Scale total scores; idiosyncratic anger measurements of situational intensity and symptom severity; and the Beck Depression Inventory-II. In order to extend the significant research findings of this pilot study, future investigations should involve larger sample sizes, populations drawn from various settings, and contact control groups.

Firm Size, Ownership, Training Duration and Training Evaluation Practices

ERIC Educational Resources Information Center

Asadullah, Muhammad Ali; Peretti, Jean Marie; Ali, Arain Ghulam; Bourgain, Marina

2015-01-01

Purpose: The purpose of this paper was to test the mediating role of training duration in relationship between firm characteristics and training evaluation practices. In this paper, the authors also investigated if this mediating effect differs with respect to the size of the firm. Design/methodology/approach: The authors collected data from 260…

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Establishment and operation of the National Accident Sampling System (NASS) team within the cities of Ft. Lauderdale/Hollywood, Florida

NASA Astrophysics Data System (ADS)

Beddow, B.; Roberts, C.; Rankin, J.; Bloch, A.; Peizer, J.

1981-01-01

The National Accident Sampling System (NASS) is described. The study area discussed is one of the original ten sites selected for NASS implementation. In addition to collecting data from the field, the original ten sites address questions of feasibility of the plan, projected results of the data collection effort, and specific operational topics, e.g., team size, sampling requirements, training approaches, quality control procedures, and field techniques. Activities and results of the first three years of the project, for both major tasks (establishment and operation) are addressed. Topics include: study area documentation; team description, function and activities; problems and solutions; and recommendations.

Scalable boson sampling with time-bin encoding using a loop-based architecture.

PubMed

Motes, Keith R; Gilchrist, Alexei; Dowling, Jonathan P; Rohde, Peter P

2014-09-19

We present an architecture for arbitrarily scalable boson sampling using two nested fiber loops. The architecture has fixed experimental complexity, irrespective of the size of the desired interferometer, whose scale is limited only by fiber and switch loss rates. The architecture employs time-bin encoding, whereby the incident photons form a pulse train, which enters the loops. Dynamically controlled loop coupling ratios allow the construction of the arbitrary linear optics interferometers required for boson sampling. The architecture employs only a single point of interference and may thus be easier to stabilize than other approaches. The scheme has polynomial complexity and could be realized using demonstrated present-day technologies.

Effectiveness of functional hand splinting and the cognitive orientation to occupational performance (CO-OP) approach in children with cerebral palsy and brain injury: two randomised controlled trial protocols

PubMed Central

2014-01-01

Background Cerebral palsy (CP) and brain injury (BI) are common conditions that have devastating effects on a child’s ability to use their hands. Hand splinting and task-specific training are two interventions that are often used to address deficits in upper limb skills, both in isolation or concurrently. The aim of this paper is to describe the method to be used to conduct two randomised controlled trials (RCT) investigating (a) the immediate effect of functional hand splints, and (b) the effect of functional hand splints used concurrently with task-specific training compared to functional hand splints alone, and to task-specific training alone in children with CP and BI. The Cognitive Orientation to Occupational Performance (CO-OP) approach will be the task-specific training approach used. Methods/Design Two concurrent trials; a two group, parallel design, RCT with a sample size of 30 participants (15 per group); and a three group, parallel design, assessor blinded, RCT with a sample size of 45 participants (15 per group). Inclusion criteria: age 4-15 years; diagnosis of CP or BI; Manual Abilities Classification System (MACS) level I – IV; hand function goals; impaired hand function; the cognitive, language and behavioural ability to participate in CO-OP. Participants will be randomly allocated to one of 3 groups; (1) functional hand splint only (n=15); (2) functional hand splint combined with task-specific training (n=15); (3) task-specific training only (n=15). Allocation concealment will be achieved using sequentially numbered, sealed opaque envelopes opened by an off-site officer after baseline measures. Treatment will be provided for a period of 2 weeks, with outcome measures taken at baseline, 1 hour after randomisation, 2 weeks and 10 weeks. The functional hand splint will be a wrist cock-up splint (+/- thumb support or supination strap). Task-specific training will involve 10 sessions of CO-OP provided in a group of 2-4 children. Primary outcome measures will be the Canadian Occupational Performance Measure (COPM) and the Goal Attainment Scale (GAS). Analysis will be conducted on an intention-to-treat basis. Discussion This paper outlines the protocol for two randomised controlled trials investigating functional hand splints and CO-OP for children with CP and BI. PMID:25023385

Face recognition based on symmetrical virtual image and original training image

NASA Astrophysics Data System (ADS)

Ke, Jingcheng; Peng, Yali; Liu, Shigang; Li, Jun; Pei, Zhao

2018-02-01

In face representation-based classification methods, we are able to obtain high recognition rate if a face has enough available training samples. However, in practical applications, we only have limited training samples to use. In order to obtain enough training samples, many methods simultaneously use the original training samples and corresponding virtual samples to strengthen the ability of representing the test sample. One is directly using the original training samples and corresponding mirror samples to recognize the test sample. However, when the test sample is nearly symmetrical while the original training samples are not, the integration of the original training and mirror samples might not well represent the test samples. To tackle the above-mentioned problem, in this paper, we propose a novel method to obtain a kind of virtual samples which are generated by averaging the original training samples and corresponding mirror samples. Then, the original training samples and the virtual samples are integrated to recognize the test sample. Experimental results on five face databases show that the proposed method is able to partly overcome the challenges of the various poses, facial expressions and illuminations of original face image.

Feasibility, acceptability and preliminary psychological benefits of mindfulness meditation training in a sample of men diagnosed with prostate cancer on active surveillance: results from a randomized controlled pilot trial.

PubMed

Victorson, David; Hankin, Vered; Burns, James; Weiland, Rebecca; Maletich, Carly; Sufrin, Nathaniel; Schuette, Stephanie; Gutierrez, Bruriah; Brendler, Charles

2017-08-01

In a pilot randomized controlled trial, examine the feasibility and preliminary efficacy of an 8-week, mindfulness training program (Mindfulness Based Stress Reduction) in a sample of men on active surveillance on important psychological outcomes including prostate cancer anxiety, uncertainty intolerance and posttraumatic growth. Men were randomized to either mindfulness (n = 24) or an attention control arm (n = 19) and completed self-reported measures of prostate cancer anxiety, uncertainty intolerance, global quality of life, mindfulness and posttraumatic growth at baseline, 8 weeks, 6 months and 12 months. Participants in the mindfulness arm demonstrated significant decreases in prostate cancer anxiety and uncertainty intolerance, and significant increases in mindfulness, global mental health and posttraumatic growth. Participants in the control condition also demonstrated significant increases in mindfulness over time. Longitudinal increases in posttraumatic growth were significantly larger in the mindfulness arm than they were in the control arm. While mindfulness training was found to be generally feasible and acceptable among participants who enrolled in the 8-week intervention as determined by completion rates and open-ended survey responses, the response rate between initial enrollment and the total number of men approached was lower than desired (47%). While larger sample sizes are necessary to examine the efficacy of mindfulness training on important psychological outcomes, in this pilot study posttraumatic growth was shown to significantly increase over time for men in the treatment group. Mindfulness training has the potential to help men cope more effectively with some of the stressors and uncertainties associated with active surveillance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A comparison of how behavioral health organizations utilize training to prepare for health care reform.

PubMed

Stanhope, Victoria; Choy-Brown, Mimi; Barrenger, Stacey; Manuel, Jennifer; Mercado, Micaela; McKay, Mary; Marcus, Steven C

2017-02-14

Under the Affordable Care Act, States have obtained Medicaid waivers to overhaul their behavioral health service systems to improve quality and reduce costs. Critical to implementation of broad service delivery reforms has been the preparation of organizations responsible for service delivery. This study focused on one large-scale initiative to overhaul its service system with the goal of improving service quality and reducing costs. The study examined the participation of behavioral health organizations in technical assistance efforts and the extent to which organizational factors related to their participation. This study matched two datasets to examine the organizational characteristics and training participation for 196 behavioral health organizations. Organizational characteristics were drawn from the Substance Abuse and Mental Health Services Administration National Mental Health Services Survey (N-MHSS). Training variables were drawn from the Clinical Technical Assistance Center's master training database. Chi-square analyses and multivariate logistic regression models were used to examine the proportion of organizations that participated in training, the organizational characteristics (size, population served, service quality, infrastructure) that predicted participation in training, and for those who participated, the type (clinical or business) and intensity of training (webinar, learning collaborative, in-person) they received. Overall 142 (72. 4%) of the sample participated in training. Organizations who pursued training were more likely to be large in size (p = .02), serve children in addition to adults (p < .01), provide child evidence-based practices (p = .01), and use computerized scheduling (p = .01). Of those trained, 95% participated in webinars, 64% participated in learning collaboratives and 35% participated in in-person trainings. More organizations participated in business trainings than clinical (63.8 vs. 59.2%). Organizations serving children had higher odds of participating in both clinical training (OR = 5.91, p < .01) and business training (OR = 4.24, p < .01) than those that did not serve children. The majority of organizations participated in trainings indicating desire for technical assistance to prepare for health care reform. Larger organizations and organizations serving children were more likely to participate potentially indicating increased interest in preparation. Over half participated in business trainings highlighting interest in learning to improve efficiency. Further understanding is needed to support organizational readiness for health care reform initiatives among behavioral health organizations.

Characterization of quantum interference control of injected currents in LT-GaAs for carrier-envelope phase measurements.

PubMed

Roos, Peter; Quraishi, Qudsia; Cundiff, Steven; Bhat, Ravi; Sipe, J

2003-08-25

We use two mutually coherent, harmonically related pulse trains to experimentally characterize quantum interference control (QIC) of injected currents in low-temperature-grown gallium arsenide. We observe real-time QIC interference fringes, optimize the QIC signal fidelity, uncover critical signal dependences regarding beam spatial position on the sample, measure signal dependences on the fundamental and second harmonic average optical powers, and demonstrate signal characteristics that depend on the focused beam spot sizes. Following directly from our motivation for this study, we propose an initial experiment to measure and ultimately control the carrier-envelope phase evolution of a single octave-spanning pulse train using the QIC phenomenon.

Modeling Electronic Quantum Transport with Machine Learning

DOE PAGES

Lopez Bezanilla, Alejandro; von Lilienfeld Toal, Otto A.

2014-06-11

We present a machine learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test data sets to the machine. The system’s representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability inmore » dealing with transport problems of undulatory nature.« less

Nurse Level of Education, Quality of Care and Patient Safety in the Medical and Surgical Wards in Malaysian Private Hospitals: A Cross-Sectional Study

PubMed Central

Rahman, Hamzah Abdul; Jarrar, Mu’taman; Don, Mohammad Sobri

2015-01-01

Background and Objective: Nursing knowledge and skills are required to sustain quality of care and patient safety. The number of nurses with Bachelor degrees in Malaysia is very limited. This study aims to predict the impact of nurse level of education on quality of care and patient safety in the medical and surgical wards in Malaysian private hospitals. Methodology: A cross-sectional survey by questionnaire was conducted. A total of 652 nurses working in the medical and surgical wards in 12 private hospitals participated in the study. Multistage stratified simple random sampling performed to invite nurses working in small size (less than 100 beds), medium size (100-199 beds) and large size (over than 200) hospitals to participate in the study. This allowed nurses from all shifts to participate in this study. Results: Nurses with higher education were not significantly associated with both quality of care and patient safety. However, a total 355 (60.9%) of respondents who participated in this study were working in teaching hospitals. Teaching hospitals offer training for all newly appointed staff. They also provide general orientation programs and training to outline the policies, procedures of the nurses’ roles and responsibilities. This made the variances between the Bachelor and Diploma nurses not significantly associated with the outcomes of care. Conclusions: Nursing educational level was not associated with the outcomes of care in Malaysian private hospitals. However, training programs and the general nursing orientation programs for nurses in Malaysia can help to upgrade the Diploma-level nurses. Training programs can increase their self confidence, knowledge, critical thinking ability and improve their interpersonal skills. So, it can be concluded that better education and training for a medical and surgical wards’ nurses is required for satisfying client expectations and sustaining the outcomes of patient care. PMID:26153190

Nurse Level of Education, Quality of Care and Patient Safety in the Medical and Surgical Wards in Malaysian Private Hospitals: A Cross-sectional Study.

PubMed

Abdul Rahman, Hamzah; Jarrar, Mu'taman; Don, Mohammad Sobri

2015-04-23

Nursing knowledge and skills are required to sustain quality of care and patient safety. The numbers of nurses with Bachelor degrees in Malaysia are very limited. This study aims to predict the impact of nurse level of education on quality of care and patient safety in the medical and surgical wards in Malaysian private hospitals. A cross-sectional survey by questionnaire was conducted. A total 652 nurses working in the medical and surgical wards in 12 private hospitals were participated in the study. Multistage stratified simple random sampling performed to invite nurses working in small size (less than 100 beds), medium size (100-199 beds) and large size (over than 200) hospitals to participate in the study. This allowed nurses from all shifts to participate in this study. Nurses with higher education were not significantly associated with both quality of care and patient safety. However, a total 355 (60.9%) of respondents participated in this study were working in teaching hospitals. Teaching hospitals offer training for all newly appointed staff. They also provide general orientation programs and training to outline the policies, procedures of the nurses' roles and responsibilities. This made the variances between the Bachelor and Diploma nurses not significantly associated with the outcomes of care. Nursing educational level was not associated with the outcomes of care in Malaysian private hospitals. However, training programs and the general nursing orientation programs for nurses in Malaysia can help to upgrade the Diploma-level nurses. Training programs can increase their self confidence, knowledge, critical thinking ability and improve their interpersonal skills. So, it can be concluded that better education and training for a medical and surgical wards' nurses is required for satisfying client expectations and sustaining the outcomes of patient care.

Self-Managed Exercises, Fitness and Strength Training, and Multifidus Muscle Size in Elite Footballers.

PubMed

Hides, Julie A; Walsh, Jazmin C; Smith, Melinda M Franettovich; Mendis, M Dilani

2017-07-01

  Low back pain (LBP) and lower limb injuries are common among Australian Football League (AFL) players. Smaller size of 1 key trunk muscle, the lumbar multifidus (MF), has been associated with LBP and injuries in footballers. The size of the MF muscle has been shown to be modifiable with supervised motor-control training programs. Among AFL players, supervised motor-control training has also been shown to reduce the incidence of lower limb injuries and was associated with increased player availability for games. However, the effectiveness of a self-managed MF exercise program is unknown.   To investigate the effect of self-managed exercises and fitness and strength training on MF muscle size in AFL players with or without current LBP.   Cross-sectional study.   Professional AFL context.   Complete data were available for 242 players from 6 elite AFL clubs.   Information related to the presence of LBP and history of injury was collected at the start of the preseason. At the end of the preseason, data were collected regarding performance of MF exercises as well as fitness and strength training. Ultrasound imaging of the MF muscle was conducted at the start and end of the preseason.   Size of the MF muscles.   An interaction effect was found between performance of MF exercises and time (F = 13.89, P ≤ .001). Retention of MF muscle size was greatest in players who practiced the MF exercises during the preseason (F = 4.77, P = .03). Increased adherence to fitness and strength training was associated with retained MF muscle size over the preseason (F = 5.35, P = .02).   Increased adherence to a self-administered MF exercise program and to fitness and strength training was effective in maintaining the size of the MF muscle in the preseason.

A systematic review and meta-analysis of online versus alternative methods for training licensed health care professionals to deliver clinical interventions.

PubMed

Richmond, Helen; Copsey, Bethan; Hall, Amanda M; Davies, David; Lamb, Sarah E

2017-11-23

Online training is growing in popularity and yet its effectiveness for training licensed health professionals (HCPs) in clinical interventions is not clear. We aimed to systematically review the literature on the effectiveness of online versus alternative training methods in clinical interventions for licensed Health Care Professionals (HCPs) on outcomes of knowledge acquisition, practical skills, clinical behaviour, self-efficacy and satisfaction. Seven databases were searched for randomised controlled trials (RCTs) from January 2000 to June 2015. Two independent reviewers rated trial quality and extracted trial data. Comparative effects were summarised as standardised mean differences (SMD) and 95% confidence intervals. Pooled effect sizes were calculated using a random-effects model for three contrasts of online versus (i) interactive workshops (ii) taught lectures and (iii) written/electronic manuals. We included 14 studies with a total of 1089 participants. Most trials studied medical professionals, used a workshop or lecture comparison, were of high risk of bias and had small sample sizes (range 21-183). Using the GRADE approach, we found low quality evidence that there was no difference between online training and an interactive workshop for clinical behaviour SMD 0.12 (95% CI -0.13 to 0.37). We found very low quality evidence of no difference between online methods and both a workshop and lecture for knowledge (workshop: SMD 0.04 (95% CI -0.28 to 0.36); lecture: SMD 0.22 (95% CI: -0.08, 0.51)). Lastly, compared to a manual (n = 3/14), we found very low quality evidence that online methods were superior for knowledge SMD 0.99 (95% CI 0.02 to 1.96). There were too few studies to draw any conclusions on the effects of online training for practical skills, self-efficacy, and satisfaction across all contrasts. It is likely that online methods may be as effective as alternative methods for training HCPs in clinical interventions for the outcomes of knowledge and clinical behaviour. However, the low quality of the evidence precludes drawing firm conclusions on the relative effectiveness of these training methods. Moreover, the confidence intervals around our effect sizes were large and could encompass important differences in effectiveness. More robust, adequately powered RCTs are needed.

Cost-efficient designs for three-arm trials with treatment delivered by health professionals: Sample sizes for a combination of nested and crossed designs

PubMed Central

Moerbeek, Mirjam

2018-01-01

Background This article studies the design of trials that compare three treatment conditions that are delivered by two types of health professionals. The one type of health professional delivers one treatment, and the other type delivers two treatments, hence, this design is a combination of a nested and crossed design. As each health professional treats multiple patients, the data have a nested structure. This nested structure has thus far been ignored in the design of such trials, which may result in an underestimate of the required sample size. In the design stage, the sample sizes should be determined such that a desired power is achieved for each of the three pairwise comparisons, while keeping costs or sample size at a minimum. Methods The statistical model that relates outcome to treatment condition and explicitly takes the nested data structure into account is presented. Mathematical expressions that relate sample size to power are derived for each of the three pairwise comparisons on the basis of this model. The cost-efficient design achieves sufficient power for each pairwise comparison at lowest costs. Alternatively, one may minimize the total number of patients. The sample sizes are found numerically and an Internet application is available for this purpose. The design is also compared to a nested design in which each health professional delivers just one treatment. Results Mathematical expressions show that this design is more efficient than the nested design. For each pairwise comparison, power increases with the number of health professionals and the number of patients per health professional. The methodology of finding a cost-efficient design is illustrated using a trial that compares treatments for social phobia. The optimal sample sizes reflect the costs for training and supervising psychologists and psychiatrists, and the patient-level costs in the three treatment conditions. Conclusion This article provides the methodology for designing trials that compare three treatment conditions while taking the nesting of patients within health professionals into account. As such, it helps to avoid underpowered trials. To use the methodology, a priori estimates of the total outcome variances and intraclass correlation coefficients must be obtained from experts’ opinions or findings in the literature. PMID:29316807

Less is more: Sampling chemical space with active learning

NASA Astrophysics Data System (ADS)

Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

2018-06-01

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

PubMed

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

["I keep cool": Relationship oriented training of prosocial behaviour and constructive conflict solving for elementary school children].

PubMed

Roth, Ina; Reichle, Barbara

2007-01-01

The evaluation of a preventive training with first graders is reported ("I keep cool"). The training focuses on the prevention of aggressive behaviour and of destructive problem solving by means of teaching prosocial behaviour and constructive problem solving. From a sample of 143 children, 92 participated in the training, 51 served as controls. Children's social competencies and behaviour problems were assessed before, after, and four months after the training via interviews with children, teachers' ratings, and separate ratings of mothers and fathers. After the training, children reported more constructive problem solving, more prosocial behavior, and a higher level of impulse control. Girls showed a lowered level of destructive problem solving behavior immediately after the training, and a lowered level of stress when confronted with intermarital conflicts of their parents at the follow-up assessment. Teachers reported less internalizing and shyness in both sexes at the follow-up assessment. Mothers reported a marginally significant lower level of oppositional-aggressive behaviour in boys immediately after the training, fathers reported a significant lower level of oppositional-aggressive behaviour and of internalizing and shyness in children of both sexes. The effect sizes of .23 < d < .94 are satisfying and comparable with those of similar programmes.

Robust feature extraction for rapid classification of damage in composites

NASA Astrophysics Data System (ADS)

Coelho, Clyde K.; Reynolds, Whitney; Chattopadhyay, Aditi

2009-03-01

The ability to detect anomalies in signals from sensors is imperative for structural health monitoring (SHM) applications. Many of the candidate algorithms for these applications either require a lot of training examples or are very computationally inefficient for large sample sizes. The damage detection framework presented in this paper uses a combination of Linear Discriminant Analysis (LDA) along with Support Vector Machines (SVM) to obtain a computationally efficient classification scheme for rapid damage state determination. LDA was used for feature extraction of damage signals from piezoelectric sensors on a composite plate and these features were used to train the SVM algorithm in parts, reducing the computational intensity associated with the quadratic optimization problem that needs to be solved during training. SVM classifiers were organized into a binary tree structure to speed up classification, which also reduces the total training time required. This framework was validated on composite plates that were impacted at various locations. The results show that the algorithm was able to correctly predict the different impact damage cases in composite laminates using less than 21 percent of the total available training data after data reduction.

Exoskeleton-assisted gait training to improve gait in individuals with spinal cord injury: a pilot randomized study.

PubMed

Chang, Shuo-Hsiu; Afzal, Taimoor; Berliner, Jeffrey; Francisco, Gerard E

2018-01-01

Robotic wearable exoskeletons have been utilized as a gait training device in persons with spinal cord injury. This pilot study investigated the feasibility of offering exoskeleton-assisted gait training (EGT) on gait in individuals with incomplete spinal cord injury (iSCI) in preparation for a phase III RCT. The objective was to assess treatment reliability and potential efficacy of EGT and conventional physical therapy (CPT). Forty-four individuals were screened, and 13 were eligible to participate in the study. Nine participants consented and were randomly assigned to receive either EGT or CPT with focus on gait. Subjects received EGT or CPT, five sessions a week (1 h/session daily) for 3 weeks. American Spinal Injury Association (ASIA) Lower Extremity Motor Score (LEMS), 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), Timed Up and Go (TUG) test, and gait characteristics including stride and step length, cadence and stance, and swing phase durations were assessed at the pre- and immediate post- training. Mean difference estimates with 95% confidence intervals were used to analyze the differences. After training, improvement was observed in the 6MWT for the EGT group. The CPT group showed significant improvement in the TUG test. Both the EGT and the CPT groups showed significant increase in the right step length. EGT group also showed improvement in the stride length. EGT could be applied to individuals with iSCI to facilitate gait recovery. The subjects were able to tolerate the treatment; however, exoskeleton size range may be a limiting factor in recruiting larger cohort of patients. Future studies with larger sample size are needed to investigate the effectiveness and efficacy of exoskeleton-assisted gait training as single gait training and combined with other gait training strategies. Clinicaltrials.org, NCT03011099, retrospectively registered on January 3, 2017.

The Influence of Training Phase on Error of Measurement in Jump Performance.

PubMed

Taylor, Kristie-Lee; Hopkins, Will G; Chapman, Dale W; Cronin, John B

2016-03-01

The purpose of this study was to calculate the coefficients of variation in jump performance for individual participants in multiple trials over time to determine the extent to which there are real differences in the error of measurement between participants. The effect of training phase on measurement error was also investigated. Six subjects participated in a resistance-training intervention for 12 wk with mean power from a countermovement jump measured 6 d/wk. Using a mixed-model meta-analysis, differences between subjects, within-subject changes between training phases, and the mean error values during different phases of training were examined. Small, substantial factor differences of 1.11 were observed between subjects; however, the finding was unclear based on the width of the confidence limits. The mean error was clearly higher during overload training than baseline training, by a factor of ×/÷ 1.3 (confidence limits 1.0-1.6). The random factor representing the interaction between subjects and training phases revealed further substantial differences of ×/÷ 1.2 (1.1-1.3), indicating that on average, the error of measurement in some subjects changes more than in others when overload training is introduced. The results from this study provide the first indication that within-subject variability in performance is substantially different between training phases and, possibly, different between individuals. The implications of these findings for monitoring individuals and estimating sample size are discussed.

The effects of emotional intelligence training on the job performance of Australian aged care workers.

PubMed

Karimi, Leila; Leggat, Sandra G; Bartram, Timothy; Rada, Jiri

2018-05-09

Emotional intelligence (EI) training is popular among human resource practitioners, but there is limited evidence of the impact of such training on health care workers. In the current article, we examine the effects of EI training on quality of resident care and worker well-being and psychological empowerment in an Australian aged care facility. We use Bar-On's (1997) conceptualization of EI. We used a quasiexperimental design in 2014-2015 with experimental (training) and control (nontraining) groups of 60 participants in each group in two geographically separate facilities. Our final poststudy sample size was 27 participants for the training group and 17 participants for the control group. Over a 6-month period, we examined whether staff improved their well-being, psychological empowerment, and job performance measured as enhanced quality of care (self-rated and client-rated) by applying skills in EI. The results showed significant improvement among workers in the training group for EI scores, quality of care, general well-being, and psychological empowerment. There were no significant differences for the control group. Through examining the impact of EI training on staff and residents of an aged care facility, we demonstrate the benefits of EI training for higher quality of care delivery. This study demonstrates the practical process through which EI training can improve the work experiences of aged care workers, as well as the quality of care for residents.

On evaluating clustering procedures for use in classification

NASA Technical Reports Server (NTRS)

Pore, M. D.; Moritz, T. E.; Register, D. T.; Yao, S. S.; Eppler, W. G. (Principal Investigator)

1979-01-01

The problem of evaluating clustering algorithms and their respective computer programs for use in a preprocessing step for classification is addressed. In clustering for classification the probability of correct classification is suggested as the ultimate measure of accuracy on training data. A means of implementing this criterion and a measure of cluster purity are discussed. Examples are given. A procedure for cluster labeling that is based on cluster purity and sample size is presented.

Naval Medical Research and Development News. Volume 7, Issue 9

DTIC Science & Technology

2015-09-01

satisfaction with the simulated training; career intentions; and, general, occupational, and task-specific self-efficacy using pretest and post - test ...samples needed to be transported to the labs for testing . What was needed was a rapid, on -site, diagnostic test that could be done quickly. "The U.S...relatively small size of the group -- usually only a handful of people per deployment - required members to juggle multiple tasks on their own, including

Bayes factors for testing inequality constrained hypotheses: Issues with prior specification.

PubMed

Mulder, Joris

2014-02-01

Several issues are discussed when testing inequality constrained hypotheses using a Bayesian approach. First, the complexity (or size) of the inequality constrained parameter spaces can be ignored. This is the case when using the posterior probability that the inequality constraints of a hypothesis hold, Bayes factors based on non-informative improper priors, and partial Bayes factors based on posterior priors. Second, the Bayes factor may not be invariant for linear one-to-one transformations of the data. This can be observed when using balanced priors which are centred on the boundary of the constrained parameter space with a diagonal covariance structure. Third, the information paradox can be observed. When testing inequality constrained hypotheses, the information paradox occurs when the Bayes factor of an inequality constrained hypothesis against its complement converges to a constant as the evidence for the first hypothesis accumulates while keeping the sample size fixed. This paradox occurs when using Zellner's g prior as a result of too much prior shrinkage. Therefore, two new methods are proposed that avoid these issues. First, partial Bayes factors are proposed based on transformed minimal training samples. These training samples result in posterior priors that are centred on the boundary of the constrained parameter space with the same covariance structure as in the sample. Second, a g prior approach is proposed by letting g go to infinity. This is possible because the Jeffreys-Lindley paradox is not an issue when testing inequality constrained hypotheses. A simulation study indicated that the Bayes factor based on this g prior approach converges fastest to the true inequality constrained hypothesis. © 2013 The British Psychological Society.

PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes.

PubMed

Gregor, Ivan; Dröge, Johannes; Schirmer, Melanie; Quince, Christopher; McHardy, Alice C

2016-01-01

Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies 'training' sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Issues in development, evaluation, and use of the NASA Preflight Adaptation Trainer (PAT)

NASA Technical Reports Server (NTRS)

Lane, Norman E.; Kennedy, Robert S.

1988-01-01

The Preflight Adaptation Trainer (PAT) is intended to reduce or alleviate space adaptation syndrome by providing opportunities for portions of that adaptation to occur under normal gravity conditions prior to space flight. Since the adaptation aspects of the PAT objectives involve modification not only of the behavior of the trainee, but also of sensiomotor skills which underly the behavioral generation, the defining of training objectives of the PAT utilizes four mechanisms: familiarization, demonstration, training and adaptation. These mechanisms serve as structural reference points for evaluation, drive the content and organization of the training procedures, and help to define the roles of the PAT instructors and operators. It was determined that three psychomotor properties are most critical for PAT evaluation: reliability; sensitivity; and relevance. It is cause for concern that the number of measures available to examine PAT effects exceed those that can be properly studied with the available sample sizes; special attention will be required in selection of the candidate measure set. The issues in PAT use and application within a training system context are addressed through linking the three training related mechanisms of familiarization, demonstration and training to the fourth mechanism, adaptation.

"Taking Training to the Next Level": The American College of Surgeons Committee on Residency Training Survey.

PubMed

Damewood, Richard B; Blair, Patrice Gabler; Park, Yoon Soo; Lupi, Linda K; Newman, Rachel Williams; Sachdeva, Ajit K

The American College of Surgeons (ACS) appointed a committee of leaders from the ACS, Association of Program Directors in Surgery, Accreditation Council for Graduate Medical Education, and American Board of Surgery to define key challenges facing surgery resident training programs and to explore solutions. The committee wanted to solicit the perspectives of surgery resident program directors (PDs) given their pivotal role in residency training. Two surveys were developed, pilot tested, and administered to PDs following Institutional Review Board approval. PDs from 247 Accreditation Council for Graduate Medical Education-accredited general surgery programs were randomized to receive 1 of the 2 surveys. Bias analyses were conducted, and adjusted Pearson χ 2 tests were used to test for differences in response patterns by program type and size. All accredited general surgery programs in the United States were included in the sampling frame of the survey; 10 programs with initial or withdrawn accreditation were excluded from the sampling frame. A total of 135 PDs responded, resulting in a 54.7% response rate (Survey A: n = 67 and Survey B: n = 68). The respondent sample was determined to be representative of program type and size. Nearly 52% of PD responses were from university-based programs, and 41% had over 6 residents per graduating cohort. More than 61% of PDs reported that, compared to 10 years ago, both entering and graduating residents are less prepared in technical skills. PDs expressed significant concerns regarding the effect of duty-hour restrictions on the overall preparation of graduating residents (61%) and quality of patient care (57%). The current 5-year training structure was viewed as needing a significant or extensive increase in opportunities for resident autonomy (63%), and the greatest barriers to resident autonomy were viewed to be patient preferences not to be cared for by residents (68%), liability concerns (68%), and Centers for Medicare and Medicaid Services regulations (65%). Although 64% of PDs believe that moderate or significant changes are needed in the current structure of residency training, 35% believe that no changes in the structure are needed. When asked for their 1 best recommendation regarding the structure of surgical residency, only 22% of PDs selected retaining the current 5-year structure. The greatest percentage of PDs (28%) selected the "4 + 2" model as their 1 best recommendation for the structure to be used. In the area of faculty development, 56% of PDs supported a significant or extensive increase in Train the Teacher programs, and 41% supported a significant or extensive increase in faculty certification in education. Information regarding the valuable perspectives of PDs gathered through these surveys should help in implementing important changes in residency training and faculty development. These efforts will need to be pursued collaboratively with involvement of key stakeholders, including the organizations represented on this ACS committee. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

The Effect of Asymmetrical Sample Training on Retention Functions for Hedonic Samples in Rats

ERIC Educational Resources Information Center

Simmons, Sabrina; Santi, Angelo

2012-01-01

Rats were trained in a symbolic delayed matching-to-sample task to discriminate sample stimuli that consisted of the presence of food or the absence of food. Asymmetrical sample training was provided in which one group was initially trained with only the food sample and the other group was initially trained with only the no-food sample. In…

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

PubMed Central

Tang, Yunwei; Jing, Linhai; Li, Hui; Liu, Qingjie; Yan, Qi; Li, Xiuxia

2016-01-01

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. PMID:27879661

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.

The generalization ability of SVM classification based on Markov sampling.

PubMed

Xu, Jie; Tang, Yuan Yan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang; Zhang, Baochang

2015-06-01

The previously known works studying the generalization ability of support vector machine classification (SVMC) algorithm are usually based on the assumption of independent and identically distributed samples. In this paper, we go far beyond this classical framework by studying the generalization ability of SVMC based on uniformly ergodic Markov chain (u.e.M.c.) samples. We analyze the excess misclassification error of SVMC based on u.e.M.c. samples, and obtain the optimal learning rate of SVMC for u.e.M.c. We also introduce a new Markov sampling algorithm for SVMC to generate u.e.M.c. samples from given dataset, and present the numerical studies on the learning performance of SVMC based on Markov sampling for benchmark datasets. The numerical studies show that the SVMC based on Markov sampling not only has better generalization ability as the number of training samples are bigger, but also the classifiers based on Markov sampling are sparsity when the size of dataset is bigger with regard to the input dimension.

Statistical technique for analysing functional connectivity of multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman

2011-03-15

A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains. Copyright © 2011 Elsevier B.V. All rights reserved.

Can You Hack It? Validating Predictors for IT Boot Camps

NASA Astrophysics Data System (ADS)

Gear, Courtney C.

Given the large number of information technology jobs open and lack of qualified individuals to fill them, coding boot camps have sprung up in response to this skill gap by offering a specialized training program in an accelerated format. This fast growth has created a need to measure these training programs and understand their effectiveness. In the present study, a series of analyses examined whether specific or combinations of predictors were valid for training performance in this coding academy. Self-rated, daily efficacy scores were used as outcome variables of training success and correlation results showed a positive relationship with efficacy scores and the logic test score as a predictor. Exploratory analyses indicated a Dunning-Kruger effect where students with lower education levels experience higher overall mood during the training program. Limitations of the study included small sample size, severe range restriction in predictor scores, lack of variance in predictor scores, and low variability in training program success. These limitations made identifying jumps between training stages difficult to identify. By identifying which predictors matter most for each stage of skill acquisition, further research should consider more objective variables such as instructor scores which can serve as a guideline to better asses what stage learners join at and how to design curriculum and assignments accordingly (Honken, 2013).

Inspiratory muscle training in patients with chronic obstructive pulmonary disease: structural adaptation and physiologic outcomes.

PubMed

Ramirez-Sarmiento, Alba; Orozco-Levi, Mauricio; Guell, Rosa; Barreiro, Esther; Hernandez, Nuria; Mota, Susana; Sangenis, Merce; Broquetas, Joan M; Casan, Pere; Gea, Joaquim

2002-12-01

The present study was aimed at evaluating the effects of a specific inspiratory muscle training protocol on the structure of inspiratory muscles in patients with chronic obstructive pulmonary disease. Fourteen patients (males, FEV1, 24 +/- 7% predicted) were randomized to either inspiratory muscle or sham training groups. Supervised breathing using a threshold inspiratory device was performed 30 minutes per day, five times a week, for 5 consecutive weeks. The inspiratory training group was subjected to inspiratory loading equivalent to 40 to 50% of their maximal inspiratory pressure. Biopsies from external intercostal muscles and vastus lateralis (control muscle) were taken before and after the training period. Muscle samples were processed for morphometric analyses using monoclonal antibodies against myosin heavy chain isoforms I and II. Increases in both the strength and endurance of the inspiratory muscles were observed in the inspiratory training group. This improvement was associated with increases in the proportion of type I fibers (by approximately 38%, p < 0.05) and in the size of type II fibers (by approximately 21%, p < 0.05) in the external intercostal muscles. No changes were observed in the control muscle. The study demonstrates that inspiratory training induces a specific functional improvement of the inspiratory muscles and adaptive changes in the structure of external intercostal muscles.

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2017-01-01

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one. PMID:28720710

Stochastic Investigation of Natural Frequency for Functionally Graded Plates

NASA Astrophysics Data System (ADS)

Karsh, P. K.; Mukhopadhyay, T.; Dey, S.

2018-03-01

This paper presents the stochastic natural frequency analysis of functionally graded plates by applying artificial neural network (ANN) approach. Latin hypercube sampling is utilised to train the ANN model. The proposed algorithm for stochastic natural frequency analysis of FGM plates is validated and verified with original finite element method and Monte Carlo simulation (MCS). The combined stochastic variation of input parameters such as, elastic modulus, shear modulus, Poisson ratio, and mass density are considered. Power law is applied to distribute the material properties across the thickness. The present ANN model reduces the sample size and computationally found efficient as compared to conventional Monte Carlo simulation.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

Advances in exercise, fitness, and performance genomics in 2010.

PubMed

Hagberg, James M; Rankinen, Tuomo; Loos, Ruth J F; Pérusse, Louis; Roth, Stephen M; Wolfarth, Bernd; Bouchard, Claude

2011-05-01

This review of the exercise genomics literature emphasizes the strongest articles published in 2010 as defined by sample size, quality of phenotype measurements, quality of the exercise program or physical activity exposure, study design, adjustment for multiple testing, quality of genotyping, and other related study characteristics. One study on voluntary running wheel behavior was performed in 448 mice from 41 inbred strains. Several quantitative trait loci for running distance, speed, and duration were identified. Several studies on the alpha-3 actinin (ACTN3) R577X nonsense polymorphism and the angiotensin-converting enzyme (ACE) I/D polymorphism were reported with no clear evidence for a joint effect, but the studies were generally underpowered. Skeletal muscle RNA abundance at baseline for 29 transcripts and 11 single nucleotide polymorphisms (SNPs) were both found to be predictive of the V˙O2max response to exercise training in one report from multiple laboratories. None of the 50 loci associated with adiposity traits are known to influence physical activity behavior. However, physical activity seems to reduce the obesity-promoting effects of at least 12 of these loci. Evidence continues to be strong for a role of gene-exercise interaction effects on the improvement in insulin sensitivity after exposure to regular exercise. SNPs in the cAMP-responsive element binding position 1 (CREB1) gene were associated with training-induced HR response, in the C-reactive protein (CRP) gene with training-induced changes in left ventricular mass, and in the methylenetetrahydrofolate reductase (MTHFR) gene with carotid stiffness in low-fit individuals. We conclude that progress is being made but that high-quality research designs and replication studies with large sample sizes are urgently needed. © 2011 by the American College of Sports Medicine

Effects of Respiratory Resistance Training With a Concurrent Flow Device on Wheelchair Athletes

PubMed Central

Litchke, Lyn G; Russian, Christopher J; Lloyd, Lisa K; Schmidt, Eric A; Price, Larry; Walker, John L

2008-01-01

Background/Objective: To determine the effect of respiratory resistance training (RRT) with a concurrent flow respiratory (CFR) device on respiratory function and aerobic power in wheelchair athletes. Methods: Ten male wheelchair athletes (8 with spinal cord injuries, 1 with a neurological disorder, and 1 with postpolio syndrome), were matched by lesion level and/or track rating before random assignment to either a RRT group (n = 5) or a control group (CON, n = 5). The RRT group performed 1 set of breathing exercises using Expand-a-Lung, a CFR device, 2 to 3 times daily for 10 weeks. Pre/posttesting included measurement of maximum voluntary ventilation (MVV), maximum inspiratory pressure (MIP), and peak oxygen consumption ( ). Results: Repeated measures ANOVA revealed a significant group difference in change for MIP from pre- to posttest (P < 0.05). The RRT group improved by 33.0 cm H2O, while the CON group improved by 0.6 cm H2O. Although not significant, the MVV increased for the RRT group and decreased for the CON group. There was no significant group difference between for pre/posttesting. Due to small sample sizes in both groups and violations of some parametric statistical assumptions, nonparametric tests were also conducted as a crosscheck of the findings. The results of the nonparametric tests concurred with the parametric results. Conclusions: These data demonstrate that 10 weeks of RRT training with a CFR device can effectively improve MIP in wheelchair athletes. Further research and a larger sample size are warranted to further characterize the impact of Expand-a-Lung on performance and other cardiorespiratory variables in wheelchair athletes. PMID:18533414

Advances in Exercise, Fitness, and Performance Genomics in 2010 (Medicine and Science in Sports and Exercise)

PubMed Central

Hagberg, James M.; Rankinen, Tuomo; Loos, Ruth J. F.; Pérusse, Louis; Roth, Stephen M.; Wolfarth, Bernd; Bouchard, Claude

2014-01-01

This review of the exercise genomics literature emphasizes the strongest papers published in 2010 as defined by sample size, quality of phenotype measurements, quality of the exercise program or physical activity exposure, study design, adjustment for multiple testing, quality of genotyping, and other related study characteristics. One study on voluntary running wheel behavior was performed in 448 mice from 41 inbred strains. Several quantitative trait loci for running distance, speed, and duration were identified. Several studies on the alpha-3 actinin (ACTN3) R577X nonsense polymorphism and the angiotensin converting enzyme (ACE) I/D polymorphism were reported with no clear evidence for a joint effect, but the studies were generally underpowered. Skeletal muscle RNA abundance at baseline for 29 transcripts and 11 single nucleotide polymorphisms (SNPs) were both found to be predictive of the VO2max response to exercise training in one report from multiple laboratories. None of the 50 loci associated with adiposity traits is known to influence physical activity behavior. However, physical activity appears to reduce the obesity-promoting effects of at least 12 of these loci. Evidence continues to be strong for a role of gene-exercise interaction effects on the improvement in insulin sensitivity following exposure to regular exercise. SNPs in the cAMP responsive element binding position 1 (CREB1) gene were associated with training-induced heart rate response, in the C-reactive protein (CRP) gene with training-induced changes in left ventricular mass, and in the methylenetetrahydrofolate reductase (MTHFR) gene with carotid stiffness in low-fit individuals. We conclude that progress is being made but that high-quality research designs and replication studies with large sample sizes are urgently needed. PMID:21499051

Handwriting training in Parkinson’s disease: A trade-off between size, speed and fluency

PubMed Central

Broeder, Sanne; Pereira, Marcelo P.; Swinnen, Stephan P.; Vandenberghe, Wim; Nieuwboer, Alice; Heremans, Elke

2017-01-01

Background In previous work, we found that intensive amplitude training successfully improved micrographia in Parkinson’s disease (PD). Handwriting abnormalities in PD also express themselves in stroke duration and writing fluency. It is currently unknown whether training changes these dysgraphic features. Objective To determine the differential effects of amplitude training on various hallmarks of handwriting abnormalities in PD. Methods We randomized 38 right-handed subjects in early to mid-stage of PD into an experimental group (n = 18), receiving training focused at improving writing size during 30 minutes/day, five days/week for six weeks, and a placebo group (n = 20), receiving stretch and relaxation exercises at equal intensity. Writing skills were assessed using a touch-sensitive tablet pre- and post-training, and after a six-week retention period. Tests encompassed a transfer task, evaluating trained and untrained sequences, and an automatization task, comparing single- and dual-task handwriting. Outcome parameters were stroke duration (s), writing velocity (cm/s) and normalized jerk (i.e. fluency). Results In contrast to the reported positive effects of training on writing size, the current results showed increases in stroke duration and normalized jerk after amplitude training, which were absent in the placebo group. These increases remained after the six-week retention period. In contrast, velocity remained unchanged throughout the study. Conclusion While intensive amplitude training is beneficial to improve writing size in PD, it comes at a cost as fluency and stroke duration deteriorated after training. The findings imply that PD patients can redistribute movement priorities after training within a compromised motor system. PMID:29272301

Handwriting training in Parkinson's disease: A trade-off between size, speed and fluency.

