Evaluation and Evolution of the Gang Resistance Education and Training (G.R.E.A.T.) Program

ERIC Educational Resources Information Center

Esbensen, Finn-Aage; Peterson, Dana; Taylor, Terrance J.; Freng, Adrienne; Osgood, D. Wayne; Carson, Dena C.; Matsuda, Kristy N.

2011-01-01

The Gang Resistance Education and Training (G.R.E.A.T.) program is a gang- and delinquency-prevention program delivered by law enforcement officers within a school setting. Originally designed in 1991 by Phoenix-area law enforcement agencies to address local needs, the program quickly spread across the United States. In this article, we describe…

Application of the One-Minute Preceptor Technique by Novice Teachers in the Gross Anatomy Laboratory

ERIC Educational Resources Information Center

Chan, Lap Ki; Yang, Jian; Irby, David M.

2015-01-01

The one-minute preceptor (OMP) was originally developed in the ambulatory care setting as a time-efficient teaching technique for learner-centered clinical training. There are also possible advantages of using the OMP in the gross anatomy laboratory. However, in a previous study it was found that providing training to experienced gross anatomy…

A Software Package for Neural Network Applications Development

NASA Technical Reports Server (NTRS)

Baran, Robert H.

1993-01-01

Original Backprop (Version 1.2) is an MS-DOS package of four stand-alone C-language programs that enable users to develop neural network solutions to a variety of practical problems. Original Backprop generates three-layer, feed-forward (series-coupled) networks which map fixed-length input vectors into fixed length output vectors through an intermediate (hidden) layer of binary threshold units. Version 1.2 can handle up to 200 input vectors at a time, each having up to 128 real-valued components. The first subprogram, TSET, appends a number (up to 16) of classification bits to each input, thus creating a training set of input output pairs. The second subprogram, BACKPROP, creates a trilayer network to do the prescribed mapping and modifies the weights of its connections incrementally until the training set is leaned. The learning algorithm is the 'back-propagating error correction procedures first described by F. Rosenblatt in 1961. The third subprogram, VIEWNET, lets the trained network be examined, tested, and 'pruned' (by the deletion of unnecessary hidden units). The fourth subprogram, DONET, makes a TSR routine by which the finished product of the neural net design-and-training exercise can be consulted under other MS-DOS applications.

Evaluating International Research Ethics Capacity Development: An Empirical Approach

PubMed Central

Ali, Joseph; Kass, Nancy E.; Sewankambo, Nelson K.; White, Tara D.; Hyder, Adnan A.

2014-01-01

The US National Institutes of health, Fogarty International Center (NIH-FIC) has, for the past 13 years, been a leading funder of international research ethics education for resource-limited settings. Nearly half of the NIH-FIC funding in this area has gone to training programs that train individuals from sub-Saharan Africa. Identifying the impact of training investments, as well as the potential predictors of post-training success, can support curricular decision-making, help establish funding priorities, and recognize the ultimate outcomes of trainees and training programs. Comprehensive evaluation frameworks and targeted evaluation tools for bioethics training programs generally, and for international research ethics programs in particular, are largely absent from published literature. This paper shares an original conceptual framework, data collection tool, and detailed methods for evaluating the inputs, processes, outputs, and outcomes of research ethics training programs serving individuals in resource-limited settings. This paper is part of a collection of papers analyzing the Fogarty International Center’s International Research Ethics Education and Curriculum Development program. PMID:24782071

The White House BRAIN Initiative has the potential to further strengthen multidisciplinary research and training in psychology.

PubMed

Flattau, Pamela

2014-12-01

Comments on the original article by Robiner et al. (see record 2014-07939-001) regarding psychologists in medical schools and academic medical center settings. The current authors also discuss how to advance training in psychology using the Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Distribution-Preserving Stratified Sampling for Learning Problems.

PubMed

Cervellera, Cristiano; Maccio, Danilo

2017-06-09

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

PubMed

Rácz, A; Bajusz, D; Héberger, K

2015-01-01

Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

PubMed

Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

2017-02-01

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

PubMed

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

PubMed Central

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Machine Learning Techniques for Persuasion Detection in Conversation

DTIC Science & Technology

2010-06-01

files maintained the original post and tile ordering within each transcript. These files were each internally shuffled prior to creating test and...of the number of post or tiles. The other 90% was used for training data. Each post and each tile appeared in only one of the 10 test sets. Each post ...concatenating 5 test sets and pairing it with the 6th test set. This process was conducted for both posts and tiles. The shortest transcript (19 posts , 0 tiles

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

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed amore » new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.« less

Computing single step operators of logic programming in radial basis function neural networks

NASA Astrophysics Data System (ADS)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-07-01

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

Emulating RRTMG Radiation with Deep Neural Networks for the Accelerated Model for Climate and Energy

NASA Astrophysics Data System (ADS)

Pal, A.; Norman, M. R.

2017-12-01

The RRTMG radiation scheme in the Accelerated Model for Climate and Energy Multi-scale Model Framework (ACME-MMF), is a bottleneck and consumes approximately 50% of the computational time. To simulate a case using RRTMG radiation scheme in ACME-MMF with high throughput and high resolution will therefore require a speed-up of this calculation while retaining physical fidelity. In this study, RRTMG radiation is emulated with Deep Neural Networks (DNNs). The first step towards this goal is to run a case with ACME-MMF and generate input data sets for the DNNs. A principal component analysis of these input data sets are carried out. Artificial data sets are created using the previous data sets to cover a wider space. These artificial data sets are used in a standalone RRTMG radiation scheme to generate outputs in a cost effective manner. These input-output pairs are used to train multiple architectures DNNs(1). Another DNN(2) is trained using the inputs to predict the error. A reverse emulation is trained to map the output to input. An error controlled code is developed with the two DNNs (1 and 2) and will determine when/if the original parameterization needs to be used.

Use of Co-occurrences for Temporal Expressions Annotation

NASA Astrophysics Data System (ADS)

Craveiro, Olga; Macedo, Joaquim; Madeira, Henrique

The annotation or extraction of temporal information from text documents is becoming increasingly important in many natural language processing applications such as text summarization, information retrieval, question answering, etc.. This paper presents an original method for easy recognition of temporal expressions in text documents. The method creates semantically classified temporal patterns, using word co-occurrences obtained from training corpora and a pre-defined seed keywords set, derived from the used language temporal references. A participation on a Portuguese named entity evaluation contest showed promising effectiveness and efficiency results. This approach can be adapted to recognize other type of expressions or languages, within other contexts, by defining the suitable word sets and training corpora.

Emergency obstetric simulation training: how do we know where we are going, if we don't know where we have been?

PubMed

Calvert, Katrina L; McGurgan, Paul M; Debenham, Edward M; Gratwick, Frances J; Maouris, Panos

2013-12-01

Obstetric emergencies contribute significantly to maternal morbidity and mortality. Current training in the management of obstetric emergencies in Australia and internationally focusses on utilising a multidisciplinary simulation-based model. Arguments for and against this type of training exist, using both economic and clinical reasoning. To identify the evidence base for the clinical impact of simulation training in obstetric emergencies and to address some of the concerns regarding appropriate delivery of obstetric emergency training in the Australian setting. A literature search was performed to identify research undertaken in the area of obstetric emergency training. The initial literature search using broad search terms identified 887 articles which were then reviewed and considered for inclusion if they provided original research with a specific emphasis on the impact of training on clinical outcomes. Ninety-two articles were identified, comprising evidence in the following clinical situations: eclampsia, shoulder dystocia, postpartum haemorrhage, maternal collapse, cord prolapse and teamwork training. Evidence exists for a benefit in knowledge or skills gained from simulation training and for the benefit of training in small units without access to high-fidelity equipment or facilities. Evidence exists for a positive impact of training in obstetric emergencies, although the majority of the available evidence applies to evaluation at the level of participants' confidence, knowledge or skills rather than at the level of impact on clinical outcomes. The model of simulation-based training is an appropriate one for the Australian setting and should be further utilised in rural and remote settings. © 2013 The Royal Australian and New Zealand College of Obstetricians and Gynaecologists.

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis.

PubMed

Nowakowska, Marzena

2017-04-01

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Critical Role of Organic Chemistry in Drug Discovery.

PubMed

Rotella, David P

2016-10-19

Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.

QNI celebrates 125 years.

PubMed

White, Alison

2012-07-01

The Queen's Nursing Institute was founded in 1887 with the grant of £70000 by Queen Victoria from the Women's Jubilee Fund. Originally called the 'Queen Victoria's Jubilee Institute for Nurses', it was set up with the objective of providing the 'training, support, maintenance and supply' of nurses for the sick poor.

Learning to Work: Transitioning Youth with Developmental Disabilities.

ERIC Educational Resources Information Center

Cohen, Monte

The paper describes Stepping Stones Growth Center, which prepared handicapped students for transition into competitive employment. The origins of the program and its emphasis on functional skill training are reviewed, followed by a description of three levels of services: a "ready" class stressing basic skills, a "set" class emphasizing…

Total Habilitation: A Concept Whose Time Has Come--Reactions to Four Responses.

ERIC Educational Resources Information Center

Drash, Philip W.; Raver, Sharon A.

1987-01-01

The original authors address several concerns expressed in four responses to their article: terminology; the need for expert and intensive pedagogy (including early intensive language training); pessimistic attitudes; the need for caution in setting total habilitation as a goal; and a research model. (KM)

Combining Multiple Knowledge Sources for Continuous Speech Recognition

DTIC Science & Technology

1989-08-01

derived by estimating probabilities from a training set, or a linguistically -based model that uses syntactic and semantic information explicitly. The...into a hierarchical set of rules tha’ wouA. :over a much larger percentage of new sentences than the original sentence patteiis. We applied this tool...statistical grammars typically used by the use of linguistic knowledge. In particular, we group the different words in the vocabulary into classes, under the

Renewed behavior produced by context change and its implications for treatment maintenance: A review.

PubMed

Podlesnik, Christopher A; Kelley, Michael E; Jimenez-Gomez, Corina; Bouton, Mark E

2017-07-01

Behavioral treatment gains established in one setting do not always maintain in other settings. The present review examines the relevance of basic and translational research to understanding failures to maintain treatment gains across settings. Specifically, studies of the renewal effect examine how transitioning away from a treatment setting could evoke a return of undesirable behavior, rather than newly trained appropriate behavior. Studies of renewal typically arrange three phases, with a response trained and reinforced under a particular set of contextual stimuli in the first phase. Next, that response is extinguished, often under a different set of contextual stimuli. Finally, that response returns despite extinction remaining in effect upon returning to the original training context or transitioning to a novel context. Thus, removing the extinction context is sufficient to produce a recurrence of the response. The findings suggest treatment effects can become specific to the context in which the treatment was delivered. This literature offers promising methods for systematically assessing the factors contributing to treatment maintenance and improving generalization of treatment gains across contexts. Therefore, the present review suggests basic and translational research on renewal provides an empirical literature to bring greater conceptual systematization to understanding generalization and maintenance of behavioral treatment. © 2017 Society for the Experimental Analysis of Behavior.

Active learning strategies for the deduplication of electronic patient data using classification trees.

PubMed

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

A Proposal to Plan and Develop a Sample Set of Drill and Testing Materials, Based on Audio and Visual Environmental and Situational Stimuli, Aimed at Training and Testing in the Creation of Original Utterances by Foreign Language Students at the Secondary and College Levels.

ERIC Educational Resources Information Center

Obrecht, Dean H.

This report contrasts the results of a rigidly specified, pattern-oriented approach to learning Spanish with an approach that emphasizes the origination of sentences by the learner in direct response to stimuli. Pretesting and posttesting statistics are presented and conclusions are discussed. The experimental method, which required the student to…

Model-based segmentation of abdominal aortic aneurysms in CTA images

NASA Astrophysics Data System (ADS)

de Bruijne, Marleen; van Ginneken, Bram; Niessen, Wiro J.; Loog, Marco; Viergever, Max A.

2003-05-01

Segmentation of thrombus in abdominal aortic aneurysms is complicated by regions of low boundary contrast and by the presence of many neighboring structures in close proximity to the aneurysm wall. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets. In contrast with the conventional ASM approach, the new appearance model trains on both true and false examples of boundary profiles. The probability that a given image profile belongs to the boundary is obtained using k nearest neighbor (kNN) probability density estimation. The performance of this scheme is compared to that of original ASMs, which minimize the Mahalanobis distance to the average true profile in the training set. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation. A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.

Teaching severely multihandicapped students to put on their own hearing aids.

PubMed Central

Tucker, D J; Berry, G W

1980-01-01

Two experiments were conducted with six severely multihandicapped students with hearing impairments to: (a) train the six students to put on their own hearing aids independently, and (b) provide an empirical evaluation of a comprehensive instructional program for putting on a hearing aid by assessing acquisition, maintenance, and generalization of that skill across environments. All six students acquired the skill rapidly, with two students requiring remedial training on one step of the program. Because for two of the original three students the newly learned skill failed initially to generalize to other environments, a second experiment was initiated to assess generalization across environments as well as to replicate the efficiency of the acquisition program. When a variation of the multiple-probe baseline technique was used, the behavior of three additional students generalized to other settings without direct training in those settings. PMID:6444931

Translating person-centered care into practice: A comparative analysis of motivational interviewing, illness-integration support, and guided self-determination.

PubMed

Zoffmann, Vibeke; Hörnsten, Åsa; Storbækken, Solveig; Graue, Marit; Rasmussen, Bodil; Wahl, Astrid; Kirkevold, Marit

2016-03-01

Person-centred care [PCC] can engage people in living well with a chronic condition. However, translating PCC into practice is challenging. We aimed to compare the translational potentials of three approaches: motivational interviewing [MI], illness integration support [IIS] and guided self-determination [GSD]. Comparative analysis included eight components: (1) philosophical origin; (2) development in original clinical setting; (3) theoretical underpinnings; (4) overarching goal and supportive processes; (5) general principles, strategies or tools for engaging peoples; (6) health care professionals' background and training; (7) fidelity assessment; (8) reported effects. Although all approaches promoted autonomous motivation, they differed in other ways. Their original settings explain why IIS and GSD strive for life-illness integration, whereas MI focuses on managing ambivalence. IIS and GSD were based on grounded theories, and MI was intuitively developed. All apply processes and strategies to advance professionals' communication skills and engagement; GSD includes context-specific reflection sheets. All offer training programs; MI and GSD include fidelity tools. Each approach has a primary application: MI, when ambivalence threatens positive change; IIS, when integrating newly diagnosed chronic conditions; and GSD, when problem solving is difficult, or deadlocked. Professionals must critically consider the context in their choice of approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach.

PubMed

Blanc-Durand, Paul; Van Der Gucht, Axel; Guedj, Eric; Abulizi, Mukedaisi; Aoun-Sebaiti, Mehdi; Lerman, Lionel; Verger, Antoine; Authier, François-Jérôme; Itti, Emmanuel

2017-01-01

Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles. 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.

A deep convolutional neural network model to classify heartbeats.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

2017-10-01

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Causes of sudden death in young female military recruits.

PubMed

Eckart, Robert E; Scoville, Stephanie L; Shry, Eric A; Potter, Robert N; Tedrow, Usha

2006-06-15

This study sought to examine the incidence of sudden death in a large, multiethnic cohort of young women. Approximately 852,300 women entered basic military training from 1977 to 2001. During this period, there were 15 sudden deaths in female recruits (median age 19 years, 73% African-American), occurring at a median of 25 days after arrival for training. Of the sudden deaths, 13 (81%) were due to reasons that may have been cardiac in origin. Presumed arrhythmic sudden death in the setting of a structurally normal heart was seen in 8 recruits (53%), and anomalous coronary origins were found in 2 recruits (13%). The mortality rate was 11.4 deaths per 100,000 recruit-years (95% confidence interval 6.9 to 18.9). The rate was significantly higher for African-American female recruits (risk ratio 10.2, p <0.001). Sudden death with a structurally normal heart was the leading cause of death in female recruits during military training.

A new biodegradation prediction model specific to petroleum hydrocarbons.

PubMed

Howard, Philip; Meylan, William; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Michael; Parkerton, Thomas F

2005-08-01

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning

PubMed Central

Cornish, Hannah; Dale, Rick; Kirby, Simon; Christiansen, Morten H.

2017-01-01

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language. PMID:28118370

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.

Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning.

PubMed

Cornish, Hannah; Dale, Rick; Kirby, Simon; Christiansen, Morten H

2017-01-01

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

Self-Estimation of Blood Alcohol Concentration: A Review

PubMed Central

Aston, Elizabeth R.; Liguori, Anthony

2013-01-01

This article reviews the history of blood alcohol concentration (BAC) estimation training, which trains drinkers to discriminate distinct BAC levels and thus avoid excessive alcohol consumption. BAC estimation training typically combines education concerning alcohol metabolism with attention to subjective internal cues associated with specific concentrations. Estimation training was originally conceived as a component of controlled drinking programs. However, dependent drinkers were unsuccessful in BAC estimation, likely due to extreme tolerance. In contrast, moderate drinkers successfully acquired this ability. A subsequent line of research translated laboratory estimation studies to naturalistic settings by studying large samples of drinkers in their preferred drinking environments. Thus far, naturalistic studies have provided mixed results regarding the most effective form of BAC feedback. BAC estimation training is important because it imparts an ability to perceive individualized impairment that may be present below the legal limit for driving. Consequently, the training can be a useful component for moderate drinkers in drunk driving prevention programs. PMID:23380489

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.

SASS Applied to Optimum Work Roll Profile Selection in the Hot Rolling of Wide Steel

NASA Astrophysics Data System (ADS)

Nolle, Lars

The quality of steel strip produced in a wide strip rolling mill depends heavily on the careful selection of initial ground work roll profiles for each of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In previous work, the profiles were successfully optimised using a self-organising migration algorithm (SOMA). In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to find the optimum profiles for a simulated rolling mill. The resulting strip quality produced using the profiles found by SASS is compared with results from previous work and the quality produced using the original profile specifications. The best set of profiles found by SASS clearly outperformed the original set and performed equally well as SOMA without the need of finding a suitable set of control parameters.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

The Emergence of Selective Attention through Probabilistic Associations between Stimuli and Actions.

PubMed

Simione, Luca; Nolfi, Stefano

2016-01-01

In this paper we show how a multilayer neural network trained to master a context-dependent task in which the action co-varies with a certain stimulus in a first context and with a second stimulus in an alternative context exhibits selective attention, i.e. filtering out of irrelevant information. This effect is rather robust and it is observed in several variations of the experiment in which the characteristics of the network as well as of the training procedure have been varied. Our result demonstrates how the filtering out of irrelevant information can originate spontaneously as a consequence of the regularities present in context-dependent training set and therefore does not necessarily depend on specific architectural constraints. The post-evaluation of the network in an instructed-delay experimental scenario shows how the behaviour of the network is consistent with the data collected in neuropsychological studies. The analysis of the network at the end of the training process indicates how selective attention originates as a result of the effects caused by relevant and irrelevant stimuli mediated by context-dependent and context-independent bidirectional associations between stimuli and actions that are extracted by the network during the learning.

Made for Leadership: The Tools and Tips You Need to Effectively Climb the College Ladder

ERIC Educational Resources Information Center

Ullman, Ellen

2015-01-01

Good leaders need to develop and improve their skills. That's why the American Association of Community Colleges (AACC) created a set of competencies for community college leaders that has served as the foundation for informal and doctoral-level training programs. When the original competencies were revised in 2012, AACC sought input from a number…

A modified electronegativity equalization method for fast and accurate calculation of atomic charges in large biological molecules.

PubMed

Ouyang, Yongzhong; Ye, Fei; Liang, Yizeng

2009-08-07

To further extend the EEM approach to improve its accuracy, a new approach, in which the different connectivities and hybridized states are introduced to represent the different chemical environments, has been developed. The C, O and N atoms are distinguished between different hybridized states. Different states of hydrogen atoms are defined according to their different connectivities. Furthermore, the sp(2) carbons in the aromatic rings are also separated from the other sp(2) carbons. Geometries and NPA charges are calculated at the B3LYP/6-31G* level, and the effective electronegativity and hardness values could be calibrated with the help of a training set of 141 organic molecules using the Differential Evolution (DE) algorithm. The quality of the modified EEM charges is evaluated by comparison with the B3LYP/6-31G* charges calculated for a series of polypeptides, not contained in the training set. For further comparison, the atomic parameters of the original EEM without including chemical environments are recalibrated under the same conditions. It is found that the accuracy of the modified EEM method improves significantly as compared to that of the original EEM method.

Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

NASA Astrophysics Data System (ADS)

Cánovas-García, Fulgencio; Alonso-Sarría, Francisco; Gomariz-Castillo, Francisco; Oñate-Valdivieso, Fernando

2017-06-01

Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other; however, they are split by bootstrapping into in-bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based); in the three cases reported, the modification we propose produces a less biased accuracy estimation.

Rapid and Portable Methods for Identification of Bacterially Influenced Calcite: Application of Laser-Induced Breakdown Spectroscopy and AOTF Reflectance Spectroscopy, Fort Stanton Cave, New Mexico

NASA Astrophysics Data System (ADS)

McMillan, N. J.; Chavez, A.; Chanover, N.; Voelz, D.; Uckert, K.; Tawalbeh, R.; Gariano, J.; Dragulin, I.; Xiao, X.; Hull, R.

2014-12-01

Rapid, in-situ methods for identification of biologic and non-biologic mineral precipitation sites permit mapping of biological hot spots. Two portable spectrometers, Laser-Induced Breakdown Spectroscopy (LIBS) and Acoustic-Optic Tunable Filter Reflectance Spectroscopy (AOTFRS) were used to differentiate between bacterially influenced and inorganically precipitated calcite specimens from Fort Stanton Cave, NM, USA. LIBS collects light emitted from the decay of excited electrons in a laser ablation plasma; the spectrum is a chemical fingerprint of the analyte. AOTFRS collects light reflected from the surface of a specimen and provides structural information about the material (i.e., the presence of O-H bonds). These orthogonal data sets provide a rigorous method to determine the origin of calcite in cave deposits. This study used a set of 48 calcite samples collected from Fort Stanton cave. Samples were examined in SEM for the presence of biologic markers; these data were used to separate the samples into biologic and non-biologic groups. Spectra were modeled using the multivariate technique Partial Least Squares Regression (PLSR). Half of the spectra were used to train a PLSR model, in which biologic samples were assigned to the independent variable "0" and non-biologic samples were assigned the variable "1". Values of the independent variable were calculated for each of the training samples, which were close to 0 for the biologic samples (-0.09 - 0.23) and close to 1 for the non-biologic samples (0.57 - 1.14). A Value of Apparent Distinction (VAD) of 0.55 was used to numerically distinguish between the two groups; any sample with an independent variable value < 0.55 was classified as having a biologic origin; a sample with a value > 0.55 was determined to be non-biologic in origin. After the model was trained, independent variable values for the remaining half of the samples were calculated. Biologic or non-biologic origin was assigned by comparison to the VAD. Using LIBS data alone, the model has a 92% success rate, correctly identifying 23 of 25 samples. Modeling of AOTFRS spectra and the combined LIBS-AOTFRS data set have similar success rates. This study demonstrates that rapid, portable LIBS and AOTFRS instruments can be used to map the spatial distribution of biologic precipitation in caves.

Contextual control of attentional allocation in human discrimination learning.

PubMed

Uengoer, Metin; Lachnit, Harald; Lotz, Anja; Koenig, Stephan; Pearce, John M

2013-01-01

In 3 human predictive learning experiments, we investigated whether the allocation of attention can come under the control of contextual stimuli. In each experiment, participants initially received a conditional discrimination for which one set of cues was trained as relevant in Context 1 and irrelevant in Context 2, and another set was relevant in Context 2 and irrelevant in Context 1. For Experiments 1 and 2, we observed that a second discrimination based on cues that had previously been trained as relevant in Context 1 during the conditional discrimination was acquired more rapidly in Context 1 than in Context 2. Experiment 3 revealed a similar outcome when new stimuli from the original dimensions were used in the test stage. Our results support the view that the associability of a stimulus can be controlled by the stimuli that accompany it.

Automatic threshold selection for multi-class open set recognition

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2017-05-01

Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Generalization error analysis: deep convolutional neural network in mammography

NASA Astrophysics Data System (ADS)

Richter, Caleb D.; Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny

2018-02-01

We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

Estimating Missing Unit Process Data in Life Cycle Assessment Using a Similarity-Based Approach.

PubMed

Hou, Ping; Cai, Jiarui; Qu, Shen; Xu, Ming

2018-05-01

In life cycle assessment (LCA), collecting unit process data from the empirical sources (i.e., meter readings, operation logs/journals) is often costly and time-consuming. We propose a new computational approach to estimate missing unit process data solely relying on limited known data based on a similarity-based link prediction method. The intuition is that similar processes in a unit process network tend to have similar material/energy inputs and waste/emission outputs. We use the ecoinvent 3.1 unit process data sets to test our method in four steps: (1) dividing the data sets into a training set and a test set; (2) randomly removing certain numbers of data in the test set indicated as missing; (3) using similarity-weighted means of various numbers of most similar processes in the training set to estimate the missing data in the test set; and (4) comparing estimated data with the original values to determine the performance of the estimation. The results show that missing data can be accurately estimated when less than 5% data are missing in one process. The estimation performance decreases as the percentage of missing data increases. This study provides a new approach to compile unit process data and demonstrates a promising potential of using computational approaches for LCA data compilation.

Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting: adaptation process and pilot study.

PubMed

van Dongen, Ellen Ji; Leerlooijer, Joanne N; Steijns, Jan M; Tieland, Michael; de Groot, Lisette Cpgm; Haveman-Nies, Annemien

2017-01-18

Combining increased dietary protein intake and resistance exercise training for elderly people is a promising strategy to prevent or counteract the loss of muscle mass and decrease the risk of disabilities. Using findings from controlled interventions in a real-life setting requires adaptations to the intervention and working procedures of healthcare professionals (HCPs). The aim of this study is to adapt an efficacious intervention for elderly people to a real-life setting (phase one) and test the feasibility and potential impact of this prototype intervention in practice in a pilot study (phase two). The Intervention Mapping approach was used to guide the adaptation in phase one. Qualitative data were collected from the original researchers, target group, and HCPs, and information was used to decide whether and how specified intervention elements needed to be adapted. In phase two, a one-group pre-test post-test pilot study was conducted (n = 25 community-dwelling elderly), to elicit further improvements to the prototype intervention. The evaluation included participant questionnaires and measurements at baseline (T0) and follow-up (T1), registration forms, interviews, and focus group discussions (T1). Qualitative data for both phases were analysed using an inductive approach. Outcome measures included physical functioning, strength, body composition, and dietary intake. Change in outcomes was assessed using Wilcoxon signed-rank tests. The most important adaptations to the original intervention were the design of HCP training and extending the original protein supplementation with a broader nutrition programme aimed at increasing protein intake, facilitated by a dietician. Although the prototype intervention was appreciated by participants and professionals, and perceived applicable for implementation, the pilot study process evaluation resulted in further adaptations, mostly concerning recruitment, training session guidance, and the nutrition programme. Pilot study outcome measures showed significant improvements in muscle strength and functioning, but no change in lean body mass. The combined nutrition and exercise intervention was successfully adapted to the real-life setting and seems to have included the most important effective intervention elements. After adaptation of the intervention using insights from the pilot study, a larger, controlled trial should be conducted to assess cost-effectiveness. Trial registration number: ClinicalTrials.gov NL51834.081.14 (April 22, 2015).

Project Amistad (Friendship), a Joint Venture between DHS and Family Outreach. Final Report: Innovations in Protective Services.

ERIC Educational Resources Information Center

Dennis-Small, Lucretia

Conducted by the Texas Department of Human Services (DHS), Project Amistad (Friendship) originally set out to recruit and train Black and Hispanic volunteers to conduct lay therapy sessions with Black and Hispanic families in which abuse and neglect of children had occurred. Start-up was significantly delayed due to personnel changes; as a result,…

Britain Between the Wars: The Historical Context of Bowlby's Theory of Attachment.