PubMed

Nackaerts, Evelien; Broeder, Sanne; Pereira, Marcelo P; Swinnen, Stephan P; Vandenberghe, Wim; Nieuwboer, Alice; Heremans, Elke

2017-01-01

In previous work, we found that intensive amplitude training successfully improved micrographia in Parkinson's disease (PD). Handwriting abnormalities in PD also express themselves in stroke duration and writing fluency. It is currently unknown whether training changes these dysgraphic features. To determine the differential effects of amplitude training on various hallmarks of handwriting abnormalities in PD. We randomized 38 right-handed subjects in early to mid-stage of PD into an experimental group (n = 18), receiving training focused at improving writing size during 30 minutes/day, five days/week for six weeks, and a placebo group (n = 20), receiving stretch and relaxation exercises at equal intensity. Writing skills were assessed using a touch-sensitive tablet pre- and post-training, and after a six-week retention period. Tests encompassed a transfer task, evaluating trained and untrained sequences, and an automatization task, comparing single- and dual-task handwriting. Outcome parameters were stroke duration (s), writing velocity (cm/s) and normalized jerk (i.e. fluency). In contrast to the reported positive effects of training on writing size, the current results showed increases in stroke duration and normalized jerk after amplitude training, which were absent in the placebo group. These increases remained after the six-week retention period. In contrast, velocity remained unchanged throughout the study. While intensive amplitude training is beneficial to improve writing size in PD, it comes at a cost as fluency and stroke duration deteriorated after training. The findings imply that PD patients can redistribute movement priorities after training within a compromised motor system.

Evaluation of the Multi-Chambered Treatment Train, a retrofit water-quality management device

USGS Publications Warehouse

Corsi, Steven R.; Greb, Steven R.; Bannerman, Roger T.; Pitt, Robert E.

1999-01-01

This paper presents the results of an evaluation of the benefits and efficiencies of a device called the Multi-Chambered Treatment Train (MCTT), which was installed below the pavement surface at a municipal maintenance garage and parking facility in Milwaukee, Wisconsin. Flow-weighted water samples were collected at the inlet and outlet of the device during 15 storms, and the efficiency of the device was based on reductions in the loads of 68 chemical constituents and organic compounds. High reduction efficiencies were achieved for all particulate-associated constituents, including total suspended solids (98 percent), total phosphorus (88 percent), and total recoverable zinc (91 percent). Reduction rates for dissolved fractions of the constituents were substantial, but somewhat lower (dissolved solids, 13 percent; dissolved phosphorus, 78 percent; dissolved zinc, 68 percent). The total dissolved solids load, which originated from road salt storage, was more than four times the total suspended solids load. No appreciable difference was detected between particle-size distributions in inflow and outflow samples.

Segmentation of thalamus from MR images via task-driven dictionary learning

NASA Astrophysics Data System (ADS)

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D.; Prince, Jerry L.

2016-03-01

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.

Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

PubMed

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D; Prince, Jerry L

2016-02-27

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.

A Pilot Trial of Mindfulness Meditation Training for ADHD in Adulthood: Impact on Core Symptoms, Executive Functioning, and Emotion Dysregulation.

PubMed

Mitchell, John T; McIntyre, Elizabeth M; English, Joseph S; Dennis, Michelle F; Beckham, Jean C; Kollins, Scott H

2017-11-01

Mindfulness meditation training is garnering increasing empirical interest as an intervention for ADHD in adulthood, although no studies of mindfulness as a standalone treatment have included a sample composed entirely of adults with ADHD or a comparison group. The aim of this study was to assess the feasibility, acceptability, and preliminary efficacy of mindfulness meditation for ADHD, executive functioning (EF), and emotion dysregulation symptoms in an adult ADHD sample. Adults with ADHD were stratified by ADHD medication status and otherwise randomized into an 8-week group-based mindfulness treatment ( n = 11) or waitlist group ( n = 9). Treatment feasibility and acceptability were positive. In addition, self-reported ADHD and EF symptoms (assessed in the laboratory and ecological momentary assessment), clinician ratings of ADHD and EF symptoms, and self-reported emotion dysregulation improved for the treatment group relative to the waitlist group over time with large effect sizes. Improvement was not observed for EF tasks. Findings support preliminary treatment efficacy, though require larger trials.

Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

PubMed Central

Shen, Sheng; Yao, Xiaohui; Sheng, Meiping; Wang, Chen

2018-01-01

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods. PMID:29570642

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

PubMed Central

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data.

PubMed

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

Context factors in consultations of general practitioner trainees and their impact on communication assessment in the authentic setting.

PubMed

Essers, Geurt; van Dulmen, Sandra; van Es, Judy; van Weel, Chris; van der Vleuten, Cees; Kramer, Anneke

2013-12-01

Acquiring adequate communication skills is an essential part of general practice (GP) specialty training. In assessing trainee proficiency, the context in which trainees communicate is usually not taken into account. The present paper aims to explore what context factors can be found in regular GP trainee consultations and how these influence their communication performance. In a randomly selected sample of 44 videotaped, real-life GP trainee consultations, we searched for context factors previously identified in GP consultations and explored how trainee ratings change if context factors are taken into account. Trainee performance was rated twice using the MAAS-Global, first without and then with incorporating context factors. Item score differences were calculated using a paired samples t-test and effect sizes were computed. All previously identified context factors were again observed in GP trainee consultations. In communication assessment scores, we found a significant difference in 5 out of 13 MAAS-Global items, mostly in a positive direction. The effect size was moderate (0.57). GP trainee communication is influenced by contextual factors; they seem to adapt to context in a professional way. GP specialty training needs to focus on a context-specific application of communication skills. Communication raters need to be taught how to incorporate context factors into their assessments. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A novel heterogeneous training sample selection method on space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Qiang; Zhang, Yongshun; Guo, Yiduo

2018-04-01

The performance of ground target detection about space-time adaptive processing (STAP) decreases when non-homogeneity of clutter power is caused because of training samples contaminated by target-like signals. In order to solve this problem, a novel nonhomogeneous training sample selection method based on sample similarity is proposed, which converts the training sample selection into a convex optimization problem. Firstly, the existing deficiencies on the sample selection using generalized inner product (GIP) are analyzed. Secondly, the similarities of different training samples are obtained by calculating mean-hausdorff distance so as to reject the contaminated training samples. Thirdly, cell under test (CUT) and the residual training samples are projected into the orthogonal subspace of the target in the CUT, and mean-hausdorff distances between the projected CUT and training samples are calculated. Fourthly, the distances are sorted in order of value and the training samples which have the bigger value are selective preference to realize the reduced-dimension. Finally, simulation results with Mountain-Top data verify the effectiveness of the proposed method.

Fourier spatial frequency analysis for image classification: training the training set

NASA Astrophysics Data System (ADS)

Johnson, Timothy H.; Lhamo, Yigah; Shi, Lingyan; Alfano, Robert R.; Russell, Stewart

2016-04-01

The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

The widespread misuse of effect sizes.

PubMed

Dankel, Scott J; Mouser, J Grant; Mattocks, Kevin T; Counts, Brittany R; Jessee, Matthew B; Buckner, Samuel L; Loprinzi, Paul D; Loenneke, Jeremy P

2017-05-01

Studies comparing multiple groups (i.e., experimental and control) often examine the efficacy of an intervention by calculating within group effect sizes using Cohen's d. This method is inappropriate and largely impacted by the pre-test variability as opposed to the variability in the intervention itself. Furthermore, the percentage change is often analyzed, but this is highly impacted by the baseline values and can be potentially misleading. Thus, the objective of this study was to illustrate the common misuse of the effect size and percent change measures. Here we provide a realistic sample data set comparing two resistance training groups with the same pre-test to post-test change. Statistical tests that are commonly performed within the literature were computed. Analyzing the within group effect size favors the control group, while the percent change favors the experimental group. The most appropriate way to present the data would be to plot the individual responses or, for larger samples, provide the mean change and 95% confidence intervals of the mean change. This details the magnitude and variability within the response to the intervention itself in units that are easily interpretable. This manuscript demonstrates the common misuse of the effect size and details the importance for investigators to always report raw values, even when alternative statistics are performed. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Data splitting for artificial neural networks using SOM-based stratified sampling.

PubMed

May, R J; Maier, H R; Dandy, G C

2010-03-01

Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified sampling, based on Neyman sampling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and sample allocation in order to minimize the bias and variance in the datasets. Using an example ANN function approximation task, the SOM-based approach is evaluated in comparison to random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality samples and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets. Copyright 2009 Elsevier Ltd. All rights reserved.

Airborne ultrafine particles in a naturally ventilated metro station: Dominant sources and mixing state determined by particle size distribution and volatility measurements.

PubMed

Mendes, Luís; Gini, Maria I; Biskos, George; Colbeck, Ian; Eleftheriadis, Konstantinos

2018-08-01

Ultrafine particle number concentrations and size distributions were measured on the platform of a metro station in Athens, Greece, and compared with those recorded at an urban background station. The volatility of the sampled particles was measured in parallel, providing further insights on the mixing state and composition of the sampled particles. Particle concentration exhibited a mean value of 1.2 × 10 4 # cm -3 and showed a weak correlation with train passage frequency, but exhibited a strong correlation with urban background particle concentrations. The size distribution appears to be strongly influenced by outdoor conditions, such as the morning traffic rush hour and new particle formation events observed at noon. The aerosol in the metro was externally mixed throughout the day, with particle populations being identified (1) as fully refractory particles being more dominant during the morning traffic rush hours, (2) as core-shell structure particles having a non-volatile core coated with volatile material, and (3) fully volatile particles. The evolution of particle volatility and size throughout the day provide additional support that most nanoparticles in the metro station originate from outdoor urban air. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

NASA Astrophysics Data System (ADS)

Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand

2018-06-01

The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ˜45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

PubMed

Khondoker, Mizanur; Dobson, Richard; Skirrow, Caroline; Simmons, Andrew; Stahl, Daniel

2016-10-01

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study. © The Author(s) 2013.

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Improved knowledge of and difficulties in palliative care among physicians during 2008 and 2015 in Japan: Association with a nationwide palliative care education program.

PubMed

Nakazawa, Yoko; Yamamoto, Ryo; Kato, Masashi; Miyashita, Mitsunori; Kizawa, Yoshiyuki; Morita, Tatsuya

2018-02-01

Palliative care education for health care professionals is a key element in improving access to quality palliative care. The Palliative Care Emphasis Program on Symptom Management and Assessment for Continuous Medical Education (PEACE) was designed to provide educational opportunities for all physicians in Japan. As of 2015, 57,764 physicians had completed it. The objective of this study was to estimate the effects of the program. This study was an analysis of 2 nationwide observational studies from 2008 and 2015. We conducted 2 questionnaire surveys for representative samples of physicians. The measurements used were the Palliative Care Knowledge Test (range, 0-100) and the Palliative Care Difficulties Scale (range, 1-4). Comparisons were made with the unpaired Student t test and with a multivariate linear regression model using 2 cohorts and a propensity score-matched sample. This study analyzed a total of 48,487 physicians in 2008 and a total of 2720 physicians in 2015. Between 2008 and 2015, physicians' knowledge and difficulties significantly improved on the Palliative Care Knowledge Test with total scores of 68 and 78, respectively (P < .001; effect size, 0.40) and on the Palliative Care Difficulties Scale with total scores of 2.65 and 2.49, respectively (P < .001; effect size, 0.29). Propensity-score matching resulted in 619 untrained physicians matched to 619 trained physicians, and physicians who trained with the PEACE program had a higher knowledge score (74 vs 86; P < .001; effect size, 0.64) and a lower difficulties score (2.6 vs 2.3; P < .001; effect size, 0.42). Physicians' knowledge of and difficulties with palliative care improved on a national level. The PEACE program may have contributed to these improvements. Cancer 2018;124:626-35. © 2017 American Cancer Society. © 2017 American Cancer Society.

Writing and reading training effects on font type and size preferences by students with low vision.

PubMed

Atasavun Uysal, Songül; Düger, Tülin

2012-06-01

The effect of writing and reading training on preferred font type and size in low-vision students was evaluated in 35 children. An ophthalmologist confirmed low vision according to ICD-10-CM. Children identified the font type and size they could best read. The writing subtest of the Jebsen-Taylor Hand Function Test, read in 1 min., and legibility as measured by the number of readable written letters were used in evaluating the children. A writing and reading treatment program was conducted, beginning with the child's preferred font type and size, for 3 months, 2 days per week, for 45 min. per day at the child's school. Before treatment, the most preferred font type was Verdana; after treatment, the preferred font type and size changed. Students had gained reading and writing speed after training, but their writing legibility was not significantly better. Training might affect the preferred font type and size of students with low vision. Surprisingly, serif and sans-serif fonts were preferred about equally after treatment.

Two-year Follow-up of a Pragmatic Randomised Controlled Trial Examining the Effect of Adding a Carer's Skill Training Intervention in Inpatients with Anorexia Nervosa.

PubMed

Magill, Nicholas; Rhind, Charlotte; Hibbs, Rebecca; Goddard, Elizabeth; Macdonald, Pamela; Arcelus, Jon; Morgan, John; Beecham, Jennifer; Schmidt, Ulrike; Landau, Sabine; Treasure, Janet

2016-03-01

Active family engagement improves outcomes from adolescent inpatient care, but the impact on adult anorexia nervosa is uncertain. The aim of this study was to describe the 2-year outcome following a pragmatic randomised controlled trial in which a skill training intervention (Experienced Caregivers Helping Others) for carers was added to inpatient care. Patient, caregiver and service outcomes were measured for 2 years following discharge from the index inpatient admission. There were small-sized/moderate-sized effects and consistent improvements in all outcomes from both patients and carers in the Experienced Caregivers Helping Others group over 2 years. The marked change in body mass index and carers' time caregiving following inpatient care was sustained. Approximately 20% of cases had further periods of inpatient care. In this predominately adult anorexia nervosa sample, enabling carers to provide active support and management skills may improve the benefits in all symptom domains that gradually follow from a period of inpatient care. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Validity and usefulness of the Line Drill test for adolescent basketball players: a Bayesian multilevel analysis.

PubMed

Carvalho, Humberto M; Gonçalves, Carlos E; Grosgeorge, Bernard; Paes, Roberto R

2017-01-01

The study examined the validity of the Line Drill test (LD) in male adolescent basketball players (10-15 years). Sensitiveness of the LD to changes in performance across a training and competition season (4 months) was also considered. Age, maturation, body size and LD were measured (n = 57). Sensitiveness of the LD was examined pre- and post-competitive season in a sub-sample (n = 44). The time at each of the four shuttle sprints of the LD (i.e. four stages) was modelled with Bayesian multilevel models. We observed very large correlation of performance at stage 4 (full LD protocol) with stage 3, but lower correlations with the early LD stages. Players' performance by somatic maturity differed substantially only when considering full LD protocol performance. Substantial improvements in all stages of the protocol were observed across the 4-month competitive season. The LD protocol should be shortened by the last full court shuttle sprint, remaining sensitive to training exposure, and independent of maturity status and body size.

Researching the Size and Scope of Online Usage in the Vocational Education and Training Sector.

ERIC Educational Resources Information Center

Hill, Robyn; Malone, Peter; Markham, Selby; Sharma, Renu; Sheard, Judithe; Young, Graeme

The size and scope of online usage in Australia's vocational education and training sector were examined in a four-stage study that included the numerous data collection activities, including the following: a literature review; interviews with 85 institutes; interviews with 10 training organizations and 20 organizations using online learning;…

The influence of cognitive load on transfer with error prevention training methods: a meta-analysis.

PubMed

Hutchins, Shaun D; Wickens, Christopher D; Carolan, Thomas F; Cumming, John M

2013-08-01

The objective was to conduct research synthesis for the U.S.Army on the effectiveness of two error prevention training strategies (training wheels and scaffolding) on the transfer of training. Motivated as part of an ongoing program of research on training effectiveness, the current work presents some of the program's research into the effects on transfer of error prevention strategies during training from a cognitive load perspective. Based on cognitive load theory, two training strategies were hypothesized to reduce intrinsic load by supporting learners early in acquisition during schema development. A transfer ratio and Hedges' g were used in the two meta-analyses conducted on transfer studies employing the two training strategies. Moderators relevant to cognitive load theory and specific to the implemented strategies were examined.The transfer ratio was the ratio of treatment transfer performance to control transfer. Hedges' g was used in comparing treatment and control group standardized mean differences. Both effect sizes were analyzed with versions of sample weighted fixed effect models. Analysis of the training wheels strategy suggests a transfer benefit. The observed benefit was strongest when the training wheels were a worked example coupled with a principle-based prompt. Analysis of the scaffolding data also suggests a transfer benefit for the strategy. Both training wheels and scaffolding demonstrated positive transfer as training strategies.As error prevention techniques, both support the intrinsic load--reducing implications of cognitive load theory. The findings are applicable to the development of instructional design guidelines in professional skill-based organizations such as the military.

A pilot randomized controlled trial comparing effectiveness of prism glasses, visual search training and standard care in hemianopia.

PubMed

Rowe, F J; Conroy, E J; Bedson, E; Cwiklinski, E; Drummond, A; García-Fiñana, M; Howard, C; Pollock, A; Shipman, T; Dodridge, C; MacIntosh, C; Johnson, S; Noonan, C; Barton, G; Sackley, C

2017-10-01

Pilot trial to compare prism therapy and visual search training, for homonymous hemianopia, to standard care (information only). Prospective, multicentre, parallel, single-blind, three-arm RCT across fifteen UK acute stroke units. Stroke survivors with homonymous hemianopia. Arm a (Fresnel prisms) for minimum 2 hours, 5 days per week over 6 weeks. Arm b (visual search training) for minimum 30 minutes, 5 days per week over 6 weeks. Arm c (standard care-information only). Adult stroke survivors (>18 years), stable hemianopia, visual acuity better than 0.5 logMAR, refractive error within ±5 dioptres, ability to read/understand English and provide consent. Primary outcomes were change in visual field area from baseline to 26 weeks and calculation of sample size for a definitive trial. Secondary measures included Rivermead Mobility Index, Visual Function Questionnaire 25/10, Nottingham Extended Activities of Daily Living, Euro Qual, Short Form-12 questionnaires and Radner reading ability. Measures were post-randomization at baseline and 6, 12 and 26 weeks. Randomization block lists stratified by site and partial/complete hemianopia. Allocations disclosed to patients. Primary outcome assessor blind to treatment allocation. Eighty-seven patients were recruited: 27-Fresnel prisms, 30-visual search training and 30-standard care; 69% male; mean age 69 years (SD 12). At 26 weeks, full results for 24, 24 and 22 patients, respectively, were compared to baseline. Sample size calculation for a definitive trial determined as 269 participants per arm for a 200 degree 2 visual field area change at 90% power. Non-significant relative change in area of visual field was 5%, 8% and 3.5%, respectively, for the three groups. Visual Function Questionnaire responses improved significantly from baseline to 26 weeks with visual search training (60 [SD 19] to 68.4 [SD 20]) compared to Fresnel prisms (68.5 [SD 16.4] to 68.2 [18.4]: 7% difference) and standard care (63.7 [SD 19.4] to 59.8 [SD 22.7]: 10% difference), P=.05. Related adverse events were common with Fresnel prisms (69.2%; typically headaches). No significant change occurred for area of visual field area across arms over follow-up. Visual search training had significant improvement in vision-related quality of life. Prism therapy produced adverse events in 69%. Visual search training results warrant further investigation. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Short Term Motor-Skill Acquisition Improves with Size of Self-Controlled Virtual Hands

PubMed Central

Ossmy, Ori; Mukamel, Roy

2017-01-01

Visual feedback in general, and from the body in particular, is known to influence the performance of motor skills in humans. However, it is unclear how the acquisition of motor skills depends on specific visual feedback parameters such as the size of performing effector. Here, 21 healthy subjects physically trained to perform sequences of finger movements with their right hand. Through the use of 3D Virtual Reality devices, visual feedback during training consisted of virtual hands presented on the screen, tracking subject’s hand movements in real time. Importantly, the setup allowed us to manipulate the size of the displayed virtual hands across experimental conditions. We found that performance gains increase with the size of virtual hands. In contrast, when subjects trained by mere observation (i.e., in the absence of physical movement), manipulating the size of the virtual hand did not significantly affect subsequent performance gains. These results demonstrate that when it comes to short-term motor skill learning, the size of visual feedback matters. Furthermore, these results suggest that highest performance gains in individual subjects are achieved when the size of the virtual hand matches their real hand size. These results may have implications for optimizing motor training schemes. PMID:28056023

Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task

NASA Astrophysics Data System (ADS)

Dutta, Sandeep; Gros, Eric

2018-03-01

Deep Learning (DL) has been successfully applied in numerous fields fueled by increasing computational power and access to data. However, for medical imaging tasks, limited training set size is a common challenge when applying DL. This paper explores the applicability of DL to the task of classifying a single axial slice from a CT exam into one of six anatomy regions. A total of 29000 images selected from 223 CT exams were manually labeled for ground truth. An additional 54 exams were labeled and used as an independent test set. The network architecture developed for this application is composed of 6 convolutional layers and 2 fully connected layers with RELU non-linear activations between each layer. Max-pooling was used after every second convolutional layer, and a softmax layer was used at the end. Given this base architecture, the effect of inclusion of network architecture components such as Dropout and Batch Normalization on network performance and training is explored. The network performance as a function of training and validation set size is characterized by training each network architecture variation using 5,10,20,40,50 and 100% of the available training data. The performance comparison of the various network architectures was done for anatomy classification as well as two computer vision datasets. The anatomy classifier accuracy varied from 74.1% to 92.3% in this study depending on the training size and network layout used. Dropout layers improved the model accuracy for all training sizes.

Comparison of different deep learning approaches for parotid gland segmentation from CT images

NASA Astrophysics Data System (ADS)

Hänsch, Annika; Schwier, Michael; Gass, Tobias; Morgas, Tomasz; Haas, Benjamin; Klein, Jan; Hahn, Horst K.

2018-02-01

The segmentation of target structures and organs at risk is a crucial and very time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and often low contrast to surrounding structures, segmentation of the parotid gland is especially challenging. Motivated by the recent success of deep learning, we study different deep learning approaches for parotid gland segmentation. Particularly, we compare 2D, 2D ensemble and 3D U-Net approaches and find that the 2D U-Net ensemble yields the best results with a mean Dice score of 0.817 on our test data. The ensemble approach reduces false positives without the need for an automatic region of interest detection. We also apply our trained 2D U-Net ensemble to segment the test data of the 2015 MICCAI head and neck auto-segmentation challenge. With a mean Dice score of 0.861, our classifier exceeds the highest mean score in the challenge. This shows that the method generalizes well onto data from independent sites. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed to properly train a neural network. We evaluate the classifier performance after training with differently sized training sets (50-450) and find that 250 cases (without using extensive data augmentation) are sufficient to obtain good results with the 2D ensemble. Adding more samples does not significantly improve the Dice score of the segmentations.

A novel inexpensive IV catheterization training model for paramedic students.

PubMed

Parwani, Vivek; Cone, David C

2006-01-01

Teaching paramedic students venipuncture and intravenous catheterization has traditionally relied on bulky, expensive phlebotomy models. A gelatin intravenous model (GIM) costing less than 50 cents is currently being used in the training of medical students and interns. The study objective was to evaluate paramedic students' perceptions of the GIM as a training tool. GIMs are created using gelatin, psyllium, Penrose drains, food coloring, salt, and water. Penrose drains are filled with artificial blood composed of salt water and food coloring. The drains are placed in an aluminum pan with a base of hardening gelatin, with half-inch drains at the bottom of the pan and quarter-inch drains higher up in layers of mixed psyllium and gelatin to simulate deep and superficial veins respectively. A convenience, volunteer sample of 14 paramedic students who previously trained with traditional phlebotomy models each made two to five attempts at intravenous insertion using the GIM. Perceptions of the GIM were measured using a Likert scale (1, worst rating; 5, best rating). Means are reported. Study subjects rated ease of use at 4.17, realism at 4.07, and effectiveness in learning intravenous insertion at 4.28. GIM as a more effective teaching tool than the conventional rubber arm yielded a rating of 4.14. This study is limited by a small sample size, and further studies evaluating the GIMs construct and content validity are needed. Despite these limitations, given the GIMs simplicity and value, paramedic instructors may wish to consider implementation of this device in their training programs.

Identifying deficiencies in national and foreign medical team responses through expert opinion surveys: implications for education and training.

PubMed

Djalali, Ahmadreza; Ingrassia, Pier Luigi; Corte, Francesco Della; Foletti, Marco; Gallardo, Alba Ripoll; Ragazzoni, Luca; Kaptan, Kubilay; Lupescu, Olivera; Arculeo, Chris; von Arnim, Gotz; Friedl, Tom; Ashkenazi, Michael; Heselmann, Deike; Hreckovski, Boris; Khorram-Manesh, Amir; Khorrram-Manesh, Amir; Komadina, Radko; Lechner, Kostanze; Patru, Cristina; Burkle, Frederick M; Fisher, Philipp

2014-08-01

Unacceptable practices in the delivery of international medical assistance are reported after every major international disaster; this raises concerns about the clinical competence and practice of some foreign medical teams (FMTs). The aim of this study is to explore and analyze the opinions of disaster management experts about potential deficiencies in the art and science of national and FMTs during disasters and the impact these opinions might have on competency-based education and training. This qualitative study was performed in 2013. A questionnaire-based evaluation of experts' opinions and experiences in responding to disasters was conducted. The selection of the experts was done using the purposeful sampling method, and the sample size was considered by data saturation. Content analysis was used to explore the implications of the data. This study shows that there is a lack of competency-based training for disaster responders. Developing and performing standardized training courses is influenced by shortcomings in budget, expertise, and standards. There is a lack of both coordination and integration among teams and their activities during disasters. The participants of this study emphasized problems concerning access to relevant resources during disasters. The major findings of this study suggest that teams often are not competent during the response phase because of education and training deficiencies. Foreign medical teams and medically related nongovernmental organizations (NGOs) do not always provide expected capabilities and services. Failures in leadership and in coordination among teams are also a problem. All deficiencies need to be applied to competency-based curricula.

Resistance Training: Physiological Responses and Adaptations (Part 2 of 4).

ERIC Educational Resources Information Center

Fleck, Stephen J.; Kraerner, William J.

1988-01-01

Resistance training causes a variety of physiological reactions, including changes in muscle size, connective tissue size, and bone mineral content. This article summarizes data from a variety of studies and research. (JL)

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

PubMed

Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

2017-06-07

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Enabling phenotypic big data with PheNorm.

PubMed

Yu, Sheng; Ma, Yumeng; Gronsbell, Jessica; Cai, Tianrun; Ananthakrishnan, Ashwin N; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Liao, Katherine P; Cai, Tianxi

2018-01-01

Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Influence of a Locomotor Training Approach on Walking Speed and Distance in People With Chronic Spinal Cord Injury: A Randomized Clinical Trial

PubMed Central

Roach, Kathryn E.

2011-01-01

Background Impaired walking limits function after spinal cord injury (SCI), but training-related improvements are possible even in people with chronic motor incomplete SCI. Objective The objective of this study was to compare changes in walking speed and distance associated with 4 locomotor training approaches. Design This study was a single-blind, randomized clinical trial. Setting This study was conducted in a rehabilitation research laboratory. Participants Participants were people with minimal walking function due to chronic SCI. Intervention Participants (n=74) trained 5 days per week for 12 weeks with the following approaches: treadmill-based training with manual assistance (TM), treadmill-based training with stimulation (TS), overground training with stimulation (OG), and treadmill-based training with robotic assistance (LR). Measurements Overground walking speed and distance were the primary outcome measures. Results In participants who completed the training (n=64), there were overall effects for speed (effect size index [d]=0.33) and distance (d=0.35). For speed, there were no significant between-group differences; however, distance gains were greatest with OG. Effect sizes for speed and distance were largest with OG (d=0.43 and d=0.40, respectively). Effect sizes for speed were the same for TM and TS (d=0.28); there was no effect for LR. The effect size for distance was greater with TS (d=0.16) than with TM or LR, for which there was no effect. Ten participants who improved with training were retested at least 6 months after training; walking speed at this time was slower than that at the conclusion of training but remained faster than before training. Limitations It is unknown whether the training dosage and the emphasis on training speed were optimal. Robotic training that requires active participation would likely yield different results. Conclusions In people with chronic motor incomplete SCI, walking speed improved with both overground training and treadmill-based training; however, walking distance improved to a greater extent with overground training. PMID:21051593

Systematic Review of Inspiratory Muscle Training After Cerebrovascular Accident.

PubMed

Martín-Valero, Rocío; De La Casa Almeida, Maria; Casuso-Holgado, Maria Jesus; Heredia-Madrazo, Alfonso

2015-11-01

This systematic review examines levels of evidence and recommendation grades of various therapeutic interventions of inspiratory muscle training in people who have had a stroke. Benefits from different levels of force and resistance in respiratory muscles are shown in this population. This review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) directives and was completed in November 2014. The search limits were studies published in English between 2004 and 2014. Relevant studies were searched for in MEDLINE, PEDro, OAIster, Scopus, PsycINFO, Web of Knowledge, CINAHL, SPORTDiscus, DOAJ, Cochrane, Embase, Academic Search Complete, Fuente Académica, and MedicLatina. Initially, 20 articles were identified. After analyzing all primary documents, 14 studies were excluded. Only 6 studies were relevant to this review. Three different types of interventions were found (maximum inspiratory training, controlled training, and nonintervention) in 3 different groups. One specific study compared 3 inspiratory muscle training groups with a group of breathing exercises (diaphragmatic exercises with pursed lips) and a control group. Future long-term studies with larger sample sizes are needed. It is necessary to apply respiratory muscle training as a service of the national health system and to consider its inclusion in the conventional neurological program. Copyright © 2015 by Daedalus Enterprises.

Modulation of Auditory Cortex Response to Pitch Variation Following Training with Microtonal Melodies

PubMed Central

Zatorre, Robert J.; Delhommeau, Karine; Zarate, Jean Mary

2012-01-01

We tested changes in cortical functional response to auditory patterns in a configural learning paradigm. We trained 10 human listeners to discriminate micromelodies (consisting of smaller pitch intervals than normally used in Western music) and measured covariation in blood oxygenation signal to increasing pitch interval size in order to dissociate global changes in activity from those specifically associated with the stimulus feature that was trained. A psychophysical staircase procedure with feedback was used for training over a 2-week period. Behavioral tests of discrimination ability performed before and after training showed significant learning on the trained stimuli, and generalization to other frequencies and tasks; no learning occurred in an untrained control group. Before training the functional MRI data showed the expected systematic increase in activity in auditory cortices as a function of increasing micromelody pitch interval size. This function became shallower after training, with the maximal change observed in the right posterior auditory cortex. Global decreases in activity in auditory regions, along with global increases in frontal cortices also occurred after training. Individual variation in learning rate was related to the hemodynamic slope to pitch interval size, such that those who had a higher sensitivity to pitch interval variation prior to learning achieved the fastest learning. We conclude that configural auditory learning entails modulation in the response of auditory cortex to the trained stimulus feature. Reduction in blood oxygenation response to increasing pitch interval size suggests that fewer computational resources, and hence lower neural recruitment, is associated with learning, in accord with models of auditory cortex function, and with data from other modalities. PMID:23227019

Effects of systemic hypoxia on human muscular adaptations to resistance exercise training

PubMed Central

Kon, Michihiro; Ohiwa, Nao; Honda, Akiko; Matsubayashi, Takeo; Ikeda, Tatsuaki; Akimoto, Takayuki; Suzuki, Yasuhiro; Hirano, Yuichi; Russell, Aaron P.

2014-01-01

Abstract Hypoxia is an important modulator of endurance exercise‐induced oxidative adaptations in skeletal muscle. However, whether hypoxia affects resistance exercise‐induced muscle adaptations remains unknown. Here, we determined the effect of resistance exercise training under systemic hypoxia on muscular adaptations known to occur following both resistance and endurance exercise training, including muscle cross‐sectional area (CSA), one‐repetition maximum (1RM), muscular endurance, and makers of mitochondrial biogenesis and angiogenesis, such as peroxisome proliferator‐activated receptor‐γ coactivator‐1α (PGC‐1α), citrate synthase (CS) activity, nitric oxide synthase (NOS), vascular endothelial growth factor (VEGF), hypoxia‐inducible factor‐1 (HIF‐1), and capillary‐to‐fiber ratio. Sixteen healthy male subjects were randomly assigned to either a normoxic resistance training group (NRT, n =7) or a hypoxic (14.4% oxygen) resistance training group (HRT, n =9) and performed 8 weeks of resistance training. Blood and muscle biopsy samples were obtained before and after training. After training muscle CSA of the femoral region, 1RM for bench‐press and leg‐press, muscular endurance, and skeletal muscle VEGF protein levels significantly increased in both groups. The increase in muscular endurance was significantly higher in the HRT group. Plasma VEGF concentration and skeletal muscle capillary‐to‐fiber ratio were significantly higher in the HRT group than the NRT group following training. Our results suggest that, in addition to increases in muscle size and strength, HRT may also lead to increased muscular endurance and the promotion of angiogenesis in skeletal muscle. PMID:24907297

More is More: The Relationship between Vocabulary Size and Word Extension

ERIC Educational Resources Information Center

Thom, Emily E.; Sandhofer, Catherine M.

2009-01-01

This study experimentally tested the relationship between children's lexicon size and their ability to learn new words within the domain of color. We manipulated the size of 25 20-month-olds' color lexicons by training them with two, four, or six different color words over the course of eight training sessions. We subsequently tested children's…

Using the Critical Incident Technique to Research Decision Making regarding Access to Training and Development in Medium-Sized Enterprises

ERIC Educational Resources Information Center

Coetzer, Alan; Redmond, Janice; Sharafizad, Jalleh

2012-01-01

Employees in small and medium-sized enterprises (SMEs) form part of a "disadvantaged" group within the workforce that receives less access to training and development (T&D) than employees in large firms. Prior research into reasons for the relatively low levels of employee participation in training and development has typically…

Short-Term Unilateral Resistance Training Results in Cross Education of Strength Without Changes in Muscle Size, Activation, or Endocrine Response.

PubMed

Beyer, Kyle S; Fukuda, David H; Boone, Carleigh H; Wells, Adam J; Townsend, Jeremy R; Jajtner, Adam R; Gonzalez, Adam M; Fragala, Maren S; Hoffman, Jay R; Stout, Jeffrey R

2016-05-01

Short-term unilateral resistance training results in cross education of strength without changes in muscle size, activation, or endocrine response. J Strength Cond Res 30(5): 1213-1223, 2016-The purpose of this study was to assess the cross education of strength and changes in the underlying mechanisms (muscle size, activation, and hormonal response) after a 4-week unilateral resistance training (URT) program. A group of 9 untrained men completed a 4-week URT program on the dominant leg (DOM), whereas cross education was measured in the nondominant leg (NON); and were compared with a control group (n = 8, CON). Unilateral isometric force (PKF), leg press (LP) and leg extension (LE) strength, muscle size (by ultrasonography) and activation (by electromyography) of the rectus femoris and vastus lateralis, and the hormonal response (testosterone, growth hormone, insulin, and insulin-like growth factor-1) were tested pretraining and posttraining. Group × time interactions were present for PKF, LP, LE, and muscle size in DOM and for LP in NON. In all interactions, the URT group improved significantly better than CON. There was a significant acute hormonal response to URT, but no chronic adaptation after the 4-week training program. Four weeks of URT resulted in an increase in strength and size of the trained musculature, and cross education of strength in the untrained musculature, which may occur without detectable changes in muscle size, activation, or the acute hormonal response.

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

PubMed

Lee, Joon; Maslove, David M; Dubin, Joel A

2015-01-01

Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data.

Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

PubMed Central

Lee, Joon; Maslove, David M.; Dubin, Joel A.

2015-01-01

Background Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. Methods and Findings We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. Conclusions The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data. PMID:25978419

Static versus dynamic sampling for data mining

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

John, G.H.; Langley, P.

1996-12-31

As data warehouses grow to the point where one hundred gigabytes is considered small, the computational efficiency of data-mining algorithms on large databases becomes increasingly important. Using a sample from the database can speed up the datamining process, but this is only acceptable if it does not reduce the quality of the mined knowledge. To this end, we introduce the {open_quotes}Probably Close Enough{close_quotes} criterion to describe the desired properties of a sample. Sampling usually refers to the use of static statistical tests to decide whether a sample is sufficiently similar to the large database, in the absence of any knowledgemore » of the tools the data miner intends to use. We discuss dynamic sampling methods, which take into account the mining tool being used and can thus give better samples. We describe dynamic schemes that observe a mining tool`s performance on training samples of increasing size and use these results to determine when a sample is sufficiently large. We evaluate these sampling methods on data from the UCI repository and conclude that dynamic sampling is preferable.« less

Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics.

PubMed

Yang, Jian; Zhang, David; Yang, Jing-Yu; Niu, Ben

2007-04-01

This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, Locality Preserving Projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications.

Computerized Cognitive Rehabilitation of Attention and Executive Function in Acquired Brain Injury: A Systematic Review.

PubMed

Bogdanova, Yelena; Yee, Megan K; Ho, Vivian T; Cicerone, Keith D

Comprehensive review of the use of computerized treatment as a rehabilitation tool for attention and executive function in adults (aged 18 years or older) who suffered an acquired brain injury. Systematic review of empirical research. Two reviewers independently assessed articles using the methodological quality criteria of Cicerone et al. Data extracted included sample size, diagnosis, intervention information, treatment schedule, assessment methods, and outcome measures. A literature review (PubMed, EMBASE, Ovid, Cochrane, PsychINFO, CINAHL) generated a total of 4931 publications. Twenty-eight studies using computerized cognitive interventions targeting attention and executive functions were included in this review. In 23 studies, significant improvements in attention and executive function subsequent to training were reported; in the remaining 5, promising trends were observed. Preliminary evidence suggests improvements in cognitive function following computerized rehabilitation for acquired brain injury populations including traumatic brain injury and stroke. Further studies are needed to address methodological issues (eg, small sample size, inadequate control groups) and to inform development of guidelines and standardized protocols.

Changing Social Networks Among Homeless Individuals: A Prospective Evaluation of a Job- and Life-Skills Training Program.