ERIC Educational Resources Information Center

Newcombe, Nora; Lerner, Jeffrey C.

John Bowlby's theory of attachment is examined in the cultural and historical context in which it was developed. Bowlby trained as a psychiatrist in England during the 1920's and published the WHO report in 1951. Thus the origins of his theory can be related to events set in motion by the First World War and occurring during the interwar period…

Assessment of Creative Thinking across Cultures Using the Torrance Tests of Creative Thinking (TTCT): Translation and Validity Issues

ERIC Educational Resources Information Center

Yarbrough, Nükhet D.

2016-01-01

As part of a project to translate and administer the Torrance Tests of Creative Thinking (TTCT) to Turkish elementary and secondary students, 35 professionals were trained in a full-day workshop to learn to score the verbal TTCT. All trainees scored the same 4 sets of TTCT verbal criterion tests for fluency, flexibility, and originality by filling…

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Estimating Spectra from Photometry

NASA Astrophysics Data System (ADS)

Kalmbach, J. Bryce; Connolly, Andrew J.

2017-12-01

Measuring the physical properties of galaxies such as redshift frequently requires the use of spectral energy distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare our results to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap, we show that the error in the estimated spectra is reduced by more than 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and by applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z ≤ 3.

Protocols for Handling Messages Between Simulation Computers

NASA Technical Reports Server (NTRS)

Balcerowski, John P.; Dunnam, Milton

2006-01-01

Practical Simulator Network (PSimNet) is a set of data-communication protocols designed especially for use in handling messages between computers that are engaging cooperatively in real-time or nearly-real-time training simulations. In a typical application, computers that provide individualized training at widely dispersed locations would communicate, by use of PSimNet, with a central host computer that would provide a common computational- simulation environment and common data. Originally intended for use in supporting interfaces between training computers and computers that simulate the responses of spacecraft scientific payloads, PSimNet could be especially well suited for a variety of other applications -- for example, group automobile-driver training in a classroom. Another potential application might lie in networking of automobile-diagnostic computers at repair facilities to a central computer that would compile the expertise of numerous technicians and engineers and act as an expert consulting technician.

[Rapid identification of hogwash oil by using synchronous fluorescence spectroscopy].

PubMed

Sun, Yan-Hui; An, Hai-Yang; Jia, Xiao-Li; Wang, Juan

2012-10-01

To identify hogwash oil quickly, the characteristic delta lambda of hogwash oil was analyzed by three dimensional fluorescence spectroscopy with parallel factor analysis, and the model was built up by using synchronous fluorescence spectroscopy with support vector machines (SVM). The results showed that the characteristic delta lambda of hogwash oil was 60 nm. Collecting original spectrum of different samples under the condition of characteristic delta lambda 60 nm, the best model was established while 5 principal components were selected from original spectrum and the radial basis function (RBF) was used as the kernel function, and the optimal penalty factor C and kernel function g were 512 and 0.5 respectively obtained by the grid searching and 6-fold cross validation. The discrimination rate of the model was 100% for both training sets and prediction sets. Thus, it is quick and accurate to apply synchronous fluorescence spectroscopy to identification of hogwash oil.

Establishing evidence-based training in cognitive behavioral therapy: A review of current empirical findings and theoretical guidance.

PubMed

Rakovshik, Sarah G; McManus, Freda

2010-07-01

Cognitive behavior therapy's (CBT) demonstrated efficacy has prompted calls for its increased dissemination to routine clinical practice settings. For the widespread dissemination of CBT to be successful in achieving effects similar to the original efficacy trials, there must also be effective dissemination of CBT training practices. However, as yet, CBT training is not evidence-based. This review examines what can be learned from existing research into the efficacy and effectiveness of CBT training. Due to the paucity of research specifically investigating CBT training, CBT effectiveness and dissemination studies are also examined to glean information about potentially effective training practices. In order to draw conclusions about effective training practices, comparisons are drawn between studies according to the clinical outcomes that they achieved. Training approaches are compared according to dose and active training elements, and theoretical models of learning are applied to interpret the findings. The limitations of the existing literature are discussed, as well as recommendations for improving training research to meet the standards evident in treatment trials (e.g., random allocation, control conditions, self-report and blind assessment, and adherence monitoring). Finally, the process of developing efficacious CBT treatment protocols is offered as a template for developing evidence-based CBT training protocols. 2010 Elsevier Ltd. All rights reserved.

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV).

PubMed

Piette, Elizabeth R; Moore, Jason H

2018-01-01

Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning analyses of GWAS can be hampered by biological and statistical factors, particularly so for the investigation of non-additive genetic interactions. Application of traditional cross validation to a GWAS data set may result in poor consistency between the training and testing data set splits due to an imbalance of the interaction genotypes relative to the data as a whole. We propose a new cross validation method, proportional instance cross validation (PICV), that preserves the original distribution of an independent variable when splitting the data set into training and testing partitions. We apply PICV to simulated GWAS data with epistatic interactions of varying minor allele frequencies and prevalences and compare performance to that of a traditional cross validation procedure in which individuals are randomly allocated to training and testing partitions. Sensitivity and positive predictive value are significantly improved across all tested scenarios for PICV compared to traditional cross validation. We also apply PICV to GWAS data from a study of primary open-angle glaucoma to investigate a previously-reported interaction, which fails to significantly replicate; PICV however improves the consistency of testing and training results. Application of traditional machine learning procedures to biomedical data may require modifications to better suit intrinsic characteristics of the data, such as the potential for highly imbalanced genotype distributions in the case of epistasis detection. The reproducibility of genetic interaction findings can be improved by considering this variable imbalance in cross validation implementation, such as with PICV. This approach may be extended to problems in other domains in which imbalanced variable distributions are a concern.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Effectiveness of suicide prevention gatekeeper-training for university administrative staff in Japan.

PubMed

Hashimoto, Naoki; Suzuki, Yuriko; Kato, Takahiro A; Fujisawa, Daisuke; Sato, Ryoko; Aoyama-Uehara, Kumi; Fukasawa, Maiko; Asakura, Satoshi; Kusumi, Ichiro; Otsuka, Kotaro

2016-01-01

Suicide is a leading cause of death among Japanese college and university students. Gatekeeper-training programs have been shown to improve detection and referral of individuals who are at risk of suicide by training non-mental-health professional persons. However, no studies have investigated the effectiveness of such programs in university settings in Japan. The aim of this study was to investigate the effectiveness of the gatekeeper-training program for administrative staff in Japanese universities. We developed a 2.5-h gatekeeper-training program based on the Mental Health First Aid program, which was originally developed for the general public. Seventy-six administrative staff at Hokkaido University participated in the program. Competence and confidence in managing suicide intervention, behavioral intention as a gatekeeper and attitude while handling suicidal students were measured by a self-reported questionnaire before, immediately after and a month after the program. We found a significant improvement in competence in the management of suicidal students. We also found improvements in confidence in management of suicidal students and behavioral intention as a gatekeeper after training, though questionnaires for those secondary outcomes were not validated. These improvements continued for a month. About 95% of the participants rated the program as useful or very useful and one-third of the participants had one or more chances to utilize their skills within a month. The current results suggest the positive effects of the training program in university settings in Japan. Future evaluation that includes comparison with standard didactic trainings and an assessment of long-term effectiveness are warranted. © 2015 The Authors. Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology.

A Novel Anti-classification Approach for Knowledge Protection.

PubMed

Lin, Chen-Yi; Chen, Tung-Shou; Tsai, Hui-Fang; Lee, Wei-Bin; Hsu, Tien-Yu; Kao, Yuan-Hung

2015-10-01

Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

Community Health Workers-Promotores de Salud in Mexico: History and Potential for Building Effective Community Actions.

PubMed

Balcazar, Hector; Perez-Lizaur, Ana Bertha; Izeta, Ericka Escalante; Villanueva, Maria Angeles

2016-01-01

This article takes a historical perspective combining 3 illustrative examples of the origins of the community health worker (CHW) model in Mexico, as a community-based participatory strategy. Three examples were identified from the sparse literature about CHWs in Mexico emphasizing their key roles and functions in various community settings. The CHW models illustrate what is known of training-development and planning, implementation, and evaluation of the CHWs model in different settings addressing cardiovascular disease and risk factors. The potential exists for integrating CHW projects to expand the health promotion model with new emphasis on municipality and regional participation.

[Evaluation of a training system for middle ear surgery with optoelectric detection].

PubMed

Strauss, G; Bahrami, N; Pössneck, A; Strauss, M; Dietz, A; Korb, W; Lüth, T; Haase, R; Moeckel, H; Grunert, R

2009-10-01

This work presents a new training concept for surgery of the temporal bone. It is based on a model of gypsum plastic with optoelectric detection of risk structures. A prototypical evaluation is given. The training models are based on high-resolution computed tomographic data of a human skull. The resulting data set was printed by a three-dimensional (3D) printer. A 3D phantom is created from gypsum powder and a bonding agent. Risks structures are the facial nerve, semicircular canal, cochlea, ossicular chain, sigmoid sinus, dura, and internal carotid artery. An electrically conductive metal (Wood's metal) and a fiber-optic cable were used as detection materials for the risk structures. For evaluating the training system, a study was done with eight inexperienced and eight experienced ear surgeons. They were asked to perform temporal bone surgery using two identical training models (group A). In group B, the same surgeons underwent surgical training with human cadavers. In the case of injuries, the number, point in time, degree (facial nerve), and injured structure were documented during the training on the model. In addition, the total time needed was noted. The training systems could be used in all cases. Evaluation of the anatomic accuracy of the models showed results that were between 49.5% and 90% agreement with the anatomic origin. Error detection was evaluated with values between 79% and 100% agreement with the perception of an experienced surgeon. The operating setting was estimated to be better than the previous"gold standard." The possibility of completely replacing the previous training method, which uses cadavers, with the examined training model was affirmed. This study shows that the examined system fulfills the conditions for a new training concept for temporal bone surgery. The system connects the preliminary work with printed and sintered models with the possibilities of microsystem engineering. In addition, the model's digital database permits a complete virtual representation of the model with appropriate further applications ("look behind the wall," virtual endoscopy).

On the Mathematical Consequences of Binning Spike Trains.

PubMed

Cessac, Bruno; Le Ny, Arnaud; Löcherbach, Eva

2017-01-01

We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process that is no longer Markov but is instead a variable-length Markov chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, that is, to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future, and we discuss how binning may affect our conclusions on this ability. We finally comment on the possible consequences of binning in the detection of spurious phase transitions or in the detection of incorrect evidence of criticality.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

Wrappers for Performance Enhancement and Oblivious Decision Graphs

DTIC Science & Technology

1995-09-01

always select all relevant features. We test di erent search engines to search the space of feature subsets and introduce compound operators to speed...distinct instances from the original dataset appearing in the test set is thus 0:632m. The 0i accuracy estimate is derived by using bootstrap sample...i for training and the rest of the instances for testing . Given a number b, the number of bootstrap samples, let 0i be the accuracy estimate for

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Locality-preserving sparse representation-based classification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Pressure-flow specificity of inspiratory muscle training.

PubMed

Tzelepis, G E; Vega, D L; Cohen, M E; Fulambarker, A M; Patel, K K; McCool, F D

1994-08-01

The inspiratory muscles (IM) can be trained by having a subject breathe through inspiratory resistive loads or by use of unloaded hyperpnea. These disparate training protocols are characterized by high inspiratory pressure (force) or high inspiratory flow (velocity), respectively. We tested the hypothesis that the posttraining improvements in IM pressure or flow performance are specific to training protocols in a way that is similar to force-velocity specificity of skeletal muscle training. IM training was accomplished in 15 normal subjects by use of three protocols: high inspiratory pressure-no flow (group A, n = 5), low inspiratory pressure-high flow (group B, n = 5), and intermediate inspiratory pressure and flow (group C, n = 5). A control group (n = 4) did no training. Before and after training, we measured esophageal pressure (Pes) and inspiratory flow (VI) during single maximal inspiratory efforts against a range of external resistances including an occluded airway. Efforts originated below relaxation volume (Vrel), and peak Pes and VI were measured at Vrel. Isovolume maximal Pes-VI plots were constructed to assess maximal inspiratory pressure-flow performance. Group A (pressure training) performed 30 maximal static inspiratory maneuvers at Vrel daily, group B (flow training) performed 30 sets of three maximal inspiratory maneuvers with no added external resistance daily, and group C (intermediate training) performed 30 maximal inspiratory efforts on a midrange external resistance (7 mm ID) daily. Subjects trained 5 days/wk for 6 wk. Data analysis included comparison of posttraining Pes-VI slopes among training groups.(ABSTRACT TRUNCATED AT 250 WORDS)

An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Fabbro, S.; Venn, K. A.; O'Briain, T.; Bialek, S.; Kielty, C. L.; Jahandar, F.; Monty, S.

2018-04-01

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Use of probabilistic neural networks for emitter correlation

NASA Astrophysics Data System (ADS)

Maloney, P. S.

1990-08-01

The Probabilistic Neural Network (PNN) as described by Specht''3 has been successfully applied to a number of emitter correlation problems involving operational data for training and testing of the neural net work. The PNN has been found to be a reliable classification tool for determining emitter type or even identifying specific emitter platforms given appropriate representative data sets for training con sisting only of parametric data from electronic intelligence (ELINT) reports. Four separate feasibility studies have been conducted to prove the usefulness of PNN in this application area: . Hull-to-emitter correlation (HULTEC) for identification of seagoing emitter platforms . Identification of landbased emitters from airborne sensors . Pulse sorting according to emitter of origin . Emitter typing based on a dynamically learning neural network. 1 .

Study of CT image texture using deep learning techniques

NASA Astrophysics Data System (ADS)

Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

Modeling habitat for Marbled Murrelets on the Siuslaw National Forest, Oregon, using lidar data

USGS Publications Warehouse

Hagar, Joan C.; Aragon, Ramiro; Haggerty, Patricia; Hollenbeck, Jeff P.

2018-03-28

Habitat models using lidar-derived variables that quantify fine-scale variation in vegetation structure can improve the accuracy of occupancy estimates for canopy-dwelling species over models that use variables derived from other remote sensing techniques. However, the ability of models developed at such a fine spatial scale to maintain accuracy at regional or larger spatial scales has not been tested. We tested the transferability of a lidar-based habitat model for the threatened Marbled Murrelet (Brachyramphus marmoratus) between two management districts within a larger regional conservation zone in coastal western Oregon. We compared the performance of the transferred model against models developed with data from the application location. The transferred model had good discrimination (AUC = 0.73) at the application location, and model performance was further improved by fitting the original model with coefficients from the application location dataset (AUC = 0.79). However, the model selection procedure indicated that neither of these transferred models were considered competitive with a model trained on local data. The new model trained on data from the application location resulted in the selection of a slightly different set of lidar metrics from the original model, but both transferred and locally trained models consistently indicated positive relationships between the probability of occupancy and lidar measures of canopy structural complexity. We conclude that while the locally trained model had superior performance for local application, the transferred model could reasonably be applied to the entire conservation zone.

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

[Data fusion and multi-components quantitative analysis for identification and quality evaluation of Gentiana rigescens from different geographical origins].

PubMed

Wang, Qin-Qin; Shen, Tao; Zuo, Zhi-Tian; Huang, Heng-Yu; Wang, Yuan-Zhong

2018-03-01

The accumulation of secondary metabolites of traditional Chinese medicine (TCM) is closely related to its origins. The identification of origins and multi-components quantitative evaluation are of great significance to ensure the quality of medicinal materials. In this study, the identification of Gentiana rigescens from different geographical origins was conducted by data fusion of Fourier transform infrared (FTIR) spectroscopy and high performance liquid chromatography (HPLC) in combination of partial least squares discriminant analysis; meanwhile quantitative analysis of index components was conducted to provide an accurate and comprehensive identification and quality evaluation strategy for selecting the best production areas of G. rigescens. In this study, the FTIR and HPLC information of 169 G. rigescens samples from Yunnan, Sichuan, Guangxi and Guizhou Provinces were collected. The raw infrared spectra were pre-treated by multiplicative scatter correction, standard normal variate (SNV) and Savitzky-Golay (SG) derivative. Then the performances of FTIR, HPLC, and low-level data fusion and mid-level data fusion for identification were compared, and the contents of gentiopicroside, swertiamarin, loganic acid and sweroside were determined by HPLC. The results showed that the FTIR spectra of G. rigescens from different geographical origins were different, and the best pre-treatment method was SNV+SG-derivative (second derivative, 15 as the window parameter, and 2 as the polynomial order). The results showed that the accuracy rate of low- and mid-level data fusion (96.43%) in prediction set was higher than that of FTIR and HPLC (94.64%) in prediction set. In addition, the accuracy of low-level data fusion (100%) in the training set was higher than that of mid-level data fusion (99.12%) in training set. The contents of the iridoid glycosides in Yunnan were the highest among different provinces. The average content of gentiopicroside, as a bioactive marker in Chinese pharmacopoeia, was 47.40 mg·g⁻¹, and the maximum was 79.83 mg·g⁻¹. The contents of loganic acid, sweroside and gentiopicroside in Yunnan were significantly different from other provinces ( P <0.05). In comparison of total content of iridoid glycosides in G. rigescens with different geographical origins in Yunnan, it was found that the amount of iridoid glycosides was higher in Eryuan Dali (68.59 mg·g⁻¹) and Yulong Lijiang (66.68 mg·g⁻¹), significantly higher than that in Wuding Chuxiong (52.99 mg·g⁻¹), Chengjiang Yuxi (52.29 mg·g⁻¹) and Xundian Kunming (46.71 mg·g⁻¹) ( P <0.05), so these two places can be used as a reference region for screening cultivation and excellent germplasm resources of G. rigescens. A comprehensive and accurate method was established by data fusion of HPLC-FTIR and quantitative analysis of HPLC for identification and quality evaluation of G. rigescens, which could provide a support for the development and utilization of G. rigescens. Copyright© by the Chinese Pharmaceutical Association.

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.

Timing of wet snow avalanche activity: An analysis from Glacier National Park, Montana, USA.

USGS Publications Warehouse

Peitzsch, Erich H.; Hendrikx, Jordy; Fagre, Daniel B.

2012-01-01

Wet snow avalanches pose a problem for annual spring road opening operations along the Going-to-the-Sun Road (GTSR) in Glacier National Park, Montana, USA. A suite of meteorological metrics and snow observations has been used to forecast for wet slab and glide avalanche activity. However, the timing of spring wet slab and glide avalanches is a difficult process to forecast and requires new capabilities. For the 2011 and 2012 spring seasons we tested a previously developed classification tree model which had been trained on data from 2003-2010. For 2011, this model yielded a 91% predictive rate for avalanche days. For 2012, the model failed to capture any of the avalanche days observed. We then investigated these misclassified avalanche days in the 2012 season by comparing them to the misclassified days from the original dataset from which the model was trained. Results showed no significant difference in air temperature variables between this year and the original training data set for these misclassified days. This indicates that 2012 was characterized by avalanche days most similar to those that the model struggled with in the original training data. The original classification tree model showed air temperature to be a significant variable in wet avalanche activity which implies that subsequent movement of meltwater through the snowpack is also important. To further understand the timing of water flow we installed two lysimeters in fall 2011 before snow accumulation. Water flow showed a moderate correlation with air temperature later in the season and no synchronous pattern associated with wet slab and glide avalanche activity. We also characterized snowpack structure as the snowpack transitioned from a dry to a wet snowpack throughout the spring. This helped to assess potential failure layers of wet snow avalanches and the timing of avalanches compared to water moving through the snowpack. These tools (classification tree model and lysimeter data), combined with standard meteorological and avalanche observations, proved useful to forecasters regarding the timing of wet snow avalanche activity along the GTSR.

Increases in lower-body strength transfer positively to sprint performance: a systematic review with meta-analysis.

PubMed

Seitz, Laurent B; Reyes, Alvaro; Tran, Tai T; Saez de Villarreal, Eduardo; Haff, G Gregory

2014-12-01

Although lower-body strength is correlated with sprint performance, whether increases in lower-body strength transfer positively to sprint performance remain unclear. This meta-analysis determined whether increases in lower-body strength (measured with the free-weight back squat exercise) transfer positively to sprint performance, and identified the effects of various subject characteristics and resistance-training variables on the magnitude of sprint improvement. A computerized search was conducted in ADONIS, ERIC, SPORTDiscus, EBSCOhost, Google Scholar, MEDLINE and PubMed databases, and references of original studies and reviews were searched for further relevant studies. The analysis comprised 510 subjects and 85 effect sizes (ESs), nested with 26 experimental and 11 control groups and 15 studies. There is a transfer between increases in lower-body strength and sprint performance as indicated by a very large significant correlation (r = -0.77; p = 0.0001) between squat strength ES and sprint ES. Additionally, the magnitude of sprint improvement is affected by the level of practice (p = 0.03) and body mass (r = 0.35; p = 0.011) of the subject, the frequency of resistance-training sessions per week (r = 0.50; p = 0.001) and the rest interval between sets of resistance-training exercises (r = -0.47; p ≤ 0.001). Conversely, the magnitude of sprint improvement is not affected by the athlete's age (p = 0.86) and height (p = 0.08), the resistance-training methods used through the training intervention, (p = 0.06), average load intensity [% of 1 repetition maximum (RM)] used during the resistance-training sessions (p = 0.34), training program duration (p = 0.16), number of exercises per session (p = 0.16), number of sets per exercise (p = 0.06) and number of repetitions per set (p = 0.48). Increases in lower-body strength transfer positively to sprint performance. The magnitude of sprint improvement is affected by numerous subject characteristics and resistance-training variables, but the large difference in number of ESs available should be taken into consideration. Overall, the reported improvement in sprint performance (sprint ES = -0.87, mean sprint improvement = 3.11 %) resulting from resistance training is of practical relevance for coaches and athletes in sport activities requiring high levels of speed.

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.

Limited Effects of Set Shifting Training in Healthy Older Adults

PubMed Central

Grönholm-Nyman, Petra; Soveri, Anna; Rinne, Juha O.; Ek, Emilia; Nyholm, Alexandra; Stigsdotter Neely, Anna; Laine, Matti

2017-01-01

Our ability to flexibly shift between tasks or task sets declines in older age. As this decline may have adverse effects on everyday life of elderly people, it is of interest to study whether set shifting ability can be trained, and if training effects generalize to other cognitive tasks. Here, we report a randomized controlled trial where healthy older adults trained set shifting with three different set shifting tasks. The training group (n = 17) performed adaptive set shifting training for 5 weeks with three training sessions a week (45 min/session), while the active control group (n = 16) played three different computer games for the same period. Both groups underwent extensive pre- and post-testing and a 1-year follow-up. Compared to the controls, the training group showed significant improvements on the trained tasks. Evidence for near transfer in the training group was very limited, as it was seen only on overall accuracy on an untrained computerized set shifting task. No far transfer to other cognitive functions was observed. One year later, the training group was still better on the trained tasks but the single near transfer effect had vanished. The results suggest that computerized set shifting training in the elderly shows long-lasting effects on the trained tasks but very little benefit in terms of generalization. PMID:28386226

Development and content validation of the power mobility training tool.

PubMed

Kenyon, Lisa K; Farris, John P; Cain, Brett; King, Emily; VandenBerg, Ashley

2018-01-01

This paper outlines the development and content validation of the power mobility training tool (PMTT), an observational tool designed to assist therapists in developing power mobility training programs for children who have multiple, severe impairments. Initial items on the PMTT were developed based on a literature review and in consultation with therapists experienced in the use of power mobility. Items were trialled in clinical settings, reviewed, and refined. Items were then operationalized and an administration manual detailing scoring for each item was created. Qualitative and quantitative methods were used to establish content validity via a 15 member, international expert panel. The content validity ratio (CVR) was determined for each possible item. Of the 19 original items, 10 achieved minimum required CVR values and were included in the final version of the PMTT. Items related to manoeuvring a power mobility device were merged and an item related to the number of switches used concurrently to operate a power mobility device were added to the PMTT. The PMTT may assist therapists in developing training programs that facilitate the acquisition of beginning power mobility skills in children who have multiple, severe impairments. Implications for Rehabilitation The Power Mobility Training Tool (PMTT) was developed to help guide the development of power mobility intervention programs for children who have multiple, severe impairments. The PMTT can be used with children who access a power mobility device using either a joystick or a switch. Therapists who have limited experience with power mobility may find the PMTT to be helpful in setting up and conducting power mobility training interventions as a feasible aspect of a plan of care for children who have multiple, severe impairments.

Shifting mindsets: a realist synthesis of evidence from self-management support training.

PubMed

Davies, Freya; Wood, Fiona; Bullock, Alison; Wallace, Carolyn; Edwards, Adrian

2018-03-01

Accompanying the growing expectation of patient self-management is the need to ensure health care professionals (HCPs) have the required attitudes and skills to provide effective self-management support (SMS). Results from existing training interventions for HCPs in SMS have been mixed and the evidence base is weaker for certain settings, including supporting people with progressive neurological conditions (PNCs). We set out to understand how training operates, and to identify barriers and facilitators to training designed to support shifts in attitudes amongst HCPs. We undertook a realist literature synthesis focused on: (i) the influence of how HCPs, teams and organisations view and adopt self-management; and (ii) how SMS needs to be tailored for people with PNCs. A traditional database search strategy was used alongside citation tracking, grey literature searching and stakeholder recommendations. We supplemented PNC-specific literature with data from other long-term conditions. Key informant interviews and stakeholder advisory group meetings informed the synthesis process. Realist context-mechanism-outcome configurations were generated and mapped onto the stages described in Mezirow's Transformative Learning Theory. Forty-four original articles were included (19 relating to PNCs), from which seven refined theories were developed. The theories identified important training elements (evidence provision, building skills and confidence, facilitating reflection and generating empathy). The significant influence of workplace factors as possible barriers or facilitators was highlighted. Embracing SMS often required challenging traditional professional role boundaries. The integration of SMS into routine care is not an automatic outcome from training. A transformative learning process is often required to trigger the necessary mindset shift. Training should focus on how individual HCPs define and value SMS and how their work context (patient group and organisational constraints) influences this process. Proactively addressing potential contextual barriers may facilitate implementation. These findings could be applied to other types of training designed to shift attitudes amongst HCPs. © 2018 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin

USGS Publications Warehouse

McKenna, James E.

2005-01-01

Diversity and fish productivity are important measures of the health and status of aquatic systems. Being able to predict the values of these indices as a function of environmental variables would be valuable to management. Diversity and productivity have been related to environmental conditions by multiple linear regression and discriminant analysis, but such methods have several shortcomings. In an effort to predict fish species diversity and estimate salmonid production for streams in the eastern basin of Lake Ontario, I constructed neural networks and trained them on a data set containing abiotic information and either fish diversity or juvenile salmonid abundance. Twenty percent of the original data were retained as a test data set and used in the training. The ability to extend these neural networks to conditions throughout the streams was tested with data not involved in the network training. The resulting neural networks were able to predict the number of salmonids with more than 84% accuracy and diversity with more than 73% accuracy, which was far superior to the performance of multiple regression. The networks also identified the environmental variables with the greatest predictive power, namely, those describing water movement, stream size, and water chemistry. Thirteen input variables were used to predict diversity and 17 to predict salmonid abundance.

Designing Multi-target Compound Libraries with Gaussian Process Models.