PubMed

Gray, Heather M; Shaffer, Paige M; Nelson, Sarah E; Shaffer, Howard J

2016-10-01

Social networks play important roles in mental and physical health among the general population. Building healthier social networks might contribute to the development of self-sufficiency among people struggling to overcome homelessness and substance use disorders. In this study of homeless adults completing a job- and life-skills program (i.e., the Moving Ahead Program at St. Francis House, Boston), we prospectively examined changes in social network quality, size, and composition. Among the sample of participants (n = 150), we observed positive changes in social network quality over time. However, social network size and composition did not change among the full sample. The subset of participants who reported abstaining from alcohol during the months before starting the program reported healthy changes in their social networks; specifically, while completing the program, they re-structured their social networks such that fewer members of their network used alcohol to intoxication. We discuss practical implications of these findings.

A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy.

PubMed

Martínez Vega, Mabel V; Sharifzadeh, Sara; Wulfsohn, Dvoralai; Skov, Thomas; Clemmensen, Line Harder; Toldam-Andersen, Torben B

2013-12-01

Visible-near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400-1100 nm. A total of 196 middle-early season and 219 late season apples (Malus domestica Borkh.) cvs 'Aroma' and 'Holsteiner Cox' samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets ('smooth fractionator', by date of measurement after harvest and random). Using the 'smooth fractionator' sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of 'Aroma' apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R(2) cal = 0.60/0.60; SEC = 0.88/0.88°Brix; Biascal = 0.00/0.00; R(2) val = 0.33/0.44; SEP = 1.14/1.03; Biasval = 0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar 'Holsteiner Cox' produced inferior results as compared with 'Aroma'. It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The 'smooth fractionator' protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible-NIR prediction models. The implication is that by using such 'efficient' sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R(2), RMSECV and RMSEP for 'Aroma' apples. © 2013 Society of Chemical Industry.

Autonomous reinforcement learning with experience replay.

PubMed

Wawrzyński, Paweł; Tanwani, Ajay Kumar

2013-05-01

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Droplet-based microfluidic washing module for magnetic particle-based assays

PubMed Central

Lee, Hun; Xu, Linfeng; Oh, Kwang W.

2014-01-01

In this paper, we propose a continuous flow droplet-based microfluidic platform for magnetic particle-based assays by employing in-droplet washing. The droplet-based washing was implemented by traversing functionalized magnetic particles across a laterally merged droplet from one side (containing sample and reagent) to the other (containing buffer) by an external magnetic field. Consequently, the magnetic particles were extracted to a parallel-synchronized train of washing buffer droplets, and unbound reagents were left in an original train of sample droplets. To realize the droplet-based washing function, the following four procedures were sequentially carried in a droplet-based microfluidic device: parallel synchronization of two trains of droplets by using a ladder-like channel network; lateral electrocoalescence by an electric field; magnetic particle manipulation by a magnetic field; and asymmetrical splitting of merged droplets. For the stable droplet synchronization and electrocoalescence, we optimized droplet generation conditions by varying the flow rate ratio (or droplet size). Image analysis was carried out to determine the fluorescent intensity of reagents before and after the washing step. As a result, the unbound reagents in sample droplets were significantly removed by more than a factor of 25 in the single washing step, while the magnetic particles were successfully extracted into washing buffer droplets. As a proof-of-principle, we demonstrate a magnetic particle-based immunoassay with streptavidin-coated magnetic particles and fluorescently labelled biotin in the proposed continuous flow droplet-based microfluidic platform. PMID:25379098

[Use of the reliable change index to evaluate the effectiveness of clinical interventions: Application of an asthma training program].

PubMed

Montero, Mikel; Iraurgi, Ioseba; Matellanes, Begoña; Montero, José Manuel

2015-12-01

To compare two methods for the evaluation of outcomes to assess effectiveness of a therapeutic intervention of a professional education program on asthma control. A naturalistic, intervention study in which asthmatic patients were attended by clinicians (IG group) who Had taken part in a special education program and a control group (CG) that received medical assistance from clinicians still waiting to be trained. Five urban Primary Care Health Centres of the same region. From an initial sample of 100 patients, 76 formed the final sample for analysis. The study included 37 males and 39 females, aged between 18 and 65 years (M=41.2 years). The two study groups were found to be homogeneous except for the sex variable. Training program for clinical treatment adherence. Peak flow as spirometric index, and structured interview. The results were initially analysed using classical techniques based on robust ANOVA models, and then by calculating the Reliable Change Index (RCI). ANOVA models, conducted separately for each sex, showed no significant differences, due to sample size. RCI methodology showed significant differences in the percentage of patients improved in both groups, as well as clinically relevant changes being observed individually. The RCI method is presented as an attractive alternative as regards the classical methods of analysis that can help in the clinical decision. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

An improved SRC method based on virtual samples for face recognition

NASA Astrophysics Data System (ADS)

Fu, Lijun; Chen, Deyun; Lin, Kezheng; Li, Ao

2018-07-01

The sparse representation classifier (SRC) performs classification by evaluating which class leads to the minimum representation error. However, in real world, the number of available training samples is limited due to noise interference, training samples cannot accurately represent the test sample linearly. Therefore, in this paper, we first produce virtual samples by exploiting original training samples at the aim of increasing the number of training samples. Then, we take the intra-class difference as data representation of partial noise, and utilize the intra-class differences and training samples simultaneously to represent the test sample in a linear way according to the theory of SRC algorithm. Using weighted score level fusion, the respective representation scores of the virtual samples and the original training samples are fused together to obtain the final classification results. The experimental results on multiple face databases show that our proposed method has a very satisfactory classification performance.

Optimized mixed Markov models for motif identification

PubMed Central

Huang, Weichun; Umbach, David M; Ohler, Uwe; Li, Leping

2006-01-01

Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods. PMID:16749929

Rehabilitation of the psychomotor consequences of falling in an elderly population: A pilot study to evaluate feasibility and tolerability of virtual reality training.

PubMed

Marivan, Kevin; Boully, Clémence; Benveniste, Samuel; Reingewirtz, Serge; Rigaud, Anne-Sophie; Kemoun, Gilles; Bloch, Frédéric

2016-01-01

A fall in elderly subjects can lead to serious psychological consequences. These symptoms can develop into Fear of Falling with behavioural disorders comparable to PTSD that may severely limit autonomy. Virtual reality training (VRT) could be seen as a worthwhile therapeutic approach for this syndrome since it has been shown to be a useful tool for motor rehabilitation or combat-related PTSD. We thus developed a training scenario for VRT with psychomotor therapists. To test the feasibility and acceptability of VRT when used by elderly adults for fall rehabilitation. Our population of 8 patients older than 75 years, with a Mini Mental Score Examination greater than 18/30 performed sessions of VRT and answered a questionnaire on the feasibility and acceptability of it. This sample showed a highly favourable response to the prototype of VRT. They found it easy to use, enjoyed the experience, and thought it realistic and helpful. The conclusions of our study are limited by sample size. However, applications with VRT can offer the potential of an acceptable technique for elderly subjects. The next step will be to show the efficacy of this method in the management of post-fall PTSD.

The Potential of Distance Education and Training for Small and Medium-Sized Enterprises in the Mediterranean Countries of the European Community. A Report for the Commission of the European Communities--Task Force Human Resources, Education, Training, and Youth.

ERIC Educational Resources Information Center

Quintino, Luisa

An evaluation was made of the training needs of the small and medium-sized enterprises (SMEs) in Portugal, Spain, Greece, and Italy and the potential of open, distance, flexible, and multimedia learning to meet those needs. The methodology included contacts with training providers, governmental institutions, and SMEs and circulation of…

Sampling algorithms for validation of supervised learning models for Ising-like systems

NASA Astrophysics Data System (ADS)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

Can mindfulness-based interventions influence cognitive functioning in older adults? A review and considerations for future research.

PubMed

Berk, Lotte; van Boxtel, Martin; van Os, Jim

2017-11-01

An increased need exists to examine factors that protect against age-related cognitive decline. There is preliminary evidence that meditation can improve cognitive function. However, most studies are cross-sectional and examine a wide variety of meditation techniques. This review focuses on the standard eight-week mindfulness-based interventions (MBIs) such as mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT). We searched the PsychINFO, CINAHL, Web of Science, COCHRANE, and PubMed databases to identify original studies investigating the effects of MBI on cognition in older adults. Six reports were included in the review of which three were randomized controlled trials. Studies reported preliminary positive effects on memory, executive function and processing speed. However, most reports had a high risk of bias and sample sizes were small. The only study with low risk of bias, large sample size and active control group reported no significant findings. We conclude that eight-week MBI for older adults are feasible, but results on cognitive improvement are inconclusive due a limited number of studies, small sample sizes, and a high risk of bias. Rather than a narrow focus on cognitive training per se, future research may productively shift to investigate MBI as a tool to alleviate suffering in older adults, and to prevent cognitive problems in later life already in younger target populations.

BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction

NASA Astrophysics Data System (ADS)

Holden, Philip B.; Birks, H. John B.; Brooks, Stephen J.; Bush, Mark B.; Hwang, Grace M.; Matthews-Bird, Frazer; Valencia, Bryan G.; van Woesik, Robert

2017-02-01

We describe the Bayesian user-friendly model for palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring ˜ 2 s to build a 100-taxon model from a 100-site training set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training sets under ideal assumptions. We then use these to demonstrate the sensitivity of reconstructions to the characteristics of the training set, considering assemblage richness, taxon tolerances, and the number of training sites. We find that a useful guideline for the size of a training set is to provide, on average, at least 10 samples of each taxon. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. An identically configured model is used in each application, the only change being the input files that provide the training-set environment and taxon-count data. The performance of BUMPER is shown to be comparable with weighted average partial least squares (WAPLS) in each case. Additional artificial datasets are constructed with similar characteristics to the real data, and these are used to explore the reasons for the differing performances of the different training sets.

USE OF NEUROFEEDBACK AND MINDFULNESS TO ENHANCE RESPONSE TO HYPNOSIS TREATMENT IN INDIVIDUALS WITH MULTIPLE SCLEROSIS: Results From a Pilot Randomized Clinical Trial.

PubMed

Jensen, Mark P; Battalio, Samuel L; Chan, Joy F; Edwards, Karlyn A; Day, Melissa A; Sherlin, Leslie H; Ehde, Dawn M

2018-01-01

This pilot study evaluated the possibility that 2 interventions hypothesized to increase slower brain oscillations (e.g., theta) may enhance the efficacy of hypnosis treatment, given evidence that hypnotic responding is associated with slower brain oscillations. Thirty-two individuals with multiple sclerosis and chronic pain, fatigue, or both, were randomly assigned to 1 of 2 interventions thought to increase slow wave activity (mindfulness meditation or neurofeedback training) or no enhancing intervention, and then given 5 sessions of self-hypnosis training targeting their presenting symptoms. The findings supported the potential for both neurofeedback and mindfulness to enhance response to hypnosis treatment. Research using larger sample sizes to determine the generalizability of these findings is warranted.

Cognitive training: How can it be adapted for surgical education?

PubMed

Wallace, Lauren; Raison, Nicholas; Ghumman, Faisal; Moran, Aidan; Dasgupta, Prokar; Ahmed, Kamran

2017-08-01

There is a need for new approaches to surgical training in order to cope with the increasing time pressures, ethical constraints, and legal limitations being placed on trainees. One of the most interesting of these new approaches is "cognitive training" or the use of psychological processes to enhance performance of skilled behaviour. Its ability to effectively improve motor skills in sport has raised the question as to whether it could also be used to improve surgical performance. The aim of this review is to provide an overview of the current evidence on the use of cognitive training within surgery, and evaluate the potential role it can play in surgical education. Scientific database searches were conducted to identify studies that investigated the use of cognitive training in surgery. The key studies were selected and grouped according to the type of cognitive training they examined. Available research demonstrated that cognitive training interventions resulted in greater performance benefits when compared to control training. In particular, cognitive training was found to improve surgical motor skills, as well as a number of non-technical outcomes. Unfortunately, key limitations restricting the generalizability of these findings include small sample size and conceptual issues arising from differing definitions of the term 'cognitive training'. When used appropriately, cognitive training can be a highly effective supplementary training tool in the development of technical skills in surgery. Although further studies are needed to refine our understanding, cognitive training should certainly play an important role in future surgical education. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Robotic Gait Training for Individuals With Cerebral Palsy: A Systematic Review and Meta-Analysis.

PubMed

Carvalho, Igor; Pinto, Sérgio Medeiros; Chagas, Daniel das Virgens; Praxedes Dos Santos, Jomilto Luiz; de Sousa Oliveira, Tainá; Batista, Luiz Alberto

2017-11-01

To identify the effects of robotic gait training practices in individuals with cerebral palsy. The search was performed in the following electronic databases: PubMed, Embase, Medline (OvidSP), Cochrane Database of Systematic Reviews, Web of Science, Scopus, Compendex, IEEE Xplore, ScienceDirect, Academic Search Premier, and Physiotherapy Evidence Database. Studies were included if they fulfilled the following criteria: (1) they investigated the effects of robotic gait training, (2) they involved patients with cerebral palsy, and (3) they enrolled patients classified between levels I and IV using the Gross Motor Function Classification System. The information was extracted from the selected articles using the descriptive-analytical method. The Critical Review Form for Quantitative Studies was used to quantitate the presence of critical components in the articles. To perform the meta-analysis, the effects of the intervention were quantified by effect size (Cohen d). Of the 133 identified studies, 10 met the inclusion criteria. The meta-analysis showed positive effects on gait speed (.21 [-.09, .51]), endurance (.21 [-.06, .49]), and gross motor function in dimension D (.18 [-.10, .45]) and dimension E (0.12 [-.15, .40]). The results obtained suggest that this training benefits people with cerebral palsy, specifically by increasing walking speed and endurance and improving gross motor function. For future studies, we suggest investigating device configuration parameters and conducting a large number of randomized controlled trials with larger sample sizes and individuals with homogeneous impairment. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

High-intensity interval training improves VO2peak, maximal lactate accumulation, time trial and competition performance in 9–11-year-old swimmers

PubMed Central

Zinner, Christoph; Heilemann, Ilka; Kjendlie, Per-Ludvik; Holmberg, Hans-Christer; Mester, Joachim

2010-01-01

Training volume in swimming is usually very high when compared to the relatively short competition time. High-intensity interval training (HIIT) has been demonstrated to improve performance in a relatively short training period. The main purpose of the present study was to examine the effects of a 5-week HIIT versus high-volume training (HVT) in 9–11-year-old swimmers on competition performance, 100 and 2,000 m time (T100 m and T2,000 m), VO2peak and rate of maximal lactate accumulation (Lacmax). In a 5-week crossover study, 26 competitive swimmers with a mean (SD) age of 11.5 ± 1.4 years performed a training period of HIIT and HVT. Competition (P < 0.01; effect size = 0.48) and T2,000 m (P = 0.04; effect size = 0.21) performance increased following HIIT. No changes were found in T100 m (P = 0.20). Lacmax increased following HIIT (P < 0.01; effect size = 0.43) and decreased after HVT (P < 0.01; effect size = 0.51). VO2peak increased following both interventions (P < 0.05; effect sizes = 0.46–0.57). The increases in competition performance, T2,000 m, Lacmax and VO2peak following HIIT were achieved in significantly less training time (~2 h/week). PMID:20683609

Intraflagellar transport particle size scales inversely with flagellar length: revisiting the balance-point length control model.

PubMed

Engel, Benjamin D; Ludington, William B; Marshall, Wallace F

2009-10-05

The assembly and maintenance of eukaryotic flagella are regulated by intraflagellar transport (IFT), the bidirectional traffic of IFT particles (recently renamed IFT trains) within the flagellum. We previously proposed the balance-point length control model, which predicted that the frequency of train transport should decrease as a function of flagellar length, thus modulating the length-dependent flagellar assembly rate. However, this model was challenged by the differential interference contrast microscopy observation that IFT frequency is length independent. Using total internal reflection fluorescence microscopy to quantify protein traffic during the regeneration of Chlamydomonas reinhardtii flagella, we determined that anterograde IFT trains in short flagella are composed of more kinesin-associated protein and IFT27 proteins than trains in long flagella. This length-dependent remodeling of train size is consistent with the kinetics of flagellar regeneration and supports a revised balance-point model of flagellar length control in which the size of anterograde IFT trains tunes the rate of flagellar assembly.

Funding Continuing Training in Small and Medium-Sized Enterprises: Discussion and Case Studies from across the EU. CEDEFOP Panorama Series.

ERIC Educational Resources Information Center

Pukkinen, Tommi; Romijn, Clemens; Elson-Rogers, Sarah

There are three main parts to this report of a study that used case studies to showcase the different approaches used to encourage more continuing training within small and medium-sized enterprises (SMEs) across the European Union (EU). Section 1 discusses the importance of funding training in SMEs and highlights the various types of funding…

Neuromuscular adaptations induced by adjacent joint training.

PubMed

Ema, R; Saito, I; Akagi, R

2018-03-01

Effects of resistance training are well known to be specific to tasks that are involved during training. However, it remains unclear whether neuromuscular adaptations are induced after adjacent joint training. This study examined the effects of hip flexion training on maximal and explosive knee extension strength and neuromuscular performance of the rectus femoris (RF, hip flexor, and knee extensor) compared with the effects of knee extension training. Thirty-seven untrained young men were randomly assigned to hip flexion training, knee extension training, or a control group. Participants in the training groups completed 4 weeks of isometric hip flexion or knee extension training. Standardized differences in the mean change between the training groups and control group were interpreted as an effect size, and the substantial effect was assumed to be ≥0.20 of the between-participant standard deviation at baseline. Both types of training resulted in substantial increases in maximal (hip flexion training group: 6.2% ± 10.1%, effect size = 0.25; knee extension training group: 20.8% ± 9.9%, effect size = 1.11) and explosive isometric knee extension torques and muscle thickness of the RF in the proximal and distal regions. Improvements in strength were accompanied by substantial enhancements in voluntary activation, which was determined using the twitch interpolation technique and RF activation. Differences in training effects on explosive torques and neural variables between the two training groups were trivial. Our findings indicate that hip flexion training results in substantial neuromuscular adaptations during knee extensions similar to those induced by knee extension training. © 2017 The Authors. Scandinavian Journal of Medicine & Science In Sports Published by John Wiley & Sons Ltd.

Continuous-time adaptive critics.

PubMed

Hanselmann, Thomas; Noakes, Lyle; Zaknich, Anthony

2007-05-01

A continuous-time formulation of an adaptive critic design (ACD) is investigated. Connections to the discrete case are made, where backpropagation through time (BPTT) and real-time recurrent learning (RTRL) are prevalent. Practical benefits are that this framework fits in well with plant descriptions given by differential equations and that any standard integration routine with adaptive step-size does an adaptive sampling for free. A second-order actor adaptation using Newton's method is established for fast actor convergence for a general plant and critic. Also, a fast critic update for concurrent actor-critic training is introduced to immediately apply necessary adjustments of critic parameters induced by actor updates to keep the Bellman optimality correct to first-order approximation after actor changes. Thus, critic and actor updates may be performed at the same time until some substantial error build up in the Bellman optimality or temporal difference equation, when a traditional critic training needs to be performed and then another interval of concurrent actor-critic training may resume.

Short progressive muscle relaxation or motor coordination training does not increase performance in a brain-computer interface based on sensorimotor rhythms (SMR).

PubMed

Botrel, L; Acqualagna, L; Blankertz, B; Kübler, A

2017-11-01

Brain computer interfaces (BCIs) allow for controlling devices through modulation of sensorimotor rhythms (SMR), yet a profound number of users is unable to achieve sufficient accuracy. Here, we investigated if visuo-motor coordination (VMC) training or Jacobsen's progressive muscle relaxation (PMR) prior to BCI use would increase later performance compared to a control group who performed a reading task (CG). Running the study in two different BCI-labs, we achieved a joint sample size of N=154 naïve participants. No significant effect of either intervention (VMC, PMR, control) was found on resulting BCI performance. Relaxation level and visuo-motor performance were associated with later BCI performance in one BCI-lab but not in the other. These mixed results do not indicate a strong potential of VMC or PMR for boosting performance. Yet further research with different training parameters or experimental designs is needed to complete the picture. Copyright © 2017 Elsevier B.V. All rights reserved.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

EXTENDING THE FLOOR AND THE CEILING FOR ASSESSMENT OF PHYSICAL FUNCTION

PubMed Central

Fries, James F.; Lingala, Bharathi; Siemons, Liseth; Glas, Cees A. W.; Cella, David; Hussain, Yusra N; Bruce, Bonnie; Krishnan, Eswar

2014-01-01

Objective The objective of the current study was to improve the assessment of physical function by improving the precision of assessment at the floor (extremely poor function) and at the ceiling (extremely good health) of the health continuum. Methods Under the NIH PROMIS program, we developed new physical function floor and ceiling items to supplement the existing item bank. Using item response theory (IRT) and the standard PROMIS methodology, we developed 30 floor items and 26 ceiling items and administered them during a 12-month prospective observational study of 737 individuals at the extremes of health status. Change over time was compared across anchor instruments and across items by means of effect sizes. Using the observed changes in scores, we back-calculated sample size requirements for the new and comparison measures. Results We studied 444 subjects with chronic illness and/or extreme age, and 293 generally fit subjects including athletes in training. IRT analyses confirmed that the new floor and ceiling items outperformed reference items (p<0.001). The estimated post-hoc sample size requirements were reduced by a factor of two to four at the floor and a factor of two at the ceiling. Conclusion Extending the range of physical function measurement can substantially improve measurement quality, can reduce sample size requirements and improve research efficiency. The paradigm shift from Disability to Physical Function includes the entire spectrum of physical function, signals improvement in the conceptual base of outcome assessment, and may be transformative as medical goals more closely approach societal goals for health. PMID:24782194

Modeling initiation trains based on HMX and TATB

NASA Astrophysics Data System (ADS)

Drake, R. C.; Maisey, M.

2017-01-01

There will always be a requirement to reduce the size of initiation trains. However, as the size is reduced the performance characteristics can be compromised. A detailed science-based understanding of the processes (ignition and growth to detonation) which determine the performance characteristics is required to enable compact and robust initiation trains to be designed. To assess the use of numerical models in the design of initiation trains a modeling study has been undertaken, with the aim of understanding the initiation of TATB and HMX charges by a confined, surface mounted detonator. The effect of detonator diameter and detonator confinement on the formation of dead zones in the acceptor explosives has been studied. The size of dead zones can be reduced by increasing the diameter of the detonator and by increasing the impedance of the confinement. The implications for the design of initiation trains are discussed.

The archaeal diversity in a cave system and its implications for life on other planets

NASA Astrophysics Data System (ADS)

Leuko, Stefan; Rettberg, Petra; De Waele, Jo; Bessone, Loredana; Sauro, Francesco; Sanna, Laura

The quest of exploring and looking for life in new places is a human desire since centuries. Nowadays, we are not only looking on planet Earth any more, but our endeavours focus on nearby planets in our solar system. At this point in time, we are not able to send manned missions to other planets, but to be ready and prepared for the day, training today is pivotal. Developed by the European Space Agency (ESA) since 2008, these CAVES missions (Cooperative Adventure for Valuing and Exercising human behaviour and performance Skills), prepare astronauts to work safely and effectively and solve problems as a multicultural team while exploring uncharted underground natural areas (i.e. caves) using space procedures. The hypogean environment is also of great interest for astrobiological research as cave conditions may resemble those in extra-terrestrial environments. Besides the main focus of exploration and skill training, future astronauts are also trained in taking microbiological samples for analysis during the exploration and for further analysis in the lab. During the 2013 mission, astronauts collected soil samples and employed flocked swaps to sample areas with little or no visible soil. Microscopic analysis back in the lab revealed a diverse spectrum of different cell shapes and sizes. Samples were further analysed employing molecular tools such as RFLP analysis, 16s rRNA clone libraries and sequence analysis. RFLP pattern analysis revealed that the community can be divided in 9 main groups and several single patterns. The largest group (40% of all analysed clones) belong to the clade of ammonia oxidizing archaea Nitrosopumilus spp.. Dividing the results by sampling point, it showed that the highest diversity of organisms was located on the flocked swaps, which is interesting as the sample was taken from a rock surface with no visible organic matter. By analysis low energy systems like a cave, we may be able to find clues for what could be necessary to survive on a different planet.

Surgeons OverSeas Assessment of Surgical Need (SOSAS) Uganda: Update for Household Survey.

PubMed

Fuller, Anthony T; Butler, Elissa K; Tran, Tu M; Makumbi, Fredrick; Luboga, Samuel; Muhumza, Christine; Chipman, Jeffrey G; Groen, Reinou S; Gupta, Shailvi; Kushner, Adam L; Galukande, Moses; Haglund, Michael M

2015-12-01

The first step in improving surgical care delivery in low- and middle-income countries (LMICs) is quantifying surgical need. The Surgeons OverSeas Assessment of Surgical Need (SOSAS) is a validated household survey that has been previously implemented in three LMICs with great success. We implemented the SOSAS survey in Uganda, a medium-sized country with comparatively more language and ethnic group diversity. The investigators partnered with the Performance Monitoring and Accountability 2020 (PMA2020) Uganda to access a data collection platform sampling 2520 households in 105 randomly selected enumeration areas. Due to geographic size consideration and language diversity, SOSAS's methodology was updated in three significant dimensions (1) technology, (2) staff management, and (3) questionnaire adaptations. The SOSAS survey was successfully implemented with non-medically trained but field proven research assistants. We sampled 2315 of 2402 eligible households (response rate 96.4 %) and 4248 of 4374 eligible individual respondents (response rate 97.1 %). The female-to-male ratio was 51.1-48.9 %. Total survey cost was USD 73,145 and data collection occurred in 14 days. SOSAS Uganda has demonstrated that non-medically trained, but university-educated, experienced researchers supervised by academic surgeons can successfully perform accurate data collection of SOSAS. SOSAS can be successfully implemented within larger and more diverse LMICs using existing national survey platforms, and SOSAS Uganda provides insights on how SOSAS can be executed specifically within other PMA2020 program countries.

Costs of Food Safety Investments in the Meat and Poultry Slaughter Industries.

PubMed

Viator, Catherine L; Muth, Mary K; Brophy, Jenna E; Noyes, Gary

2017-02-01

To develop regulations efficiently, federal agencies need to know the costs of implementing various regulatory alternatives. As the regulatory agency responsible for the safety of meat and poultry products, the U.S. Dept. of Agriculture's Food Safety and Inspection Service is interested in the costs borne by meat and poultry establishments. This study estimated the costs of developing, validating, and reassessing hazard analysis and critical control points (HACCP), sanitary standard operating procedures (SSOP), and sampling plans; food safety training for new employees; antimicrobial equipment and solutions; sanitizing equipment; third-party audits; and microbial tests. Using results from an in-person expert consultation, web searches, and contacts with vendors, we estimated capital equipment, labor, materials, and other costs associated with these investments. Results are presented by establishment size (small and large) and species (beef, pork, chicken, and turkey), when applicable. For example, the cost of developing food safety plans, such as HACCP, SSOP, and sampling plans, can range from approximately $6000 to $87000, depending on the type of plan and establishment size. Food safety training costs from approximately $120 to $2500 per employee, depending on the course and type of employee. The costs of third-party audits range from approximately $13000 to $24000 per audit, and establishments are often subject to multiple audits per year. Knowing the cost of these investments will allow researchers and regulators to better assess the effects of food safety regulations and evaluate cost-effective alternatives. © 2017 Institute of Food Technologists®.

The blood pressure response to acute and chronic aerobic exercise: A meta-analysis of candidate gene association studies.

PubMed

Bruneau, Michael L; Johnson, Blair T; Huedo-Medina, Tania B; Larson, Kara A; Ash, Garrett I; Pescatello, Linda S

2016-05-01

To meta-analyze candidate gene association studies on the change in blood pressure beyond the immediate post-exercise phase after versus before aerobic exercise. Meta-analysis. A systematic search was conducted. Studies retrieved included acute (short-term or postexercise hypotension) or chronic (long-term or training) aerobic exercise interventions; and blood pressure measured before and after aerobic exercise training, or before and after exercise or control under ambulatory conditions by genotype. Effect sizes were determined for genotype and adjusted for sample features. Qualifying studies (k=17, n=3524) on average included middle-aged, overweight men (44.2%) and women (55.8%) with prehypertension (134.9±11.7/78.6±9.5mmHg). Training interventions (k=12) were performed at 60.4±12.9% of maximum oxygen consumption (VO2max) for 41.9±12.5minsession(-1), 3.6±1.2daysweek(-1) for 15.7±7.6week; and post-exercise hypotension interventions (k=5) were performed at 53.5±14.4% VO2max for 38.5±5.4minsession(-1). Sample characteristics explained 54.2-59.0% of the variability in the blood pressure change after versus before acute exercise or control under ambulatory conditions, and 57.4-67.1% of the variability in the blood pressure change after versus before training (p<0.001). Only angiotensinogen M235T (rs699) associated with the change in diastolic blood pressure after versus before training (R(2)=0.1%, p=0.05), but this association did not remain statistically significant after adjustment for multiple comparisons. Sample characteristics explained most of the variability in the change of BP beyond the immediate post-exercise phase after versus before acute and chronic aerobic exercise. Angiotensinogen M235T (rs699) was the only genetic variant that associated with the change in diastolic blood pressure after versus before training, accounting for <1% of the variance. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Principal component analysis for designed experiments.

PubMed

Konishi, Tomokazu

2015-01-01

Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes. Together, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Validation of a Multimarker Model for Assessing Risk of Type 2 Diabetes from a Five-Year Prospective Study of 6784 Danish People (Inter99)

PubMed Central

Urdea, Mickey; Kolberg, Janice; Wilber, Judith; Gerwien, Robert; Moler, Edward; Rowe, Michael; Jorgensen, Paul; Hansen, Torben; Pedersen, Oluf; Jørgensen, Torben; Borch-Johnsen, Knut

2009-01-01

Background Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx™ Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Methods Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. Results The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. Conclusions The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be “at risk” using the clinical factors. PMID:20144324

Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99).

PubMed

Urdea, Mickey; Kolberg, Janice; Wilber, Judith; Gerwien, Robert; Moler, Edward; Rowe, Michael; Jorgensen, Paul; Hansen, Torben; Pedersen, Oluf; Jørgensen, Torben; Borch-Johnsen, Knut

2009-07-01

Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors. Copyright 2009 Diabetes Technology Society.

Can reading-specific training stimuli improve the effect of perceptual learning on peripheral reading speed?

PubMed

Bernard, Jean-Baptiste; Arunkumar, Amit; Chung, Susana T L

2012-08-01

In a previous study, Chung, Legge, and Cheung (2004) showed that training using repeated presentation of trigrams (sequences of three random letters) resulted in an increase in the size of the visual span (number of letters recognized in a glance) and reading speed in the normal periphery. In this study, we asked whether we could optimize the benefit of trigram training on reading speed by using trigrams more specific to the reading task (i.e., trigrams frequently used in the English language) and presenting them according to their frequencies of occurrence in normal English usage and observers' performance. Averaged across seven observers, our training paradigm (4 days of training) increased the size of the visual span by 6.44 bits, with an accompanied 63.6% increase in the maximum reading speed, compared with the values before training. However, these benefits were not statistically different from those of Chung, Legge, and Cheung (2004) using a random-trigram training paradigm. Our findings confirm the possibility of increasing the size of the visual span and reading speed in the normal periphery with perceptual learning, and suggest that the benefits of training on letter recognition and maximum reading speed may not be linked to the types of letter strings presented during training. Copyright © 2012 Elsevier Ltd. All rights reserved.

How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

PubMed

Veturi, Yogasudha; Ritchie, Marylyn D

2018-01-01

Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.

Explanation of Two Anomalous Results in Statistical Mediation Analysis.

PubMed

Fritz, Matthew S; Taylor, Aaron B; Mackinnon, David P

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M , a , increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y , b , was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a . Implications of these findings are discussed.

Operation of a sampling train for the analysis of environmental species in coal gasification gas-phase process streams

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

Pochan, M.J.; Massey, M.J.

1979-02-01

This report discusses the results of actual raw product gas sampling efforts and includes: Rationale for raw product gas sampling efforts; design and operation of the CMU gas sampling train; development and analysis of a sampling train data base; and conclusions and future application of results. The results of sampling activities at the CO/sub 2/-Acceptor and Hygas pilot plants proved that: The CMU gas sampling train is a valid instrument for characterization of environmental parameters in coal gasification gas-phase process streams; depending on the particular process configuration, the CMU gas sampling train can reduce gasifier effluent characterization activity to amore » single location in the raw product gas line; and in contrast to the slower operation of the EPA SASS Train, CMU's gas sampling train can collect representative effluent data at a rapid rate (approx. 2 points per hour) consistent with the rate of change of process variables, and thus function as a tool for process engineering-oriented analysis of environmental characteristics.« less

OSIRIS-REx Flight Dynamics and Navigation Design

NASA Astrophysics Data System (ADS)

Williams, B.; Antreasian, P.; Carranza, E.; Jackman, C.; Leonard, J.; Nelson, D.; Page, B.; Stanbridge, D.; Wibben, D.; Williams, K.; Moreau, M.; Berry, K.; Getzandanner, K.; Liounis, A.; Mashiku, A.; Highsmith, D.; Sutter, B.; Lauretta, D. S.

2018-06-01

OSIRIS-REx is the first NASA mission to return a sample of an asteroid to Earth. Navigation and flight dynamics for the mission to acquire and return a sample of asteroid 101955 Bennu establish many firsts for space exploration. These include relatively small orbital maneuvers that are precise to ˜1 mm/s, close-up operations in a captured orbit about an asteroid that is small in size and mass, and planning and orbit phasing to revisit the same spot on Bennu in similar lighting conditions. After preliminary surveys and close approach flyovers of Bennu, the sample site will be scientifically characterized and selected. A robotic shock-absorbing arm with an attached sample collection head mounted on the main spacecraft bus acquires the sample, requiring navigation to Bennu's surface. A touch-and-go sample acquisition maneuver will result in the retrieval of at least 60 grams of regolith, and up to several kilograms. The flight activity concludes with a return cruise to Earth and delivery of the sample return capsule (SRC) for landing and sample recovery at the Utah Test and Training Range (UTTR).

An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Boyte, Stephen; Picotte, Joshua J.; Howard, Danny; Smith, Kelcy; Nelson, Kurtis

2016-01-01

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

Appearance-based representative samples refining method for palmprint recognition

NASA Astrophysics Data System (ADS)

Wen, Jiajun; Chen, Yan

2012-07-01

The sparse representation can deal with the lack of sample problem due to utilizing of all the training samples. However, the discrimination ability will degrade when more training samples are used for representation. We propose a novel appearance-based palmprint recognition method. We aim to find a compromise between the discrimination ability and the lack of sample problem so as to obtain a proper representation scheme. Under the assumption that the test sample can be well represented by a linear combination of a certain number of training samples, we first select the representative training samples according to the contributions of the samples. Then we further refine the training samples by an iteration procedure, excluding the training sample with the least contribution to the test sample for each time. Experiments on PolyU multispectral palmprint database and two-dimensional and three-dimensional palmprint database show that the proposed method outperforms the conventional appearance-based palmprint recognition methods. Moreover, we also explore and find out the principle of the usage for the key parameters in the proposed algorithm, which facilitates to obtain high-recognition accuracy.

Development and Pilot Testing of a Standardized Training Program for a Patient-Mentoring Intervention to Increase Adherence to Outpatient HIV Care

PubMed Central

Mignogna, Joseph; Stanley, Melinda A.; Davila, Jessica; Wear, Jackie; Amico, K. Rivet; Giordano, Thomas P.

2012-01-01

Abstract Although peer interventionists have been successful in medication treatment-adherence interventions, their role in complex behavior-change approaches to promote entry and reentry into HIV care requires further investigation. The current study sought to describe and test the feasibility of a standardized peer-mentor training program used for MAPPS (Mentor Approach for Promoting Patient Self-Care), a study designed to increase engagement and attendance at HIV outpatient visits among high-risk HIV inpatients using HIV-positive peer interventionists to deliver a comprehensive behavioral change intervention. Development of MAPPS and its corresponding training program included collaborations with mentors from a standing outpatient mentor program. The final training program included (1) a half-day workshop; (2) practice role-plays; and (3) formal, standardized patient role-plays, using trained actors with “real-time” video observation (and ratings from trainers). Mentor training occurred over a 6-week period and required demonstration of adherence and skill, as rated by MAPPS trainers. Although time intensive, ultimate certification of mentors suggested the program was both feasible and effective. Survey data indicated mentors thought highly of the training program, while objective rating data from trainers indicated mentors were able to understand and display standards associated with intervention fidelity. Data from the MAPPS training program provide preliminary evidence that peer mentors can be trained to levels necessary to ensure intervention fidelity, even within moderately complex behavioral-change interventions. Although additional research is needed due to limitations of the current study (e.g., limited generalizability due to sample size and limited breadth of clinical training opportunities), data from the current trial suggest that training programs such as MAPPS appear both feasible and effective. PMID:22248331

Differential effect of motivational features on training improvements in school-based cognitive training

PubMed Central

Katz, Benjamin; Jaeggi, Susanne; Buschkuehl, Martin; Stegman, Alyse; Shah, Priti

2014-01-01

Cognitive training often utilizes game-like motivational features to keep participants engaged. It is unclear how these elements, such as feedback, reward, and theming impact player performance during training. Recent research suggests that motivation and engagement are closely related to improvements following cognitive training. We hypothesized that training paradigms featuring game-like motivational elements would be more effective than a version with no motivational elements. Five distinct motivational features were chosen for examination: a real-time scoring system, theme changes, prizes, end-of-session certificates, and scaffolding to explain the lives and leveling system included in the game. One version of the game was created with all these motivational elements included, and one was created with all of them removed. Other versions removed a single element at a time. Seven versions of a game-like n-back working memory task were then created and administered to 128 students in second through eight grade at school-based summer camps in southeastern Michigan. The inclusion of real-time scoring during play, a popular motivational component in both entertainment games and cognitive training, was found to negatively impact training improvements over the three day period. Surprisingly, scaffolding to explain lives and levels also negatively impacted training gains. The other game adjustments did not significantly impact training improvement compared to the original version of the game with all features included. These findings are preliminary and are limited by both the small sample size and the brevity of the intervention. Nonetheless, these findings suggest that certain motivational elements may distract from the core cognitive training task, reducing task improvement, especially at the initial stage of learning. PMID:24795603

Differences in safety training among smaller and larger construction firms with non-native workers: Evidence of overlapping vulnerabilities

PubMed Central

Guerin, Rebecca J.; Keller, Brenna M.; Flynn, Michael A.; Salgado, Cathy; Hudson, Dennis

2017-01-01

Collaborative efforts between the National Institute for Occupational Safety and Health (NIOSH) and the American Society of Safety Engineers (ASSE) led to a report focusing on overlapping occupational vulnerabilities, specifically small construction businesses employing young, non-native workers. Following the report, an online survey was conducted by ASSE with construction business representatives focusing on training experiences of non-native workers. Results were grouped by business size (50 or fewer employees or more than 50 employees). Smaller businesses were less likely to employ a supervisor who speaks the same language as immigrant workers (p < .001). Non-native workers in small businesses received fewer hours of both initial safety training (p = .005) and monthly ongoing safety training (p = .042). Immigrant workers in smaller businesses were less likely to receive every type of safety training identified in the survey (including pre-work safety orientation [p < .001], job-specific training [p < .001], OSHA 10-hour training [p = .001], and federal/state required training [p < .001]). The results highlight some of the challenges a vulnerable worker population faces in a small business, and can be used to better focus intervention efforts. Among businesses represented in this sample, there are deflcits in the amount, frequency, and format of workplace safety and health training provided to non-native workers in smaller construction businesses compared to those in larger businesses. The types of training conducted for non-native workers in small business were less likely to take into account the language and literacy issues faced by these workers. The findings suggest the need for a targeted approach in providing occupational safety and health training to non-native workers employed by smaller construction businesses. PMID:29375194

Respiratory muscle training improves respiratory muscle endurance but not exercise tolerance in children with cystic fibrosis.

PubMed

Bieli, Christian; Summermatter, Selina; Boutellier, Urs; Moeller, Alexander

2017-03-01

Respiratory muscle endurance (RME) training has been shown to increase exercise endurance and lung function in adults with cystic fibrosis (CF). We conducted an interventional study to investigate the effectiveness of RME training on CF-related health outcomes in children. In a crossover trial, 22 children, aged 9-18 years, with CF performed 8 weeks of RME training and standard chest physiotherapy in a randomized sequence separated by a 1 week washout period. All children underwent training sessions using the RME training device before beginning the study. The primary outcomes were RME (in minutes) and exercise endurance (in minutes). Data were analyzed according to the intention-to-treat principle. Sixteen of 22 children (73%) completed the study. Study dropouts tended to be older with more advanced lung disease. After RME training, respiratory muscle endurance significantly increased by 7.03 ± 8.15 min (mean ± standard deviation, P < 0.001), whereas exercise endurance was unchanged by RME training (0.80 ± 2.58 min, P = 0.169). No significant improvement in secondary outcomes (lung function, CF quality of life, and CF clinical score) were observed. The small sample size and short intervention time have to be acknowledged as limitations of our study. RME training led to a significant increase in respiratory muscle endurance in children with CF. However, RME training did not improve exercise endurance or other CF-related health outcomes. Thus, our results do not support the routine use of RME training in the care of children with CF. Future studies in larger populations and with prolonged intervention time may overcome the limitations of our study. Pediatr Pulmonol. 2017;52:331-336. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Carotid-cardiac baroreflex response and LBNP tolerance following resistance training

NASA Technical Reports Server (NTRS)

Tatro, D. L.; Dudley, G. A.; Convertino, V. A.