PubMed

Bieler, Michael; Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Kriegl, Jan M; Schneider, Gisbert

2016-05-01

We present the application of machine learning models to selecting G protein-coupled receptor (GPCR)-focused compound libraries. The library design process was realized by ant colony optimization. A proprietary Boehringer-Ingelheim reference set consisting of 3519 compounds tested in dose-response assays at 11 GPCR targets served as training data for machine learning and activity prediction. We compared the usability of the proprietary data with a public data set from ChEMBL. Gaussian process models were trained to prioritize compounds from a virtual combinatorial library. We obtained meaningful models for three of the targets (5-HT2c , MCH, A1), which were experimentally confirmed for 12 of 15 selected and synthesized or purchased compounds. Overall, the models trained on the public data predicted the observed assay results more accurately. The results of this study motivate the use of Gaussian process regression on public data for virtual screening and target-focused compound library design. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Effects of Goal Setting on Performance and Job Satisfaction

ERIC Educational Resources Information Center

Ivancevich, John M.

1976-01-01

Studied the effect of goal-setting training on the performance and job satisfaction of sales personnel. One group was trained in participative goal setting; one group was trained in assigned goal setting; and one group received no training. Both trained groups showed temporary improvements in performance and job satisfaction. For availability see…

Training Effectiveness of The Inertial Training and Measurement System

PubMed Central

Naczk, Mariusz; Brzenczek-Owczarzak, Wioletta; Arlet, Jarosław; Naczk, Alicja; Adach, Zdzisław

2014-01-01

The purpose of this study was to evaluate the efficacy of inertial training with different external loads using a new original device - the Inertial Training and Measurement System (ITMS). Forty-six physical education male students were tested. The participants were randomly divided into three training groups and a control group (C group). The training groups performed inertial training with three different loads three times weekly for four weeks. The T0 group used only the mass of the ITMS flywheel (19.4 kg), the T5 and T10 groups had an additional 5 and 10 kg on the flywheel, respectively. Each training session included three exercise sets involving the shoulder joint adductors. Before and after training, the maximal torque and power were measured on an isokinetic dynamometer during adduction of the shoulder joint. Simultaneously, the electromyography activity of the pectoralis major muscle was recorded. Results of the study indicate that ITMS training induced a significant increase in maximal muscle torque in the T0, T5, T10 groups (15.5%, 13.0%, and 14.0%, respectively). Moreover, ITMS training caused a significant increase in power in the T0, T5, T10 groups (16.6%, 19.5%, and 14.5%, respectively). The percentage changes in torque and power did not significantly differ between training groups. Electromyography activity of the pectoralis major muscle increased only in the T0 group after four weeks of training. Using the ITMS device in specific workouts allowed for an increase of shoulder joint adductors torque and power in physical education students. PMID:25713662

Improving the histopathologic diagnosis of pediatric malignancies in a low-resource setting by combining focused training and telepathology strategies.

PubMed

Santiago, Teresa C; Jenkins, Jesse J; Pedrosa, Francisco; Billups, Catherine; Quintana, Yuri; Ribeiro, Raul C; Qaddoumi, Ibrahim

2012-08-01

Accurate diagnosis is critical for optimal management of pediatric cancer. Pathologists with experience in pediatric oncology are in short supply in the developing world. Telepathology is increasingly used for consultations but its overall contribution to diagnostic accuracy is unknown. We developed a strategy to provide a focused training in pediatric cancer and telepathology support to pathologists in the developing world. After the training period, we compared trainee's diagnoses with those of an experienced pathologist. We next compared the effectiveness of static versus dynamic telepathology review in 127 cases. Results were compared by Fisher's exact test. The diagnoses of the trainee and the expert pathologist differed in only 6.5% of cases (95% CI, 1.2-20.0%). The overall concordance between the telepathology and original diagnoses was 90.6% (115/127; 95% CI, 84.1-94.6%). Brief, focused training in pediatric cancer histopathology can improve diagnostic accuracy. Dynamic and static telepathology analyses are equally effective for diagnostic review. Copyright © 2012 Wiley Periodicals, Inc.

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

Image simulation for automatic license plate recognition

NASA Astrophysics Data System (ADS)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Take-Home Training in Laparoscopy.

PubMed

Thinggaard, Ebbe

2017-04-01

When laparoscopy was first introduced, skills were primarily taught using the apprenticeship model. A limitation of this method when compared to open surgery, was that it requires more time to practise and more frequent learning opportunities in clinical practice. The unique set of skills required in laparoscopy highlighted the need for new training methods that reduce the need for supervision and do not put the patient at risk. Simulation training was developed to meet this need. The overall purpose of this thesis was to explore simulation-based laparoscopic training at home. The thesis consists of five papers: a review, a validation study, a study of methodology, a randomised controlled trial and a mixed-methods study. Our aims were to review the current knowledge on training off-site, to develop and explore validity for a training and assessment system, to investigate the effect of take-home training in a simulation-based laparoscopic training programme, and to explore the use of take-home training. The first paper in this thesis is a scoping review. The aim of the review was to explore the current knowledge on off-site laparoscopic skills training. We found that off-site training was feasible but that changes were required in order for it to become an effective method of training. Furthermore, the select-ed instructional design varied and training programmes were designed using a variety of educational theories. Based on our findings, we recommended that courses and training curricula should follow established education theories such as proficiency-based learning and deliberate practice. Principles of directed self-regulated learning could be used to improve off-site laparoscopic training programmes. In the second study, we set out to develop and explore validity evidence of the TABLT test. The TABLT test was developed for basic laparoscopic skills training in a cross-specialty curriculum. We found validity evidence to support the TABLT test as a summative test in a basic laparoscopic training programme. We also established a credible pass/fail level using the contrasting groups method. We concluded that the TABTL test could be used to assess novice laparoscopic trainees across different specialties and help trainees acquire basic laparoscopic competencies prior to supervised surgery. In the third study, we aimed to explore the consequences of the choice of standard setting method and whether there is a difference in terms of how high a score experienced and novice laparoscopic surgeons expect that novices should achieve during training. We used three different standard setting methods and found that pass/fail levels vary depending on the choice of standard setting method. We also asked experienced and novice laparoscopic surgeons how high a score they expected a novice laparoscopic surgeon should achieve on a test during training. We found a significant difference, with experienced surgeons setting a lower pass/fail level. We concluded that an established standard setting method supported by evidence should be used when setting a pass/fail level. In the first and second papers of this thesis, we found that off-site training is feasible and explored validity for the TABLT test. We used this knowledge in the fourth study to design a randomised controlled trial. The aim of the trial was to investigate the effect of take-home training in a simulation-based laparoscopic course. We hypothesised that training at home could help trainees plan their training according to their own schedule and thereby increase the effect of training. We found that participants had a distributed training pattern; they trained more frequently and in shorter sessions. We also found that participants were able to rate their own performance during unsupervised training and that selfrating was reliable. The fifth and final study of the thesis was a mixed-methods study that aimed to explore the use of take-home training. To meet this aim, we recruited participants from the intervention arm in our randomised controlled trial. All participants had access to the simulation centre and were given a port-able trainer to train on at home. Participants were asked to use a logbook during training. At the end of the course, they were invited to take part in focus group interviews and individual interviews. Based on data from logbooks, a descriptive statistical analysis was conducted and data from interviews were analysed using a content analysis. We found that participants took an individualised approach to training when training at home. They structured their training according to their needs and external requirements. We concluded that mandatory training requirements and testing help determine when and how much participants train. We also found that self-rating can guide unsupervised training by giving clear goals to be reached during training. From the papers included in the thesis, we found that the literature describes training at home as a feasible method of acquiring laparoscopic skills. Nonetheless, changes to current training programmes are needed in order to make this method effective. We then developed and explored validity evidence for the TABLT test. We also established a reasonable pass/fail level and went on to explore the immediate consequences of the pass/fail level. Using our knowledge from the review, we conducted a randomised controlled trial and a mixed-method study. Based on these studies we found that training at home allows for distributed learning, that self-rating guides unsupervised training, and that mandatory training requirements and testing strongly influence training patterns. Access to training, guidance during training, and mandatory training requirements will make take-home training not just feasible but also effective. Articles published in the Danish Medical Journal are “open access”. This means that the articles are distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Garvin, Gregory; Romano, Walter; Li, Shuo

2011-01-01

This study investigates novel object-interaction priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in the image. Based on a global measure of similarity between distributions, the proposed priors are intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive an original fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed priors relax the need of costly pose estimation (or registration) procedures and large training sets (we used a single subject for training), and can tolerate shape deformations, unlike template-based priors. Our formulation leads to an NP-hard problem which does not afford a form directly amenable to graph cut optimization. We proceeded to a relaxation of the problem via an auxiliary function, thereby obtaining a nearly real-time solution with few graph cuts. Quantitative evaluations over 60 intervertebral discs acquired from 10 subjects demonstrated that the proposed algorithm yields a high correlation with independent manual segmentations by an expert. We further demonstrate experimentally the invariance of the proposed geometric attributes. This supports the fact that a single subject is sufficient for training our algorithm, and confirms the relevance of the proposed priors to disc segmentation.

Effect of postgraduate training on job and career satisfaction among health-system pharmacists.

PubMed

Padiyara, Rosalyn S; Komperda, Kathy E

2010-07-01

The effect of postgraduate training on job and career satisfaction among health-system pharmacists was evaluated. A mail-based questionnaire was sent to a random sample of pharmacist members of the American Society of Health-System Pharmacists. Previously validated questions for job and career satisfaction among pharmacists were utilized. The questionnaire was designed to obtain information regarding general employment, work environment, job satisfaction, career satisfaction, postgraduate training, and demographic characteristics. Pharmacists who had completed either a pharmacy residency or fellowship were classified as having postgraduate training. Questionnaires returned within two months of the original mailing date were included in the analysis. Responses from pharmacists who were retired, employed in a nonpharmacy career, or unemployed were excluded. Data were analyzed using SPSS software. Of the 2499 questionnaires mailed, 36 were undeliverable; 1058 were completed, yielding a response rate of 43%. Of these, 48 were excluded, resulting in 1010 questionnaires suitable for analysis. Approximately 37% of respondents indicated completion of postgraduate training. The most common practice setting was a community, not-for-profit hospital (40.9%). Overall, 90.7% of respondents indicated they were either satisfied or highly satisfied with their current employment. Approximately 45% of pharmacists with postgraduate training indicated they were highly satisfied with their employment, compared with 32.7% of pharmacists without postgraduate training (p < 0.001). Pharmacists who completed postgraduate training were more satisfied with their job than those who did not complete such training.

Preceptor use of classroom assessment techniques to stimulate higher-order thinking in the clinical setting.

PubMed

Davidson, Judy E

2009-03-01

The purpose of this article is to provide examples of learning activities to be used as formative (interim) evaluation of an in-hospital orientation or cross-training program. Examples are provided in the form of vignettes that have been derived from strategies described in the literature as classroom assessment techniques. Although these classroom assessment techniques were originally designed for classroom experiences, they are proposed as methods for preceptors to stimulate the development of higher-order thinking such as synthesizing information, solving problems, and learning how to learn.

Bi-national cross-validation of an evidence-based conduct problem prevention model.

PubMed

Porta, Carolyn M; Bloomquist, Michael L; Garcia-Huidobro, Diego; Gutiérrez, Rafael; Vega, Leticia; Balch, Rosita; Yu, Xiaohui; Cooper, Daniel K

2018-04-01

To (a) explore the preferences of Mexican parents and Spanish-speaking professionals working with migrant Latino families in Minnesota regarding the Mexican-adapted brief model versus the original conduct problems intervention and (b) identifying the potential challenges, and preferred solutions, to implementation of a conduct problems preventive intervention. The core practice elements of a conduct problems prevention program originating in the United States were adapted for prevention efforts in Mexico. Three focus groups were conducted in the United States, with Latino parents (n = 24; 2 focus groups) and professionals serving Latino families (n = 9; 1 focus group), to compare and discuss the Mexican-adapted model and the original conduct problems prevention program. Thematic analysis was conducted on the verbatim focus group transcripts in the original language spoken. Participants preferred the Mexican-adapted model. The following key areas were identified for cultural adaptation when delivering a conduct problems prevention program with Latino families: recruitment/enrollment strategies, program delivery format, and program content (i.e., child skills training, parent skills training, child-parent activities, and child-parent support). For both models, strengths, concerns, barriers, and strategies for overcoming concerns and barriers were identified. We summarize recommendations offered by participants to strengthen the effective implementation of a conduct problems prevention model with Latino families in the United States. This project demonstrates the strength in binational collaboration to critically examine cultural adaptations of evidence-based prevention programs that could be useful to diverse communities, families, and youth in other settings. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

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

Li, Nan; Carmona, Ruben; Sirak, Igor

Purpose: To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. Methods and Materials: We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volumemore » histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V{sub 10} (percentage volume of PBM receiving at least 10 Gy dose) and V{sub 20} (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V{sub 40} (absolute volume of bowel receiving at least 40 Gy dose) and V{sub 45} (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBP{sub FINAL}), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBP{sub FINAL} plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. Results: KBP{sub FINAL} plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBP{sub FINAL} versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBP{sub FINAL} plans, indicating lowered probability of hematologic toxicity. Conclusions: This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.« less

Trends in Characteristics and Country of Origin Among Foreign-Trained Nurses in the United States, 1990 and 2000

PubMed Central

Polsky, Daniel; Ross, Sara J.; Brush, Barbara L.; Sochalski, Julie

2007-01-01

Objectives. We describe long-term trends in the characteristics of foreign-trained new entrants to the registered nurse (RN) workforce in the United States. Methods. Using the 1990 and 2000 US Census 5% Public Use Microdata Sample files, we compared trends in characteristics of US- and foreign-trained new entrants to the RN labor force (n=40827) and identified trends in the country of origin of the foreign-trained new entrants. Results. Foreign-trained RNs grew as a percentage of new entrants to the RN workforce, from 8.8% in 1990 to 15.2% in 2000. Compared with US-trained RNs, foreign-trained RNs were 3 times as likely to work in nursing homes and were more likely to have earned a bachelor’s degree. In 2000, 21% of foreign-trained RNs originated from low-income countries, a doubling of the rate since 1990. Conclusions. Foreign-trained RNs now account for a substantial and growing proportion of the US RN workforce. Our findings suggest foreign-trained RNs entering the United States are not of lower quality than US-trained RNs. However, growth in the proportion of RNs from low-income countries may have negative consequences in those countries. PMID:17395844

Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.