1992-01-01

The purpose of this study was to examine the effect of lower body resistance training on cardiovascular control mechanisms and blood pressure maintenance during an orthostatic challenge. Lower body negative pressure (LBNP) tolerance, carotid-cardiac baroreflex function (using neck chamber pressure), and calf compliance were measured in eight healthy males before and after 19 wk of knee extension and leg press training. Resistance training sessions consisted of four or five sets of 6-12 repetitions of each exercise, performed two times per week. Training increased strength 25 +/- 3 (SE) percent (P = 0.0003) and 31 +/- 6 percent (P = 0.0004), respectively, for the leg press and knee extension exercises. Average fiber size in biopsy samples of m. vastus lateralis increased 21 +/- 5 percent (P = 0.0014). Resistance training had no significant effect on LBNP tolerance. However, calf compliance decreased in five of the seven subjects measured, with the group average changing from 4.4 +/- 0.6 ml.mm Hg-1 to 3.9 +/- 0.3 ml.mm Hg-1 (P = 0.3826). The stimulus-response relationship of the carotid-cardiac baroreflex response shifted to the left on the carotid pressure axis as indicated by a reduction of 6 mm Hg in baseline systolic blood pressure (P = 0.0471). In addition, maximum slope increased from 5.4 +/- 1.3 ms.mm Hg-1 before training to 6.6 +/- 1.6 ms.mm Hg-1 after training (P = 0.0141). Our results suggest the possibility that high resistance, lower extremity exercise training can cause a chronic increase in sensitivity and resetting of the carotid-cardiac baroreflex.

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate this approach,using a publicly available head and neck CT dataset. We also show that a deep neural network of similar depth, if trained directly using backpropagation, cannot acheive the tasks achieved using our layer wise training paradigm.

Short-term adaptations following Complex Training in team-sports: A meta-analysis

PubMed Central

Martinez-Rodriguez, Alejandro; Calleja-González, Julio; Alcaraz, Pedro E.

2017-01-01

Objective The purpose of this meta-analysis was to study the short-term adaptations on sprint and vertical jump (VJ) performance following Complex Training (CT) in team-sports. CT is a resistance training method aimed at developing both strength and power, which has a direct effect on sprint and VJ. It consists on alternating heavy resistance training exercises with plyometric/power ones, set for set, on the same workout. Methods A search of electronic databases up to July 2016 (PubMed-MEDLINE, SPORTDiscus, Web of Knowledge) was conducted. Inclusion criteria: 1) at least one CT intervention group; 2) training protocols ≥4-wks; 3) sample of team-sport players; 4) sprint or VJ as an outcome variable. Effect sizes (ES) of each intervention were calculated and subgroup analyses were performed. Results A total of 9 studies (13 CT groups) met the inclusion criteria. Medium effect sizes (ES) (ES = 0.73) were obtained for pre-post improvements in sprint, and small (ES = 0.41) in VJ, following CT. Experimental-groups presented better post-intervention sprint (ES = 1.01) and VJ (ES = 0.63) performance than control-groups. Sprint large ESs were exhibited in younger athletes (<20 years old; ES = 1.13); longer CT interventions (≥6 weeks; ES = 0.95); conditioning activities with intensities ≤85% 1RM (ES = 0.96) and protocols with frequencies of <3 sessions/week (ES = 0.84). Medium ESs were obtained in Division I players (ES = 0.76); training programs >12 total sessions (ES = 0.74). VJ Large ESs in programs with >12 total sessions (ES = 0.81). Medium ESs obtained for under-Division I individuals (ES = 0.56); protocols with intracomplex rest intervals ≥2 min (ES = 0.55); conditioning activities with intensities ≤85% 1RM (ES = 0.64); basketball/volleyball players (ES = 0.55). Small ESs were found for younger athletes (ES = 0.42); interventions ≥6 weeks (ES = 0.45). Conclusions CT interventions have positive medium effects on sprint performance and small effects on VJ in team-sport athletes. This training method is a suitable option to include in the season planning. PMID:28662108

Teaching menstrual care skills to intellectually disabled female students.

PubMed

Altundağ, Sebahat; Çalbayram, Nazan Çakırer

2016-07-01

The aim of this study was to teach pad replacement skills to intellectually disabled adolescent female students during their menstruation periods by demonstrating on a dummy. It may be difficult to make intellectually disabled adolescents achieve self-care during menstruation. In addition, there are difficulties experienced in explaining menstruation, such as physical changes and the practice of cleaning during this period. The study used a 'One group pretest and post-test model'. The study was performed in a special educational institution. The population consisted of 77 female students in the high school section. Calculation of a sample size was not attempted, and 54 students with no attendance issues agreed to take part in the study and were included. In this work, we found that pad replacement training significantly changed the scores of mentally disabled adolescents before and after training. Our training yielded positive results, and the population improved their skills at all stages of skill building. Training adolescents with mental disabilities helped them gain hygiene habits. Performance of these trainings occurs at the beginning of menstrual hygiene education. To achieve improved success in life, it is important that adolescents assume the responsibility of self-care and manage sustained care activity on their own. © 2016 John Wiley & Sons Ltd.

30 CFR 75.338 - Training.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Training. 75.338 Section 75.338 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.338 Training. (a) Certified persons conducting sampling shall be trained in the use of appropriate sampling equipment, procedures, location of sampling...

30 CFR 75.338 - Training.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Training. 75.338 Section 75.338 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.338 Training. (a) Certified persons conducting sampling shall be trained in the use of appropriate sampling equipment, procedures, location of sampling...

The Effectiveness of Teamwork Training on Teamwork Behaviors and Team Performance: A Systematic Review and Meta-Analysis of Controlled Interventions

PubMed Central

McEwan, Desmond; Ruissen, Geralyn R.; Eys, Mark A.; Zumbo, Bruno D.; Beauchamp, Mark R.

2017-01-01

The objective of this study was to conduct a systematic review and meta-analysis of teamwork interventions that were carried out with the purpose of improving teamwork and team performance, using controlled experimental designs. A literature search returned 16,849 unique articles. The meta-analysis was ultimately conducted on 51 articles, comprising 72 (k) unique interventions, 194 effect sizes, and 8439 participants, using a random effects model. Positive and significant medium-sized effects were found for teamwork interventions on both teamwork and team performance. Moderator analyses were also conducted, which generally revealed positive and significant effects with respect to several sample, intervention, and measurement characteristics. Implications for effective teamwork interventions as well as considerations for future research are discussed. PMID:28085922

Audiovisual Interval Size Estimation Is Associated with Early Musical Training.

PubMed

Abel, Mary Kathryn; Li, H Charles; Russo, Frank A; Schlaug, Gottfried; Loui, Psyche

2016-01-01

Although pitch is a fundamental attribute of auditory perception, substantial individual differences exist in our ability to perceive differences in pitch. Little is known about how these individual differences in the auditory modality might affect crossmodal processes such as audiovisual perception. In this study, we asked whether individual differences in pitch perception might affect audiovisual perception, as it relates to age of onset and number of years of musical training. Fifty-seven subjects made subjective ratings of interval size when given point-light displays of audio, visual, and audiovisual stimuli of sung intervals. Audiovisual stimuli were divided into congruent and incongruent (audiovisual-mismatched) stimuli. Participants' ratings correlated strongly with interval size in audio-only, visual-only, and audiovisual-congruent conditions. In the audiovisual-incongruent condition, ratings correlated more with audio than with visual stimuli, particularly for subjects who had better pitch perception abilities and higher nonverbal IQ scores. To further investigate the effects of age of onset and length of musical training, subjects were divided into musically trained and untrained groups. Results showed that among subjects with musical training, the degree to which participants' ratings correlated with auditory interval size during incongruent audiovisual perception was correlated with both nonverbal IQ and age of onset of musical training. After partialing out nonverbal IQ, pitch discrimination thresholds were no longer associated with incongruent audio scores, whereas age of onset of musical training remained associated with incongruent audio scores. These findings invite future research on the developmental effects of musical training, particularly those relating to the process of audiovisual perception.

Audiovisual Interval Size Estimation Is Associated with Early Musical Training

PubMed Central

Abel, Mary Kathryn; Li, H. Charles; Russo, Frank A.; Schlaug, Gottfried; Loui, Psyche

2016-01-01

Although pitch is a fundamental attribute of auditory perception, substantial individual differences exist in our ability to perceive differences in pitch. Little is known about how these individual differences in the auditory modality might affect crossmodal processes such as audiovisual perception. In this study, we asked whether individual differences in pitch perception might affect audiovisual perception, as it relates to age of onset and number of years of musical training. Fifty-seven subjects made subjective ratings of interval size when given point-light displays of audio, visual, and audiovisual stimuli of sung intervals. Audiovisual stimuli were divided into congruent and incongruent (audiovisual-mismatched) stimuli. Participants’ ratings correlated strongly with interval size in audio-only, visual-only, and audiovisual-congruent conditions. In the audiovisual-incongruent condition, ratings correlated more with audio than with visual stimuli, particularly for subjects who had better pitch perception abilities and higher nonverbal IQ scores. To further investigate the effects of age of onset and length of musical training, subjects were divided into musically trained and untrained groups. Results showed that among subjects with musical training, the degree to which participants’ ratings correlated with auditory interval size during incongruent audiovisual perception was correlated with both nonverbal IQ and age of onset of musical training. After partialing out nonverbal IQ, pitch discrimination thresholds were no longer associated with incongruent audio scores, whereas age of onset of musical training remained associated with incongruent audio scores. These findings invite future research on the developmental effects of musical training, particularly those relating to the process of audiovisual perception. PMID:27760134

Skeletal muscle pathology in endurance athletes with acquired training intolerance

PubMed Central

Grobler, L; Collins, M; Lambert, M; Sinclair-Smith, C; Derman, W; St, C; Noakes, T

2004-01-01

Background: It is well established that prolonged, exhaustive endurance exercise is capable of inducing skeletal muscle damage and temporary impairment of muscle function. Although skeletal muscle has a remarkable capacity for repair and adaptation, this may be limited, ultimately resulting in an accumulation of chronic skeletal muscle pathology. Case studies have alluded to an association between long term, high volume endurance training and racing, acquired training intolerance, and chronic skeletal muscle pathology. Objective: To systematically compare the skeletal muscle structural and ultrastructural status of endurance athletes with acquired training intolerance (ATI group) with asymptomatic endurance athletes matched for age and years of endurance training (CON group). Methods: Histological and electron microscopic analyses were carried out on a biopsy sample of the vastus lateralis from 18 ATI and 17 CON endurance athletes. The presence of structural and ultrastructural disruptions was compared between the two groups of athletes. Results: Significantly more athletes in the ATI group than in the CON group presented with fibre size variation (15 v 6; p = 0.006), internal nuclei (9 v 2; p = 0.03), and z disc streaming (6 v 0; p = 0.02). Conclusions: There is an association between increased skeletal muscle disruptions and acquired training intolerance in endurance athletes. Further studies are required to determine the nature of this association and the possible mechanisms involved. PMID:15562162

Stakeholder-focused evaluation of an online course for health care providers.

PubMed

Dunet, Diane O; Reyes, Michele

2006-01-01

Different people who have a stake or interest in a training course (stakeholders) may have markedly different definitions of what constitutes "training success" and how they will use evaluation results. Stakeholders at multiple levels within and outside of the organization guided the development of an evaluation plan for a Web-based training course on hemochromatosis. Stakeholder interests and values were reflected in the type, level, and rigor of evaluation methods selected. Our mixed-method evaluation design emphasized small sample sizes and repeated measures. Limited resources for evaluation were leveraged by focusing on the data needs of key stakeholders, understanding how they wanted to use evaluation results, and collecting data needed for stakeholder decision making. Regular feedback to key stakeholders provided opportunities for updating the course evaluation plan to meet emerging needs for new or different information. Early and repeated involvement of stakeholders in the evaluation process also helped build support for the final product. Involving patient advocacy groups, managers, and representative course participants improved the course and enhanced product dissemination. For training courses, evaluation planning is an opportunity to tailor methods and data collection to meet the information needs of particular stakeholders. Rigorous evaluation research of every training course may be infeasible or unwarranted; however, course evaluations can be improved by good planning. A stakeholder-focused approach can build a picture of the results and impact of training while fostering the practical use of evaluation data.

Using visual processing training to enhance standard cognitive remediation outcomes in schizophrenia: A pilot study.

PubMed

Contreras, Natalia A; Tan, Eric J; Lee, Stuart J; Castle, David J; Rossell, Susan L

2018-04-01

Approaches to cognitive remediation (CR) that address sensory perceptual skills before higher cognitive skills, have been found to be effective in enhancing cognitive performance in schizophrenia. To date, however, most of the conducted trials have concentrated on auditory processing. The aim of this study was to explore whether the addition of visual processing training could enhance standard cognitive remediation outcomes in a schizophrenia population. Twenty participants were randomised to either receive 20h of computer-assisted cognitive remediation alone or 20h of visual processing training modules and cognitive remediation training. All participants were assessed at baseline and at the end of cognitive remediation training on cognitive and psychosocial (i.e. self-esteem, quality of life) measures. At the end of the study participants across both groups improved significantly in overall cognition and psychosocial functioning. No significant differences were observed between groups on any of the measures. Of potential interest, however, was that the Cohen's d assessing the between group difference in the rates of change were moderate/large for a greater improvement in Visual Learning, Working Memory and Social Cognition for the visual training plus cognitive remediation group. On the basis of our effect sizes on three domains of cognition, we recommend replicating this intervention with a larger sample. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of two techniques of robot-aided upper limb exercise training after stroke.

PubMed

Stein, Joel; Krebs, Hermano Igo; Frontera, Walter R; Fasoli, Susan E; Hughes, Richard; Hogan, Neville

2004-09-01

This study examined whether incorporating progressive resistive training into robot-aided exercise training provides incremental benefits over active-assisted robot-aided exercise for the upper limb after stroke. A total of 47 individuals at least 1 yr poststroke were enrolled in this 6-wk training protocol. Paretic upper limb motor abilities were evaluated using clinical measures and a robot-based assessment to determine eligibility for robot-aided progressive resistive training at study entry. Subjects capable of participating in resistance training were randomized to receive either active-assisted robot-aided exercises or robot-aided progressive resistance training. Subjects who were incapable of participating in resistance training underwent active-assisted robotic therapy and were again screened for eligibility after 3 wks of robotic therapy. Those subjects capable of participating in resistance training at 3 wks were then randomized to receive either robot-aided resistance training or to continue with robot-aided active-assisted training. One subject withdrew due to unrelated medical issues, and data for the remaining 46 subjects were analyzed. Subjects in all groups showed improvement in measures of motor control (mean increase in Fugl-Meyer of 3.3; 95% confidence interval, 2.2-4.4) and maximal force (mean increase in maximal force of 3.5 N, P = 0.027) over the course of robot-aided exercise training. No differences in outcome measures were observed between the resistance training groups and the matched active-assisted training groups. Subjects' ability to perform the robotic task at the time of group assignment predicted the magnitude of the gain in motor control. The incorporation of robot-aided progressive resistance exercises into a program of robot-aided exercise did not favorably or negatively affect the gains in motor control or strength associated with this training, though interpretation of these results is limited by sample size. Individuals with better motor control at baseline experienced greater increases in motor control with robotic training.

Short-arc measurement and fitting based on the bidirectional prediction of observed data

NASA Astrophysics Data System (ADS)

Fei, Zhigen; Xu, Xiaojie; Georgiadis, Anthimos

2016-02-01

To measure a short arc is a notoriously difficult problem. In this study, the bidirectional prediction method based on the Radial Basis Function Neural Network (RBFNN) to the observed data distributed along a short arc is proposed to increase the corresponding arc length, and thus improve its fitting accuracy. Firstly, the rationality of regarding observed data as a time series is discussed in accordance with the definition of a time series. Secondly, the RBFNN is constructed to predict the observed data where the interpolation method is used for enlarging the size of training examples in order to improve the learning accuracy of the RBFNN’s parameters. Finally, in the numerical simulation section, we focus on simulating how the size of the training sample and noise level influence the learning error and prediction error of the built RBFNN. Typically, the observed data coming from a 5{}^\\circ short arc are used to evaluate the performance of the Hyper method known as the ‘unbiased fitting method of circle’ with a different noise level before and after prediction. A number of simulation experiments reveal that the fitting stability and accuracy of the Hyper method after prediction are far superior to the ones before prediction.

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Space Shuttle inflatable training articles

NASA Technical Reports Server (NTRS)

West, M. L.

1984-01-01

The design, development, construction, and testing of the Long Duration Exposure Facility inflatable and the space telescope training articles are discussed. While these articles are of similar nature, materials, and construction, they vary in size and present different problems with regards to size, shape, gross/net lift, and balance.

Cognitive rehabilitation in schizophrenia: a quantitative analysis of controlled studies.

PubMed

Krabbendam, Lydia; Aleman, André

2003-09-01

Cognitive rehabilitation is now recognized as an important tool in the treatment of schizophrenia, and findings in this area are emerging rapidly. There is a need for a systematic review of the effects of the different training programs. To review quantitatively the controlled studies on cognitive rehabilitation in schizophrenia for the effect of training on performance on tasks other than those practiced in the training procedure. A meta-analysis was conducted on 12 controlled studies of cognitive rehabilitation in schizophrenia taking into account the effects of type of rehabilitation approach (rehearsal or strategy learning) and duration of training. The mean weighted effect size was 0.45, with a 95% confidence interval from 0.26 to 0.64. Effect sizes differed slightly, depending on rehabilitation approach, in favor of strategy learning, but this difference did not reach statistical significance. Duration of training did not influence effect size. Cognitive rehabilitation can improve task performance in patients with schizophrenia and this effect is apparent on tasks outside those practiced during the training procedure. Future studies should include more real-world outcomes and perform longitudinal evaluations.

Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model

NASA Astrophysics Data System (ADS)

Gelß, Patrick; Matera, Sebastian; Schütte, Christof

2016-06-01

In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO2(110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased stiffness.

EEG neurofeedback effects in the treatment of adolescent anorexia nervosa.

PubMed

Lackner, Nina; Unterrainer, Human-Friedrich; Skliris, Dimitris; Shaheen, Sandra; Dunitz-Scheer, Marguerite; Wood, Guilherme; Scheer, Peter Jaron Zwi; Wallner-Liebmann, Sandra Johanna; Neuper, Christa

2016-01-01

A pre-post design including 22 females was used to evaluate the effectiveness of neurofeedback in the treatment of adolescent anorexia nervosa. Resting EEG measures and a psychological test-battery assessing eating behavior traits, clinical symptoms, emotionality, and mood were obtained. While both the experimental (n = 10) and control group (n = 12) received their usual maintenance treatment, the experimental group received 10 sessions of individual alpha frequency training over a period of 5 weeks as additional treatment. Significant training effects were shown in eating behavior traits, emotion regulation, and in relative theta power in the eyes closed condition. Although the results are limited due to the small sample size, these are the first empirical data demonstrating the benefits of neurofeedback as a treatment adjunct in individuals with anorexia nervosa.

Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies.

PubMed

Ganna, Andrea; Lee, Donghwan; Ingelsson, Erik; Pawitan, Yudi

2015-07-01

It is common and advised practice in biomedical research to validate experimental or observational findings in a population different from the one where the findings were initially assessed. This practice increases the generalizability of the results and decreases the likelihood of reporting false-positive findings. Validation becomes critical when dealing with high-throughput experiments, where the large number of tests increases the chance to observe false-positive results. In this article, we review common approaches to determine statistical thresholds for validation and describe the factors influencing the proportion of significant findings from a 'training' sample that are replicated in a 'validation' sample. We refer to this proportion as rediscovery rate (RDR). In high-throughput studies, the RDR is a function of false-positive rate and power in both the training and validation samples. We illustrate the application of the RDR using simulated data and real data examples from metabolomics experiments. We further describe an online tool to calculate the RDR using t-statistics. We foresee two main applications. First, if the validation study has not yet been collected, the RDR can be used to decide the optimal combination between the proportion of findings taken to validation and the size of the validation study. Secondly, if a validation study has already been done, the RDR estimated using the training data can be compared with the observed RDR from the validation data; hence, the success of the validation study can be assessed. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A hidden Markov model for decoding and the analysis of replay in spike trains.

PubMed

Box, Marc; Jones, Matt W; Whiteley, Nick

2016-12-01

We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

Mental training in surgical education: a systematic review.

PubMed

Davison, Sara; Raison, Nicholas; Khan, Muhammad S; Dasgupta, Prokar; Ahmed, Kamran

2017-11-01

Pressures on surgical education from restricted working hours and increasing scrutiny of outcomes have been compounded by the development of highly technical surgical procedures requiring additional specialist training. Mental training (MT), the act of performing motor tasks in the 'mind's eye', offers the potential for training outside the operating room. However, the technique is yet to be formally incorporated in surgical curricula. This study aims to review the available literature to determine the role of MT in surgical education. EMBASE and Medline databases were searched. The primary outcome measure was surgical proficiency following training. Secondary analyses examined training duration, forms of MT and trainees level of experience. Study quality was assessed using Consolidated Standards of Reporting Trials scores or Quality Assessment Tool for Before-After (Pre-Post) Studies with No Control Group. Fourteen trials with 618 participants met the inclusion criteria, of which 11 were randomized and three longitudinal. Ten studies found MT to be beneficial. Mental rehearsal was the most commonly used form of training. No significant correlation was found between the length of MT and outcomes. MT benefitted expert surgeons more than medical students or novice surgeons. The majority studies demonstrate MT to be beneficial in surgical education especially amongst more experienced surgeons within a well-structured MT programme. However, overall studies were low quality, lacked sufficient methodology and suffered from small sample sizes. For these reasons, further research is required to determine optimal role of MT as a supplementary educational tool within the surgical curriculum. © 2017 Royal Australasian College of Surgeons.

Randomised social-skills training and parental training plus standard treatment versus standard treatment of children with attention deficit hyperactivity disorder - The SOSTRA trial protocol

PubMed Central

2011-01-01

Background Children with attention deficit hyperactivity disorder (ADHD) are hyperactive and impulsive, cannot maintain attention, and have difficulties with social interactions. Medical treatment may alleviate symptoms of ADHD, but seldom solves difficulties with social interactions. Social-skills training may benefit ADHD children in their social interactions. We want to examine the effects of social-skills training on difficulties related to the children's ADHD symptoms and social interactions. Methods/Design The design is randomised two-armed, parallel group, assessor-blinded trial. Children aged 8-12 years with a diagnosis of ADHD are randomised to social-skills training and parental training plus standard treatment versus standard treatment alone. A sample size calculation estimated that at least 52 children must be included to show a 4-point difference in the primary outcome on the Conners 3rd Edition subscale for 'hyperactivity-impulsivity' between the intervention group and the control group. The outcomes will be assessed 3 and 6 months after randomisation. The primary outcome measure is ADHD symptoms. The secondary outcome is social skills. Tertiary outcomes include the relationship between social skills and symptoms of ADHD, the ability to form attachment, and parents' ADHD symptoms. Discussion We hope that the results from this trial will show that the social-skills training together with medication may have a greater general effect on ADHD symptoms and social and emotional competencies than medication alone. Trial registration ClinicalTrials (NCT): NCT00937469 PMID:21255399

MYmind: Mindfulness training for Youngsters with autism spectrum disorders and their parents.

PubMed

de Bruin, Esther I; Blom, René; Smit, Franka Ma; van Steensel, Francisca Ja; Bögels, Susan M

2015-11-01

Despite the dramatic increase in autism spectrum disorder in youth and the extremely high costs, hardly any evidence-based interventions are available. The aim of this study is to examine the effects of mindfulness training for adolescents with autism spectrum disorder, combined with Mindful Parenting training. A total of 23 adolescents with autism spectrum disorder, referred to a mental health clinic, received nine weekly sessions of mindfulness training in group format. Their parents (18 mothers, 11 fathers) participated in parallel Mindful Parenting training. A pre-test, post-test, and 9-week follow-up design was used. Data were analyzed using multi-level analyses. Attendance rate was 88% for adolescents and fathers and 86% for mothers. Adolescents reported an increase in quality of life and a decrease in rumination, but no changes in worry, autism spectrum disorder core symptoms, or mindful awareness. Although parents reported no change in adolescent's autism spectrum disorder core symptoms, they reported improved social responsiveness, social communication, social cognition, preoccupations, and social motivation. About themselves, parents reported improvement in general as well as in parental mindfulness. They reported improved competence in parenting, overall parenting styles, more specifically a less lax, verbose parenting style, and an increased quality of life. Mindfulness training for adolescents with autism spectrum disorder combined with Mindful Parenting is feasible. Although the sample size was small and no control group was included, the first outcomes of this innovative training are positive. © The Author(s) 2014.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

NASA Astrophysics Data System (ADS)

Ndaw, Joseph D.; Faye, Andre; Maïga, Amadou S.

2017-05-01

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-DOA) with ANN models. In this paper we present a fast artificial neural network approach for 2D-DOA estimation with reduced training sets sizes. We exploit the symmetry properties of Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.

Rationale and design of a randomized controlled, clinical trial investigating a comprehensive exercise stimulus for improving mobility disability outcomes in persons with multiple sclerosis.

PubMed

Motl, Robert W; Pilutti, Lara A; Sandroff, Brian M; Klaren, Rachel; Balantrapu, Swathi; McAuley, Edward; Sosnoff, Jacob J; Fernhall, Bo

2013-05-01

This randomized controlled trial (RCT) examines the effect of a comprehensive exercise training stimulus on physiological function and mobility disability (i.e., problems walking) in individuals with multiple sclerosis (MS) who have walking impairment. This trial will recruit 30 persons with MS across central Illinois who have an Expanded Disability Status Scale score between 4.0 and 6.0, and those persons will be randomized into either the intervention or control arm of the study; the participants will not be blinded regarding group assignment. The intervention will incorporate equal amounts of aerobic, resistance, and balance modes of training delivered 3 times/week with a gradual progression of duration and intensity across a 6-month period. The control will involve stretching along with minimal muscle strengthening stimuli and will be delivered on the same frequency and duration. The primary outcomes will be clinical, kinematic, patient-rated, and physiological measures of mobility disability. The secondary outcomes will be measures of physiological function including aerobic capacity, muscle strength, and balance. This study will lay the foundation for the design of a subsequent Phase II or Phase III RCT by (a) providing effect sizes that can be included in a power analysis for sample size estimation and (b) investigating whether aerobic capacity, muscle strength, and balance are possible factors associated with the beneficial effect of exercise training on walking outcomes. Taken as a whole, the proposed study and our subsequent research agenda has the potential for advancing the management of mobility disability using exercise training in the 2nd stage of MS. Copyright © 2013 Elsevier Inc. All rights reserved.

End-of-Life Conversation Game Increases Confidence for Having End-of-Life Conversations for Chaplains-in-Training.

PubMed

Van Scoy, Lauren Jodi; Watson-Martin, Elizabeth; Bohr, Tiffany A; Levi, Benjamin H; Green, Michael J

2018-04-01

Discussing end-of-life issues with patients is an essential role for chaplains. Few tools are available to help chaplains-in-training develop end-of-life communication skills. This study aimed to determine whether playing an end-of-life conversation game increases the confidence for chaplain-in-trainings to discuss end-of-life issues with patients. We used a convergent mixed methods design. Chaplains-in-training played the end-of-life conversation game twice over 2 weeks. For each game, pre- and postgame questionnaires measured confidence discussing end-of-life issues with patients and emotional affect. Between games, chaplains-in-training discussed end-of-life issues with an inpatient. One week after game 2, chaplains-in-training were individually interviewed. Quantitative data were analyzed using descriptive statistics and Wilcoxon rank-sum t tests. Content analysis identified interview themes. Quantitative and qualitative data sets were then integrated using a joint display. Twenty-three chaplains-in-training (52% female; 87% Caucasian; 70% were in year 1 of training) completed the study. Confidence scores (scale: 15-75; 75 = very confident) increased significantly after each game, increasing by 10.0 points from pregame 1 to postgame 2 ( P < .001). Positive affect subscale scores also increased significantly after each game, and shyness subscale scores decreased significantly after each game. Content analysis found that chaplains-in-training found the game to be a positive, useful experience and reported that playing twice was beneficial (not redundant). Mixed methods analysis suggest that an end-of-life conversation game is a useful tool that can increase chaplain-in-trainings' confidence for initiating end-of-life discussions with patients. A larger sample size is needed to confirm these findings.

Simulation-based training for nurses: Systematic review and meta-analysis.

PubMed

Hegland, Pål A; Aarlie, Hege; Strømme, Hilde; Jamtvedt, Gro

2017-07-01

Simulation-based training is a widespread strategy to improve health-care quality. However, its effect on registered nurses has previously not been established in systematic reviews. The aim of this systematic review is to evaluate effect of simulation-based training on nurses' skills and knowledge. We searched CDSR, DARE, HTA, CENTRAL, CINAHL, MEDLINE, Embase, ERIC, and SveMed+ for randomised controlled trials (RCT) evaluating effect of simulation-based training among nurses. Searches were completed in December 2016. Two reviewers independently screened abstracts and full-text, extracted data, and assessed risk of bias. We compared simulation-based training to other learning strategies, high-fidelity simulation to other simulation strategies, and different organisation of simulation training. Data were analysed through meta-analysis and narrative syntheses. GRADE was used to assess the quality of evidence. Fifteen RCTs met the inclusion criteria. For the comparison of simulation-based training to other learning strategies on nurses' skills, six studies in the meta-analysis showed a significant, but small effect in favour of simulation (SMD -1.09, CI -1.72 to -0.47). There was large heterogeneity (I 2 85%). For the other comparisons, there was large between-study variation in results. The quality of evidence for all comparisons was graded as low. The effect of simulation-based training varies substantially between studies. Our meta-analysis showed a significant effect of simulation training compared to other learning strategies, but the quality of evidence was low indicating uncertainty. Other comparisons showed inconsistency in results. Based on our findings simulation training appears to be an effective strategy to improve nurses' skills, but further good-quality RCTs with adequate sample sizes are needed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive accuracy of combined genetic and environmental risk scores.

PubMed

Dudbridge, Frank; Pashayan, Nora; Yang, Jian

2018-02-01

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

Predictive accuracy of combined genetic and environmental risk scores

PubMed Central

Pashayan, Nora; Yang, Jian

2017-01-01

ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

Coupling a Neural Network with Atmospheric Flow Simulations to Locate and Quantify CH4 Emissions at Well Pads

NASA Astrophysics Data System (ADS)

Travis, B. J.; Sauer, J.; Dubey, M. K.

2017-12-01

Methane (CH4) leaks from oil and gas production fields are a potentially significant source of atmospheric methane. US DOE's ARPA-E office is supporting research to locate methane emissions at 10 m size well pads to within 1 m. A team led by Aeris Technologies, and that includes LANL, Planetary Science Institute and Rice University has developed an autonomous leak detection system (LDS) employing a compact laser absorption methane sensor, a sonic anemometer and multiport sampling. The LDS system analyzes monitoring data using a convolutional neural network (cNN) to locate and quantify CH4 emissions. The cNN was trained using three sources: (1) ultra-high-resolution simulations of methane transport provided by LANL's coupled atmospheric transport model HIGRAD, for numerous controlled methane release scenarios and methane sampling configurations under variable atmospheric conditions, (2) Field tests at the METEC site in Ft. Collins, CO., and (3) Field data from other sites where point-source surface methane releases were monitored downwind. A cNN learning algorithm is well suited to problems in which the training and observed data are noisy, or correspond to complex sensor data as is typical of meteorological and sensor data over a well pad. Recent studies with our cNN emphasize the importance of tracking wind speeds and directions at fine resolution ( 1 second), and accounting for variations in background CH4 levels. A few cases illustrate the importance of sufficiently long monitoring; short monitoring may not provide enough information to determine accurately a leak location or strength, mainly because of short-term unfavorable wind directions and choice of sampling configuration. Length of multiport duty cycle sampling and sample line flush time as well as number and placement of monitoring sensors can significantly impact ability to locate and quantify leaks. Source location error at less than 10% requires about 30 or more training cases.

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Effectiveness of Positive Thinking Training Program on Nurses' Quality of Work Life through Smartphone Applications

PubMed Central

Dehghan, Azizallah

2017-01-01

Aim Job stress is a part of nurses' professional life that causes the decrease of the nurses' job satisfaction and quality of work life. This study aimed to determine the effect of positive thinking via social media applications on the nurses' quality of work life. Methods This was a pretest-posttest quasi-experimental study design with a control group. The samples were selected among the nurses in two hospitals in Fasa University of Medical Sciences and divided randomly into two interventional (n = 50) and control (n = 50) groups. Positive thinking training through telegrams was sent to the intervention group during a period of 3 months. Data were collected by using Brooks and Anderson's questionnaire of work life quality and analyzed by SPSS 18. Results The mean total scores of pretest and posttest in the intervention group improved noticeably and there were significant differences between mean scores of quality of work life in pretest and posttest scores in interventional groups (p < 0.001) and in dimensions of work life quality, home life (p < 0.001), work design (p < 0.001), work context (p < 0.001), and work world (p = 0.003). Conclusion This study concluded that positive thinking training via social media application enhanced nurses' quality of work life. This study is necessary to carry out on a larger sample size for generalizing findings better. PMID:28589174

Effectiveness of Positive Thinking Training Program on Nurses' Quality of Work Life through Smartphone Applications.

PubMed

Motamed-Jahromi, Mohadeseh; Fereidouni, Zhila; Dehghan, Azizallah

2017-01-01

Job stress is a part of nurses' professional life that causes the decrease of the nurses' job satisfaction and quality of work life. This study aimed to determine the effect of positive thinking via social media applications on the nurses' quality of work life. This was a pretest-posttest quasi-experimental study design with a control group. The samples were selected among the nurses in two hospitals in Fasa University of Medical Sciences and divided randomly into two interventional ( n = 50) and control ( n = 50) groups. Positive thinking training through telegrams was sent to the intervention group during a period of 3 months. Data were collected by using Brooks and Anderson's questionnaire of work life quality and analyzed by SPSS 18. The mean total scores of pretest and posttest in the intervention group improved noticeably and there were significant differences between mean scores of quality of work life in pretest and posttest scores in interventional groups ( p < 0.001) and in dimensions of work life quality, home life ( p < 0.001), work design ( p < 0.001), work context ( p < 0.001), and work world ( p = 0.003). This study concluded that positive thinking training via social media application enhanced nurses' quality of work life. This study is necessary to carry out on a larger sample size for generalizing findings better.

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

A DMA-train for precision measurement of sub-10 nm aerosol dynamics

NASA Astrophysics Data System (ADS)

Stolzenburg, Dominik; Steiner, Gerhard; Winkler, Paul M.

2017-05-01

Measurements of aerosol dynamics in the sub-10 nm size range are crucially important for quantifying the impact of new particle formation onto the global budget of cloud condensation nuclei. Here we present the development and characterization of a differential mobility analyzer train (DMA-train), operating six DMAs in parallel for high-time-resolution particle-size-distribution measurements below 10 nm. The DMAs are operated at six different but fixed voltages and hence sizes, together with six state-of-the-art condensation particle counters (CPCs). Two Airmodus A10 particle size magnifiers (PSM) are used for channels below 2.5 nm while sizes above 2.5 nm are detected by TSI 3776 butanol-based or TSI 3788 water-based CPCs. We report the transfer functions and characteristics of six identical Grimm S-DMAs as well as the calibration of a butanol-based TSI model 3776 CPC, a water-based TSI model 3788 CPC and an Airmodus A10 PSM. We find cutoff diameters similar to those reported in the literature. The performance of the DMA-train is tested with a rapidly changing aerosol of a tungsten oxide particle generator during warmup. Additionally we report a measurement of new particle formation taken during a nucleation event in the CLOUD chamber experiment at CERN. We find that the DMA-train is able to bridge the gap between currently well-established measurement techniques in the cluster-particle transition regime, providing high time resolution and accurate size information of neutral and charged particles even at atmospheric particle concentrations.

Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture.

PubMed

Vallejo, Roger L; Leeds, Timothy D; Gao, Guangtu; Parsons, James E; Martin, Kyle E; Evenhuis, Jason P; Fragomeni, Breno O; Wiens, Gregory D; Palti, Yniv

2017-02-01

Previously, we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative that enables exploitation of within-family genetic variation. We compared three GS models [single-step genomic best linear unbiased prediction (ssGBLUP), weighted ssGBLUP (wssGBLUP), and BayesB] to predict genomic-enabled breeding values (GEBV) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBV to traditional estimates of breeding values (EBV) from a pedigree-based BLUP (P-BLUP) model. We also assessed the impact of sampling design on the accuracy of GEBV predictions. For these comparisons, we used BCWD survival phenotypes recorded on 7893 fish from 102 families, of which 1473 fish from 50 families had genotypes [57 K single nucleotide polymorphism (SNP) array]. Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBV and mated to produce 138 progeny testing families. In the following generation, 9968 progeny were phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents. The accuracy of GEBV from all tested GS models were substantially higher than the P-BLUP model EBV. The highest increase in accuracy relative to the P-BLUP model was achieved with BayesB (97.2 to 108.8%), followed by wssGBLUP at iteration 2 (94.4 to 97.1%) and 3 (88.9 to 91.2%) and ssGBLUP (83.3 to 85.3%). Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67 to 0.72), but with n = ~500 the accuracy dropped to 0.53 to 0.61 if the training and testing fish were full-sibs, and even substantially lower, to 0.22 to 0.25, when they were not full-sibs. Using progeny performance data, we showed that the accuracy of genomic predictions is substantially higher than estimates obtained from the traditional pedigree-based BLUP model for BCWD resistance. Overall, we found that using a much smaller training sample size compared to similar studies in livestock, GS can substantially improve the selection accuracy and genetic gains for this trait in a commercial rainbow trout breeding population.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

PubMed Central

Shi, Guokai; Xu, Tingfa; Luo, Jiqiang; Li, Yuankun

2017-01-01

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). PMID:29231876

Computerized cognitive training in children and adolescents with attention deficit/hyperactivity disorder as add-on treatment to stimulants: feasibility study and protocol description.

PubMed

Rosa, Virginia de Oliveira; Schmitz, Marcelo; Moreira-Maia, Carlos Roberto; Wagner, Flavia; Londero, Igor; Bassotto, Caroline de Fraga; Moritz, Guilherme; de Souza, Caroline Dos Santos; Rohde, Luis Augusto Paim

2017-01-01

Cognitive training has received increasing attention as a non-pharmacological approach for the treatment of attention deficit/hyperactivity disorder (ADHD) in children and adolescents. Few studies have assessed cognitive training as add-on treatment to medication in randomized placebo controlled trials. The purpose of this preliminary study was to explore the feasibility of implementing a computerized cognitive training program for ADHD in our environment, describe its main characteristics and potential efficacy in a small pilot study. Six ADHD patients aged 10-12-years old receiving stimulants and presenting residual symptoms were enrolled in a randomized clinical trial to either a standard cognitive training program or a controlled placebo condition for 12 weeks. The primary outcome was core ADHD symptoms measured using the Swanson, Nolan and Pelham Questionnaire (SNAP-IV scale). We faced higher resistance than expected to patient enrollment due to logistic issues to attend face-to-face sessions in the hospital and to fill the requirement of medication status and absence of some comorbidities. Both groups showed decrease in parent reported ADHD symptoms without statistical difference between them. In addition, improvements on neuropsychological tests were observed in both groups - mainly on trained tasks. This protocol revealed the need for new strategies to better assess the effectiveness of cognitive training such as the need to implement the intervention in a school environment to have an assessment with more external validity. Given the small sample size of this pilot study, definitive conclusions on the effects of cognitive training as add-on treatment to stimulants would be premature.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

PubMed

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

PubMed Central

Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%. PMID:29795581

Perceptual Learning in Children With Infantile Nystagmus: Effects on Reading Performance.