PubMed

de Bruijne, Marleen; van Ginneken, Bram; Viergever, Max A; Niessen, Wiro J

2003-07-01

Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.

Transfer and Use of Training Technology: A Model for Matching Training Approaches with Training Settings. Technical Report No. 74-24.

ERIC Educational Resources Information Center

Haverland, Edgar M.

The report describes a project designed to facilitate the transfer and utilization of training technology by developing a model for evaluating training approaches or innovtions in relation to the requirements, resources, and constraints of specific training settings. The model consists of two parallel sets of open-ended questions--one set…

Discipline-specific competency-based curricula for leadership learning in medical specialty training.

PubMed

Turner, Sandra; Chan, Ming-Ka; McKimm, Judy; Dickson, Graham; Shaw, Timothy

2018-05-08

Purpose Doctors play a central role in leading improvements to healthcare systems. Leadership knowledge and skills are not inherent, however, and need to be learned. General frameworks for medical leadership guide curriculum development in this area. Explicit discipline-linked competency sets and programmes provide context for learning and likely enhance specialty trainees' capability for leadership at all levels. The aim of this review was to summarise the scholarly literature available around medical specialty-specific competency-based curricula for leadership in the post-graduate training space. Design/methodology/approach A systematic literature search method was applied using the Medline, EMBASE and ERIC (education) online databases. Documents were reviewed for a complete match to the research question. Partial matches to the study topic were noted for comparison. Findings In this study, 39 articles were retrieved in full text for detailed examination, of which 32 did not comply with the full inclusion criteria. Seven articles defining discipline-linked competencies/curricula specific to medical leadership training were identified. These related to the areas of emergency medicine, general practice, maternal and child health, obstetrics and gynaecology, pathology, radiology and radiation oncology. Leadership interventions were critiqued in relation to key features of their design, development and content, with reference to modern leadership concepts. Practical implications There is limited discipline-specific guidance for the learning and teaching of leadership within medical specialty training programmes. The competency sets identified through this review may aid the development of learning interventions and tools for other medical disciplines. Originality/value The findings of this study provide a baseline for the further development, implementation and evaluation work required to embed leadership learning across all medical specialty training programmes.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

PubMed

Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

2018-03-27

P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

PubMed

Lopes, F B; Wu, X-L; Li, H; Xu, J; Perkins, T; Genho, J; Ferretti, R; Tait, R G; Bauck, S; Rosa, G J M

2018-02-01

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. © 2018 Blackwell Verlag GmbH.

Determinants of an urban origin student choosing rural practice: a scoping review.

PubMed

Myhre, Douglas L; Bajaj, Sameer; Jackson, Wesley

2015-01-01

The shortage of physicians in rural and remote communities is an ongoing problem. Many studies have shown that the rural background of a student (ie rural origin) is a primary factor in recruiting physicians for practice in rural communities. Scoping reviews are primarily done to gauge the extent of literature on the research question at hand, typically with an intent that future research in that area is a constructive addition to pre-existing knowledge. This scoping review focuses on factors that predispose urban-origin students to choose a carrier in rural medicine. The study used Arksey and O'Malley's guidelines for a scoping review of the literature, which, in contrast to a traditional systematic review, is brief yet comprehensive. Medline (Ovid) and PubMed databases were used to review literature published between 1 January 1970 and 30 November 2014. After removing duplicates, articles were screened based on inclusion and exclusion criteria set up by the research team. The literature search resulted in 435 articles, 418 of which were excluded, leaving 17 articles for comprehensive review. Out of these 17 studies, the following four factors that suggest why urban-origin medical students may choose rural practice were generated: geographic diffusion of physicians in response to economic forces such as debt repayment and financial incentives (five studies), scope of practice and personal satisfaction (five studies), undergraduate and postgraduate rural training (nine studies) and premedical school mindset to practice rurally (five studies). Urban-origin students may choose rural practice because of market forces as well as financial incentives. The participation in undergraduate and postgraduate rural training is reported to positively alter the attitude of urban-origin students. A small subset of these students has a predetermined mindset to practice rurally at the time of matriculation. Obstacles for choosing a rural carrier include, but are not limited to lack of job and education opportunities for spouses/partners, lack of recreational and educational opportunities for children, and obscure opportunities for continuing medical education.

Advancing Continence in Typically Developing Children: Adapting the Procedures of Foxx and Azrin for Primary Care.

PubMed

Warzak, William J; Forcino, Stacy S; Sanberg, Sela Ann; Gross, Amy C

2016-01-01

To (1) identify and summarize procedures of Foxx and Azrin's classic toilet training protocol that continue to be used in training typically developing children and (2) adapt recent findings with the original Foxx and Azrin procedures to inform practical suggestions for the rapid toilet training of typically developing children in the primary care setting. Literature searches of PsychINFO and MEDLINE databases used the search terms "(toilet* OR potty* AND train*)." Selection criteria were only peer-reviewed experimental articles that evaluated intensive toilet training with typically developing children. Exclusion criteria were (1) nonpeer reviewed research, (2) studies addressing encopresis and/or enuresis, (3) studies excluding typically developing children, and (4) studies evaluating toilet training during infancy. In addition to the study of Foxx and Azrin, only 4 publications met the above criteria. Toilet training procedures from each article were reviewed to determine which toilet training methods were similar to components described by Foxx and Azrin. Common training elements include increasing the frequency of learning opportunities through fluid loading and having differential consequences for being dry versus being wet and for voiding in the toilet versus elsewhere. There is little research on intensive toilet training of typically developing children. Practice sits and positive reinforcement for voids in the toilet are commonplace, consistent with the Foxx and Azrin protocol, whereas positive practice as a corrective procedure for wetting accidents often is omitted. Fluid loading and differential consequences for being dry versus being wet and for voiding in the toilet also are suggested procedures, consistent with the Foxx and Azrin protocol.

Group Support Systems (GSS)

NASA Technical Reports Server (NTRS)

Hamel, Gary P.; Wijesinghe, R.

1996-01-01

Groupware is a term describing an emerging computer software technology enhancing the ability of people to work together as a group, (a software driven 'group support system'). This project originated at the beginning of 1992 and reports were issued describing the activity through May 1995. These reports stressed the need for process as well as technology. That is, while the technology represented a computer assisted method for groups to work together, the Group Support System (GSS) technology als required an understanding of the facilitation process electronic meetings demand. Even people trained in traditional facilitation techniques did not necessarily aimlessly adopt groupware techniques. The latest phase of this activity attempted to (1) improve the facilitation process by developing training support for a portable groupware computer system, and (2) to explore settings and uses for the portable groupware system using different software, such as Lotus Notes.

The effect of aggression management training programmes for nursing staff and students working in an acute hospital setting. A narrative review of current literature.

PubMed

Heckemann, B; Zeller, A; Hahn, S; Dassen, T; Schols, J M G A; Halfens, R J G

2015-01-01

Patient aggression is a longstanding problem in general hospital nursing. Staff training is recommended to tackle workplace aggression originating from patients or visitors, yet evidence on training effects is scarce. To review and collate current research evidence on the effect of aggression management training for nurses and nursing students working in general hospitals, and to derive recommendations for further research. Systematic, narrative review. Embase, MEDLINE, the Cochrane library, CINAHL, PsycINFO, pubmed, psycArticles, Psychology and Behavioural Sciences Collection were searched for articles evaluating training programs for staff and students in acute hospital adult nursing in a 'before/after' design. Studies published between January 2000 and September 2011 in English, French or German were eligible of inclusion. The methodological quality of included studies was assessed with the 'Quality Assessment Tool for Quantitative Studies'. Main outcomes i.e. attitudes, confidence, skills and knowledge were collated. Nine studies were included. Two had a weak, six a moderate, and one a strong study design. All studies reported increased confidence, improved attitude, skills, and knowledge about risk factors post training. There was no significant change in incidence of patient aggression. Our findings corroborate findings of reviews on training in mental health care, which point to a lack of high quality research. Training does not reduce the incidence of aggressive acts. Aggression needs to be tackled at an organizational level. Copyright © 2014 Elsevier Ltd. All rights reserved.

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

PubMed

Yao, Chen; Zhu, Xiaojin; Weigel, Kent A

2016-11-07

Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with measured phenotypes. Semi-supervised learning can be helpful for genomic prediction of novel traits, such as RFI, for which the size of reference population is limited, in particular, when the animals to be predicted and the animals in the reference population originate from the same herd-environment.

The effect of open kinetic chain knee extensor resistance training at different training loads on anterior knee laxity in the uninjured.

PubMed

Barcellona, Massimo G; Morrissey, Matthew C

2016-04-01

The commonly used open kinetic chain knee extensor (OKCKE) exercise loads the sagittal restraints to knee anterior tibial translation. To investigate the effect of different loads of OKCKE resistance training on anterior knee laxity (AKL) in the uninjured knee. non-clinical trial. Randomization into one of three supervised training groups occurred with training 3 times per week for 12 weeks. Subjects in the LOW and HIGH groups performed OKCKE resistance training at loads of 2 sets of 20 repetition maximum (RM) and 20 sets of 2RM, respectively. Subjects in the isokinetic training group (ISOK) performed isokinetic OKCKE resistance training using 2 sets of 20 maximal efforts. AKL was measured using the KT2000 arthrometer with concurrent measurement of lateral hamstrings muscle activity at baseline, 6 weeks and 12 weeks. Twenty six subjects participated (LOW n = 9, HIGH n = 10, ISOK n = 7). The main finding from this study is that a 12-week OKCKE resistance training programme at loads of 20 sets of 2RM, leads to an increase in manual maximal AKL. OKCKE resistance training at high loads (20 sets of 2RM) increases AKL while low load OKCKE resistance training (2 sets of 20RM) and isokinetic OKCKE resistance training at 2 sets of 20RM does not. Copyright © 2015 Elsevier Ltd. All rights reserved.

Increased Variability and Asymmetric Expansion of the Hippocampal Spatial Representation in a Distal Cue-Dependent Memory Task.

PubMed

Park, Seong-Beom; Lee, Inah

2016-08-01

Place cells in the hippocampus fire at specific positions in space, and distal cues in the environment play critical roles in determining the spatial firing patterns of place cells. Many studies have shown that place fields are influenced by distal cues in foraging animals. However, it is largely unknown whether distal-cue-dependent changes in place fields appear in different ways in a memory task if distal cues bear direct significance to achieving goals. We investigated this possibility in this study. Rats were trained to choose different spatial positions in a radial arm in association with distal cue configurations formed by visual cue sets attached to movable curtains around the apparatus. The animals were initially trained to associate readily discernible distal cue configurations (0° vs. 80° angular separation between distal cue sets) with different food-well positions and then later experienced ambiguous cue configurations (14° and 66°) intermixed with the original cue configurations. Rats showed no difficulty in transferring the associated memory formed for the original cue configurations when similar cue configurations were presented. Place field positions remained at the same locations across different cue configurations, whereas stability and coherence of spatial firing patterns were significantly disrupted when ambiguous cue configurations were introduced. Furthermore, the spatial representation was extended backward and skewed more negatively at the population level when processing ambiguous cue configurations, compared with when processing the original cue configurations only. This effect was more salient for large cue-separation conditions than for small cue-separation conditions. No significant rate remapping was observed across distal cue configurations. These findings suggest that place cells in the hippocampus dynamically change their detailed firing characteristics in response to a modified cue environment and that some of the firing properties previously reported in a foraging task might carry more functional weight than others when tested in a distal-cue-dependent memory task. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Patient-specific atrium models for training and pre-procedure surgical planning

NASA Astrophysics Data System (ADS)

Laing, Justin; Moore, John; Bainbridge, Daniel; Drangova, Maria; Peters, Terry

2017-03-01

Minimally invasive cardiac procedures requiring a trans-septal puncture such as atrial ablation and MitraClip® mitral valve repair are becoming increasingly common. These procedures are performed on the beating heart, and require clinicians to rely on image-guided techniques. For cases of complex or diseased anatomy, in which fluoroscopic and echocardiography images can be difficult to interpret, clinicians may benefit from patient-specific atrial models that can be used for training, surgical planning, and the validation of new devices and guidance techniques. Computed tomography (CT) images of a patient's heart were segmented and used to generate geometric models to create a patient-specific atrial phantom. Using rapid prototyping, the geometric models were converted into physical representations and used to build a mold. The atria were then molded using tissue-mimicking materials and imaged using CT. The resulting images were segmented and used to generate a point cloud data set that could be registered to the original patient data. The absolute distance of the two point clouds was compared and evaluated to determine the model's accuracy. The result when comparing the molded model point cloud to the original data set, resulted in a maximum Euclidean distance error of 4.5 mm, an average error of 0.5 mm and a standard deviation of 0.6 mm. Using our workflow for creating atrial models, potential complications, particularly for complex repairs, may be accounted for in pre-operative planning. The information gained by clinicians involved in planning and performing the procedure should lead to shorter procedural times and better outcomes for patients.

Beyond "the men of steel". The origins and significance of house staff training stress.

PubMed

Levin, R

1988-03-01

Stress is a common and significant component of house staff training. It has a dual capacity to support and hinder the trainee's education and well-being. However, there has been infrequent attention to the purpose and significance of training stress in the medical literature. The myths and traditions of medicine that foster sayings such as "in the days of the giants" or "the men of steel" do not sufficiently explain the dynamics of house staff stress. This article examines the origins, effects, and meaning of house officer stress. Stress seems to originate from as well as influence: the psychology of physicians, patient care, hospital economics, and the relationship between trainees and educators. Adaptations to stress acquired in training influence the house officer's future professional and personal well-being. Evaluation of training stress can help clarify related issues such as physician impairment and mentoring in medical education.

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

Supervised local error estimation for nonlinear image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Pluim, Josien P. W.

2017-02-01

Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.

Quantitative analysis of single- vs. multiple-set programs in resistance training.

PubMed

Wolfe, Brian L; LeMura, Linda M; Cole, Phillip J

2004-02-01

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.

[Assessment of the efficiency of the auditory training in children with dyslalia and auditory processing disorders].

PubMed

Włodarczyk, Elżbieta; Szkiełkowska, Agata; Skarżyński, Henryk; Piłka, Adam

2011-01-01

To assess effectiveness of the auditory training in children with dyslalia and central auditory processing disorders. Material consisted of 50 children aged 7-9-years-old. Children with articulation disorders stayed under long-term speech therapy care in the Auditory and Phoniatrics Clinic. All children were examined by a laryngologist and a phoniatrician. Assessment included tonal and impedance audiometry and speech therapists' and psychologist's consultations. Additionally, a set of electrophysiological examinations was performed - registration of N2, P2, N2, P2, P300 waves and psychoacoustic test of central auditory functions: FPT - frequency pattern test. Next children took part in the regular auditory training and attended speech therapy. Speech assessment followed treatment and therapy, again psychoacoustic tests were performed and P300 cortical potentials were recorded. After that statistical analyses were performed. Analyses revealed that application of auditory training in patients with dyslalia and other central auditory disorders is very efficient. Auditory training may be a very efficient therapy supporting speech therapy in children suffering from dyslalia coexisting with articulation and central auditory disorders and in children with educational problems of audiogenic origin. Copyright © 2011 Polish Otolaryngology Society. Published by Elsevier Urban & Partner (Poland). All rights reserved.

Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: pattern-recognition analyses for screening quality control purposes.

PubMed

Flumignan, Danilo Luiz; Boralle, Nivaldo; Oliveira, José Eduardo de

2010-06-30

In this work, the combination of carbon nuclear magnetic resonance ((13)C NMR) fingerprinting with pattern-recognition analyses provides an original and alternative approach to screening commercial gasoline quality. Soft Independent Modelling of Class Analogy (SIMCA) was performed on spectroscopic fingerprints to classify representative commercial gasoline samples, which were selected by Hierarchical Cluster Analyses (HCA) over several months in retails services of gas stations, into previously quality-defined classes. Following optimized (13)C NMR-SIMCA algorithm, sensitivity values were obtained in the training set (99.0%), with leave-one-out cross-validation, and external prediction set (92.0%). Governmental laboratories could employ this method as a rapid screening analysis to discourage adulteration practices. Copyright 2010 Elsevier B.V. All rights reserved.

Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.

PubMed

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; Bergkamp, Mayra; Wissink, Joost; Obels, Jiri; Keizer, Karlijn; de Leeuw, Frank-Erik; Ginneken, Bram van; Marchiori, Elena; Platel, Bram

2017-01-01

Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines. In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

A token centric part-of-speech tagger for biomedical text.

PubMed

Barrett, Neil; Weber-Jahnke, Jens

2014-05-01

Difficulties with part-of-speech (POS) tagging of biomedical text is accessing and annotating appropriate training corpora. These difficulties may result in POS taggers trained on corpora that differ from the tagger's target biomedical text (cross-domain tagging). In such cases where training and target corpora differ tagging accuracy decreases. This paper presents a POS tagger for cross-domain tagging called TcT. TcT estimates a tag's likelihood for a given token by combining token collocation probabilities and the token's tag probabilities calculated using a Naive Bayes classifier. We compared TcT to three POS taggers used in the biomedical domain (mxpost, Brill and TnT). We trained each tagger on a non-biomedical corpus and evaluated it on biomedical corpora. TcT was more accurate in cross-domain tagging than mxpost, Brill and TnT (respective averages 83.9, 81.0, 79.5 and 78.8). Our analysis of tagger performance suggests that lexical differences between corpora have more effect on tagging accuracy than originally considered by previous research work. Biomedical POS tagging algorithms may be modified to improve their cross-domain tagging accuracy without requiring extra training or large training data sets. Future work should reexamine POS tagging methods for biomedical text. This differs from the work to date that has focused on retraining existing POS taggers. Copyright © 2014 Elsevier B.V. All rights reserved.

A comparative study of two hazard handling training methods for novice drivers.

PubMed

Wang, Y B; Zhang, W; Salvendy, G

2010-10-01

The effectiveness of two hazard perception training methods, simulation-based error training (SET) and video-based guided error training (VGET), for novice drivers' hazard handling performance was tested, compared, and analyzed. Thirty-two novice drivers participated in the hazard perception training. Half of the participants were trained using SET by making errors and/or experiencing accidents while driving with a desktop simulator. The other half were trained using VGET by watching prerecorded video clips of errors and accidents that were made by other people. The two groups had exposure to equal numbers of errors for each training scenario. All the participants were tested and evaluated for hazard handling on a full cockpit driving simulator one week after training. Hazard handling performance and hazard response were measured in this transfer test. Both hazard handling performance scores and hazard response distances were significantly better for the SET group than the VGET group. Furthermore, the SET group had more metacognitive activities and intrinsic motivation. SET also seemed more effective in changing participants' confidence, but the result did not reach the significance level. SET exhibited a higher training effectiveness of hazard response and handling than VGET in the simulated transfer test. The superiority of SET might benefit from the higher levels of metacognition and intrinsic motivation during training, which was observed in the experiment. Future research should be conducted to assess whether the advantages of error training are still effective under real road conditions.

Set Shifting Training with Categorization Tasks

PubMed Central

Soveri, Anna; Waris, Otto; Laine, Matti

2013-01-01

The very few cognitive training studies targeting an important executive function, set shifting, have reported performance improvements that also generalized to untrained tasks. The present randomized controlled trial extends set shifting training research by comparing previously used cued training with uncued training. A computerized adaptation of the Wisconsin Card Sorting Test was utilized as the training task in a pretest-posttest experimental design involving three groups of university students. One group received uncued training (n = 14), another received cued training (n = 14) and the control group (n = 14) only participated in pre- and posttests. The uncued training group showed posttraining performance increases on their training task, but neither training group showed statistically significant transfer effects. Nevertheless, comparison of effect sizes for transfer effects indicated that our results did not differ significantly from the previous studies. Our results suggest that the cognitive effects of computerized set shifting training are mostly task-specific, and would preclude any robust generalization effects with this training. PMID:24324717

Development of Infrared Library Search Prefilters for Automotive Clear Coats from Simulated Attenuated Total Reflection (ATR) Spectra.

PubMed

Perera, Undugodage Don Nuwan; Nishikida, Koichi; Lavine, Barry K

2018-06-01

A previously published study featuring an attenuated total reflection (ATR) simulation algorithm that mitigated distortions in ATR spectra was further investigated to evaluate its efficacy to enhance searching of infrared (IR) transmission libraries. In the present study, search prefilters were developed from transformed ATR spectra to identify the assembly plant of a vehicle from ATR spectra of the clear coat layer. A total of 456 IR transmission spectra from the Paint Data Query (PDQ) database that spanned 22 General Motors assembly plants and served as a training set cohort were transformed into ATR spectra by the simulation algorithm. These search prefilters were formulated using the fingerprint region (1500 cm -1 to 500 cm -1 ). Both the transformed ATR spectra (training set) and the experimental ATR spectra (validation set) were preprocessed for pattern recognition analysis using the discrete wavelet transform, which increased the signal-to-noise of the ATR spectra by concentrating the signal in specific wavelet coefficients. Attenuated total reflection spectra of 14 clear coat samples (validation set) measured with a Nicolet iS50 Fourier transform IR spectrometer were correctly classified as to assembly plant(s) of the automotive vehicle from which the paint sample originated using search prefilters developed from 456 simulated ATR spectra. The ATR simulation (transformation) algorithm successfully facilitated spectral library matching of ATR spectra against IR transmission spectra of automotive clear coats in the PDQ database.

29 CFR 31.3 - General standards.

Code of Federal Regulations, 2010 CFR

2010-07-01

... which resulted in limiting participation by persons of a particular race, color or national origin. (7..., color, or national origin. (2) Manpower Development and Training Act, work-incentive under Social... respect to any trainee or enrollee under the Manpower Development and Training Act, Area Redevelopment Act...

29 CFR 31.3 - General standards.

Code of Federal Regulations, 2011 CFR

2011-07-01

... which resulted in limiting participation by persons of a particular race, color or national origin. (7..., color, or national origin. (2) Manpower Development and Training Act, work-incentive under Social... respect to any trainee or enrollee under the Manpower Development and Training Act, Area Redevelopment Act...

29 CFR 31.3 - General standards.

Code of Federal Regulations, 2014 CFR

2014-07-01

... which resulted in limiting participation by persons of a particular race, color or national origin. (7..., color, or national origin. (2) Manpower Development and Training Act, work-incentive under Social... respect to any trainee or enrollee under the Manpower Development and Training Act, Area Redevelopment Act...

29 CFR 31.3 - General standards.

Code of Federal Regulations, 2013 CFR

2013-07-01

... which resulted in limiting participation by persons of a particular race, color or national origin. (7..., color, or national origin. (2) Manpower Development and Training Act, work-incentive under Social... respect to any trainee or enrollee under the Manpower Development and Training Act, Area Redevelopment Act...

29 CFR 31.3 - General standards.

Code of Federal Regulations, 2012 CFR

2012-07-01

... which resulted in limiting participation by persons of a particular race, color or national origin. (7..., color, or national origin. (2) Manpower Development and Training Act, work-incentive under Social... respect to any trainee or enrollee under the Manpower Development and Training Act, Area Redevelopment Act...

REsearch into implementation STrategies to support patients of different ORigins and language background in a variety of European primary care settings (RESTORE): study protocol.

PubMed

MacFarlane, Anne; O'Donnell, Catherine; Mair, Frances; O'Reilly-de Brún, Mary; de Brún, Tomas; Spiegel, Wolfgang; van den Muijsenbergh, Maria; van Weel-Baumgarten, Evelyn; Lionis, Christos; Burns, Nicola; Gravenhorst, Katja; Princz, Christine; Teunissen, Erik; van den Driessen Mareeuw, Francine; Saridaki, Aristoula; Papadakaki, Maria; Vlahadi, Maria; Dowrick, Christopher

2012-11-20

The implementation of guidelines and training initiatives to support communication in cross-cultural primary care consultations is ad hoc across a range of international settings with negative consequences particularly for migrants. This situation reflects a well-documented translational gap between evidence and practice and is part of the wider problem of implementing guidelines and the broader range of professional educational and quality interventions in routine practice. In this paper, we describe our use of a contemporary social theory, Normalization Process Theory and participatory research methodology--Participatory Learning and Action--to investigate and support implementation of such guidelines and training initiatives in routine practice. This is a qualitative case study, using multiple primary care sites across Europe. Purposive and maximum variation sampling approaches will be used to identify and recruit stakeholders-migrant service users, general practitioners, primary care nurses, practice managers and administrative staff, interpreters, cultural mediators, service planners, and policy makers. We are conducting a mapping exercise to identify relevant guidelines and training initiatives. We will then initiate a PLA-brokered dialogue with stakeholders around Normalization Process Theory's four constructs--coherence, cognitive participation, collective action, and reflexive monitoring. Through this, we will enable stakeholders in each setting to select a single guideline or training initiative for implementation in their local setting. We will prospectively investigate and support the implementation journeys for the five selected interventions. Data will be generated using a Participatory Learning and Action approach to interviews and focus groups. Data analysis will follow the principles of thematic analysis, will occur in iterative cycles throughout the project and will involve participatory co-analysis with key stakeholders to enhance the authenticity and veracity of findings. This research employs a unique combination of Normalization Process Theory and Participatory Learning and Action, which will provide a novel approach to the analysis of implementation journeys. The findings will advance knowledge in the field of implementation science because we are using and testing theoretical and methodological approaches so that we can critically appraise their scope to mediate barriers and improve the implementation processes.

SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

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

Fan, J; Fan, J; Hu, W

Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditionalmore » probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.« less

52. Copy Photograph, L.A. Daily News, ca. 1944 (original print ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

52. Copy Photograph, L.A. Daily News, ca. 1944 (original print in UCLA Special Collections, Daily News Photograph Collection) TRAIN GATE AREA OF TRAIN CONCOURSE, LOOKING NORTHEAST - Los Angeles Union Passenger Terminal, Tracks & Shed, 800 North Alameda Street, Los Angeles, Los Angeles County, CA

Insights about Psychotherapy Training and Curricular Sequencing: Portal of Discovery

ERIC Educational Resources Information Center

McGowen, K. Ramsey; Miller, Merry Noel; Floyd, Michael; Miller, Barney; Coyle, Brent

2009-01-01

Objective: The authors discuss the curricular implications of a research project originally designed to evaluate the instructional strategy of using standardized patients in a psychotherapy training seminar. Methods: The original project included second-year residents enrolled in an introductory psychotherapy seminar that employed sequential…

Single- vs. Multiple-Set Strength Training in Women.

ERIC Educational Resources Information Center

Schlumberger, Andreas; Stec, Justyna; Schmidtbleicher, Dietmar

2001-01-01

Compared the effects of single- and multiple-set strength training in women with basic experience in resistance training. Both training groups had significant strength improvements in leg extension. In the seated bench press, only the three-set group showed a significant increase in maximal strength. There were higher strength gains overall in the…

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

Consumer Perceptions About Pilot Training: An Emotional Response

NASA Astrophysics Data System (ADS)

Rosser, Timothy G.

Civilian pilot training has followed a traditional path for several decades. With a potential pilot shortage approaching, ICAO proposed a new paradigm in pilot training methodology called the Multi-Crew Pilot License. This new methodology puts a pilot in the cockpit of an airliner with significantly less flight time experience than the traditional methodology. The purpose of this study was to determine to what extent gender, country of origin and pilot training methodology effect an aviation consumer's willingness to fly. Additionally, this study attempted to determine what emotions mediate a consumer's decision. This study surveyed participants from India and the United States to measure their willingness to fly using the Willingness to Fly Scale shown to be valid and reliable by Rice et al. (2015). The scale uses a five point Likert-type scale. In order to determine the mediating emotions, Ekman and Friesen's (1979) universal emotions, which are happiness, surprise, fear, disgust, anger, and sadness were used. Data were analyzed using SPSS. Descriptive statistics are provided for respondent's age and willingness to fly values. An ANOVA was conducted to test the first four hypotheses and Hayes (2004, 2008) bootstrapping process was used for the mediation analysis. Results indicated a significant main effect for training, F(1,972) = 227.76, p . .001, etap 2 = 0.190, country of origin, F(1, 972) = 28.86, p < .001, .p 2 = 0.029, and a two-way interaction was indicated between training and country of origin, F(7, 972) = 46.71, p < .001, etap 2 = 0.252. Mediation analysis indicated the emotions anger, fear, happiness, and surprise mediated the relationship between training and country of origin, and training. The findings of this study are important to designers of MPL training programs and airline marketers.