PubMed

Huurneman, Bianca; Boonstra, F Nienke; Goossens, Jeroen

2016-08-01

Perceptual learning improves visual acuity and reduces crowding in children with infantile nystagmus (IN). Here, we compare reading performance of 6- to 11-year-old children with IN with normal controls, and evaluate whether perceptual learning improves their reading. Children with IN were divided in two training groups: a crowded training group (n = 18; albinism: n = 8; idiopathic IN: n = 10) and an uncrowded training group (n = 17; albinism: n = 9; idiopathic IN: n = 8). Also 11 children with normal vision participated. Outcome measures were: reading acuity (the smallest readable font size), maximum reading speed, critical print size (font size below which reading is suboptimal), and acuity reserve (difference between reading acuity and critical print size). We used multiple regression analyses to test if these reading parameters were related to the children's uncrowded distance acuity and/or crowding scores. Reading acuity and critical print size were 0.65 ± 0.04 and 0.69 ± 0.08 log units larger for children with IN than for children with normal vision. Maximum reading speed and acuity reserve did not differ between these groups. After training, reading acuity improved by 0.12 ± 0.02 logMAR and critical print size improved by 0.11 ± 0.04 logMAR in both IN training groups. The changes in reading acuity, critical print size, and acuity reserve of children with IN were tightly related to changes in their uncrowded distance acuity and the changes in magnitude and extent of crowding. Our findings are the first to show that visual acuity is not the only factor that restricts reading in children with IN, but that crowding also limits their reading performance. By targeting both of these spatial bottlenecks in children with IN, our perceptual learning paradigms significantly improved their reading acuity and critical print size. This shows that perceptual learning can effectively transfer to reading.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Role of Ingested Amino Acids and Protein in the Promotion of Resistance Exercise–Induced Muscle Protein Anabolism123

PubMed Central

Rasmussen, Blake B

2016-01-01

The goal of this critical review is to comprehensively assess the evidence for the molecular, physiologic, and phenotypic skeletal muscle responses to resistance exercise (RE) combined with the nutritional intervention of protein and/or amino acid (AA) ingestion in young adults. We gathered the literature regarding the translational response in human skeletal muscle to acute exposure to RE and protein/AA supplements and the literature describing the phenotypic skeletal muscle adaptation to RE and nutritional interventions. Supplementation of protein/AAs with RE exhibited clear protein dose–dependent effects on translational regulation (protein synthesis) through mammalian target of rapamycin complex 1 (mTORC1) signaling, which was most apparent through increases in p70 ribosomal protein S6 kinase 1 (S6K1) phosphorylation, compared with postexercise recovery in the fasted or carbohydrate-fed state. These acute findings were critically tested via long-term exposure to RE training (RET) and protein/AA supplementation, and it was determined that a diminishing protein/AA supplement effect occurs over a prolonged exposure stimulus after exercise training. Furthermore, we found that protein/AA supplements, combined with RET, produced a positive, albeit minor, effect on the promotion of lean mass growth (when assessed in >20 participants/treatment); a negligible effect on muscle mass; and a negligible to no additional effect on strength. A potential concern we discovered was that the majority of the exercise training studies were underpowered in their ability to discern effects of protein/AA supplementation. Regardless, even when using optimal methodology and large sample sizes, it is clear that the effect size for protein/AA supplementation is low and likely limited to a subset of individuals because the individual variability is high. With regard to nutritional intakes, total protein intake per day, rather than protein timing or quality, appears to be more of a factor on this effect during long-term exercise interventions. There were no differences in strength or mass/muscle mass on RET outcomes between protein types when a leucine threshold (>2 g/dose) was reached. Future research with larger sample sizes and more homogeneity in design is necessary to understand the underlying adaptations and to better evaluate the individual variability in the muscle-adaptive response to protein/AA supplementation during RET. PMID:26764320

A mindfulness-based stress prevention training for medical students (MediMind): study protocol for a randomized controlled trial.

PubMed

Kuhlmann, Sophie Merle; Bürger, Arne; Esser, Günter; Hammerle, Florian

2015-02-08

Medical training is very demanding and associated with a high prevalence of psychological distress. Compared to the general population, medical students are at a greater risk of developing a psychological disorder. Various attempts of stress management training in medical school have achieved positive results on minimizing psychological distress; however, there are often limitations. Therefore, the use of a rigorous scientific method is needed. The present study protocol describes a randomized controlled trial to examine the effectiveness of a specifically developed mindfulness-based stress prevention training for medical students that includes selected elements of cognitive behavioral strategies (MediMind). This study protocol presents a prospective randomized controlled trial, involving four assessment time points: baseline, post-intervention, one-year follow-up and five-year follow-up. The aims include evaluating the effect on stress, coping, psychological morbidity and personality traits with validated measures. Participants are allocated randomly to one of three conditions: MediMind, Autogenic Training or control group. Eligible participants are medical or dental students in the second or eighth semester of a German university. They form a population of approximately 420 students in each academic term. A final total sample size of 126 (at five-year follow-up) is targeted. The trainings (MediMind and Autogenic Training) comprise five weekly sessions lasting 90 minutes each. MediMind will be offered to participants of the control group once the five-year follow-up is completed. The allotment is randomized with a stratified allocation ratio by course of studies, semester, and gender. After descriptive statistics have been evaluated, inferential statistical analysis will be carried out with a repeated measures ANOVA-design with interactions between time and group. Effect sizes will be calculated using partial η-square values. Potential limitations of this study are voluntary participation and the risk of attrition, especially concerning participants that are allocated to the control group. Strengths are the study design, namely random allocation, follow-up assessment, the use of control groups and inclusion of participants at different stages of medical training with the possibility of differential analysis. This trial is recorded at German Clinical Trials Register under the number DRKS00005354 (08 November 2013).

Barriers to Employee Training in Small and Medium Sized Enterprises: Insights and Evidences from Mauritius

ERIC Educational Resources Information Center

Padachi, Kesseven; Bhiwajee, Soolakshna Lukea

2016-01-01

Purpose: Training is an important component of successful business concerns. However, although there is growing acceptance amongst scholars that small- and medium-sized enterprises (SMEs) are engines that drive economies across nations, through their contribution in terms of job creation and poverty reduction; extant research portray that these…

A simple method to derive bounds on the size and to train multilayer neural networks

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Antsaklis, Panos J.

1991-01-01

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely, the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to no.1 is greater than p - 1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving no.1 + 1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.

Working memory updating and binding training: Bayesian evidence supporting the absence of transfer.

PubMed

De Simoni, Carla; von Bastian, Claudia C

2018-06-01

As working memory (WM) predicts a wide range of other abilities, it has become a popular target for training interventions. However, its effectiveness to elicit generalized cognitive benefits is still under debate. Previous research yielded inconsistent findings and focused only little on the mechanisms underlying transfer effects. To disentangle training effects on WM capacity and efficiency, we evaluated near transfer to untrained, structurally different WM tasks and far transfer to closely related abilities (i.e., reasoning, processing speed, task switching, and inhibitory control) in addition to process-specific effects on 3 WM mechanisms (i.e., focus switching, removal of WM contents, and interference resolution). We randomly assigned 197 young adults to 1 of 2 experimental groups (updating or item-to-context binding) or to an active control group practicing visual search tasks. Before and after 5 weeks of adaptive training, performance was assessed measuring each of the cognitive processes and abilities of interest with 4 tasks covering verbal-numerical and visual-spatial materials. Despite the relatively large sample size, large practice effects in the trained tasks, and at least moderate correlations between WM training tasks and transfer measures, we found consistent evidence for the absence of any training-induced improvements across all ranges of transfer and mechanisms. Instead, additional analyses of error patterns and self-reported strategy use indicated that WM training encouraged the development of stimulus-specific expertise and use of paradigm-specific strategies. Thus, the results suggest that the WM training interventions examined here enhanced neither WM capacity nor the WM mechanisms assumed to underlie transfer. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Virtual Sensorimotor Balance Training for Children With Fetal Alcohol Spectrum Disorders: Feasibility Study.

PubMed

McCoy, Sarah Westcott; Jirikowic, Tracy; Price, Robert; Ciol, Marcia A; Hsu, Lin-Ya; Dellon, Brian; Kartin, Deborah

2015-11-01

Diminished sensory adaptation has been associated with poor balance control for children with fetal alcohol spectrum disorders (FASD). A virtual reality system, Sensorimotor Training to Affect Balance, Engagement and Learning (STABEL), was developed to train sensory control for balance. The purpose of this study was to examine the STABEL system in children with FASD and children with typical development (TD) to (1) determine the feasibility of the STABEL system and (2) explore the immediate effects of the STABEL system on sensory attention and postural control. This is a technical report with observational study data. Eleven children with FASD and 11 children with TD, aged 8 to 16 years, completed 30 minutes of STABEL training. The children answered questions about their experience using STABEL. Sensory attention and postural control were measured pre- and post-STABEL training with the Multimodal Balance Entrainment Response system and compared using repeated-measures analysis of variance. All children engaged in game play and tolerated controlled sensory input during the STABEL protocol. Immediate effects post-STABEL training in both groups were increased postural sway velocity and some changes in entrainment gain. Children with FASD showed higher entrainment gain to vestibular stimuli. There were no significant changes in sensory attention fractions. The small sample size, dose of STABEL training, and exploratory statistical analyses are study limitations, but findings warrant larger systematic study to examine therapeutic effects. Children completed the training protocol, demonstrating the feasibility of the STABEL system. Differences in postural sway velocity post-STABEL training may have been affected by fatigue, warranting further investigation. Limited immediate effects suggest more practice is needed to affect sensory attention; however, entrainment gain changes suggest the STABEL system provoked vestibular responses during balance practice. © 2015 American Physical Therapy Association.

A pilot study examining the effects of low-volume high-intensity interval training and continuous low to moderate intensity training on quality of life, functional capacity and cardiovascular risk factors in cancer survivors.

PubMed

Toohey, Kellie; Pumpa, Kate L; Arnolda, Leonard; Cooke, Julie; Yip, Desmond; Craft, Paul S; Semple, Stuart

2016-01-01

The aim of this study was to evaluate the effects of low-volume high-intensity interval training and continuous low to moderate intensity training on quality of life, functional capacity and cardiovascular disease risk factors in cancer survivors. Cancer survivors within 24 months post-diagnosis were randomly assigned into the low-volume high-intensity interval training group ( n  = 8) or the continuous low to moderate intensity training group ( n  = 8) group for 36 sessions (12 weeks) of supervised exercise. The low-volume high-intensity interval training (LVHIIT) group performed 7 × 30 s intervals (≥85% maximal heart rate) and the continuous low to moderate intensity training (CLMIT) group performed continuous aerobic training for 20 min (≤55% maximal heart rate) on a stationary bike or treadmill. Significant improvements (time) were observed for 13 of the 23 dependent variables (ES 0.05-0.61, p  ≤ 0.05). An interaction effect was observed for six minute walk test (18.53% [32.43-4.63] ES 0.50, p  ≤ 0.01) with the LVHIIT group demonstrating greater improvements. These preliminary findings suggest that both interventions can induce improvements in quality of life, functional capacity and selected cardiovascular disease risk factors. The LVHIIT program was well tolerated by the participants and our results suggest that LVHIIT is the preferred modality to improve fitness (6MWT); it remains to be seen which intervention elicits the most clinically relevant outcomes for patients. A larger sample size with a control group is required to confirm the significance of these findings.

Embodied cognitive flexibility and neuroplasticity following Quadrato Motor Training

PubMed Central

Ben-Soussan, Tal D.; Berkovich-Ohana, Aviva; Piervincenzi, Claudia; Glicksohn, Joseph; Carducci, Filippo

2015-01-01

Quadrato Motor Training (QMT) is a whole-body movement contemplative practice aimed at increasing health and well-being. Previous research studying the effect of one QMT session suggested that one of its means for promoting health is by enhancing cognitive flexibility, an important dimension of creativity. Yet, little is known about the effect of a longer QMT practice on creativity, or the relative contribution of the cognitive and motor aspects of the training. Here, we continue this line of research in two inter-related studies, examining the effects of prolonged QMT. In the first, we investigated the effect of 4-weeks of daily QMT on creativity using the Alternate Uses (AUs) Task. In order to determine whether changes in creativity were driven by the cognitive or the motor aspects of the training, we used two control groups: Verbal Training (VT, identical cognitive training with verbal response) and Simple Motor Training (SMT, similar motor training with reduced choice requirements). Twenty-seven participants were randomly assigned to one of the groups. Following training, cognitive flexibility significantly increased in the QMT group, which was not the case for either the SMT or VT groups. In contrast to one QMT session, ideational fluency was also significantly increased. In the second study, we conducted a pilot longitudinal structural magnetic resonance imaging and diffusion tensor imaging (4-weeks QMT). We report gray matter volume and fractional anisotropy changes, in several regions, including the cerebellum, previously related to interoceptive accuracy. The anatomical changes were positively correlated with cognitive flexibility scores. Albeit the small sample size and preliminary nature of the findings, these results provide support for the hypothesized creativity-motor connection. The results are compared to other contemplative studies, and discussed in light of theoretical models integrating cognitive flexibility, embodiment and the motor system. PMID:26257679

A pilot study examining the effects of low-volume high-intensity interval training and continuous low to moderate intensity training on quality of life, functional capacity and cardiovascular risk factors in cancer survivors

PubMed Central

Pumpa, Kate L.; Arnolda, Leonard; Cooke, Julie; Yip, Desmond; Craft, Paul S.; Semple, Stuart

2016-01-01

Purpose The aim of this study was to evaluate the effects of low-volume high-intensity interval training and continuous low to moderate intensity training on quality of life, functional capacity and cardiovascular disease risk factors in cancer survivors. Methods Cancer survivors within 24 months post-diagnosis were randomly assigned into the low-volume high-intensity interval training group (n = 8) or the continuous low to moderate intensity training group (n = 8) group for 36 sessions (12 weeks) of supervised exercise. The low-volume high-intensity interval training (LVHIIT) group performed 7 × 30 s intervals (≥85% maximal heart rate) and the continuous low to moderate intensity training (CLMIT) group performed continuous aerobic training for 20 min (≤55% maximal heart rate) on a stationary bike or treadmill. Results Significant improvements (time) were observed for 13 of the 23 dependent variables (ES 0.05–0.61, p ≤ 0.05). An interaction effect was observed for six minute walk test (18.53% [32.43–4.63] ES 0.50, p ≤ 0.01) with the LVHIIT group demonstrating greater improvements. Conclusion These preliminary findings suggest that both interventions can induce improvements in quality of life, functional capacity and selected cardiovascular disease risk factors. The LVHIIT program was well tolerated by the participants and our results suggest that LVHIIT is the preferred modality to improve fitness (6MWT); it remains to be seen which intervention elicits the most clinically relevant outcomes for patients. A larger sample size with a control group is required to confirm the significance of these findings. PMID:27781180

Six Sessions of Sprint Interval Training Improves Running Performance in Trained Athletes.

PubMed

Koral, Jerome; Oranchuk, Dustin J; Herrera, Roberto; Millet, Guillaume Y

2018-03-01

Koral, J, Oranchuk, DJ, Herrera, R, and Millet, GY. Six sessions of sprint interval training improves running performance in trained athletes. J Strength Cond Res 32(3): 617-623, 2018-Sprint interval training (SIT) is gaining popularity with endurance athletes. Various studies have shown that SIT allows for similar or greater endurance, strength, and power performance improvements than traditional endurance training but demands less time and volume. One of the main limitations in SIT research is that most studies were performed in a laboratory using expensive treadmills or ergometers. The aim of this study was to assess the performance effects of a novel short-term and highly accessible training protocol based on maximal shuttle runs in the field (SIT-F). Sixteen (12 male, 4 female) trained trail runners completed a 2-week procedure consisting of 4-7 bouts of 30 seconds at maximal intensity interspersed by 4 minutes of recovery, 3 times a week. Maximal aerobic speed (MAS), time to exhaustion at 90% of MAS before test (Tmax at 90% MAS), and 3,000-m time trial (TT3000m) were evaluated before and after training. Data were analyzed using a paired samples t-test, and Cohen's (d) effect sizes were calculated. Maximal aerobic speed improved by 2.3% (p = 0.01, d = 0.22), whereas peak power (PP) and mean power (MP) increased by 2.4% (p = 0.009, d = 0.33) and 2.8% (p = 0.002, d = 0.41), respectively. TT3000m was 6% shorter (p < 0.001, d = 0.35), whereas Tmax at 90% MAS was 42% longer (p < 0.001, d = 0.74). Sprint interval training in the field significantly improved the 3,000-m run, time to exhaustion, PP, and MP in trained trail runners. Sprint interval training in the field is a time-efficient and cost-free means of improving both endurance and power performance in trained athletes.

Six Sessions of Sprint Interval Training Improves Running Performance in Trained Athletes

PubMed Central

Oranchuk, Dustin J.; Herrera, Roberto; Millet, Guillaume Y.

2018-01-01

Abstract Koral, J, Oranchuk, DJ, Herrera, R, and Millet, GY. Six sessions of sprint interval training improves running performance in trained athletes. J Strength Cond Res 32(3): 617–623, 2018—Sprint interval training (SIT) is gaining popularity with endurance athletes. Various studies have shown that SIT allows for similar or greater endurance, strength, and power performance improvements than traditional endurance training but demands less time and volume. One of the main limitations in SIT research is that most studies were performed in a laboratory using expensive treadmills or ergometers. The aim of this study was to assess the performance effects of a novel short-term and highly accessible training protocol based on maximal shuttle runs in the field (SIT-F). Sixteen (12 male, 4 female) trained trail runners completed a 2-week procedure consisting of 4–7 bouts of 30 seconds at maximal intensity interspersed by 4 minutes of recovery, 3 times a week. Maximal aerobic speed (MAS), time to exhaustion at 90% of MAS before test (Tmax at 90% MAS), and 3,000-m time trial (TT3000m) were evaluated before and after training. Data were analyzed using a paired samples t-test, and Cohen's (d) effect sizes were calculated. Maximal aerobic speed improved by 2.3% (p = 0.01, d = 0.22), whereas peak power (PP) and mean power (MP) increased by 2.4% (p = 0.009, d = 0.33) and 2.8% (p = 0.002, d = 0.41), respectively. TT3000m was 6% shorter (p < 0.001, d = 0.35), whereas Tmax at 90% MAS was 42% longer (p < 0.001, d = 0.74). Sprint interval training in the field significantly improved the 3,000-m run, time to exhaustion, PP, and MP in trained trail runners. Sprint interval training in the field is a time-efficient and cost-free means of improving both endurance and power performance in trained athletes. PMID:29076961

Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.

PubMed

Zhu, Yongjun; Yan, Erjia; Wang, Fei

2017-07-03

Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec. We download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects. Models trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task). Increasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article bodies, abstracts excel in accuracy but lose in coverage of identifiable relations.

Cognitive Training in Parkinson's Disease: A Review of Studies from 2000 to 2014

PubMed Central

MacDonald, Penny A.

2016-01-01

Cognitive deficits are prevalent among patients with Parkinson's disease (PD), in both early and late stages of the disease. These deficits are associated with lower quality of life, loss of independence, and institutionalization. To date, there is no effective pharmacological treatment for the range of cognitive impairments presented in PD. Cognitive training (CT) has been explored as an alternative approach to remediating cognition in PD. In this review we present a detailed summary of 13 studies of CT that have been conducted between 2000 and 2014 and a critical examination of the evidence for the effectiveness and applicability of CT in PD. Although the evidence shows that CT leads to short-term, moderate improvements in some cognitive functions, methodological inconsistencies weaken these results. We discuss several key limitations of the literature to date, propose methods of addressing these questions, and outline the future directions that studies of CT in PD should pursue. Studies need to provide more detail about the cognitive profile of participants, include larger sample sizes, be hypothesis driven, and be clearer about the training interventions and the outcome measures. PMID:27688923

Relaxation and health-related quality of life in multiple sclerosis: the example of autogenic training.

PubMed

Sutherland, Georgina; Andersen, Mark B; Morris, Tony

2005-06-01

This study was a pilot project to explore the effect of an autogenic training program (AT; a relaxation intervention) on the health-related quality of life (HRQOL) and well-being for people with multiple sclerosis. Participants either met weekly for sessions in AT for 10 weeks (n = 11) or were assigned to the control group (n = 11). The AT group was also asked to practice the technique daily at home. Scales designed to measure HRQOL and aspects of well-being (mood and depressed affect) were taken preintervention and at week 8 of the 10-week program. ANCOVAs using a measure of social support and pretest scores as covariates revealed that at the posttest the AT group reported more energy and vigor than the control group and were less limited in their roles due to physical and emotional problems. Future research should involve studies conducted over an extended period, together with sufficiently sized samples to explore the effect of frequency of practice of relaxation training on HRQOL and well-being for people with multiple sclerosis.

[Physiotherapy, exercise and strength training and physical therapies in the treatment of fibromyalgia syndrome].

PubMed

Schiltenwolf, M; Häuser, W; Felde, E; Flügge, C; Häfner, R; Settan, M; Offenbächer, M

2008-06-01

A guideline for the treatment and diagnostic procedures for fibromyalgia syndrome (FMS) was developed in cooperation with 10 German medical and psychological associations and 2 patient self-help groups. A systematic literature search including all controlled studies evaluating physiotherapy, exercise and strength training as well as physical therapies was performed in the Cochrane Collaboration Reviews (1993-12/2006), Medline (1980-12/2006), PsychInfo (1966-12/2006) and Scopus (1980-12/ 2006). Levels of evidence were assigned according to the classification system of the Oxford Centre for Evidence-Based Medicine. Grading of the strengths of recommendations was done according to the German program for disease management guidelines. Standardized procedures to reach a consensus on recommendations were used. Aerobic exercise training is strongly recommended (grade A) and the temporary use of whole body hyperthermia, balneotherapy and spa therapy is recommended (grade B). The significance which can be assigned to most of the studies on the various procedures for therapy is restricted due to short study duration (mean 6-12 weeks) and small sample sizes.

[Menarche, menstrual and sociodemographic characteristics of Puerto Rican female athletes in the XV and XVI Central American and Caribbean Games].

PubMed

Rivera, M A; Mendez Zamora, I; Matos, R M; Rivera, A

1993-09-01

This investigation described maturation, menstrual and socio-demographic characteristics of 65 Puerto Rican women athletes that were interviewed during the XVI Central American and Caribbean Games (CACG), Mexico City in 1990. The results were compared with those of Puerto Rican women athletes (n = 52) at the XV CACG, Santiago Dominican Republic, 1986. The quantitative variables (age, age at initiation of training, years of training, age at menarche, birth order, and family size) were not statistically different (t-independent, p > or = 0.05). The observed frequencies for the qualitative variables (menstrual characteristics, degree of certainty in the recall of age of menarche, use of oral contraceptives, and marital status) were very similar. the women at the XVI CAC in Mexico demonstrated similar maturational, menstrual and socio-demographic characteristics to the those athletes evaluated four years earlier in Santiago and based on their long history of training, both samples were representative of athletically mature athletes. The findings were very similar to those reported for olympic athletes and such data expands the available information on Puerto Rican women athletes.

Collaborative assessment of California spiny lobster population and fishery responses to a marine reserve network.

PubMed

Kay, Matthew C; Lenihan, Hunter S; Guenther, Carla M; Wilson, Jono R; Miller, Christopher J; Shrout, Samuel W

2012-01-01

Assessments of the conservation and fisheries effects of marine reserves typically focus on single reserves where sampling occurs over narrow spatiotemporal scales. A strategy for broadening the collection and interpretation of data is collaborative fisheries research (CFR). Here we report results of a CFR program formed in part to test whether reserves at the Santa Barbara Channel Islands, USA, influenced lobster size and trap yield, and whether abundance changes in reserves led to spillover that influenced trap yield and effort distribution near reserve borders. Industry training of scientists allowed us to sample reserves with fishery relevant metrics that we compared with pre-reserve fishing records, a concurrent port sampling program, fishery effort patterns, the local ecological knowledge (LEK) of fishermen, and fishery-independent visual surveys of lobster abundance. After six years of reserve protection, there was a four- to eightfold increase in trap yield, a 5-10% increase in the mean size (carapace length) of legal sized lobsters, and larger size structure of lobsters trapped inside vs. outside of three replicate reserves. Patterns in trap data were corroborated by visual scuba surveys that indicated a four- to sixfold increase in lobster density inside reserves. Population increases within reserves did not lead to increased trap yields or effort concentrations (fishing the line) immediately outside reserve borders. The absence of these catch and effort trends, which are indicative of spillover, may be due to moderate total mortality (Z = 0.59 for legal sized lobsters outside reserves), which was estimated from analysis of growth and length frequency data collected as part of our CFR program. Spillover at the Channel Islands reserves may be occurring but at levels that are insufficient to influence the fishery dynamics that we measured. Future increases in fishing effort (outside reserves) and lobster biomass (inside reserves) are likely and may lead to increased spillover, and CFR provides an ideal platform for continued assessment of fishery-reserve interactions.

Resistance training alters skeletal muscle structure and function in human heart failure: effects at the tissue, cellular and molecular levels

PubMed Central

Toth, Michael J; Miller, Mark S; VanBuren, Peter; Bedrin, Nicholas G; LeWinter, Martin M; Ades, Philip A; Palmer, Bradley M

2012-01-01

Reduced skeletal muscle function in heart failure (HF) patients may be partially explained by altered myofilament protein content and function. Resistance training increases muscle function, although whether these improvements are achieved by correction of myofilament deficits is not known. To address this question, we examined 10 HF patients and 14 controls prior to and following an 18 week high-intensity resistance training programme. Evaluations of whole muscle size and strength, single muscle fibre size, ultrastructure and tension and myosin–actin cross-bridge mechanics and kinetics were performed. Training improved whole muscle isometric torque in both groups, although there were no alterations in whole muscle size or single fibre cross-sectional area or isometric tension. Unexpectedly, training reduced the myofibril fractional area of muscle fibres in both groups. This structural change manifested functionally as a reduction in the number of strongly bound myosin–actin cross-bridges during Ca2+ activation. When post-training single fibre tension data were corrected for the loss of myofibril fractional area, we observed an increase in tension with resistance training. Additionally, training corrected alterations in cross-bridge kinetics (e.g. myosin attachment time) in HF patients back to levels observed in untrained controls. Collectively, our results indicate that improvements in myofilament function in sedentary elderly with and without HF may contribute to increased whole muscle function with resistance training. More broadly, these data highlight novel cellular and molecular adaptations in muscle structure and function that contribute to the resistance-trained phenotype. PMID:22199163

Binning in Gaussian Kernel Regularization

DTIC Science & Technology

2005-04-01

OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, but reduces the...71.40%) on 966 randomly sampled data. Using the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the...the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, and reduces

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones

PubMed Central

Shen, Chao; Yu, Tianwen; Yuan, Sheng; Li, Yunpeng; Guan, Xiaohong

2016-01-01

The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research. PMID:27005626

Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones.

PubMed

Shen, Chao; Yu, Tianwen; Yuan, Sheng; Li, Yunpeng; Guan, Xiaohong

2016-03-09

The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users' passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.

Asymmetrical Sample Training and Asymmetrical Retention Functions in One-to-One and Many-to-One Matching in Pigeons

ERIC Educational Resources Information Center

Grant, Douglas S.

2006-01-01

Pigeons were trained in a matching task with either color (group color-first) or line (group line-first) samples. After asymmetrical training in which each group was initially trained with the same sample on all trials, marked retention asymmetries were obtained. In both groups, accuracy dropped precipitously on trials involving the initially…

Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model

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

Gelß, Patrick, E-mail: p.gelss@fu-berlin.de; Matera, Sebastian, E-mail: matera@math.fu-berlin.de; Schütte, Christof, E-mail: schuette@mi.fu-berlin.de

2016-06-01

In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO{sub 2}(110) surface.more » We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased stiffness.« less

An ecological evaluation of the metabolic benefits due to robot-assisted gait training.

PubMed

Peri, E; Biffi, E; Maghini, C; Marzorati, M; Diella, E; Pedrocchi, A; Turconi, A C; Reni, G

2015-08-01

Cerebral palsy (CP), one of the most common neurological disorders in childhood, features affected individual's motor skills and muscle actions. This results in elevated heart rate and rate of oxygen uptake during sub-maximal exercise, thus indicating a mean energy expenditure higher than healthy subjects. Rehabilitation, currently involving also robot-based devices, may have an impact also on these aspects. In this study, an ecological setting has been proposed to evaluate the energy expenditure of 4 children with CP before and after a robot-assisted gait training. Even if the small sample size makes it difficult to give general indications, results presented here are promising. Indeed, children showed an increasing trend of the energy expenditure per minute and a decreasing trend of the energy expenditure per step, in accordance to the control group. These data suggest a metabolic benefit of the treatment that may increase the locomotion efficiency of disabled children.

Connecting Students to Mental Health Care: Pilot Findings from an Engagement Program for School Nurses

PubMed Central

Kim, Rachel E.; Becker, Kimberly D.; Stephan, Sharon H.; Hakimian, Serop; Apocada, Dee; Escudero, Pia V.; Chorpita, Bruce F.

2015-01-01

Schools function as the major provider of mental health services (MHS) for youth, but can struggle with engaging them in services. School nurses are well-positioned to facilitate referrals for MHS. This pilot study examined the feasibility, acceptability, and preliminary efficacy of an engagement protocol (EP) designed to enhance school nurses’ utilization of evidence-based engagement practices when referring youth to MHS. Participants were six school nurses and twenty-five adolescents in a large, urban school district. School nurses reported positive attitudes towards the EP, suggesting that they found it feasible and acceptable. Though there were small increases in school nurses’ use of engagement practices and in adolescents’ readiness for services following training, due to limited sample size, differences were not statistically significant. Still, pilot results suggest preliminary efficacy of training school nurses to strategically implement evidence-based engagement practices to increase adolescents’ engagement in MHS. PMID:26251671

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

NASA Astrophysics Data System (ADS)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Silica dust exposure: Effect of filter size to compliance determination

NASA Astrophysics Data System (ADS)

Amran, Suhaily; Latif, Mohd Talib; Khan, Md Firoz; Leman, Abdul Mutalib; Goh, Eric; Jaafar, Shoffian Amin

2016-11-01

Monitoring of respirable dust was performed using a set of integrated sampling system consisting of sampling pump attached with filter media and separating device such as cyclone or special cassette. Based on selected method, filter sizes are either 25 mm or 37 mm poly vinyl chloride (PVC) filter. The aim of this study was to compare performance of two types of filter during personal respirable dust sampling for silica dust under field condition. The comparison strategy focused on the final compliance judgment based on both dataset. Eight hour parallel sampling of personal respirable dust exposure was performed among 30 crusher operators at six quarries. Each crusher operator was attached with parallel set of integrated sampling train containing either 25 mm or 37 mm PVC filter. Each set consisted of standard flow SKC sampler, attached with SKC GS3 cyclone and 2 pieces cassette loaded with 5.0 µm of PVC filter. Samples were analyzed by gravimetric technique. Personal respirable dust exposure between the two types of filters indicated significant positive correlation (p < 0.05) with moderate relationship (r2 = 0.6431). Personal exposure based on 25 mm PVC filter indicated 0.1% non-compliance to overall data while 37 mm PVC filter indicated similar findings at 0.4 %. Both data showed similar arithmetic mean(AM) and geometric mean(GM). In overall we concluded that personal respirable dust exposure either based on 25mm or 37mm PVC filter will give similar compliance determination. Both filters are reliable to be used in respirable dust monitoring for silica dust related exposure.

Analysis and Evaluation of Databases on Business and Management Training Schemes for Small and Medium-Sized Enterprises in the European Community.

ERIC Educational Resources Information Center

Allesch, Jurgen; Preiss-Allesch, Dagmar

This report describes a study that identified major databases in operation in the 12 European Community countries that provide small- and medium-sized enterprises with information on opportunities for obtaining training and continuing education. Thirty-five databases were identified through information obtained from telephone interviews or…

Effects of 𝛽-Hydroxy-𝛽-methylbutyrate-free Acid Supplementation on Strength, Power and Hormonal Adaptations Following Resistance Training

PubMed Central

Asadi, Abbas; Arazi, Hamid

2017-01-01

Background: β-Hydroxy-β-methylbutyrate-free acid (HMB-FA) has been ingested prior to exercise to reduce muscle damage, however the effects of HMB-FA supplementation on hormonal, strength and power adaptation are unclear. Methods: Sixteen healthy men were matched and randomized into two groups and performed six-week resistance training while supplementing with either HMB-FA or placebo (3 g per day). The subjects were evaluated for 1 repetition maximum (1RM) bench press and leg press and vertical jump (VJ) prior to and after training intervention. In addition, blood samples were obtained before and after resistance training to evaluate resting growth hormone (GH), insulin like growth factor 1 (IGF-1), testosterone (TEST), cortisol (CORT), and adrenocorticotropic hormone (ACTH) responses. The HMB-FA supplementation group showed greater gains compared with the placebo group in peak power (effect size ES = 0.26 vs. 0.01) and 1RM leg press (ES = 1.52 vs. 0.96). In addition, the HMB-FA supplementation group indicated greater decrements in ACTH and CORT responses to training in comparison to the placebo group (p < 0.05). Likewise, in GH (ES = 1.41 vs. 0.12) and IGF-1 (ES = 0.83 vs. 0.41), the HMB-FA indicated greater training effects when compared with the placebo group. Conclusions: These findings provide further support for the potential anabolic benefits associated with HMB-FA supplementation. PMID:29207472

Position-Dependent Cardiovascular Response and Time-Motion Analysis During Training Drills and Friendly Matches in Elite Male Basketball Players.

PubMed

Torres-Ronda, Lorena; Ric, Angel; Llabres-Torres, Ivan; de Las Heras, Bernat; Schelling I Del Alcazar, Xavi

2016-01-01

The purpose of this study was to measure differences in the cardiovascular workload (heart rate [HR]) and time-motion demands between positional groups, during numerous basketball training drills, and compare the results with in-game competition demands. A convenience sample of 14 top-level professional basketball players from the same club (Spanish First Division, ACB) participated in the study. A total of 146 basketball exercises per player (performed over an 8-week period in 32 team training sessions throughout the competitive season) and 7 friendly matches (FM) played during the preparatory phase were analyzed. The results reveal that HRavg and HRpeak were the highest in FM (158 ± 10; 198 ± 9 b · min(-1), respectively). Time-motion analysis showed 1v1 to be the most demanding drill (53 ± 8 and 46 ± 12 movements per minute for full and half court, respectively). During FM, players performed 33 ± 7 movements per minute. Positional differences exist for both HR and time-motion demands, ranging from moderate to very large for all basketball drills compared with FM. Constraints such as number of players, court size, work-to-rest ratios, and coach intervention are key factors influencing cardiovascular responses and time-motion demands during basketball training sessions. These results demonstrate that systematic monitoring of the physical demands and physiological responses during training and competition can inform and potentially improve coaching strategy, basketball-specific training drills, and ultimately, match performance.

Face and construct validity of a computer-based virtual reality simulator for ERCP.

PubMed

Bittner, James G; Mellinger, John D; Imam, Toufic; Schade, Robert R; Macfadyen, Bruce V

2010-02-01

Currently, little evidence supports computer-based simulation for ERCP training. To determine face and construct validity of a computer-based simulator for ERCP and assess its perceived utility as a training tool. Novice and expert endoscopists completed 2 simulated ERCP cases by using the GI Mentor II. Virtual Education and Surgical Simulation Laboratory, Medical College of Georgia. Outcomes included times to complete the procedure, reach the papilla, and use fluoroscopy; attempts to cannulate the papilla, pancreatic duct, and common bile duct; and number of contrast injections and complications. Subjects assessed simulator graphics, procedural accuracy, difficulty, haptics, overall realism, and training potential. Only when performance data from cases A and B were combined did the GI Mentor II differentiate novices and experts based on times to complete the procedure, reach the papilla, and use fluoroscopy. Across skill levels, overall opinions were similar regarding graphics (moderately realistic), accuracy (similar to clinical ERCP), difficulty (similar to clinical ERCP), overall realism (moderately realistic), and haptics. Most participants (92%) claimed that the simulator has definite training potential or should be required for training. Small sample size, single institution. The GI Mentor II demonstrated construct validity for ERCP based on select metrics. Most subjects thought that the simulated graphics, procedural accuracy, and overall realism exhibit face validity. Subjects deemed it a useful training tool. Study repetition involving more participants and cases may help confirm results and establish the simulator's ability to differentiate skill levels based on ERCP-specific metrics.

Young weightlifters' performance across time.

PubMed

Byrd, Ronald; Pierce, Kyle; Rielly, Lee; Brady, Jenny

2003-01-01

Prestigious professional organisations have questioned the efficacy of resistive training by children or have often neglected to address weightlifting in their position papers on resistive training for children. The purpose of this paper was to address the deficit in data regarding the efficacy of training children for weightlifting and to report data regarding to safety in this population. Eleven subjects (3 female, 8 male) who had trained at the USA Weightlifting Development Centre in Shreveport Louisiana for a minimum of 22 months (mean = 28.8; SD +/- 4.4) served as subjects for this study. Means for the pool of subjects subjected to t-test to compare data obtained at each subject's initial competition with that obtained at the individual's most recent competition revealed significant positive changes in body weight, snatch weight, clean and jerk weight, and total weight lifted. The latter three were significant both in absolute weight and in weight lifted per kg of body weight. Total weight lifted at competitions plotted separately for boys and for girls across time indicated an apparently steeper slope of improvement for boys. The latter were not tested for significance because of the small sample sizes. The lack of injury in training and in 534 competitive lifts was discussed. None required medical attention or loss of training time. It was concluded that there can be no doubt regarding the efficacy of weightlifting as carried out at the USA Weightlifting Development Centre. The importance of proper application of scientific theory of conditioning in a conservative manner for this population was emphasised.

fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study.

PubMed

Zilverstand, Anna; Sorger, Bettina; Slaats-Willemse, Dorine; Kan, Cornelis C; Goebel, Rainer; Buitelaar, Jan K

2017-01-01

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by poor cognitive control/attention and hypofunctioning of the dorsal anterior cingulate cortex (dACC). In the current study, we investigated for the first time whether real-time fMRI neurofeedback (rt-fMRI) training targeted at increasing activation levels within dACC in adults with ADHD leads to a reduction of clinical symptoms and improved cognitive functioning. An exploratory randomized controlled treatment study with blinding of the participants was conducted. Participants with ADHD (n = 7 in the neurofeedback group, and n = 6 in the control group) attended four weekly MRI training sessions (60-min training time/session), during which they performed a mental calculation task at varying levels of difficulty, in order to learn how to up-regulate dACC activation. Only neurofeedback participants received continuous feedback information on actual brain activation levels within dACC. Before and after the training, ADHD symptoms and relevant cognitive functioning was assessed. Results showed that both groups achieved a significant increase in dACC activation levels over sessions. While there was no significant difference between the neurofeedback and control group in clinical outcome, neurofeedback participants showed stronger improvement on cognitive functioning. The current study demonstrates the general feasibility of the suggested rt-fMRI neurofeedback training approach as a potential novel treatment option for ADHD patients. Due to the study's small sample size, potential clinical benefits need to be further investigated in future studies. ISRCTN12390961.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

An empirical study of race times in recreational endurance runners.

PubMed

Vickers, Andrew J; Vertosick, Emily A

2016-01-01

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time. Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races). Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

A cognitive-behavioural program for adolescents with chronic pain-a pilot study.

PubMed

Merlijn, Vivian P B M; Hunfeld, Joke A M; van der Wouden, Johannes C; Hazebroek-Kampschreur, Alice A J M; van Suijlekom-Smit, Lisette W A; Koes, Bart W; Passchier, Jan

2005-11-01

The purpose of this pilot study is to evaluate the feasibility of a cognitive-behavioural training program for adolescents with chronic pain irrespective of pain localisation. A secondary aim was to give an impression of the effect of the program on pain and quality of life. Eight adolescents (14-18 years) with chronic non-organic pain recruited from the general population (and their parents) participated in this pilot study. The intervention included five group meetings alternated with four telephone contacts (during the self-management weeks) over a period of 9 weeks. The training aimed to change pain behaviour through pain education, relaxation strategies, problem-solving techniques, assertiveness training, cognitive restructuring and by stimulating the adolescent's physical activity level. The training further addresses the social context of pain by inviting parents to attend two meetings for the parents only, and by asking the adolescents to bring a peer to one of the meetings. Adolescents and their parents were positive about the program. Adolescents felt they were more in control of their pain and parents valued the support they experienced in helping their children to master the pain. The training was considered to be feasible in daily life. Further, the preliminary data showed an effect on pain and quality of life in the expected direction. The results underline the need for a definitive study with a larger sample size and a random controlled design.

Water-based exercise training for chronic obstructive pulmonary disease.

PubMed

McNamara, Renae J; McKeough, Zoe J; McKenzie, David K; Alison, Jennifer A

2013-12-18

Land-based exercise training improves exercise capacity and quality of life in people with chronic obstructive pulmonary disease (COPD). Water-based exercise training is an alternative mode of physical exercise training that may appeal to the older population attending pulmonary rehabilitation programmes, those who are unable to complete land-based exercise programmes and people with COPD with comorbid physical and medical conditions. To assess the effects of water-based exercise training in people with COPD. A search of the Cochrane Airways Group Specialised Register of trials, which is derived from systematic searches of bibliographic databases, including the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL, AMED and PsycINFO, was conducted (from inception to August 2013). Handsearching was done to identify further qualifying studies from reference lists of relevant studies. Review authors included randomised or quasi-randomised controlled trials in which water-based exercise training of at least four weeks' duration was compared with no exercise training or any other form of exercise training in people with COPD. Swimming was excluded. We used standard methodological procedures expected by The Cochrane Collaboration. Five studies were included with a total of 176 participants (71 people participated in water-based exercise training and 54 in land-based exercise training; 51 completed no exercise training). All studies compared supervised water-based exercise training versus land-based exercise training and/or no exercise training in people with COPD (with average forced expiratory volume in one second (FEV1) %predicted ranging from 39% to 62%). Sample sizes ranged from 11 to 53 participants. The exercise training programmes lasted from four to 12 weeks, and the mean age of participants ranged from 57 to 73 years. A moderate risk of bias was due to lack of reporting of randomisation, allocation and blinding procedures in some studies, as well as small sample sizes.Compared with no exercise, water-based exercise training improved the six-minute walk distance (mean difference (MD) 62 metres; 95% confidence interval (CI) 44 to 80 metres; three studies; 99 participants; moderate quality evidence), the incremental shuttle walk distance (MD 50 metres; 95% CI 20 to 80 metres; one study; 30 participants; high quality evidence) and the endurance shuttle walk distance (MD 371 metres; 95% CI 121 to 621 metres; one study; 30 participants; high quality evidence). Quality of life was also improved after water-based exercise training compared with no exercise (standardised mean difference (SMD) -0.97, 95% CI -0.37 to -1.57; two studies; 49 participants; low quality evidence). Compared with land-based exercise training, water-based exercise training did not significantly change the six-minute walk distance (MD 11 metres; 95% CI -11 to 33 metres; three studies; 62 participants; moderate quality evidence) or the incremental shuttle walk distance (MD 9 metres; 95% CI -15 to 34 metres; two studies; 59 participants; low quality evidence). However, the endurance shuttle walk distance improved following water-based exercise training compared with land-based exercise training (MD 313 metres; 95% CI 232 to 394 metres; two studies; 59 participants; moderate quality evidence). No significant differences were found between water-based exercise training and land-based exercise training for quality of life, as measured by the St George's Respiratory Questionnaire or by three of four domains of the Chronic Respiratory Disease Questionnaire (CRDQ); however, the fatigue domain of the CRDQ showed a statistically significant difference in favour of water-based exercise (MD -3.00; 95% CI -5.26 to -0.74; one study; 30 participants). Only one study reported long-term outcomes after water-based exercise training for quality of life and body composition, and no significant change was observed between baseline results and six-month follow-up results. One minor adverse event was reported for water-based exercise training (based on reporting from two studies; 20 participants). Impact of disease severity could not be examined because data were insufficient. There is limited quality evidence that water-based exercise training is safe and improves exercise capacity and quality of life in people with COPD immediately after training. There is limited quality evidence that water-based exercise training offers advantages over land-based exercise training in improving endurance exercise capacity, but we remain uncertain as to whether it leads to better quality of life. Little evidence exists examining the long-term effect of water-based exercise training.

Improving patient-centered communication while using an electronic health record: Report from a curriculum evaluation.

PubMed

Fogarty, Colleen T; Winters, Paul; Farah, Subrina

2016-05-01

Researchers and clinicians are concerned about the impact of electronic health record use and patient-centered communication. Training about patient-centered clinical communication skills with the electronic health record may help clinicians adapt and remain patient-centered. We developed an interactive workshop eliciting challenges and opportunities of working with the electronic health record in clinical practice, introduction of specific patient-centered behaviors and mindful practice techniques, and video demonstrating contrasts in common behavior and "better practices." One hundred thirty-nine resident physicians and faculty supervisors in five residency training programs at the University of Rochester Medical Center participated in the workshops. Participants were asked to complete an 11-item survey of behaviors related to their use of the electronic health record prior to training and after attending training. We used paired t-tests to assess changes in self-reported behavior from pre-intervention to post-intervention. We trained 139 clinicians in the workshops; 110 participants completed the baseline assessment and 39 completed both the baseline and post-intervention assessment. Data from post-curriculum respondents found a statistically significant increase in "I told the patient when turning my attention from the patient to the computer," from 60% of the time prior to the training to 70% of the time after. Data from our program evaluation demonstrated improvement in one communication behavior. Sample size limited the detection of other changes; further research should investigate effective training techniques for patient-centered communication while using the electronic health record. © The Author(s) 2016.

Visual-spatial ability is more important than motivation for novices in surgical simulator training: a preliminary study.

PubMed

Schlickum, Marcus; Hedman, Leif; Felländer-Tsai, Li

2016-02-21

To investigate whether surgical simulation performance and previous video gaming experience would correlate with higher motivation to further train a specific simulator task and whether visual-spatial ability would rank higher in importance to surgical performance than the above. It was also examined whether or not motivation would correlate with a preference to choose a surgical specialty in the future and if simulator training would increase the interest in choosing that same work field. Motivation and general interest in surgery was measured pre- and post-training in 30 medical students at Karolinska Institutet who were tested in a laparoscopic surgical simulator in parallel with measurement of visual-spatial ability and self-estimated video gaming experience. Correlations between simulator performance metrics, visual-spatial ability and motivation were statistically analyzed using regression analysis. A good result in the first simulator trial correlated with higher self-determination index (r =-0.46, p=0.05) in male students. Visual-spatial ability was the most important underlying factor followed by intrinsic motivation score and finally video gaming experience (p=0.02, p=0.05, p=0.11) regarding simulator performance in male students. Simulator training increased interest in surgery when studying all subjects (p=0.01), male subjects (p=0.02) as well as subjects with low video gaming experience (p=0.02). This preliminary study highlights individual differences regarding the effect of simulator training on motivation that can be taken into account when designing simulator training curricula, although the sample size is quite small and findings should be interpreted carefully.

Tabu search and binary particle swarm optimization for feature selection using microarray data.

PubMed

Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

2009-12-01

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

PubMed

Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

2017-05-31

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

Predicting Kenya Short Rains Using the Indian Ocean SST

NASA Astrophysics Data System (ADS)

Peng, X.; Albertson, J. D.; Steinschneider, S.

2017-12-01

The rainfall over the Eastern Africa is charaterized by the typical bimodal monsoon system. Literatures have shown that the monsoon system is closely connected with the large-scale atmospheric motion which is believed to be driven by sea surface temperature anomalies (SSTA). Therefore, we may make use of the predictability of SSTA in estimating future Easter Africa monsoon. In this study, we tried predict the Kenya short rains (Oct, Nov and Dec rainfall) based on the Indian Ocean SSTA. The Least Absolute Shrinkage and Selection Operator (LASSO) regression is used to avoid over-fitting issues. Models for different lead times are trained using a 28-year training set (2006-1979) and are tested using a 10-year test set (2007-2016). Satisfying prediciton skills are achieved at relatively long lead times (i.e., 8 and 10 months) in terms of correlation coefficient and sign accuracy. Unlike some of the previous work, the prediction models are obtained from a data-driven method. Limited predictors are selected for each model and can be used in understanding the underlying physical connection. Still, further investigation is needed since the sampling variability issue cannot be excluded due to the limited sample size.

A national survey of clinical pharmacy services in county hospitals in China.

PubMed

Yao, Dongning; Xi, Xiaoyu; Huang, Yuankai; Hu, Hao; Hu, Yuanjia; Wang, Yitao; Yao, Wenbing

2017-01-01

Clinical pharmacy is not only a medical science but also an elaborate public health care system firmly related to its subsystems of education, training, qualification authentication, scientific research, management, and human resources. China is a developing country with a tremendous need for improvements in the public health system, including the clinical pharmacy service system. The aim of this research was to evaluate the infrastructure and personnel qualities of clinical pharmacy services in China. Public county hospitals in China. A national survey of clinical pharmacists in county hospitals was conducted. It was sampled through a stratified sampling strategy. Responses were analyzed using descriptive and inferential statistics. The main outcome measures include the coverage of clinical pharmacy services, the overall staffing of clinical pharmacists, the software and hardware of clinical pharmacy services, the charge mode of clinical pharmacy services, and the educational background, professional training acquisition, practical experience, and entry path of clinical pharmacists. The overall coverage of clinical pharmacy services on both the department scale (median = 18.25%) and the patient scale (median = 15.38%) does not meet the 100% coverage that is required by the government. In 57.73% of the sample hospitals, the staffing does not meet the requirement, and the size of the clinical pharmacist group is smaller in larger hospitals. In addition, 23.4% of the sample hospitals do not have management rules for the clinical pharmacists, and 43.1% do not have rational drug use software, both of which are required by the government. In terms of fees, 89.9% of the sample hospitals do not charge for the services. With regard to education, 8.5% of respondents are with unqualified degree, and among respondents with qualified degree, 37.31% are unqualified in the major; 43% of respondents lack the clinical pharmacist training required by the government. Most respondents (93.5%) have a primary or medium professional title. The median age and work seniority of respondents are 31 and four years, respectively. Only 18.5% of respondents chose this occupation by personal consideration or willingness. The main findings in this research include the overall low coverage of clinical pharmacy services, the low rate of clinical pharmacy service software, hardware, and personnel as well as a wide variance in educational training of pharmacists at county hospitals.

Developing a Neural Network to Act as a Noise Filter

DTIC Science & Technology

1992-10-02

s... ... j.. .3885*I888+.. I. . . fff~8*I*8s+ ... i £888&aiz$$88 1. *.󈧧 z*I* sseI . .** m $as+.’ . . .𔄂seEgzsssas* ... .. +8891MI1$883< I. .. (*8...6: Architectures that provided the best results Using a Training Set of 5 Samples (no bias) Size of] m 1 ax 1 lumber Learning1 PatternI Overlap...Riddle IAbs I RfAS I of I Time Layer Layer Layer Error Error _ycles __(Seel_ m 5 x 4 (0,21 5 x 9 0.100 0.039 132 1043.0 lone of the neural netvork

Biofeedback and dance performance: a preliminary investigation.

PubMed

Raymond, Joshua; Sajid, Imran; Parkinson, Lesley A; Gruzelier, John H

2005-03-01

Alpha-theta neurofeedback has been shown to produce professionally significant performance improvements in music students. The present study aimed to extend this work to a different performing art and compare alpha-theta neurofeedback with another form of biofeedback: heart rate variability (HRV) biofeedback. Twenty-four ballroom and Latin dancers were randomly allocated to three groups, one receiving neurofeedback, one HRV biofeedback and one no intervention. Dance was assessed before and after training. Performance improvements were found in the biofeedback groups but not in the control group. Neurofeedback and HRV biofeedback benefited performance in different ways. A replication with larger sample sizes is required.

Safety programmes in the Egyptian construction industry.

PubMed

Hassanein, Amr A G; Hanna, Ragaa S

2007-12-01

This study is aimed at exploring the nature of the safety programmes applied by large-size contractors operating in Egypt. Results revealed that safety programmes applied by those contractors were less formal than the programmes applied by their American counterparts. Only three contractors out of the surveyed sample had accident records broken down by projects, provided workers with formal safety orientation, and trained safety personnel on first-aid. The study recommended that reforms to the scheme of the employers' contribution to social insurance are necessary. This is meant to serve as a strong incentive for safety management.

Automatic detection of mycobacterium tuberculosis using artificial intelligence

PubMed Central

Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen

2018-01-01

Background Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. Methods We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Results Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. Conclusions TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified. PMID:29707349

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Automatic detection of mycobacterium tuberculosis using artificial intelligence.

PubMed

Xiong, Yan; Ba, Xiaojun; Hou, Ao; Zhang, Kaiwen; Chen, Longsen; Li, Ting

2018-03-01

Tuberculosis (TB) is a global issue that seriously endangers public health. Pathology is one of the most important means for diagnosing TB in clinical practice. To confirm TB as the diagnosis, finding specially stained TB bacilli under a microscope is critical. Because of the very small size and number of bacilli, it is a time-consuming and strenuous work even for experienced pathologists, and this strenuosity often leads to low detection rate and false diagnoses. We investigated the clinical efficacy of an artificial intelligence (AI)-assisted detection method for acid-fast stained TB bacillus. We built a convolutional neural networks (CNN) model, named tuberculosis AI (TB-AI), specifically to recognize TB bacillus. The training set contains 45 samples, including 30 positive cases and 15 negative cases, where bacilli are labeled by human pathologists. Upon training the neural network model, 201 samples (108 positive cases and 93 negative cases) were collected as test set and used to examine TB-AI. We compared the diagnosis of TB-AI to the ground truth result provided by human pathologists, analyzed inconsistencies between AI and human, and adjusted the protocol accordingly. Trained TB-AI were run on the test data twice. Examined against the double confirmed diagnosis by pathologists both via microscopes and digital slides, TB-AI achieved 97.94% sensitivity and 83.65% specificity. TB-AI can be a promising support system to detect stained TB bacilli and help make clinical decisions. It holds the potential to relieve the heavy workload of pathologists and decrease chances of missed diagnosis. Samples labeled as positive by TB-AI must be confirmed by pathologists, and those labeled as negative should be reviewed to make sure that the digital slides are qualified.

Size speed bias or size arrival effect-How judgments of vehicles' approach speed and time to arrival are influenced by the vehicles' size.

PubMed

Petzoldt, Tibor

2016-10-01

Crashes at railway level crossings are a key problem for railway operations. It has been suggested that a potential explanation for such crashes might lie in a so-called size speed bias, which describes the phenomenon that observers underestimate the speed of larger objects, such as aircraft or trains. While there is some evidence that this size speed bias indeed exists, it is somewhat at odds with another well researched phenomenon, the size arrival effect. When asked to judge the time it takes an approaching object to arrive at a predefined position (time to arrival, TTA), observers tend to provide lower estimates for larger objects. In that case, road users' crossing decisions when confronted with larger vehicles should be rather conservative, which has been confirmed in multiple studies on gap acceptance. The aim of the experiment reported in this paper was to clarify the relationship between size speed bias and size arrival effect. Employing a relative judgment task, both speed and TTA estimates were assessed for virtual depictions of a train and a truck, using a car as a reference to compare against. The results confirmed the size speed bias for the speed judgments, with both train and truck being perceived as travelling slower than the car. A comparable bias was also present in the TTA estimates for the truck. In contrast, no size arrival effect could be found for the train or the truck, neither in the speed nor the TTA judgments. This finding is inconsistent with the fact that crossing behaviour when confronted with larger vehicles appears to be consistently more conservative. This discrepancy might be interpreted as an indication that factors other than perceived speed or TTA play an important role for the differences in gap acceptance between different types of vehicles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies

NASA Astrophysics Data System (ADS)

Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

2016-03-01

The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

Company Training. A Key Strategy for Success. Workforce Brief #2.

ERIC Educational Resources Information Center

Bergman, Terri

General research and anecdotal reports have confirmed that both technical and basic skills training offer many benefits to companies of all sizes. Company training can improve employee performance, firm productivity, product quality, and company profitability. Training supports "high-performance" work practices such as the following: total quality…

A pilot study to evaluate the utility of live training (LIVEX) in the operational preparedness of UK military trauma teams.

PubMed

Smith, J E; Withnall, R D J; Rickard, R F; Lamb, D; Sitch, A; Hodgetts, T J

2016-12-01

With the end of UK military operations in Iraq and Afghanistan, it is essential that peacetime training of Defence Medical Services (DMS) trauma teams ensures appropriate future preparedness. A new model of pre-deployment training involves placement of formed military trauma teams into civilian trauma centres. This study evaluates the benefit of 'live training during an exercise period' (LIVEX) for DMS trauma teams. A cross-sectional questionnaire-based survey of participants was conducted. Quantitative data were collected prior to the start and on the final day. Written reports were collected from the coordinators. Thematic analysis was used to identify emergent themes in a supplementary, qualitative analysis. Each team comprised 13 personnel and results should be interpreted with knowledge of this small sample size. The response rate for both the pre-LIVEX and post-LIVEX questionnaire was 100%. By the end of the week, 89% of participants (n=23) stated LIVEX was an 'appropriate or very appropriate' way of preparing for an operational role compared with 40% (n=9) before the exercise (p<0.01). However, completing LIVEX made no difference to participants' personal perception of their own operational preparedness. Thematic analysis suggested greater training benefit for more junior members of the team; from Regulars and Reservists training together; and from two-way exchange of information between DMS and National Health Service medical staffs. Completing LIVEX made no statistically significant difference to participants' personal perception of their own operational preparedness, but the perception of LIVEX as an appropriate training platform improved significantly after conducting the training exercise. 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/.

The effects of adding single-joint exercises to a multi-joint exercise resistance training program on upper body muscle strength and size in trained men.

PubMed

de França, Henrique Silvestre; Branco, Paulo Alexandre Nordeste; Guedes Junior, Dilmar Pinto; Gentil, Paulo; Steele, James; Teixeira, Cauê Vazquez La Scala

2015-08-01

The aim of this study was compare changes in upper body muscle strength and size in trained men performing resistance training (RT) programs involving multi-joint plus single-joint (MJ+SJ) or only multi-joint (MJ) exercises. Twenty young men with at least 2 years of experience in RT were randomized in 2 groups: MJ+SJ (n = 10; age, 27.7 ± 6.6 years) and MJ (n = 10; age, 29.4 ± 4.6 years). Both groups trained for 8 weeks following a linear periodization model. Measures of elbow flexors and extensors 1-repetition maximum (1RM), flexed arm circumference (FAC), and arm muscle circumference (AMC) were taken pre- and post-training period. Both groups significantly increased 1RM for elbow flexion (4.99% and 6.42% for MJ and MJ+SJ, respectively), extension (10.60% vs 9.79%, for MJ and MJ+SJ, respectively), FAC (1.72% vs 1.45%, for MJ and MJ+SJ, respectively), and AMC (1.33% vs 3.17% for MJ and MJ+SJ, respectively). Comparison between groups revealed no significant difference in any variable. In conclusion, 8 weeks of RT involving MJ or MJ+SJ resulted in similar alterations in muscle strength and size in trained participants. Therefore, the addition of SJ exercises to a RT program involving MJ exercises does not seem to promote additional benefits to trained men, suggesting MJ-only RT to be a time-efficient approach.

A longitudinal study of skeletal muscle following spinal cord injury and locomotor training.

PubMed

Liu, M; Bose, P; Walter, G A; Thompson, F J; Vandenborne, K

2008-07-01

Experimental rat model of spinal cord contusion injury (contusion SCI). The objectives of this study were (1) to characterize the longitudinal changes in rat lower hindlimb muscle morphology following contusion SCI by using magnetic resonance imaging and (2) to determine the therapeutic potential of two types of locomotor training, treadmill and cycling. University research setting. After moderate midthoracic contusion SCI, Sprague-Dawley rats were assigned to either treadmill training, cycle training or an untrained group. Lower hindlimb muscle size was examined prior to SCI and at 1-, 2-, 4-, 8-, and 12-week post injury. Following contusion SCI, we observed significant atrophy in all rat hindlimb muscles with the posterior muscles (triceps surae and flexor digitorum) showing greater atrophy than the anterior muscles (tibialis anterior and extensor digitorum). The greatest amount of atrophy was measured at 2-week post injury (range from 11 to 26%), and spontaneous recovery in muscle size was observed by 4 weeks post-SCI. Both cycling and treadmill training halted the atrophic process and accelerated the rate of recovery. The therapeutic influence of both training interventions was observed within 1 week of training and no significant difference was noted between the two interventions, except in the tibialis anterior muscle. Finally, a positive correlation was found between locomotor functional scores and hindlimb muscle size following SCI. Both treadmill and cycle training diminish the extent of atrophy and facilitate muscle plasticity after contusion SCI.

The development of radioactive sample surrogates for training and exercises

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

Martha Finck; Bevin Brush; Dick Jansen

2012-03-01

The development of radioactive sample surrogates for training and exercises Source term information is required for to reconstruct a device used in a dispersed radiological dispersal device. Simulating a radioactive environment to train and exercise sampling and sample characterization methods with suitable sample materials is a continued challenge. The Idaho National Laboratory has developed and permitted a Radioactive Response Training Range (RRTR), an 800 acre test range that is approved for open air dispersal of activated KBr, for training first responders in the entry and exit from radioactively contaminated areas, and testing protocols for environmental sampling and field characterization. Membersmore » from the Department of Defense, Law Enforcement, and the Department of Energy participated in the first contamination exercise that was conducted at the RRTR in the July 2011. The range was contaminated using a short lived radioactive Br-82 isotope (activated KBr). Soil samples contaminated with KBr (dispersed as a solution) and glass particles containing activated potassium bromide that emulated dispersed radioactive materials (such as ceramic-based sealed source materials) were collected to assess environmental sampling and characterization techniques. This presentation summarizes the performance of a radioactive materials surrogate for use as a training aide for nuclear forensics.« less

Development and validation of a knowledge test for health professionals regarding lifestyle modification.

PubMed

Talip, Whadi-ah; Steyn, Nelia P; Visser, Marianne; Charlton, Karen E; Temple, Norman

2003-09-01

We wanted to develop and validate a test that assesses the knowledge and practices of health professionals (HPs) with regard to the role of nutrition, physical activity, and smoking cessation (lifestyle modification) in chronic diseases of lifestyle. A descriptive cross-sectional validation study was carried out. The validation design consisted of two phases, namely 1) test planning and development and 2) test evaluation. The study sample consisted of five groups of HPs: dietitians, dietetic interns, general practitioners, medical students, and nurses. The overall response rate was 58%, resulting in a sample size of 186 participants. A test was designed to evaluate the knowledge and practices of HPs. The test was first evaluated by an expert group to ensure content, construct, and face validity. Thereafter, the questionnaire was tested on five groups of HPs to test for criterion validity. Internal consistency was evaluated by Cronbach's alpha. An expert panel ensured content, construct, and face validity of the test. Groups with the most training and exposure to nutrition (dietitians and dietetic interns) had the highest group mean score, ranging from 61% to 88%, whereas those with limited nutrition training (general practitioners, medical students, and nurses) had significantly lower scores, ranging from 26% to 80%. This result demonstrated criterion validity. Internal consistency of the overall test demonstrated a Cronbach's alpha of 0.99. Most HPs identified the mass media as their main source of information on lifestyle modification. These HPs also identified lack of time, lack of patient compliance, and lack of knowledge as barriers that prevent them from providing counseling on lifestyle modification. The results of this study showed that this test instrument identifies groups of health professionals with adequate training (knowledge) in lifestyle modification and those who require further training (knowledge).

Diagnosis of Parkinsonian disorders using a channelized Hotelling observer model: Proof of principle

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

Bal, H.; Bal, G.; Acton, P. D.

2007-10-15

Imaging dopamine transporters using PET and SPECT probes is a powerful technique for the early diagnosis of Parkinsonian disorders. In order to perform automated accurate diagnosis of these diseases, a channelized Hotelling observer (CHO) based model was developed and evaluated using the SPECT tracer [Tc-99m]TRODAT-1. Computer simulations were performed using a digitized striatal phantom to characterize early stages of the disease (20 lesion-present cases with varying lesion size and contrast). Projection data, modeling the effects of attenuation and geometric response function, were obtained for each case. Statistical noise levels corresponding to those observed clinically were added to the projection datamore » to obtain 100 noise realizations for each case. All the projection data were reconstructed, and a subset of the transaxial slices containing the striatum was summed and used for further analysis. CHO models, using the Laguerre-Gaussian functions as channels, were designed for two cases: (1) By training the model using individual lesion-present samples and (2) by training the model using pooled lesion-present samples. A decision threshold obtained for each CHO model was used to classify the study population (n=40). It was observed that individual lesion trained CHO models gave high diagnostic accuracy for lesions that were larger than those used to train the model and vice-versa. On the other hand, the pooled CHO model was found to give a high diagnostic accuracy for all the lesion cases (average diagnostic accuracy=0.95{+-}0.07; p<0.0001 Fisher's exact test). Based on our results, we conclude that a CHO model has the potential to provide early and accurate diagnosis of Parkinsonian disorders, thereby improving patient management.« less

The impact of brain size on pilot performance varies with aviation training and years of education

PubMed Central

Adamson, Maheen M.; Samarina, Viktoriya; Xiangyan, Xu; Huynh, Virginia; Kennedy, Quinn; Weiner, Michael; Yesavage, Jerome; Taylor, Joy L.

2010-01-01

Previous studies have consistently reported age-related changes in cognitive abilities and brain structure. Previous studies also suggest compensatory roles for specialized training, skill, and years of education in the age-related decline of cognitive function. The Stanford/VA Aviation Study examines the influence of specialized training and skill level (expertise) on age-related changes in cognition and brain structure. This preliminary report examines the effect of aviation expertise, years of education, age, and brain size on flight simulator performance in pilots aged 45–68 years. Fifty-one pilots were studied with structural magnetic resonance imaging, flight simulator, and processing speed tasks. There were significant main effects of age (p < .01) and expertise (p < .01), but not of whole brain size (p > .1) or education (p > .1), on flight simulator performance. However, even though age and brain size were correlated (r = −0.41), age differences in flight simulator performance were not explained by brain size. Both aviation expertise and education were involved in an interaction with brain size in predicting flight simulator performance (p < .05). These results point to the importance of examining measures of expertise and their interactions to assess age-related cognitive changes. PMID:20193103

The effects of mindfulness and relaxation training for insomnia (MRTI) on postmenopausal women: a pilot study.

PubMed

Garcia, Marcelo C; Kozasa, Elisa H; Tufik, Sergio; Mello, Luiz Eugênio A M; Hachul, Helena

2018-05-21

The aim of the study was to evaluate the effects of mindfulness and relaxation training for insomnia on insomnia and quality of life in postmenopausal women. Thirty postmenopausal women aged 50 to 65 years, who were not using hormone therapy, and had a diagnosis of insomnia and an apnea-hypopnea index of less than 15, were randomly assigned to two groups: a mindfulness intervention group and a control group. They were assessed before the intervention, and 8 weeks after its completion using questionnaires assessing sleep quality (Pittsburgh Sleep Quality Index), insomnia (Insomnia Severity Index), quality of life in menopause (Menopause-Specific Quality of Life), menopausal symptoms (Kupperman Menopausal Index), and level of attention (Mindfulness Awareness Attention Scale). They were also assessed through ambulatory polysomnography. This is a pilot study and is limited by its small sample size. The results of the questionnaires showed significant differences in the group that received mindfulness training compared with the control group, namely, improvements in sleep quality, a reduction in the severity of insomnia, a better quality of life, improved attention levels, and a reduction in menopausal and vasomotor symptoms. Polysomnography results showed no differences between the groups. Eight weeks mindfulness meditation training improved sleep quality, quality of life, attention levels, and reduced vasomotor symptoms in postmenopausal women with insomnia.

Evaluation of the Commitment to Living (CTL) curriculum: a 3-hour training for mental health professionals to address suicide risk.

PubMed

Pisani, Anthony R; Cross, Wendi F; Watts, Arthur; Conner, Kenneth

2012-01-01

Finding effective and efficient options for training mental health professionals to assess and manage suicide risk is a high priority. To test whether an innovative, brief workshop can improve provider knowledge, confidence, and written risk assessment in a multidisciplinary sample of ambulatory and acute services professionals and trainees. We conducted a pre/post evaluation of a 3 h workshop designed to improve clinical competence in suicide risk assessment by using visual concept mapping, medical records documentation, and site-specific crisis response options. Participants (N = 338 diverse mental health professionals) completed pre- and postworkshop questionnaires measuring their knowledge and confidence. Before and after the workshop, participants completed documentation for a clinical vignette. Trained coders rated the quality of risk assessment formulation before and after training. Participants' knowledge, confidence, and objectively-rated documentation skills improved significantly (p < .001), with large effect sizes. Participants' expectation of their ability to transfer workshop content to their clinical practice was high (mean = 4.10 on 1-5 scale). Commitment to Living is a promising, innovative, and efficient curriculum for educating practicing clinicians to assess and respond to suicide risk. Well-designed, brief, suicide risk management programs can improve clinicians' knowledge, confidence, and skill.

Effectiveness of a workplace training programme in improving social, communication and emotional skills for adults with autism and intellectual disability in Hong Kong--a pilot study.

PubMed

Liu, Karen P Y; Wong, Denys; Chung, Anthony C Y; Kwok, Natalie; Lam, Madeleine K Y; Yuen, Cheri M C; Arblaster, Karen; Kwan, Aldous C S

2013-12-01

This pilot study explored the effectiveness of workplace training programme that aimed to enhance the work-related behaviours in individuals with autism and intellectual disabilities. Fourteen participants with autism and mild to moderate intellectual disability (mean age = 24.6 years) were recruited. The workplace training programme included practices in work context and group educational sessions. A pre-test-post-test design was used with the Work Personality Profile, the Scale of Independent Behaviour Revised and the Observational Emotional Inventory Revised to evaluate the targeted behaviours. Improvement in social and communication skills specific to the workplace was achieved. For emotional control, participants became less confused and had a better self-concept. However, improvement in other general emotional behaviours, such as impulse control, was limited. The results indicated that a structured workplace training programme aimed at improving social, communication and emotional behaviours can be helpful for people with autism and intellectual disability. Further study with a larger sample size and a control group is recommended. The development of specific programme to cater for the emotional control needs at workplace for people with autism is also suggested. Copyright © 2013 John Wiley & Sons, Ltd.

Estimation of signal-dependent noise level function in transform domain via a sparse recovery model.

PubMed

Yang, Jingyu; Gan, Ziqiao; Wu, Zhaoyang; Hou, Chunping

2015-05-01

This paper proposes a novel algorithm to estimate the noise level function (NLF) of signal-dependent noise (SDN) from a single image based on the sparse representation of NLFs. Noise level samples are estimated from the high-frequency discrete cosine transform (DCT) coefficients of nonlocal-grouped low-variation image patches. Then, an NLF recovery model based on the sparse representation of NLFs under a trained basis is constructed to recover NLF from the incomplete noise level samples. Confidence levels of the NLF samples are incorporated into the proposed model to promote reliable samples and weaken unreliable ones. We investigate the behavior of the estimation performance with respect to the block size, sampling rate, and confidence weighting. Simulation results on synthetic noisy images show that our method outperforms existing state-of-the-art schemes. The proposed method is evaluated on real noisy images captured by three types of commodity imaging devices, and shows consistently excellent SDN estimation performance. The estimated NLFs are incorporated into two well-known denoising schemes, nonlocal means and BM3D, and show significant improvements in denoising SDN-polluted images.

Using Computer-Based Continuing Professional Education of Training Staff to Develop Small- and Medium-Sized Enterprises in Thailand

ERIC Educational Resources Information Center

Sooraksa, Nanta

2012-01-01

This paper describes a career development program for staff involved in providing training for small- and medium-sized enterprises (SMEs) in Thailand. Most of these staff were professional vocational teachers in schools. The program uses information communication technology (ICT), and its main objective is to teach Moodle software as a tool for…

An Inter-Industry Comparison of VET in Australian SMEs: Inter-Industry Comparison

ERIC Educational Resources Information Center

Jones, Janice

2006-01-01

Purpose: The purpose of this paper is to compare and contrast the extent and nature of Vocational Education and Training (VET) vis-a-vis other forms of training in three size categories of small-to-medium-sized enterprises (SMEs) from two industry sectors. Design/methodology/approach: The longitudinal panel data employed in this paper are drawn…

Entrepreneurial Training for the Growth of Small and Medium-Sized Enterprises: Lessons from Central and Eastern Europe. Report.

ERIC Educational Resources Information Center

European Training Foundation, Turin (Italy).

This report brings together a number of principles as to best practice in supporting, through training, growth of small and medium-sized enterprises (SMEs) in Central and Eastern Europe. Chapter 2 identifies key principles to be drawn from the West through a literature review. Chapter 3 reviews the "practice" of entrepreneurial training…

Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

NASA Astrophysics Data System (ADS)

Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

2017-03-01

Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

Single muscle fiber adaptations with marathon training.

PubMed

Trappe, Scott; Harber, Matthew; Creer, Andrew; Gallagher, Philip; Slivka, Dustin; Minchev, Kiril; Whitsett, David

2006-09-01

The purpose of this investigation was to characterize the effects of marathon training on single muscle fiber contractile function in a group of recreational runners. Muscle biopsies were obtained from the gastrocnemius muscle of seven individuals (22 +/- 1 yr, 177 +/- 3 cm, and 68 +/- 2 kg) before, after 13 wk of run training, and after 3 wk of taper. Slow-twitch myosin heavy chain [(MHC) I] and fast-twitch (MHC IIa) muscle fibers were analyzed for size, strength (P(o)), speed (V(o)), and power. The run training program led to the successful completion of a marathon (range 3 h 56 min to 5 h 35 min). Oxygen uptake during submaximal running and citrate synthase activity were improved (P < 0.05) with the training program. Muscle fiber size declined (P < 0.05) by approximately 20% in both fiber types after training. P(o) was maintained in both fiber types with training and increased (P < 0.05) by 18% in the MHC IIa fibers after taper. This resulted in >60% increase (P < 0.05) in force per cross-sectional area in both fiber types. Fiber V(o) increased (P < 0.05) by 28% in MHC I fibers with training and was unchanged in MHC IIa fibers. Peak power increased (P < 0.05) in MHC I and IIa fibers after training with a further increase (P < 0.05) in MHC IIa fiber power after taper. These data show that marathon training decreased slow-twitch and fast-twitch muscle fiber size but that it maintained or improved the functional profile of these fibers. A taper period before the marathon further improved the functional profile of the muscle, which was targeted to the fast-twitch muscle fibers.

Effectiveness of CME on "Pediatric Emergencies and Management" Among the Health Personnels in Community Health Centre, Karikalampakkam, Puducherry.

PubMed

Vasudevaiah, V; Dash, Manjubala

2014-05-01

To assess the level of knowledge among health personnels on pediatric emergencies and their management and to evaluate the effectiveness of CME programme on the same. This study was conducted at Karikalampakkam village of Puducherry. Karikalampakkam is a Community Health Center with seven subcenters under it. The research design was one of the Quasi Experimental Design pre and post test with one group. All the health personnels like ANM, PHN, Health educators were considered as subjects for the study. The sample size was 40 and selected by purposive sampling technique. Pretest was conducted before the CME programme with the structured interview schedule. Post test was conducted after completion of the programme with the help of same tool. The pretest mean knowledge score among the health personnels was 3.15 ± 0.89 with the mean percentage 7.8 % whereas the posttest mean knowledge score was 4.47 ± 1.58 with mean percentage 11.17 %. The Z value was -2.555 and the p value was 0.011 (p < 0.05) which was significant at 0.05 level. Though the health personnels are already trained, during pretest their knowledge level was found to be poor and after training, the results show that their knowledge improved. Thus, there is a necessity to conduct inservice training programmes to update knowledge and skill of health personnels.

Key considerations for the experimental training and evaluation of cancer odour detection dogs: lessons learnt from a double-blind, controlled trial of prostate cancer detection

PubMed Central

2014-01-01

Background Cancer detection using sniffer dogs is a potential technology for clinical use and research. Our study sought to determine whether dogs could be trained to discriminate the odour of urine from men with prostate cancer from controls, using rigorous testing procedures and well-defined samples from a major research hospital. Methods We attempted to train ten dogs by initially rewarding them for finding and indicating individual prostate cancer urine samples (Stage 1). If dogs were successful in Stage 1, we then attempted to train them to discriminate prostate cancer samples from controls (Stage 2). The number of samples used to train each dog varied depending on their individual progress. Overall, 50 unique prostate cancer and 67 controls were collected and used during training. Dogs that passed Stage 2 were tested for their ability to discriminate 15 (Test 1) or 16 (Tests 2 and 3) unfamiliar prostate cancer samples from 45 (Test 1) or 48 (Tests 2 and 3) unfamiliar controls under double-blind conditions. Results Three dogs reached training Stage 2 and two of these learnt to discriminate potentially familiar prostate cancer samples from controls. However, during double-blind tests using new samples the two dogs did not indicate prostate cancer samples more frequently than expected by chance (Dog A sensitivity 0.13, specificity 0.71, Dog B sensitivity 0.25, specificity 0.75). The other dogs did not progress past Stage 1 as they did not have optimal temperaments for the sensitive odour discrimination training. Conclusions Although two dogs appeared to have learnt to select prostate cancer samples during training, they did not generalise on a prostate cancer odour during robust double-blind tests involving new samples. Our study illustrates that these rigorous tests are vital to avoid drawing misleading conclusions about the abilities of dogs to indicate certain odours. Dogs may memorise the individual odours of large numbers of training samples rather than generalise on a common odour. The results do not exclude the possibility that dogs could be trained to detect prostate cancer. We recommend that canine olfactory memory is carefully considered in all future studies and rigorous double-blind methods used to avoid confounding effects. PMID:24575737

Chi-Squared Test of Fit and Sample Size-A Comparison between a Random Sample Approach and a Chi-Square Value Adjustment Method.

PubMed

Bergh, Daniel

2015-01-01

Chi-square statistics are commonly used for tests of fit of measurement models. Chi-square is also sensitive to sample size, which is why several approaches to handle large samples in test of fit analysis have been developed. One strategy to handle the sample size problem may be to adjust the sample size in the analysis of fit. An alternative is to adopt a random sample approach. The purpose of this study was to analyze and to compare these two strategies using simulated data. Given an original sample size of 21,000, for reductions of sample sizes down to the order of 5,000 the adjusted sample size function works as good as the random sample approach. In contrast, when applying adjustments to sample sizes of lower order the adjustment function is less effective at approximating the chi-square value for an actual random sample of the relevant size. Hence, the fit is exaggerated and misfit under-estimated using the adjusted sample size function. Although there are big differences in chi-square values between the two approaches at lower sample sizes, the inferences based on the p-values may be the same.

Learning from Past Classification Errors: Exploring Methods for Improving the Performance of a Deep Learning-based Building Extraction Model through Quantitative Analysis of Commission Errors for Optimal Sample Selection

NASA Astrophysics Data System (ADS)

Swan, B.; Laverdiere, M.; Yang, L.

2017-12-01

In the past five years, deep Convolutional Neural Networks (CNN) have been increasingly favored for computer vision applications due to their high accuracy and ability to generalize well in very complex problems; however, details of how they function and in turn how they may be optimized are still imperfectly understood. In particular, their complex and highly nonlinear network architecture, including many hidden layers and self-learned parameters, as well as their mathematical implications, presents open questions about how to effectively select training data. Without knowledge of the exact ways the model processes and transforms its inputs, intuition alone may fail as a guide to selecting highly relevant training samples. Working in the context of improving a CNN-based building extraction model used for the LandScan USA gridded population dataset, we have approached this problem by developing a semi-supervised, highly-scalable approach to select training samples from a dataset of identified commission errors. Due to the large scope this project, tens of thousands of potential samples could be derived from identified commission errors. To efficiently trim those samples down to a manageable and effective set for creating additional training sample, we statistically summarized the spectral characteristics of areas with rates of commission errors at the image tile level and grouped these tiles using affinity propagation. Highly representative members of each commission error cluster were then used to select sites for training sample creation. The model will be incrementally re-trained with the new training data to allow for an assessment of how the addition of different types of samples affects the model performance, such as precision and recall rates. By using quantitative analysis and data clustering techniques to select highly relevant training samples, we hope to improve model performance in a manner that is resource efficient, both in terms of training process and in sample creation.

What Is the Proportion of Studies Reporting Patient and Practitioner Satisfaction with Software Support Tools Used in the Management of Knee Pain and Is This Related to Sample Size, Effect Size, and Journal Impact Factor?

PubMed

Bright, Philip; Hambly, Karen

2017-12-21

E-health software tools have been deployed in managing knee conditions. Reporting of patient and practitioner satisfaction in studies regarding e-health usage is not widely explored. The objective of this review was to identify studies describing patient and practitioner satisfaction with software use concerning knee pain. A computerized search was undertaken: four electronic databases were searched from January 2007 until January 2017. Key words were decision dashboard, clinical decision, Web-based resource, evidence support, and knee. Full texts were scanned for effect of size reporting and satisfaction scales from participants and practitioners. Binary regression was run; impact factor and sample size were predictors with indicators for satisfaction and effect size reporting as dependent variables. Seventy-seven articles were retrieved; 37 studies were included in final analysis. Ten studies reported patient satisfaction ratings (27.8%): a single study reported both patient and practitioner satisfaction (2.8%). Randomized control trials were the most common design (35%) and knee osteoarthritis the most prevalent condition (38%). Electronic patient-reported outcome measures and Web-based training were the most common interventions. No significant dependency was found within the regression models (p > 0.05). The proportion of reporting of patient satisfaction was low; practitioner satisfaction was poorly represented. There may be implications for the suitability of administering e-health, a medium for capturing further meta-evidence needs to be established and used as best practice for implicated studies in future. This is the first review of its kind to address patient and practitioner satisfaction with knee e-health.

Alterations in Neural Control of Constant Isometric Contraction with the Size of Error Feedback

PubMed Central

Hwang, Ing-Shiou; Lin, Yen-Ting; Huang, Wei-Min; Yang, Zong-Ru; Hu, Chia-Ling; Chen, Yi-Ching

2017-01-01

Discharge patterns from a population of motor units (MUs) were estimated with multi-channel surface electromyogram and signal processing techniques to investigate parametric differences in low-frequency force fluctuations, MU discharges, and force-discharge relation during static force-tracking with varying sizes of execution error presented via visual feedback. Fourteen healthy adults produced isometric force at 10% of maximal voluntary contraction through index abduction under three visual conditions that scaled execution errors with different amplification factors. Error-augmentation feedback that used a high amplification factor (HAF) to potentiate visualized error size resulted in higher sample entropy, mean frequency, ratio of high-frequency components, and spectral dispersion of force fluctuations than those of error-reducing feedback using a low amplification factor (LAF). In the HAF condition, MUs with relatively high recruitment thresholds in the dorsal interosseous muscle exhibited a larger coefficient of variation for inter-spike intervals and a greater spectral peak of the pooled MU coherence at 13–35 Hz than did those in the LAF condition. Manipulation of the size of error feedback altered the force-discharge relation, which was characterized with non-linear approaches such as mutual information and cross sample entropy. The association of force fluctuations and global discharge trace decreased with increasing error amplification factor. Our findings provide direct neurophysiological evidence that favors motor training using error-augmentation feedback. Amplification of the visualized error size of visual feedback could enrich force gradation strategies during static force-tracking, pertaining to selective increases in the discharge variability of higher-threshold MUs that receive greater common oscillatory inputs in the β-band. PMID:28125658

Learning to identify crowded letters: Does the learning depend on the frequency of training?

PubMed Central

Chung, Susana T. L.; Truong, Sandy R.

2012-01-01

Performance for many visual tasks improves with training. The magnitude of improvement following training depends on the training task, number of trials per training session and the total amount of training. Does the magnitude of improvement also depend on the frequency of training sessions? In this study, we compared the learning effect for three groups of normally sighted observers who repeatedly practiced the task of identifying crowded letters in the periphery for six sessions (1000 trials per session), according to three different training schedules — one group received one session of training everyday, the second group received a training session once a week and the third group once every two weeks. Following six sessions of training, all observers improved in their performance of identifying crowded letters in the periphery. Most importantly, the magnitudes of improvement were similar across the three training groups. The improvement was accompanied by a reduction in the spatial extent of crowding, an increase in the size of visual span and a reduction in letter-size threshold. The magnitudes of these accompanied improvements were also similar across the three training groups. Our finding that the effectiveness of visual perceptual learning is similar for daily, weekly and biweekly training has significant implication for adopting perceptual learning as an option to improve visual functions for clinical patients. PMID:23206551

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

PubMed

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases. PMID:26571112

How a health and safety management training program may improve the working environment in small- and medium-sized companies.

PubMed

Torp, Steffen

2008-03-01

The objective of this controlled intervention study was to investigate the effects of a 2-year training program in health and safety (H&S) management for managers at small- and medium-sized companies. A total of 113 managers of motor vehicle repair garages participated in the training and another 113 garage managers served as a comparison group. The effects were measured using questionnaires sent before and after the intervention to the managers and blue-collar workers at the garages. The intervention group managers reported significantly greater improvement of their H&S management system than the managers in the comparison group. The results also indicate that the management training positively affected how the workers regarded their supportive working environment. H&S management training may positively affect measures at both garage and individual levels.

The efficacy of a HUBER exercise system mediated sensorimotor training protocol on proprioceptive system, lumbar movement control and quality of life in patients with chronic non-specific low back pain.

PubMed

Letafatkar, Amir; Nazarzadeh, Maryam; Hadadnezhad, Malihe; Farivar, Niloufar

2017-08-03

There is a relation between deficits of the proprioceptive system and movement control dysfunction in patients with chronic low back pain (LBP) but, the exact mechanism of this relation is unknown. Exercise therapy has been recognized as an effective method for low back pain treatment. In spite of this, it is not clear which of the various exercise therapy programs lead to better results. Therefore, the present analyze the efficacy of a HUBER study aims to exercise system mediated sensorimotor training protocol on proprioceptive system, lumbar movement control (LMC) and quality of life (QOL) in patients with chronic non-specific LBP. Quasi-experimental study. 53 patients with chronic non-specific LBP (mean age 37.55 ± 6.67 years,and Body Mass Index (BMI) 22.4 ± 3.33) were selected by using Roland-Morris Disability Questionnaire (RMQ) and were assigned into two experimental (N= 27) and control groups (N= 26) The experimental group underwent a five-week (10 sessions) Sensorimotor training by using the Human Body Equalizer (HUBER) spine force under the supervision of an investigator. The movement control battery tests, the HUBER machine testing option, goniometer and visual analogue scale used for movement control, neuromuscular coordination, proprioception and LBP assessment respectively. The assessments were completed in pre-test and after five weeks. The paired and sample T tests were used for data analysis in SPSS program version 18 (Significance level were set at a P value < 0.05). The HUBER system mediated sensorimotor training demonstrated significant improvement in the proprioceptive system, LMC and QOL (P= 0.001). Also There was a significant reduction in the pain scores of subjects with chronic non-specific LBP in the sensorimotor group (P= 0.001). In this study, only the short term effects of the sensorimotor training were examined. The results suggest that a sensorimotor training program causes significant improvement in patients with chronic non-specific LBP. Future research should be carried out with a larger sample size to examine the long term effects of the sensorimotor training program on treatment of patients with chronic non-specific LBP. Considering the efficacy of the sensorimotor training, it is recommended that this intervention should be applied to treatment of patients with chronic non-specific LBP in the future.

Three Minutes of All-Out Intermittent Exercise per Week Increases Skeletal Muscle Oxidative Capacity and Improves Cardiometabolic Health

PubMed Central

Gillen, Jenna B.; Percival, Michael E.; Skelly, Lauren E.; Martin, Brian J.; Tan, Rachel B.; Tarnopolsky, Mark A.; Gibala, Martin J.

2014-01-01

We investigated whether a training protocol that involved 3 min of intense intermittent exercise per week — within a total training time commitment of 30 min including warm up and cool down — could increase skeletal muscle oxidative capacity and markers of health status. Overweight/obese but otherwise healthy men and women (n = 7 each; age  = 29±9 y; BMI  = 29.8±2.7 kg/m2) performed 18 training sessions over 6 wk on a cycle ergometer. Each session began with a 2 min warm-up at 50 W, followed by 3×20 s “all-out” sprints against 5.0% body mass (mean power output: ∼450–500 W) interspersed with 2 min of recovery at 50 W, followed by a 3 min cool-down at 50 W. Peak oxygen uptake increased by 12% after training (32.6±4.5 vs. 29.1±4.2 ml/kg/min) and resting mean arterial pressure decreased by 7% (78±10 vs. 83±10 mmHg), with no difference between groups (both p<0.01, main effects for time). Skeletal muscle biopsy samples obtained before and 72 h after training revealed increased maximal activity of citrate synthase and protein content of cytochrome oxidase 4 (p<0.01, main effect), while the maximal activity of β-hydroxy acyl CoA dehydrogenase increased in men only (p<0.05). Continuous glucose monitoring measured under standard dietary conditions before and 48–72 h following training revealed lower 24 h average blood glucose concentration in men following training (5.4±0.6 vs. 5.9±0.5 mmol/L, p<0.05), but not women (5.5±0.4 vs. 5.5±0.6 mmol/L). This was associated with a greater increase in GLUT4 protein content in men compared to women (138% vs. 23%, p<0.05). Short-term interval training using a 10 min protocol that involved only 1 min of hard exercise, 3x/wk, stimulated physiological changes linked to improved health in overweight adults. Despite the small sample size, potential sex-specific adaptations were apparent that warrant further investigation. PMID:25365337

Hand tool permits shrink sizing of assembled tubing

NASA Technical Reports Server (NTRS)

Millett, A.; Odor, M.

1966-01-01

Portable tool sizes tubing ends without disassembling the tubing installation. The shrink sizing tool is clamped to the tubing and operated by a ratchet wrench. A gear train forces the tubing end against an appropriate die or mandrel to effect the sizing.

Hypnotherapy for insomnia: a systematic review and meta-analysis of randomized controlled trials.

PubMed

Lam, Tak-Ho; Chung, Ka-Fai; Yeung, Wing-Fai; Yu, Branda Yee-Man; Yung, Kam-Ping; Ng, Tommy Ho-Yee

2015-10-01

To examine the efficacy and safety of hypnotherapy for insomnia as compared to placebo, pharmacological or non-pharmacological intervention, or no treatment. A systematic search on major electronic databases was conducted up until March 2014. Inclusion criteria are: (1) randomized controlled trials (RCTs) or quasi-RCTs; (2) intervention targeted at improving sleep; (3) hypnosis as an intervention; and (4) English language articles. Sleep diary variable is the primary outcome measure. Six RCTs of hypnotherapy and seven on autogenic training or guided imagery, comprising 502 subjects, were included. Eleven of the 13 studies had low methodological quality, as indicated by a modified Jadad score below 3, and high risks of bias in blinding and design of the control interventions. No adverse events related to hypnosis were reported, though seldom investigated. Meta-analyses found hypnotherapy significantly shortened sleep latency compared to waitlist (standardized mean difference, SMD=-0.88, 95% confidence interval (CI): -1.56, -0.19, P=0.01, I(2)=15%), but no difference compared to sham intervention (SMD: -1.08, 95% CI: -3.15, 0.09, P=0.31, I(2)=90%). Similar results were found for autogenic training or guided imagery (SMD with waitlist=-1.16, 95% CI: -1.92, -0.40, P=0.003, I(2)=0%; SMD with sham intervention=-0.50, 95% CI: -1.19, 0.19, P=0.15, I(2)=0%). Generalizability of the positive results is doubtful due to the relatively small sample size and methodological limitations. Future studies with larger sample size and better study design and methodology are called for. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reduction in training time of a deep learning model in detection of lesions in CT

NASA Astrophysics Data System (ADS)

Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

2018-02-01

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

Sampling Methods and the Accredited Population in Athletic Training Education Research

ERIC Educational Resources Information Center

Carr, W. David; Volberding, Jennifer

2009-01-01

Context: We describe methods of sampling the widely-studied, yet poorly defined, population of accredited athletic training education programs (ATEPs). Objective: There are two purposes to this study; first to describe the incidence and types of sampling methods used in athletic training education research, and second to clearly define the…

Concept of an Exchange Network for the Development of Vocational Training in Small and Medium-Sized Enterprises.

ERIC Educational Resources Information Center

Boudet, Rene

An examination of the ways in which vocational training can be extended to small and medium-sized enterprises in the European Economic Community, this document consists of: an introduction; four parts containing multiple chapters; 10 case studies; and a bibliography. Following the introduction, which is an update of a report made in 1985, part one…

Assessing the Value of Workforce Training. A Guide for Small and Mid-sized Companies and the Providers that Serve Them.

ERIC Educational Resources Information Center

Askov, Eunice N.; Hoops, John; Alamprese, Judith

This booklet provides an introduction to evaluating a work force training program, both to assess its impact and to improve its effectiveness. The guide provides instructions for assessing a single training program, rather than a training department in a company or a training provider. It is targeted at small and midsized companies and the…

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

48 CFR 537.201 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., recent performance of work of similar size and scope, specific training and other factors that the contracting officer determines are necessary to the successful performance of the task or contract at issue.... Requisite training and capability means training and capability necessary to successfully perform the task...

Pressure-controlled treadmill training in chronic stroke: a case study with AlterG.

PubMed

Lathan, Cherise; Myler, Andrew; Bagwell, Jennifer; Powers, Christopher M; Fisher, Beth E

2015-04-01

Body-weight-supported treadmill training has been shown to be an effective intervention to improve walking characteristics for individuals who have experienced a stroke. A pressure-controlled treadmill utilizes a sealed chamber in which air pressure can be altered in a controlled manner to counteract the effects of gravity. The focus of this case study was to assess the immediate and short-term impact of a pressure-controlled treadmill to improve gait parameters, reduce fall risk, improve participation, and reduce the self-perceived negative impact of stroke in an individual with chronic stroke. The subject was an 81-year-old man (14.5 months poststroke). He had slow walking speed, poor endurance, and multiple gait deviations. The subject trained 4 times per week for 4 weeks (40 minutes per session) on a pressure-controlled treadmill (AlterG M320) to counter the influence of gravity on the lower extremities. Following training, self-selected gait speed increased from 0.50 m/s to 0.96 m/s, as measured by the 10-meter walk test. Stride length increased from 0.58 m to 0.95 m after training and to 1.00 m at 1-month follow-up. Peak hip flexion increased from 3.7° to 24.6° after training and to 19.4° at 1-month follow-up. Peak knee flexion increased from 19.4° to 34.3° after training and to 42.7° at 1-month follow-up. Measures of endurance, fall risk, and percentage of perceived recovery also were found to improve posttraining. Training with a pressure-controlled treadmill may be a viable alternative to traditional body-weight-supported treadmill training for persons poststroke. Additional studies with larger sample sizes are needed to elucidate the role of pressure-controlled treadmill training in this population. Video abstract available for more insights from the authors (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A97).

Enhancing Cognitive Abilities with Comprehensive Training: A Large, Online, Randomized, Active-Controlled Trial

PubMed Central

Hardy, Joseph L.; Nelson, Rolf A.; Thomason, Moriah E.; Sternberg, Daniel A.; Katovich, Kiefer; Farzin, Faraz; Scanlon, Michael

2015-01-01

Background A variety of studies have demonstrated gains in cognitive ability following cognitive training interventions. However, other studies have not shown such gains, and questions remain regarding the efficacy of specific cognitive training interventions. Cognitive training research often involves programs made up of just one or a few exercises, targeting limited and specific cognitive endpoints. In addition, cognitive training studies typically involve small samples that may be insufficient for reliable measurement of change. Other studies have utilized training periods that were too short to generate reliable gains in cognitive performance. Methods The present study evaluated an online cognitive training program comprised of 49 exercises targeting a variety of cognitive capacities. The cognitive training program was compared to an active control condition in which participants completed crossword puzzles. All participants were recruited, trained, and tested online (N = 4,715 fully evaluable participants). Participants in both groups were instructed to complete one approximately 15-minute session at least 5 days per week for 10 weeks. Results Participants randomly assigned to the treatment group improved significantly more on the primary outcome measure, an aggregate measure of neuropsychological performance, than did the active control group (Cohen’s d effect size = 0.255; 95% confidence interval = [0.198, 0.312]). Treatment participants showed greater improvements than controls on speed of processing, short-term memory, working memory, problem solving, and fluid reasoning assessments. Participants in the treatment group also showed greater improvements on self-reported measures of cognitive functioning, particularly on those items related to concentration compared to the control group (Cohen’s d = 0.249; 95% confidence interval = [0.191, 0.306]). Conclusion Taken together, these results indicate that a varied training program composed of a number of tasks targeted to different cognitive functions can show transfer to a wide range of untrained measures of cognitive performance. Trial Registration ClinicalTrials.gov NCT-02367898 PMID:26333022

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Effects of an Off-Axis Pivoting Elliptical Training Program on Gait Function in Persons With Spastic Cerebral Palsy: A Preliminary Study.

PubMed

Tsai, Liang-Ching; Ren, Yupeng; Gaebler-Spira, Deborah J; Revivo, Gadi A; Zhang, Li-Qun

2017-07-01

This preliminary study examined the effects of off-axis elliptical training on reducing transverse-plane gait deviations and improving gait function in 8 individuals with cerebral palsy (CP) (15.5 ± 4.1 years) who completed an training program using a custom-made elliptical trainer that allows transverse-plane pivoting of the footplates during exercise. Lower-extremity off-axis control during elliptical exercise was evaluated by quantifying the root-mean-square and maximal angular displacement of the footplate pivoting angle. Lower-extremity pivoting strength was assessed. Gait function and balance were evaluated using 10-m walk test, 6-minute-walk test, and Pediatric Balance Scale. Toe-in angles during gait were quantified. Participants with CP demonstrated a significant decrease in the pivoting angle (root mean square and maximal angular displacement; effect size, 1.00-2.00) and increase in the lower-extremity pivoting strength (effect size = 0.91-1.09) after training. Reduced 10-m walk test time (11.9 ± 3.7 seconds vs. 10.8 ± 3.0 seconds; P = 0.004; effect size = 1.46), increased Pediatric Balance Scale score (43.6 ± 12.9 vs. 45.6 ± 10.8; P = 0.042; effect size = 0.79), and decreased toe-in angle (3.7 ± 10.5 degrees vs. 0.7 ± 11.7 degrees; P = 0.011; effect size = 1.22) were observed after training. We present an intervention to challenge lower-extremity off-axis control during a weight-bearing and functional activity for individuals with CP. Our preliminary findings suggest that this intervention was effective in enhancing off-axis control, gait function, and balance and reducing in-toeing gait in persons with CP.

Computer-Aided Diagnosis Of Leukemic Blood Cells

NASA Astrophysics Data System (ADS)

Gunter, U.; Harms, H.; Haucke, M.; Aus, H. M.; ter Meulen, V.

1982-11-01

In a first clinical test, computer programs are being used to diagnose leukemias. The data collected include blood samples from patients suffering from acute myelomonocytic-, acute monocytic- and acute promyelocytic, myeloblastic, prolymphocytic, chronic lymphocytic leukemias and leukemic transformed immunocytoma. The proper differentiation of the leukemic cells is essential because the therapy depends on the type of leukemia. The algorithms analyse the fine chromatin texture and distribution in the nuclei as well as size and shape parameters from the cells and nuclei. Cells with similar nuclei from different leukemias can be distinguished from each other by analyzing the cell cytoplasm images. Recognition of these subtle differences in the cells require an image sampling rate of 15-30 pixel/micron. The results for the entire data set correlate directly to established hematological parameters and support the previously published initial training set .

Multi-view L2-SVM and its multi-view core vector machine.

PubMed

Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

PubMed

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

A randomised controlled trial of adjunctive yoga and adjunctive physical exercise training for cognitive dysfunction in schizophrenia.

PubMed

Bhatia, Triptish; Mazumdar, Sati; Wood, Joel; He, Fanyin; Gur, Raquel E; Gur, Ruben C; Nimgaonkar, Vishwajit L; Deshpande, Smita N

2017-04-01

Yoga and physical exercise have been used as adjunctive intervention for cognitive dysfunction in schizophrenia (SZ), but controlled comparisons are lacking. Aims A single-blind randomised controlled trial was designed to evaluate whether yoga training or physical exercise training enhance cognitive functions in SZ, based on a prior pilot study. Consenting, clinically stable, adult outpatients with SZ (n=286) completed baseline assessments and were randomised to treatment as usual (TAU), supervised yoga training with TAU (YT) or supervised physical exercise training with TAU (PE). Based on the pilot study, the primary outcome measure was speed index for the cognitive domain of 'attention' in the Penn computerised neurocognitive battery. Using mixed models and contrasts, cognitive functions at baseline, 21 days (end of training), 3 and 6 months post-training were evaluated with intention-to-treat paradigm. Speed index of attention domain in the YT group showed greater improvement than PE at 6 months follow-up (p<0.036, effect size 0.51). In the PE group, 'accuracy index of attention domain showed greater improvement than TAU alone at 6-month follow-up (p<0.025, effect size 0.61). For several other cognitive domains, significant improvements were observed with YT or PE compared with TAU alone (p<0.05, effect sizes 0.30-1.97). Both YT and PE improved attention and additional cognitive domains well past the training period, supporting our prior reported beneficial effect of YT on speed index of attention domain. As adjuncts, YT or PE can benefit individuals with SZ.

Cooperative VET in Training Networks: Analysing the Free-Rider Problem in a Sociology-of-Conventions Perspective

ERIC Educational Resources Information Center

Leemann, Regula Julia; Imdorf, Christian

2015-01-01

In training networks, particularly small and medium-sized enterprises pool their resources to train apprentices within the framework of the dual VET system, while an intermediary organisation is tasked with managing operations. Over the course of their apprenticeship, the apprentices switch from one training company to another on a (half-) yearly…

The Power of Low Back Pain Trials: A Systematic Review of Power, Sample Size, and Reporting of Sample Size Calculations Over Time, in Trials Published Between 1980 and 2012.

PubMed

Froud, Robert; Rajendran, Dévan; Patel, Shilpa; Bright, Philip; Bjørkli, Tom; Eldridge, Sandra; Buchbinder, Rachelle; Underwood, Martin

2017-06-01

A systematic review of nonspecific low back pain trials published between 1980 and 2012. To explore what proportion of trials have been powered to detect different bands of effect size; whether there is evidence that sample size in low back pain trials has been increasing; what proportion of trial reports include a sample size calculation; and whether likelihood of reporting sample size calculations has increased. Clinical trials should have a sample size sufficient to detect a minimally important difference for a given power and type I error rate. An underpowered trial is one within which probability of type II error is too high. Meta-analyses do not mitigate underpowered trials. Reviewers independently abstracted data on sample size at point of analysis, whether a sample size calculation was reported, and year of publication. Descriptive analyses were used to explore ability to detect effect sizes, and regression analyses to explore the relationship between sample size, or reporting sample size calculations, and time. We included 383 trials. One-third were powered to detect a standardized mean difference of less than 0.5, and 5% were powered to detect less than 0.3. The average sample size was 153 people, which increased only slightly (∼4 people/yr) from 1980 to 2000, and declined slightly (∼4.5 people/yr) from 2005 to 2011 (P < 0.00005). Sample size calculations were reported in 41% of trials. The odds of reporting a sample size calculation (compared to not reporting one) increased until 2005 and then declined (Equation is included in full-text article.). Sample sizes in back pain trials and the reporting of sample size calculations may need to be increased. It may be justifiable to power a trial to detect only large effects in the case of novel interventions. 3.

Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients?

PubMed Central

Peri, Elisabetta; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Nava, Claudia; Longoni, Valentina; Monticone, Marco; Ferrante, Simona

2016-01-01

Recent studies advocated the use of active cycling coupled with functional electrical stimulation to induce neuroplasticity and enhance functional improvements in stroke adult patients. The aim of this work was to evaluate whether the benefits induced by such a treatment are superior to standard physiotherapy. A single-blinded randomized controlled trial has been performed on post-acute elderly stroke patients. Patients underwent FES-augmented cycling training combined with voluntary pedaling or standard physiotherapy. The intervention consisted of fifteen 30-minutes sessions carried out within 3 weeks. Patients were evaluated before and after training, through functional scales, gait analysis and a voluntary pedaling test. Results were compared with an age-matched healthy group. Sixteen patients completed the training. After treatment, a general improvement of all clinical scales was obtained for both groups. Only the mechanical efficiency highlighted a group effect in favor of the experimental group. Although a group effect was not found for any other cycling or gait parameters, the experimental group showed a higher percentage of change with respect to the control group (e.g. the gait velocity was improved of 35.4% and 25.4% respectively, and its variation over time was higher than minimal clinical difference for the experimental group only). This trend suggests that differences in terms of motor recovery between the two groups may be achieved increasing the training dose. In conclusion, this study, although preliminary, showed that FES-augmented active cycling training seems to be effective in improving cycling and walking ability in post-acute elderly stroke patients. A higher sample size is required to confirm results. PMID:27990234

Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients?

PubMed

Peri, Elisabetta; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Nava, Claudia; Longoni, Valentina; Monticone, Marco; Ferrante, Simona

2016-06-13

Recent studies advocated the use of active cycling coupled with functional electrical stimulation to induce neuroplasticity and enhance functional improvements in stroke adult patients. The aim of this work was to evaluate whether the benefits induced by such a treatment are superior to standard physiotherapy. A single-blinded randomized controlled trial has been performed on post-acute elderly stroke patients. Patients underwent FES-augmented cycling training combined with voluntary pedaling or standard physiotherapy. The intervention consisted of fifteen 30-minutes sessions carried out within 3 weeks. Patients were evaluated before and after training, through functional scales, gait analysis and a voluntary pedaling test. Results were compared with an age-matched healthy group. Sixteen patients completed the training. After treatment, a general improvement of all clinical scales was obtained for both groups. Only the mechanical efficiency highlighted a group effect in favor of the experimental group. Although a group effect was not found for any other cycling or gait parameters, the experimental group showed a higher percentage of change with respect to the control group (e.g. the gait velocity was improved of 35.4% and 25.4% respectively, and its variation over time was higher than minimal clinical difference for the experimental group only). This trend suggests that differences in terms of motor recovery between the two groups may be achieved increasing the training dose. In conclusion, this study, although preliminary, showed that FES-augmented active cycling training seems to be effective in improving cycling and walking ability in post-acute elderly stroke patients. A higher sample size is required to confirm results.

fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study

PubMed Central

Slaats-Willemse, Dorine; Kan, Cornelis C.; Goebel, Rainer; Buitelaar, Jan K.

2017-01-01

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by poor cognitive control/attention and hypofunctioning of the dorsal anterior cingulate cortex (dACC). In the current study, we investigated for the first time whether real-time fMRI neurofeedback (rt-fMRI) training targeted at increasing activation levels within dACC in adults with ADHD leads to a reduction of clinical symptoms and improved cognitive functioning. An exploratory randomized controlled treatment study with blinding of the participants was conducted. Participants with ADHD (n = 7 in the neurofeedback group, and n = 6 in the control group) attended four weekly MRI training sessions (60-min training time/session), during which they performed a mental calculation task at varying levels of difficulty, in order to learn how to up-regulate dACC activation. Only neurofeedback participants received continuous feedback information on actual brain activation levels within dACC. Before and after the training, ADHD symptoms and relevant cognitive functioning was assessed. Results showed that both groups achieved a significant increase in dACC activation levels over sessions. While there was no significant difference between the neurofeedback and control group in clinical outcome, neurofeedback participants showed stronger improvement on cognitive functioning. The current study demonstrates the general feasibility of the suggested rt-fMRI neurofeedback training approach as a potential novel treatment option for ADHD patients. Due to the study’s small sample size, potential clinical benefits need to be further investigated in future studies. Trial Registration: ISRCTN12390961 PMID:28125735

Extracting information in spike time patterns with wavelets and information theory.

PubMed

Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

2015-02-01

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

Observation of electron cloud instabilities and emittance dilution at the Cornell electron-positron Storage ring Test Accelerator

DOE PAGES

Holtzapple, R. L.; Billing, M. G.; Campbell, R. C.; ...

2016-04-11

Electron cloud related emittance dilution and instabilities of bunch trains limit the performance of high intensity circular colliders. One of the key goals of the Cornell electron-positron storage ring Test Accelerator (CesrTA) research program is to improve our understanding of how the electron cloud alters the dynamics of bunches within the train. Single bunch beam diagnostics have been developed to measure the beam spectra, vertical beam size, two important dynamical effects of beams interacting with the electron cloud, for bunch trains on a turn-by-turn basis. Experiments have been performed at CesrTA to probe the interaction of the electron cloud withmore » stored positron bunch trains. The purpose of these experiments was to characterize the dependence of beam-electron cloud interactions on the machine parameters such as bunch spacing, vertical chromaticity, and bunch current. The beam dynamics of the stored beam, in the presence of the electron cloud, was quantified using: 1) a gated beam position monitor (BPM) and spectrum analyzer to measure the bunch-by-bunch frequency spectrum of the bunch trains, 2) an x-ray beam size monitor to record the bunch-by-bunch, turn-by-turn vertical size of each bunch within the trains. In this study we report on the observations from these experiments and analyze the effects of the electron cloud on the stability of bunches in a train under many different operational conditions.« less

Observation of Electron Cloud Instabilities and Emittance Dilution at the Cornell Electron-Positron Storage Ring Test Accelerator

NASA Astrophysics Data System (ADS)

Holtzapple, R. L.; Billing, M. G.; Campbell, R. C.; Dugan, G. F.; Flanagan, J.; McArdle, K. E.; Miller, M. I.; Palmer, M. A.; Ramirez, G. A.; Sonnad, K. G.; Totten, M. M.; Tucker, S. L.; Williams, H. A.

2016-04-01

Electron cloud related emittance dilution and instabilities of bunch trains limit the performance of high intensity circular colliders. One of the key goals of the Cornell electron-positron storage ring Test Accelerator (CesrTA) research program is to improve our understanding of how the electron cloud alters the dynamics of bunches within the train. Single bunch beam diagnotics have been developed to measure the beam spectra, vertical beam size, two important dynamical effects of beams interacting with the electron cloud, for bunch trains on a turn-by-turn basis. Experiments have been performed at CesrTA to probe the interaction of the electron cloud with stored positron bunch trains. The purpose of these experiments was to characterize the dependence of beam-electron cloud interactions on the machine parameters such as bunch spacing, vertical chromaticity, and bunch current. The beam dynamics of the stored beam, in the presence of the electron cloud, was quantified using: 1) a gated beam position monitor (BPM) and spectrum analyzer to measure the bunch-by-bunch frequency spectrum of the bunch trains; 2) an x-ray beam size monitor to record the bunch-by-bunch, turn-by-turn vertical size of each bunch within the trains. In this paper we report on the observations from these experiments and analyze the effects of the electron cloud on the stability of bunches in a train under many different operational conditions.

Transcranial Direct Current Stimulation to Enhance Dual-Task Gait Training in Parkinson's Disease: A Pilot RCT.

PubMed

Schabrun, Siobhan M; Lamont, Robyn M; Brauer, Sandra G

2016-01-01

To investigate the feasibility and safety of a combined anodal transcranial direct current stimulation (tDCS) and dual task gait training intervention in people with Parkinson's Disease (PD) and to provide data to support a sample size calculation for a fully powered trial should trends of effectiveness be present. A pilot, randomized, double-blind, sham-controlled parallel group trial with 12 week follow-up. A university physiotherapy department. Sixteen participants diagnosed with PD received nine dual task gait training sessions over 3 weeks. Participants were randomized to receive either active or sham tDCS applied for the first 20 minutes of each session. The primary outcome was gait speed while undertaking concurrent cognitive tasks (word lists, counting, conversation). Secondary measures included step length, cadence, Timed Up and Go, bradykinesia and motor speed. Gait speed, step length and cadence improved in both groups, under all dual task conditions. This effect was maintained at follow-up. There was no difference between the active and sham tDCS groups. Time taken to perform the TUGwords also improved, with no difference between groups. The active tDCS group did however increase their correct cognitive response rate during the TUGwords and TUGcount. Bradykinesia improved after training in both groups. Three weeks of dual task gait training resulted in improved gait under dual task conditions, and bradykinesia, immediately following training and at 12 weeks follow-up. The only parameter enhanced by tDCS was the number of correct responses while performing the dual task TUG. tDCS applied to M1 may not be an effective adjunct to dual task gait training in PD. Australia-New Zealand Clinical Trials Registry ACTRN12613001093774.

Swimming training prevents metabolic imprinting induced by hypernutrition during lactation.

PubMed

Fischer, Stefani Valeria; Capriglioni Cancian, Cláudia Regina; Montes, Elisangela Gueiber; de Carvalho Leite, Nayara; Grassiolli, Sabrina

2015-02-01

Reduction in litter size during lactation induces hypernutrition of the offspring culminating with altered metabolic programming during adult life. Overnourished rats present alterations in the endocrine pancreas and major predisposition to the development of type 2 diabetes. Our study evaluated the impact of swimming training on insulin secretion control in overnourished rats. At postnatal day 3 male rat pup litters were redistributed randomly into Small Litters (SL, 3 pups) or Normal Litters (NL, 9 pups) to induce early overfeeding during lactation. Both groups were subjected to swimming training (3 times/week/30 min) post-weaning (21 days) for 72 days. At 92 days of life pancreatic islets were isolated using collagenase technique and incubated with glucose in the presence or absence of acetylcholine (Ach, 0.1-1000 μM) or glucagon-like peptide 1 (GLP1, 10 nM). Adipose tissue depots (white and brown) and endocrine pancreas samples were examined by histological analysis. Food intake and body weight were measured. Blood biochemical parameters were also evaluated. Swimming training prevented metabolic program alteration by hypernutrition during lactation. Exercise reduced obesity and hyperglycemia in overnourished rats. Pancreatic islets isolated from overnourished rats showed a reduction in glucose-induced insulin secretion and cholinergic responses while the insulinotropic action of GLP1 was increased. Physical training effectively restored glucose-induced insulin secretion and GLP1-stimulated action in pancreatic islets from overnourished rats. However, swimming training did not correct the weak cholinergic response in pancreatic islets isolated from overnourished rats. Swimming training avoids obesity development, corrects glucose-induced insulin secretion, as well as, GLP1 insulinotropic response in overnourished rats. Copyright © 2014 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.

Effect of Exercise and Cognitive Training on Falls and Fall-Related Factors in Older Adults With Mild Cognitive Impairment: A Systematic Review.

PubMed

Lipardo, Donald S; Aseron, Anne Marie C; Kwan, Marcella M; Tsang, William W

2017-10-01

To evaluate the effect of exercise and cognitive training on falls reduction and on factors known to be associated with falls among community-dwelling older adults with mild cognitive impairment (MCI). Seven databases (PubMed, CINAHL, Cochrane Library, Web of Science, ProQuest, ProQuest Dissertations and Theses, Digital Dissertation Consortium) and reference lists of pertinent articles were searched. Randomized controlled trials (RCTs) on the effect of exercise, cognitive training, or a combination of both on falls and factors associated with falls such as balance, lower limb muscle strength, gait, and cognitive function among community-dwelling older adults with MCI were included. Data were extracted using the modified Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) tool. Study quality was assessed using the JBI-MAStARI appraisal instrument. Seventeen RCTs (1679 participants; mean age ± SD, 74.4±2.4y) were included. Exercise improved gait speed and global cognitive function in MCI; both are known factors associated with falls. Cognitive training alone had no significant effect on cognitive function, while combined exercise and cognitive training improved balance in MCI. Neither fall rate nor the number of fallers was reported in any of the studies included. This review suggests that exercise, and combined exercise and cognitive training improve specific factors associated with falls such as gait speed, cognitive function, and balance in MCI. Further research on the direct effect of exercise and cognitive training on the fall rate and incidence in older adults with MCI with larger sample sizes is highly recommended. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Visual-spatial ability is more important than motivation for novices in surgical simulator training: a preliminary study

PubMed Central

Hedman, Leif; Felländer-Tsai, Li

2016-01-01

Objectives To investigate whether surgical simulation performance and previous video gaming experience would correlate with higher motivation to further train a specific simulator task and whether visual-spatial ability would rank higher in importance to surgical performance than the above. It was also examined whether or not motivation would correlate with a preference to choose a surgical specialty in the future and if simulator training would increase the interest in choosing that same work field. Methods Motivation and general interest in surgery was measured pre- and post-training in 30 medical students at Karolinska Institutet who were tested in a laparoscopic surgical simulator in parallel with measurement of visual-spatial ability and self-estimated video gaming experience.  Correlations between simulator performance metrics, visual-spatial ability and motivation were statistically analyzed using regression analysis. Results A good result in the first simulator trial correlated with higher self-determination index (r =-0.46, p=0.05) in male students. Visual-spatial ability was the most important underlying factor followed by intrinsic motivation score and finally video gaming experience (p=0.02, p=0.05, p=0.11) regarding simulator performance in male students. Simulator training increased interest in surgery when studying all subjects (p=0.01), male subjects (p=0.02) as well as subjects with low video gaming experience (p=0.02). Conclusions This preliminary study highlights individual differences regarding the effect of simulator training on motivation that can be taken into account when designing simulator training curricula, although the sample size is quite small and findings should be interpreted carefully.  PMID:26897701

Progressive resistance training in Parkinson's disease: a systematic review and meta-analysis

PubMed Central

Saltychev, Mikhail; Bärlund, Esa; Paltamaa, Jaana; Katajapuu, Niina; Laimi, Katri

2016-01-01

Objectives To investigate if there is evidence on effectiveness of progressive resistance training in rehabilitation of Parkinson disease. Design Systematic review and meta-analysis. Data sources: Central, Medline, Embase, Cinahl, Web of Science, Pedro until May 2014. Randomised controlled or controlled clinical trials. The methodological quality of studies was assessed according to the Cochrane Collaboration's domain-based evaluation framework. Data synthesis: random effects meta-analysis with test for heterogeneity using the I² and pooled estimate as the raw mean difference. Participants Adults with primary/idiopathic Parkinson's disease of any severity, excluding other concurrent neurological condition. Interventions Progressive resistance training defined as training consisting of a small number of repetitions until fatigue, allowing sufficient rest between exercises for recovery, and increasing the resistance as the ability to generate force improves. Comparison Progressive resistance training versus no treatment, placebo or other treatment in randomised controlled or controlled clinical trials. Primary and secondary outcome measures Any outcome. Results Of 516 records, 12 were considered relevant. Nine of them had low risk of bias. All studies were randomised controlled trials conducted on small samples with none or 1 month follow-up after the end of intervention. Of them, six were included in quantitative analysis. Pooled effect sizes of meta-analyses on fast and comfortable walking speed, the 6 min walking test, Timed Up and Go test and maximal oxygen consumption were below the level of minimal clinical significance. Conclusions There is so far no evidence on the superiority of progressive resistance training compared with other physical training to support the use of this technique in rehabilitation of Parkinson's disease. Systematic review registration number PROSPERO 2014:CRD42014009844. PMID:26743698

A Comparison of Match-to-Sample and Respondent-Type Training of Equivalence Classes

ERIC Educational Resources Information Center

Clayton, Michael C.; Hayes, Linda J.

2004-01-01

Throughout the 25-year history of research on stimulus equivalence, one feature of the training procedure has remained constant, namely, the requirement of operant responding during the training procedures. The present investigation compared the traditional match-to-sample (MTS) training with a more recent respondent-type (ReT) procedure. Another…

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Multilingual Twitter Sentiment Classification: The Role of Human Annotators

PubMed Central

Mozetič, Igor; Grčar, Miha; Smailović, Jasmina

2016-01-01

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered. PMID:27149621

Changes in area affect figure-ground assignment in pigeons.

PubMed

Castro, Leyre; Lazareva, Olga F; Vecera, Shaun P; Wasserman, Edward A

2010-03-05

A critical cue for figure-ground assignment in humans is area: smaller regions are more likely to be perceived as figures than are larger regions. To see if pigeons are similarly sensitive to this cue, we trained birds to report whether a target appeared on a colored figure or on a differently colored background. The initial training figure was either smaller than (Experiments 1 and 2) or the same area as (Experiment 2) the background. After training, we increased or decreased the size of the figure. When the original training shape was smaller than the background, pigeons' performance improved with smaller figures (and worsened with larger figures); when the original training shape was the same area as the background, pigeons' performance worsened when they were tested with smaller figures. A smaller figural region appeared to improve the figure-ground discrimination only when size was a relevant cue in the initial discrimination.

Changes in Area Affect Figure-Ground Assignment in Pigeons

PubMed Central

Castro, Leyre; Lazareva, Olga F.; Vecera, Shaun P.; Wasserman, Edward A.

2010-01-01

A critical cue for figure-ground assignment in humans is area: Smaller regions are more likely to be perceived as figures than are larger regions. To see if pigeons are similarly sensitive to this cue, we trained birds to report whether a target appeared on a colored figure or on a differently colored background. The initial training figure was either smaller than (Experiments 1 and 2) or the same area as (Experiment 2) the background. After training, we increased or decreased the size of the figure. When the original training shape was smaller than the background, pigeons’ performance improved with smaller figures (and worsened with larger figures); when the original training shape was the same area as the background, pigeons’ performance worsened when they were tested with smaller figures. A smaller figural region appeared to improve the figure-ground discrimination only when size was a relevant cue in the initial discrimination. PMID:20060406

Macroscopic inhomogeneous deformation behavior arising in single crystal Ni-Mn-Ga foils under tensile loading

NASA Astrophysics Data System (ADS)

Murasawa, Go; Yeduru, Srinivasa R.; Kohl, Manfred

2016-12-01

This study investigated macroscopic inhomogeneous deformation occurring in single-crystal Ni-Mn-Ga foils under uniaxial tensile loading. Two types of single-crystal Ni-Mn-Ga foil samples were examined as-received and after thermo-mechanical training. Local strain and the strain field were measured under tensile loading using laser speckle and digital image correlation. The as-received sample showed a strongly inhomogeneous strain field with intermittence under progressive deformation, but the trained sample result showed strain field homogeneity throughout the specimen surface. The as-received sample is a mainly polycrystalline-like state composed of the domain structure. The sample contains many domain boundaries and large domain structures in the body. Its structure would cause large local strain band nucleation with intermittence. However, the trained one is an ideal single-crystalline state with a transformation preferential orientation of variants after almost all domain boundary and large domain structures vanish during thermo-mechanical training. As a result, macroscopic homogeneous deformation occurs on the trained sample surface during deformation.

Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique

PubMed Central

Aczel, Balazs; Bago, Bence; Szollosi, Aba; Foldes, Andrei; Lukacs, Bence

2015-01-01

The aim of this study was to initiate the exploration of debiasing methods applicable in real-life settings for achieving lasting improvement in decision making competence regarding multiple decision biases. Here, we tested the potentials of the analogical encoding method for decision debiasing. The advantage of this method is that it can foster the transfer from learning abstract principles to improving behavioral performance. For the purpose of the study, we devised an analogical debiasing technique for 10 biases (covariation detection, insensitivity to sample size, base rate neglect, regression to the mean, outcome bias, sunk cost fallacy, framing effect, anchoring bias, overconfidence bias, planning fallacy) and assessed the susceptibility of the participants (N = 154) to these biases before and 4 weeks after the training. We also compared the effect of the analogical training to the effect of ‘awareness training’ and a ‘no-training’ control group. Results suggested improved performance of the analogical training group only on tasks where the violations of statistical principles are measured. The interpretation of these findings require further investigation, yet it is possible that analogical training may be the most effective in the case of learning abstract concepts, such as statistical principles, which are otherwise difficult to master. The study encourages a systematic research of debiasing trainings and the development of intervention assessment methods to measure the endurance of behavior change in decision debiasing. PMID:26300816

Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

PubMed

Kim, D H; MacKinnon, T

2018-05-01

To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs. The top layer of the Inception v3 network was re-trained using lateral wrist radiographs to produce a model for the classification of new studies as either "fracture" or "no fracture". The model was trained on a total of 11,112 images, after an eightfold data augmentation technique, from an initial set of 1,389 radiographs (695 "fracture" and 694 "no fracture"). The training data set was split 80:10:10 into training, validation, and test groups, respectively. An additional 100 wrist radiographs, comprising 50 "fracture" and 50 "no fracture" images, were used for final testing and statistical analysis. The area under the receiver operator characteristic curve (AUC) for this test was 0.954. Setting the diagnostic cut-off at a threshold designed to maximise both sensitivity and specificity resulted in values of 0.9 and 0.88, respectively. The AUC scores for this test were comparable to state-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs. This was achieved using only a moderate sample size. This technique is largely transferable, and therefore, has many potential applications in medical imaging, which may lead to significant improvements in workflow productivity and in clinical risk reduction. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Effects of Krankcycle Training on Performance and Body Composition in Wheelchair Users.

PubMed

Čichoň, Rostislav; Maszczyk, Adam; Stastny, Petr; Uhlíř, Petr; Petr, Miroslav; Doubrava, Ondřej; Mostowik, Aleksandra; Gołaś, Artur; Cieszczyk, Paweł; Żmijewski, Piotr

2015-11-22

Innovation in training equipment is important for increasing training effectiveness, performance and changes in body composition, especially in wheelchair users with paraplegia. The main objective of a workout session is to induce an adaptation stimulus, which requires overload of involved muscles by voluntary effort, yet this overload may be highly influenced by the size of the spinal cord lesion. Krancykl construction is designed to allow exercise on any wheelchair and with adjustable height or width of crank handles, where even the grip handle may be altered. The aim of this study was to determine the differences in body composition, performance and the rate of perceived exertion (RPE) in paraplegics with a different level of paralyses after a 12 week training programme of a unilateral regime on Krankcycle equipment (a crank machine). The study sample included four men and one women at a different spine lesion level. The 12 weeks programme was successfully completed by four participants, while one subject got injured during the intervention process. Three participants were paraplegics and one was quadriplegic with innervation of the biceps humeri, triceps humeri and deltoideus. The Krankcycle 30 min programme was followed by four other exercises, which were performed after themselves rather than in a circuit training manner as the latter would result in much longer rest periods between exercises, because paraplegics have to be fixed by straps during exercise on hydraulic machines. The RPE after the workout decreased following the twelve week adaptation period.

Effects of Krankcycle Training on Performance and Body Composition in Wheelchair Users

PubMed Central

Čichoň, Rostislav; Maszczyk, Adam; Stastny, Petr; Uhlíř, Petr; Petr, Miroslav; Doubrava, Ondřej; Mostowik, Aleksandra; Gołaś, Artur; Cieszczyk, Paweł; Żmijewski, Piotr

2015-01-01

Innovation in training equipment is important for increasing training effectiveness, performance and changes in body composition, especially in wheelchair users with paraplegia. The main objective of a workout session is to induce an adaptation stimulus, which requires overload of involved muscles by voluntary effort, yet this overload may be highly influenced by the size of the spinal cord lesion. Krancykl construction is designed to allow exercise on any wheelchair and with adjustable height or width of crank handles, where even the grip handle may be altered. The aim of this study was to determine the differences in body composition, performance and the rate of perceived exertion (RPE) in paraplegics with a different level of paralyses after a 12 week training programme of a unilateral regime on Krankcycle equipment (a crank machine). The study sample included four men and one women at a different spine lesion level. The 12 weeks programme was successfully completed by four participants, while one subject got injured during the intervention process. Three participants were paraplegics and one was quadriplegic with innervation of the biceps humeri, triceps humeri and deltoideus. The Krankcycle 30 min programme was followed by four other exercises, which were performed after themselves rather than in a circuit training manner as the latter would result in much longer rest periods between exercises, because paraplegics have to be fixed by straps during exercise on hydraulic machines. The RPE after the workout decreased following the twelve week adaptation period. PMID:26834875

Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

Equal Opportunities and Vocational Training. Qualifications and Educational Needs of Co-Working Spouses of Owners of Small and Medium-Sized Enterprises.

ERIC Educational Resources Information Center

Riis-Jorgensen, Karin

A study examined the training needs of women working in moderate-sized enterprises owned by their husbands. Information collected from interviews with spouses of business owners in Belgium, Denmark, the Federal Republic of Germany, France, and Italy confirmed the original hypothesis that in the kind of enterprise studied it is the man who owns the…

Innovative strength training-induced neuroplasticity and increased muscle size and strength in children with spastic cerebral palsy: an experimenter-blind case study--three-month follow-up.

PubMed

Lee, Dong Ryul; Kim, Yun Hee; Kim, Dong A; Lee, Jung Ah; Hwang, Pil Woo; Lee, Min Jin; You, Sung Hyun

2014-01-01

In children with cerebral palsy (CP), the never-learned-to-use (NLTU) effect and underutilization suppress the normal development of cortical plasticity in the paretic limb, which further inhibits its functional use and increases associated muscle weakness. To highlight the effects of a novel comprehensive hand repetitive intensive strengthening training system on neuroplastic changes associated with upper extremity (UE) muscle strength and motor performance in children with spastic hemiplegic CP. Two children with spastic hemiplegic CP were recruited. Intervention with the comprehensive hand repetitive intensive strengthening training system was provided for 60 min a day, three times a week, for 10 weeks. Neuroplastic changes, muscle size, strength, and associated motor function were measured using functional magnetic resonance imaging (MRI), ultrasound imaging, and standardized motor tests, respectively. The functional MRI data showed that the comprehensive hand repetitive intensive strengthening training intervention produced measurable neuroplastic changes in the neural substrates associated with motor control and learning. These neuroplastic changes were associated with increased muscle size, strength and motor function. These results provide compelling evidence of neuroplastic changes and associated improvements in muscle size and motor function following innovative upper extremity strengthening exercise.

Publication Bias in Psychology: A Diagnosis Based on the Correlation between Effect Size and Sample Size

PubMed Central

Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas

2014-01-01

Background The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. Methods We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. Results We found a negative correlation of r = −.45 [95% CI: −.53; −.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. Conclusion The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology. PMID:25192357

Publication bias in psychology: a diagnosis based on the correlation between effect size and sample size.

PubMed

Kühberger, Anton; Fritz, Astrid; Scherndl, Thomas

2014-01-01

The p value obtained from a significance test provides no information about the magnitude or importance of the underlying phenomenon. Therefore, additional reporting of effect size is often recommended. Effect sizes are theoretically independent from sample size. Yet this may not hold true empirically: non-independence could indicate publication bias. We investigate whether effect size is independent from sample size in psychological research. We randomly sampled 1,000 psychological articles from all areas of psychological research. We extracted p values, effect sizes, and sample sizes of all empirical papers, and calculated the correlation between effect size and sample size, and investigated the distribution of p values. We found a negative correlation of r = -.45 [95% CI: -.53; -.35] between effect size and sample size. In addition, we found an inordinately high number of p values just passing the boundary of significance. Additional data showed that neither implicit nor explicit power analysis could account for this pattern of findings. The negative correlation between effect size and samples size, and the biased distribution of p values indicate pervasive publication bias in the entire field of psychology.

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.

Fundamental differences in axial and appendicular bone density in stress fractured and uninjured Royal Marine recruits--a matched case-control study.

PubMed

Davey, Trish; Lanham-New, Susan A; Shaw, Anneliese M; Cobley, Rosalyn; Allsopp, Adrian J; Hajjawi, Mark O R; Arnett, Timothy R; Taylor, Pat; Cooper, Cyrus; Fallowfield, Joanne L

2015-04-01

Stress fracture is a common overuse injury within military training, resulting in significant economic losses to the military worldwide. Studies to date have failed to fully identify the bone density and bone structural differences between stress fractured personnel and controls due to inadequate adjustment for key confounding factors; namely age, body size and physical fitness; and poor sample size. The aim of this study was to investigate bone differences between male Royal Marine recruits who suffered a stress fracture during the 32 weeks of training and uninjured control recruits, matched for age, body weight, height and aerobic fitness. A total of 1090 recruits were followed through training and 78 recruits suffered at least one stress fracture. Bone mineral density (BMD) was measured at the lumbar spine (LS), femoral neck (FN) and whole body (WB) using Dual X-ray Absorptiometry in 62 matched pairs; tibial bone parameters were measured using peripheral Quantitative Computer Tomography in 51 matched pairs. Serum C-terminal peptide concentration was measured as a marker of bone resorption at baseline, week-15 and week-32. ANCOVA was used to determine differences between stress fractured recruits and controls. BMD at the LS, WB and FN sites was consistently lower in the stress fracture group (P<0.001). Structural differences between the stress fracture recruits and controls were evident in all slices of the tibia, with the most prominent differences seen at the 38% tibial slice. There was a negative correlation between the bone cross-sectional area and BMD at the 38% tibial slice. There was no difference in serum CTx concentration between stress fracture recruits and matched controls at any stage of training. These results show evidence of fundamental differences in bone mass and structure in stress fracture recruits, and provide useful data on bone risk factor profiles for stress fracture within a healthy military population. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Optimum sample size allocation to minimize cost or maximize power for the two-sample trimmed mean test.

PubMed

Guo, Jiin-Huarng; Luh, Wei-Ming

2009-05-01

When planning a study, sample size determination is one of the most important tasks facing the researcher. The size will depend on the purpose of the study, the cost limitations, and the nature of the data. By specifying the standard deviation ratio and/or the sample size ratio, the present study considers the problem of heterogeneous variances and non-normality for Yuen's two-group test and develops sample size formulas to minimize the total cost or maximize the power of the test. For a given power, the sample size allocation ratio can be manipulated so that the proposed formulas can minimize the total cost, the total sample size, or the sum of total sample size and total cost. On the other hand, for a given total cost, the optimum sample size allocation ratio can maximize the statistical power of the test. After the sample size is determined, the present simulation applies Yuen's test to the sample generated, and then the procedure is validated in terms of Type I errors and power. Simulation results show that the proposed formulas can control Type I errors and achieve the desired power under the various conditions specified. Finally, the implications for determining sample sizes in experimental studies and future research are discussed.

Tracing the trajectory of skill learning with a very large sample of online game players.

PubMed

Stafford, Tom; Dewar, Michael

2014-02-01

In the present study, we analyzed data from a very large sample (N = 854,064) of players of an online game involving rapid perception, decision making, and motor responding. Use of game data allowed us to connect, for the first time, rich details of training history with measures of performance from participants engaged for a sustained amount of time in effortful practice. We showed that lawful relations exist between practice amount and subsequent performance, and between practice spacing and subsequent performance. Our methodology allowed an in situ confirmation of results long established in the experimental literature on skill acquisition. Additionally, we showed that greater initial variation in performance is linked to higher subsequent performance, a result we link to the exploration/exploitation trade-off from the computational framework of reinforcement learning. We discuss the benefits and opportunities of behavioral data sets with very large sample sizes and suggest that this approach could be particularly fecund for studies of skill acquisition.

Rethinking police training policies: large class sizes increase risk of police sexual misconduct.

PubMed

Reingle Gonzalez, Jennifer M; Bishopp, Stephen A; Jetelina, Katelyn K

2016-09-01

The limited research on police sexual misconduct (PSM), a common form of police misconduct, suggests that no evidence-based strategies for prevention are available for use by police departments. To identify new avenues for prevention, we critically evaluated 'front-end' police recruiting, screening, hiring and training procedures. Internal Affairs records were linked with administrative reports and police academy graduation data for officers accused of sexual assault or misconduct between 1994 and 2014. Logistic and proportional hazards regression methods were used to identify predictors of discharge for sustained allegations of PSM and time to discharge, respectively. Officer's graduating class size was positively associated with odds of discharge for PSM. For every one-officer increase in class size, the rate of discharge for PSM increased by 9% [hazard ratio (HR) = 1.09, P < 0.01]. For particularly large classes (>35 graduates), discharge rates were at least four times greater than smaller classes (HR = 4.43, P < 0.05). Large class sizes and more annual graduates increase rates of PSM. Officer recruitment strategies or training quality may be compromised during periods of intensive hiring. Trainee to instructor ratios or maximum class sizes may be instituted by academies to ensure that all police trainees receive the required supervision, one-on-one training, feedback and attention necessary to maximize public safety. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Pragmatic Approach to Sales Training

ERIC Educational Resources Information Center

Buzzotta, V. R.; And Others

1974-01-01

A systematic ten-step approach to behavioral sales training is offered: (1) sales-behavior training goals, (2) cognitive maps, (3) sizing-up of skills, (4) selling techniques, (5) realistic practice, (6) feedback, (7) individual business goals, (8) plan of action, (9) review of results, and (10) research results. (MW)

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 1 2014-07-01 2014-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 1 2013-07-01 2013-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 1 2012-07-01 2012-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 1 2011-07-01 2011-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2010 CFR

2010-07-01

... Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... COMMUTATION INSTEAD OF UNIFORMS FOR MEMBERS OF THE SENIOR RESERVE OFFICERS' TRAINING CORPS Pt. 110, App. E Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

Building Capacity for Workplace Health Promotion: Findings From the Work@Health® Train-the-Trainer Program

PubMed Central

Lang, Jason; Cluff, Laurie; Rineer, Jennifer; Brown, Darigg; Jones-Jack, Nkenge

2017-01-01

Small- and mid-sized employers are less likely to have expertise, capacity, or resources to implement workplace health promotion programs, compared with large employers. In response, the Centers for Disease Control and Prevention developed the Work@Health® employer training program to determine the best way to deliver skill-based training to employers of all sizes. The core curriculum was designed to increase employers’ knowledge of the design, implementation, and evaluation of workplace health strategies. The first arm of the program was direct employer training. In this article, we describe the results of the second arm—the program’s train-the-trainer (T3) component, which was designed to prepare new certified trainers to provide core workplace health training to other employers. Of the 103 participants who began the T3 program, 87 fully completed it and delivered the Work@Health core training to 233 other employers. Key indicators of T3 participants’ knowledge and attitudes significantly improved after training. The curriculum delivered through the T3 model has the potential to increase the health promotion capacity of employers across the nation, as well as organizations that work with employers, such as health departments and business coalitions. PMID:28829622

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Building Capacity for Workplace Health Promotion: Findings From the Work@Health® Train-the-Trainer Program.

PubMed

Lang, Jason; Cluff, Laurie; Rineer, Jennifer; Brown, Darigg; Jones-Jack, Nkenge

2017-11-01

Small- and mid-sized employers are less likely to have expertise, capacity, or resources to implement workplace health promotion programs, compared with large employers. In response, the Centers for Disease Control and Prevention developed the Work@Health ® employer training program to determine the best way to deliver skill-based training to employers of all sizes. The core curriculum was designed to increase employers' knowledge of the design, implementation, and evaluation of workplace health strategies. The first arm of the program was direct employer training. In this article, we describe the results of the second arm-the program's train-the-trainer (T3) component, which was designed to prepare new certified trainers to provide core workplace health training to other employers. Of the 103 participants who began the T3 program, 87 fully completed it and delivered the Work@Health core training to 233 other employers. Key indicators of T3 participants' knowledge and attitudes significantly improved after training. The curriculum delivered through the T3 model has the potential to increase the health promotion capacity of employers across the nation, as well as organizations that work with employers, such as health departments and business coalitions.

Effect of confinement in small space flight size cages on insulin sensitivity of exercise-trained rats

NASA Technical Reports Server (NTRS)

Mondon, C. E.; Dolkas, C. B.; Reaven, G. M.

1983-01-01

The effect of confinement in small cages (simulating the size to be used in future space Shuttle missions) on insulin sensitivity was studied in rats having an increased insulin sensitivity due to exercise training prior to confinement. Oral glucose tolerance tests (OGTT) were given to both control and exercise-trained rats before and after placement in the small cages for 7 days. The insulin resistance was assessed by the product of the area of the insulin and glucose curves of the OGTT (IG index). Results show that the values obtained before confinement were one-half as high in exercise-trained rats as those in control rats, reflecting an increased sensitivity to insulin with exercise training. After 7 days confinement, the IG index was found to be not significantly different from initial values for both control and exercise-trained rats. These findings suggest that increased insulin sensitivity in exercise-trained rats persists 7 days after cessation of running activity. The data also indicate that exercise training, before flight, may be beneficial in minimizing the loss of insulin sensitivity expected with decreased use of gravity dependent muscles during exposure to hypogravity in space flight.

Immunological discrimination of Atlantic striped bass stocks

USGS Publications Warehouse

Schill, W.B.; Dorazio, R.M.

1990-01-01

Stocks of Atlantic striped bass Morone saxatilis that were assumed to be geographically isolated during spawning showed strong antigenic differences in blood serum albumin. A discriminant function was estimated from the immunologic responses of northern (Canadian and Hudson River) and southern (Chesapeake Bay and Roanoke River) stocks to two reference antisera. The function correctly classified 92% of the northern and 95% of the southern fish in the training set. Cross-validation revealed similar percentages of correct classification for fish that were of known origin but not used to estimate the discriminant function. Monte Carlo experiments were used to evaluate the ability of the discriminant function to predict the relative contribution of northern fish in samples of various size and stock composition. Averages of predicted proportions of northern fish in the samples agreed well with actual proportions. Coefficients of variation (100 × SD/mean) in the predicted proportions ranged from 1.5 to 36% for samples of 50–400 fish that contained at least 10% northern stock. In samples that contained only 2% northern stock, however, at least 1,600 fish were required to achieve similar levels of precision.

Effects of a Specifically Designed Physical Conditioning Program on the Load Carriage and Lifting Performance of Female Soldiers.

DTIC Science & Technology

1997-11-01

66 TRAINING AND TESTING RELATED INJURIES ................ 68 iv Pre-tests ................................................ 68 T raining...74 BASIC TRAINING VS. THE EXPERIMENTAL PROGRAM ......... 74 INDIVIDUAL DIFFERENCES IN RESPONSIVENESS TO TRAINING.. 74 INJURY RISK IN HIGH-LEVEL...USED FOR TRAINING ............ SAMPLE WORKOUTS .................................... vi Sample Monday and Thursday Weightlifting and Running W orkout

Evaluation of pyramid training as a method to increase diagnostic sampling capacity during an emergency veterinary response to a swine disease outbreak.

PubMed

Canon, Abbey J; Lauterbach, Nicholas; Bates, Jessica; Skoland, Kristin; Thomas, Paul; Ellingson, Josh; Ruston, Chelsea; Breuer, Mary; Gerardy, Kimberlee; Hershberger, Nicole; Hayman, Kristen; Buckley, Alexis; Holtkamp, Derald; Karriker, Locke

2017-06-15

OBJECTIVE To develop and evaluate a pyramid training method for teaching techniques for collection of diagnostic samples from swine. DESIGN Experimental trial. SAMPLE 45 veterinary students. PROCEDURES Participants went through a preinstruction assessment to determine their familiarity with the equipment needed and techniques used to collect samples of blood, nasal secretions, feces, and oral fluid from pigs. Participants were then shown a series of videos illustrating the correct equipment and techniques for collecting samples and were provided hands-on pyramid-based instruction wherein a single swine veterinarian trained 2 or 3 participants on each of the techniques and each of those participants, in turn, trained additional participants. Additional assessments were performed after the instruction was completed. RESULTS Following the instruction phase, percentages of participants able to collect adequate samples of blood, nasal secretions, feces, and oral fluid increased, as did scores on a written quiz assessing participants' ability to identify the correct equipment, positioning, and procedures for collection of samples. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that the pyramid training method may be a feasible way to rapidly increase diagnostic sampling capacity during an emergency veterinary response to a swine disease outbreak.

Recent trends in psychiatry residency workforce with special reference to international medical graduates.

PubMed

Rao, Nyapati R

2003-01-01

This study examines trends in the supply, distribution, and demographics of psychiatry residents during the 1990s. It evaluates the extent to which the predicted downsizing of psychiatry residency training programs actually occurred and how it affected training programs of different sizes and locations. Data for this study were obtained from the American Medical Association's (AMA) Annual Survey of Graduate Medical Education (GME) Programs, the AMA GME directory, and the APA Graduate Medical Census. The study compares the roles played by international medical graduates (IMGs) in contrast to U.S. medical graduates (USMGs) in these trends. There was a significant decline in the number of residents during the years studied. The median training program size also decreased. International medical graduates found broad acceptance in training programs of all locations and sizes, including medical school based programs. Implications of the findings are discussed regarding the impact of current graduate medical education (GME) and immigration policies on future workforce patterns. The field will have to decide whether it can afford anymore residency downsizing in light of emerging evidence of a shortage of psychiatrists.

The effects of video-game training on broad cognitive transfer in multiple sclerosis: A pilot randomized controlled trial.

PubMed

Janssen, Alisha; Boster, Aaron; Lee, HyunKyu; Patterson, Beth; Prakash, Ruchika Shaurya

2015-01-01

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that results in diffuse nerve damage and associated physical and cognitive impairments. Of the few comprehensive rehabilitation options that exist for populations with lower baseline cognitive functioning, those that have been successful at eliciting broad cognitive improvements have focused on a multimodal training approach, emphasizing complex cognitive processing that utilizes multiple domains simultaneously. The current study sought to determine the feasibility of an 8-week, hybrid-variable priority training (HVT) program, with a secondary aim to assess the success of this training paradigm at eliciting broad cognitive transfer effects. Capitalizing on the multimodal training modalities offered by the Space Fortress platform, we compared the HVT strategy-based intervention with a waitlist control group, to primarily assess skill acquisition and secondarily determine presence of cognitive transfer. Twenty-eight participants met inclusionary criteria for the study and were randomized to either training or waitlist control groups. To assess broad transfer effects, a battery of neuropsychological tests was administered pre- and post-intervention. The results indicated an overall improvement in skill acquisition and evidence for the feasibility of the intervention, but a lack of broad transfer to tasks of cognitive functioning. Participants in the training group, however, did show improvements on a measure of spatial short-term memory. The current investigation provided support for the feasibility of a multimodal training approach, using the HVT strategy, within the MS population, but lacked broad transfer to multiple domains of cognitive functioning. Future improvements to obtain greater cognitive transfer efficacy would include a larger sample size, a longer course of training to evoke greater game score improvement, the inclusion of only cognitively impaired individuals, and integration of subjective measures of improvement in addition to objective tests of cognitive performance.

Effects of equal-volume resistance training with different training frequencies in muscle size and strength in trained men

PubMed Central

Fisher, James; Steele, James; Campos, Mario H.; Silva, Marcelo H.; Paoli, Antonio; Giessing, Jurgen; Bottaro, Martim

2018-01-01

Background The objective of the present study was to compare the effects of equal-volume resistance training (RT) performed with different training frequencies on muscle size and strength in trained young men. Methods Sixteen men with at least one year of RT experience were divided into two groups, G1 and G2, that trained each muscle group once and twice a week, respectively, for 10 weeks. Elbow flexor muscle thickness (MT) was measured using a B-Mode ultrasound and concentric peak torque of elbow extensors and flexors were assessed by an isokinetic dynamometer. Results ANOVA did not reveal group by time interactions for any variable, indicating no difference between groups for the changes in MT or PT of elbow flexors and extensors. Notwithstanding, MT of elbow flexors increased significantly (3.1%, P < 0.05) only in G1. PT of elbow flexors and extensors did not increase significantly for any group. Discussion The present study suggest that there were no differences in the results promoted by equal-volume resistance training performed once or twice a week on upper body muscle strength in trained men. Only the group performing one session per week significantly increased the MT of their elbow flexors. However, with either once or twice a week training, adaptations appear largely minimal in previously trained males.

Effects of equal-volume resistance training with different training frequencies in muscle size and strength in trained men.

PubMed

Gentil, Paulo; Fisher, James; Steele, James; Campos, Mario H; Silva, Marcelo H; Paoli, Antonio; Giessing, Jurgen; Bottaro, Martim

2018-01-01

The objective of the present study was to compare the effects of equal-volume resistance training (RT) performed with different training frequencies on muscle size and strength in trained young men. Sixteen men with at least one year of RT experience were divided into two groups, G1 and G2, that trained each muscle group once and twice a week, respectively, for 10 weeks. Elbow flexor muscle thickness (MT) was measured using a B-Mode ultrasound and concentric peak torque of elbow extensors and flexors were assessed by an isokinetic dynamometer. ANOVA did not reveal group by time interactions for any variable, indicating no difference between groups for the changes in MT or PT of elbow flexors and extensors. Notwithstanding, MT of elbow flexors increased significantly (3.1%, P  < 0.05) only in G1. PT of elbow flexors and extensors did not increase significantly for any group. The present study suggest that there were no differences in the results promoted by equal-volume resistance training performed once or twice a week on upper body muscle strength in trained men. Only the group performing one session per week significantly increased the MT of their elbow flexors. However, with either once or twice a week training, adaptations appear largely minimal in previously trained males.

Is modified brief assertiveness training for nurses effective? A single-group study with long-term follow-up.

PubMed

Yoshinaga, Naoki; Nakamura, Yohei; Tanoue, Hiroki; MacLiam, Fionnula; Aoishi, Keiko; Shiraishi, Yuko

2018-01-01

To evaluate the long-term effectiveness of modified brief assertiveness training (with cognitive techniques) for nurses. Most assertiveness training takes a long time to conduct; thus, briefer training is required for universal on-the-job training in the workplace. In this single-group study, nurses received two 90-min training sessions with a 1-month interval between sessions. The degree of assertiveness was assessed by using the Rathus Assertiveness Schedule as the primary outcome, at four time points: pre- and post-training, 3-month follow-up and 6-month follow-up. A total of 33 nurses received the training, and the mean Rathus Assertiveness Schedule score improved from -14.2 (SD = 16.5) pre-training to -10.5 (SD = 18.0) post-training (p < .05). These improvements were maintained until the 6-month follow-up. The pre-post effect size of 0.22 (indicating small effect) was larger than the effect sizes ranging from -0.56 to 0.17 (no effect) reported in previous studies that used brief training. Modified brief assertiveness training seems feasible and may achieve long-term favourable outcomes in improving assertiveness among nurses. The ease of implementation of assertiveness training is important because creating an open environment for communication leads to improved job satisfaction, improved nursing care and increased patient safety. © 2017 The Authors. Journal of Nursing Management Published by John Wiley & Sons Ltd.

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Will Big Data Close the Missing Heritability Gap?

PubMed

Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I; Hsu, Stephen; de Los Campos, Gustavo

2017-11-01

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity ( e.g. , number of SNPs). We applied the methodology to data from the interim release of the UK Biobank. Focusing on human height as a model trait and using 80,000 records for model training, we achieved a prediction R-sq. in testing ( n = 22,221) of 0.24 (95% C.I.: 0.23-0.25). Our estimates show that prediction R-sq. increases with sample size, reaching an estimated plateau at values that ranged from 0.1 to 0.37 for models using 500 and 50,000 (GWA-selected) SNPs, respectively. Soon much larger data sets will become available. Using the estimated surface response, we forecast that larger sample sizes will lead to further improvements in prediction R-sq. We conclude that big data will lead to a substantial reduction of the gap between trait heritability and the proportion of interindividual differences that can be explained with a genomic predictor. However, even with the power of big data, for complex traits we anticipate that the gap between prediction R-sq. and trait heritability will not be fully closed. Copyright © 2017 by the Genetics Society of America.

Will Big Data Close the Missing Heritability Gap?

PubMed Central

Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I.; Hsu, Stephen; de los Campos, Gustavo

2017-01-01

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity (e.g., number of SNPs). We applied the methodology to data from the interim release of the UK Biobank. Focusing on human height as a model trait and using 80,000 records for model training, we achieved a prediction R-sq. in testing (n = 22,221) of 0.24 (95% C.I.: 0.23–0.25). Our estimates show that prediction R-sq. increases with sample size, reaching an estimated plateau at values that ranged from 0.1 to 0.37 for models using 500 and 50,000 (GWA-selected) SNPs, respectively. Soon much larger data sets will become available. Using the estimated surface response, we forecast that larger sample sizes will lead to further improvements in prediction R-sq. We conclude that big data will lead to a substantial reduction of the gap between trait heritability and the proportion of interindividual differences that can be explained with a genomic predictor. However, even with the power of big data, for complex traits we anticipate that the gap between prediction R-sq. and trait heritability will not be fully closed. PMID:28893854

Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter

NASA Astrophysics Data System (ADS)

Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio

2012-01-01

Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.

Effects of Strength Training on Running Economy in Highly Trained Runners: A Systematic Review With Meta-Analysis of Controlled Trials.

PubMed

Balsalobre-Fernández, Carlos; Santos-Concejero, Jordan; Grivas, Gerasimos V

2016-08-01

Balsalobre-Fernández, C, Santos-Concejero, J, and Grivas, GV. Effects of strength training on running economy in highly trained runners: a systematic review with meta-analysis of controlled trials. J Strength Cond Res 30(8): 2361-2368, 2016-The purpose of this study was to perform a systematic review and meta-analysis of controlled trials to determine the effect of strength training programs on the running economy (RE) of high-level middle- and long-distance runners. Four electronic databases were searched in September 2015 (PubMed, SPORTDiscus, MEDLINE, and CINAHL) for original research articles. After analyzing 699 resultant original articles, studies were included if the following criteria were met: (a) participants were competitive middle- or long-distance runners; (b) participants had a V[Combining Dot Above]O2max >60 ml·kg·min; (c) studies were controlled trials published in peer-reviewed journals; (d) studies analyzed the effects of strength training programs with a duration greater than 4 weeks; and (e) RE was measured before and after the strength training intervention. Five studies met the inclusion criteria, resulting in a total sample size of 93 competitive, high-level middle- and long-distance runners. Four of the 5 included studies used low to moderate training intensities (40-70% one repetition maximum), and all of them used low to moderate training volume (2-4 resistance lower-body exercises plus up to 200 jumps and 5-10 short sprints) 2-3 times per week for 8-12 weeks. The meta-analyzed effect of strength training programs on RE in high-level middle- and long-distance runners showed a large, beneficial effect (standardized mean difference [95% confidence interval] = -1.42 [-2.23 to -0.60]). In conclusion, a strength training program including low to high intensity resistance exercises and plyometric exercises performed 2-3 times per week for 8-12 weeks is an appropriate strategy to improve RE in highly trained middle- and long-distance runners.

Single-Site, Results-Level Evaluation of Quality Awareness Training.

ERIC Educational Resources Information Center

Murray, Brian; Raffaele, Gary C.

1997-01-01

An interrupted time-series design pooling 6 12-year series evaluated the long-term effects of a quality training intervention in a factory. Training positively affected quality of goods and dollar utility. Production process was an important contextual factor in assessing the effect size of the intervention. (SK)

Face-to-Face or Distance Training: Two Different Approaches To Motivate SMEs to Learn.

ERIC Educational Resources Information Center

Lawless, Naomi; Allan, John; O'Dwyer, Michele

2000-01-01

Two approaches to training for small/medium-sized enterprises were compared: a British distance learning program and an Irish program offering face-to-face training for micro-enterprises. Both used constructivist, collaborative, and reflective methods. Advantages and disadvantages of each approach were identified. (SK)

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

NASA Astrophysics Data System (ADS)

Baasch, B.; Müller, H.; von Dobeneck, T.

2018-07-01

In this work, we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine-learning techniques. Non-negative matrix factorization is used to determine grain-size end-members from sediment surface samples. Four end-members were found, which well represent the variety of sediments in the study area. A radial basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.

Predictive modelling of grain size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

NASA Astrophysics Data System (ADS)

Baasch, B.; M"uller, H.; von Dobeneck, T.

2018-04-01

In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.

The efficacy of prospective memory rehabilitation plus metacognitive skills training for adults with traumatic brain injury: study protocol for a randomized controlled trial.

PubMed

Fleming, Jennifer; Ownsworth, Tamara; Doig, Emmah; Hutton, Lauren; Griffin, Janelle; Kendall, Melissa; Shum, David H K

2017-01-05

Impairment of prospective memory (PM) is common following traumatic brain injury (TBI) and negatively impacts on independent living. Compensatory approaches to PM rehabilitation have been found to minimize the impact of PM impairment in adults with TBI; however, poor self-awareness after TBI poses a major barrier to the generalization of compensatory strategies in daily life. Metacognitive skills training (MST) is a cognitive rehabilitation approach that aims to facilitate the development of self-awareness in adults with TBI. This paper describes the protocol of a study that aims to evaluate the efficacy of a MST approach to compensatory PM rehabilitation for improving everyday PM performance and psychosocial outcomes after TBI. This randomized controlled trial has three treatment groups: compensatory training plus metacognitive skills training (COMP-MST), compensatory training only (COMP), and waitlist control. Participants in the COMP-MST and COMP groups will complete a 6-week intervention consisting of six 2-h weekly training sessions. Each 1.5-h session will involve compensatory strategy training and 0.5 h will incorporate either MST (COMP-MST group) or filler activity as an active control (COMP group). Participants in the waitlist group receive care as usual for 6 weeks, followed by the COMP-MST intervention. Based on the sample size estimate, 90 participants with moderate to severe TBI will be randomized into the three groups using a stratified sampling approach. The primary outcomes include measures of PM performance in everyday life and level of psychosocial reintegration. Secondary outcomes include measures of PM function on psychometric testing, strategy use, self-awareness, and level of support needs following TBI. Blinded assessments will be conducted pre and post intervention, and at 3-month and 6-month follow-ups. This study seeks to determine the efficacy of COMP-MST for improving and maintaining everyday PM performance and level of psychosocial integration in adults with moderate to severe TBI. The findings will advance theoretical understanding of the role of self-awareness in compensatory PM rehabilitation and skills generalization. COMP-MST has the potential to reduce the cost of rehabilitation and lifestyle support following TBI because the intervention could enhance generalization success and lifelong application of PM compensatory strategies. New Zealand Clinical Trials Registry, ACTRN12615000996561 . Registered on 23 September 2015; retrospectively registered 2 months after commencement.

Effects of a structured 20-session slow-cortical-potential-based neurofeedback program on attentional performance in children and adolescents with attention-deficit hyperactivity disorder: retrospective analysis of an open-label pilot-approach and 6-month follow-up.

PubMed

Albrecht, Johanna S; Bubenzer-Busch, Sarah; Gallien, Anne; Knospe, Eva Lotte; Gaber, Tilman J; Zepf, Florian D

2017-01-01

The aim of this approach was to conduct a structured electroencephalography-based neurofeedback training program for children and adolescents with attention-deficit hyperactivity disorder (ADHD) using slow cortical potentials with an intensive first (almost daily sessions) and second phase of training (two sessions per week) and to assess aspects of attentional performance. A total of 24 young patients with ADHD participated in the 20-session training program. During phase I of training (2 weeks, 10 sessions), participants were trained on weekdays. During phase II, neurofeedback training occurred twice per week (5 weeks). The patients' inattention problems were measured at three assessment time points before (pre, T0) and after (post, T1) the training and at a 6-month follow-up (T2); the assessments included neuropsychological tests (Alertness and Divided Attention subtests of the Test for Attentional Performance; Sustained Attention Dots and Shifting Attentional Set subtests of the Amsterdam Neuropsychological Test) and questionnaire data (inattention subscales of the so-called Fremdbeurteilungsbogen für Hyperkinetische Störungen and Child Behavior Checklist/4-18 [CBCL/4-18]). All data were analyzed retrospectively. The mean auditive reaction time in a Divided Attention task decreased significantly from T0 to T1 (medium effect), which was persistent over time and also found for a T0-T2 comparison (larger effects). In the Sustained Attention Dots task, the mean reaction time was reduced from T0-T1 and T1-T2 (small effects), whereas in the Shifting Attentional Set task, patients were able to increase the number of trials from T1-T2 and significantly diminished the number of errors (T1-T2 & T0-T2, large effects). First positive but very small effects and preliminary results regarding different parameters of attentional performance were detected in young individuals with ADHD. The limitations of the obtained preliminary data are the rather small sample size, the lack of a control group/a placebo condition and the open-label approach because of the clinical setting and retrospective analysis. The value of the current approach lies in providing pilot data for future studies involving larger samples.

Plasmodium species differentiation by non-expert on-line volunteers for remote malaria field diagnosis.

PubMed

Ortiz-Ruiz, Alejandra; Postigo, María; Gil-Casanova, Sara; Cuadrado, Daniel; Bautista, José M; Rubio, José Miguel; Luengo-Oroz, Miguel; Linares, María

2018-01-30

Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. In addition, differential diagnosis of Plasmodium species has a high level of misdiagnosis. Real time remote microscopical diagnosis through on-line crowdsourcing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification-a critical step during the diagnosis protocol in order to choose the appropriate medication-is possible through the information provided by non-trained on-line volunteers. 88 volunteers have performed a series of questionnaires over 110 images to differentiate species (Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, Plasmodium knowlesi) and parasite staging from thin blood smear images digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Visual cues evaluated in the surveys include texture and colour, parasite shape and red blood size. On-line volunteers are able to discriminate Plasmodium species (P. falciparum, P. malariae, P. vivax, P. ovale, P. knowlesi) and stages in thin-blood smears according to visual cues observed on digitalized images of parasitized red blood cells. Friendly textual descriptions of the visual cues and specialized malaria terminology is key for volunteers learning and efficiency. On-line volunteers with short-training are able to differentiate malaria parasite species and parasite stages from digitalized thin smears based on simple visual cues (shape, size, texture and colour). While the accuracy of a single on-line expert is far from perfect, a single parasite classification obtained by combining the opinions of multiple on-line volunteers over the same smear, could improve accuracy and reliability of Plasmodium species identification in remote malaria diagnosis.

Exercise effects on cognitive functioning in young adults with first-episode psychosis: FitForLife.

PubMed

Hallgren, Mats; Skott, Maria; Ekblom, Örjan; Firth, Joseph; Schembri, Adrian; Forsell, Yvonne

2018-05-06

Exercise has mood-enhancing effects and can improve cognitive functioning, but the effects in first-episode psychosis (FEP) remain understudied. We examined the feasibility and cognitive effects of exercise in FEP. Multi-center, open-label intervention study. Ninety-one outpatients with FEP (mean age = 30 years, 65% male) received usual care plus a 12-week supervised circuit-training program, consisting of high-volume resistance exercises, aerobic training, and stretching. Primary study outcome was cognitive functioning assessed by Cogstate Brief Battery (processing speed, attention, visual learning, working memory) and Trailmaking A and B tasks (visual attention and task shifting). Within-group changes in cognition were assessed using paired sample t tests with effect sizes (Hedges' g) reported for significant values. Relationships between exercise frequency and cognitive improvement were assessed using analysis of covariance. Moderating effects of gender were explored with stratified analyses. Participants exercised on average 13.5 (s.d. = 11.7) times. Forty-eight percent completed 12 or more sessions. Significant post-intervention improvements were seen for processing speed, visual learning, and visual attention; all with moderate effect sizes (g = 0.47-0.49, p < 0.05). Exercise participation was also associated with a positive non-significant trend for working memory (p < 0.07). Stratified analyses indicated a moderating effect of gender. Positive changes were seen among females only for processing speed, visual learning, working memory, and visual attention (g = 0.43-0.69). A significant bivariate correlation was found between total training frequency and improvements in visual attention among males (r = 0.40, p < 0.05). Supported physical exercise is a feasible and safe adjunct treatment for FEP with potential cognitive benefits, especially among females.

Classifying Radio Galaxies with the Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Aniyan, A. K.; Thorat, K.

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

The Effect of Exercise Intensity on Total PYY and GLP-1 in Healthy Females: A Pilot Study.

PubMed

Hallworth, Jillian R; Copeland, Jennifer L; Doan, Jon; Hazell, Tom J

2017-01-01

We compared the acute response of anorexigenic signals (total PYY and GLP-1) in response to submaximal and supramaximal exercise. Nine females completed three sessions: (1) moderate-intensity continuous training (MICT; 30 min; 65%  VO 2max ); (2) sprint interval training (SIT; 6 × 30 sec "all-out" cycling sprints with 4 min recovery); or (3) control (CTRL; no exercise). PYY and GLP-1 were measured via blood samples drawn before, immediately after, and 90 min after exercise. Perceptions of hunger were rated using a visual analogue scale at all blood sampling time points. There was a session × time interaction for GLP-1 ( p = 0.004) where SIT and MICT ( p < 0.015 and p < 0.001) were higher compared to CTRL both immediately and 90 min after exercise. There was a main effect of time for PYY where 90 min after exercise it was decreased versus before and immediately after exercise. There was a session × time interaction for hunger with lower ratings following SIT versus MICT ( p = 0.027) and CTRL ( p = 0.031) 90 min after exercise. These results suggest that though GLP-1 is elevated after exercise in women, it is not affected by exercise intensity though hunger was lower 90 min after exercise with SIT. As the sample size is small further study is needed to confirm these findings.