Self-Organizing-Map Program for Analyzing Multivariate Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Jacob, Joseph C.; Block, Gary L.; Braverman, Amy J.

2005-01-01

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

A Classification method for eye movements direction during REM sleep trained on wake electro-oculographic recordings.

PubMed

Betta, M; Laurino, M; Gemignani, A; Landi, A; Menicucci, D

2015-01-01

Rapid eye movements (REMs) are a peculiar and intriguing aspect of REM sleep, even if their physiological function still remains unclear. During this work, a new automatic tool was developed, aimed at a complete description of REMs activity during the night, both in terms of their timing of occurrence that in term of their directional properties. A classification stage of each singular movement detected during the night according to its main direction, was in fact added to our procedure of REMs detection and ocular artifact removal. A supervised classifier was constructed, using as training and validation set EOG data recorded during voluntary saccades of five healthy volunteers. Different classification methods were tested and compared. The further information about REMs directional characteristic provided by the procedure would represent a valuable tool for a deeper investigation into REMs physiological origin and functional meaning.

The use of Metro-Apex in health administration and planning education and training.

PubMed

Washburn, A W; McGinty, R T

1977-01-01

Metro-Apex is a computerized gaming-simulation designed to give practitioners and students an understanding of the environment of health care delivery systems. The exercise allows participants to explore the interaction of health roles and the health system's interaction with the larger community system. Originally developed as an air pollution control exercise, it has evolved to be a game about communities and how they operate. In 1972, the Department of Health, Education, and Welfare funded the Center for Multidisciplinary Educational Exercises (COMEX), of the University of Southern California to modify Metro-Apex for use with health service planners, health care administrators, and students in programs leading to these positions. The game runs in several rounds of from three to eight hours for groups of from 40 to 120 persons. Used in both educational and training settings, Metro-Apex is found to be a flexible addition to the health educator's tools.

Meta-analysis of operative experiences of general surgery trainees during training.

PubMed

Elsey, E J; Griffiths, G; Humes, D J; West, J

2017-01-01

General surgical training curricula around the world set defined operative numbers to be achieved before completion of training. However, there are few studies reporting total operative experience in training. This systematic review aimed to quantify the published global operative experience at completion of training in general surgery. Electronic databases were searched systematically for articles in any language relating to operative experience in trainees completing postgraduate general surgical training. Two reviewers independently assessed citations for inclusion using agreed criteria. Studies were assessed for quantitative data in addition to study design and purpose. A meta-analysis was performed using a random-effects model of studies with appropriate data. The search resulted in 1979 titles for review. Of these, 24 studies were eligible for inclusion in the review and data from five studies were used in the meta-analysis. Studies with published data of operative experience at completion of surgical training originated from the USA (19), UK (2), the Netherlands (1), Spain (1) and Thailand (1). Mean total operative experience in training varied from 783 procedures in Thailand to 1915 in the UK. Meta-analysis produced a mean pooled estimate of 1366 (95 per cent c.i. 1026 to 1707) procedures per trainee at completion of training. There was marked heterogeneity between studies (I 2  = 99·6 per cent). There is a lack of robust data describing the operative experiences of general surgical trainees outside the USA. The number of surgical procedures performed by general surgeons in training varies considerably across the world. © 2016 The Authors. BJS published by John Wiley & Sons Ltd on behalf of BJS Society Ltd.

Thinking Outside of Outpatient: Underutilized Settings for Psychotherapy Education.

PubMed

Blumenshine, Philip; Lenet, Alison E; Havel, Lauren K; Arbuckle, Melissa R; Cabaniss, Deborah L

2017-02-01

Although psychiatry residents are expected to achieve competency in conducting psychotherapy during their training, it is unclear how psychotherapy teaching is integrated across diverse clinical settings. Between January and March 2015, 177 psychiatry residency training directors were sent a survey asking about psychotherapy training practices in their programs, as well as perceived barriers to psychotherapy teaching. Eighty-two training directors (44%) completed the survey. While 95% indicated that psychotherapy was a formal learning objective for outpatient clinic rotations, fifty percent or fewer noted psychotherapy was a learning objective in other settings. Most program directors would like to see psychotherapy training included (particularly supportive psychotherapy and cognitive behavioral therapy) on inpatient (82%) and consultation-liaison settings (57%). The most common barriers identified to teaching psychotherapy in these settings were time and perceived inadequate staff training and interest. Non-outpatient rotations appear to be an underutilized setting for psychotherapy teaching.

A Clinical Tool for the Prediction of Venous Thromboembolism in Pediatric Trauma Patients.

PubMed

Connelly, Christopher R; Laird, Amy; Barton, Jeffrey S; Fischer, Peter E; Krishnaswami, Sanjay; Schreiber, Martin A; Zonies, David H; Watters, Jennifer M

2016-01-01

Although rare, the incidence of venous thromboembolism (VTE) in pediatric trauma patients is increasing, and the consequences of VTE in children are significant. Studies have demonstrated increasing VTE risk in older pediatric trauma patients and improved VTE rates with institutional interventions. While national evidence-based guidelines for VTE screening and prevention are in place for adults, none exist for pediatric patients, to our knowledge. To develop a risk prediction calculator for VTE in children admitted to the hospital after traumatic injury to assist efforts in developing screening and prophylaxis guidelines for this population. Retrospective review of 536,423 pediatric patients 0 to 17 years old using the National Trauma Data Bank from January 1, 2007, to December 31, 2012. Five mixed-effects logistic regression models of varying complexity were fit on a training data set. Model validity was determined by comparison of the area under the receiver operating characteristic curve (AUROC) for the training and validation data sets from the original model fit. A clinical tool to predict the risk of VTE based on individual patient clinical characteristics was developed from the optimal model. Diagnosis of VTE during hospital admission. Venous thromboembolism was diagnosed in 1141 of 536,423 children (overall rate, 0.2%). The AUROCs in the training data set were high (range, 0.873-0.946) for each model, with minimal AUROC attenuation in the validation data set. A prediction tool was developed from a model that achieved a balance of high performance (AUROCs, 0.945 and 0.932 in the training and validation data sets, respectively; P = .048) and parsimony. Points are assigned to each variable considered (Glasgow Coma Scale score, age, sex, intensive care unit admission, intubation, transfusion of blood products, central venous catheter placement, presence of pelvic or lower extremity fractures, and major surgery), and the points total is converted to a VTE risk score. The predicted risk of VTE ranged from 0.0% to 14.4%. We developed a simple clinical tool to predict the risk of developing VTE in pediatric trauma patients. It is based on a model created using a large national database and was internally validated. The clinical tool requires external validation but provides an initial step toward the development of the specific VTE protocols for pediatric trauma patients.

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints.

PubMed

Duan, Yuping; Bouslimi, Dalel; Yang, Guanyu; Shu, Huazhong; Coatrieux, Gouenou

2017-07-01

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

A new biologic prognostic model based on immunohistochemistry predicts survival in patients with diffuse large B-cell lymphoma.

PubMed

Perry, Anamarija M; Cardesa-Salzmann, Teresa M; Meyer, Paul N; Colomo, Luis; Smith, Lynette M; Fu, Kai; Greiner, Timothy C; Delabie, Jan; Gascoyne, Randy D; Rimsza, Lisa; Jaffe, Elaine S; Ott, German; Rosenwald, Andreas; Braziel, Rita M; Tubbs, Raymond; Cook, James R; Staudt, Louis M; Connors, Joseph M; Sehn, Laurie H; Vose, Julie M; López-Guillermo, Armando; Campo, Elias; Chan, Wing C; Weisenburger, Dennis D

2012-09-13

Biologic factors that predict the survival of patients with a diffuse large B-cell lymphoma, such as cell of origin and stromal signatures, have been discovered by gene expression profiling. We attempted to simulate these gene expression profiling findings and create a new biologic prognostic model based on immunohistochemistry. We studied 199 patients (125 in the training set, 74 in the validation set) with de novo diffuse large B-cell lymphoma treated with rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or CHOP-like therapies, and immunohistochemical stains were performed on paraffin-embedded tissue microarrays. In the model, 1 point was awarded for each adverse prognostic factor: nongerminal center B cell-like subtype, SPARC (secreted protein, acidic, and rich in cysteine) < 5%, and microvascular density quartile 4. The model using these 3 biologic markers was highly predictive of overall survival and event-free survival in multivariate analysis after adjusting for the International Prognostic Index in both the training and validation sets. This new model delineates 2 groups of patients, 1 with a low biologic score (0-1) and good survival and the other with a high score (2-3) and poor survival. This new biologic prognostic model could be used with the International Prognostic Index to stratify patients for novel or risk-adapted therapies.

Compositional Signatures of Conventional, Free Range, and Organic Pork Meat Using Fingerprint Techniques.

PubMed

Oliveira, Gislene B; Alewijn, Martin; Boerrigter-Eenling, Rita; van Ruth, Saskia M

2015-08-25

Consumers' interest in the way meat is produced is increasing in Europe. The resulting free range and organic meat products retail at a higher price, but are difficult to differentiate from their counterparts. To ascertain authenticity and prevent fraud, relevant markers need to be identified and new analytical methodology developed. The objective of this pilot study was to characterize pork belly meats of different animal welfare classes by their fatty acid (Fatty Acid Methyl Ester-FAME), non-volatile compound (electrospray ionization-tandem mass spectrometry-ESI-MS/MS), and volatile compound (proton-transfer-reaction mass spectrometry-PTR-MS) fingerprints. Well-defined pork belly meat samples (13 conventional, 15 free range, and 13 organic) originating from the Netherlands were subjected to analysis. Fingerprints appeared to be specific for the three categories, and resulted in 100%, 95.3%, and 95.3% correct identity predictions of training set samples for FAME, ESI-MS/MS, and PTR-MS respectively and slightly lower scores for the validation set. Organic meat was also well discriminated from the other two categories with 100% success rates for the training set for all three analytical approaches. Ten out of 25 FAs showed significant differences in abundance between organic meat and the other categories, free range meat differed significantly for 6 out of the 25 FAs. Overall, FAME fingerprinting presented highest discrimination power.

Compositional Signatures of Conventional, Free Range, and Organic Pork Meat Using Fingerprint Techniques

PubMed Central

Oliveira, Gislene B.; Alewijn, Martin; Boerrigter-Eenling, Rita; van Ruth, Saskia M.

2015-01-01

Consumers’ interest in the way meat is produced is increasing in Europe. The resulting free range and organic meat products retail at a higher price, but are difficult to differentiate from their counterparts. To ascertain authenticity and prevent fraud, relevant markers need to be identified and new analytical methodology developed. The objective of this pilot study was to characterize pork belly meats of different animal welfare classes by their fatty acid (Fatty Acid Methyl Ester—FAME), non-volatile compound (electrospray ionization-tandem mass spectrometry—ESI-MS/MS), and volatile compound (proton-transfer-reaction mass spectrometry—PTR-MS) fingerprints. Well-defined pork belly meat samples (13 conventional, 15 free range, and 13 organic) originating from the Netherlands were subjected to analysis. Fingerprints appeared to be specific for the three categories, and resulted in 100%, 95.3%, and 95.3% correct identity predictions of training set samples for FAME, ESI-MS/MS, and PTR-MS respectively and slightly lower scores for the validation set. Organic meat was also well discriminated from the other two categories with 100% success rates for the training set for all three analytical approaches. Ten out of 25 FAs showed significant differences in abundance between organic meat and the other categories, free range meat differed significantly for 6 out of the 25 FAs. Overall, FAME fingerprinting presented highest discrimination power. PMID:28231211

Methods Beyond Methods: A Model for Africana Graduate Methods Training.

PubMed

Best, Latrica E; Byrd, W Carson

2014-06-01

A holistic graduate education can impart not just tools and knowledge, but critical positioning to fulfill many of the original missions of Africana Studies programs set forth in the 1960s and 1970s. As an interdisciplinary field with many approaches to examining the African Diaspora, the methodological training of graduate students can vary across graduate programs. Although taking qualitative methods courses are often required of graduate students in Africana Studies programs, and these programs offer such courses, rarely if ever are graduate students in these programs required to take quantitative methods courses, let alone have these courses offered in-house. These courses can offer Africana Studies graduate students new tools for their own research, but more importantly, improve their knowledge of quantitative research of diasporic communities. These tools and knowledge can assist with identifying flawed arguments about African-descended communities and their members. This article explores the importance of requiring and offering critical quantitative methods courses in graduate programs in Africana Studies, and discusses the methods requirements of one graduate program in the field as an example of more rigorous training that other programs could offer graduate students.

Implementing a Digital Phasemeter in an FPGA

NASA Technical Reports Server (NTRS)

Rao, Shanti R.

2008-01-01

Firmware for implementing a digital phasemeter within a field-programmable gate array (FPGA) has been devised. In the original application of this firmware, the phase that one seeks to measure is the difference between the phases of two nominally-equal-frequency heterodyne signals generated by two interferometers. In that application, zero-crossing detectors convert the heterodyne signals to trains of rectangular pulses, the two pulse trains are fed to a fringe counter (the major part of the phasemeter) controlled by a clock signal having a frequency greater than the heterodyne frequency, and the fringe counter computes a time-averaged estimate of the difference between the phases of the two pulse trains. The firmware also does the following: Causes the FPGA to compute the frequencies of the input signals; Causes the FPGA to implement an Ethernet (or equivalent) transmitter for readout of phase and frequency values; and Provides data for use in diagnosis of communication failures. The readout rate can be set, by programming, to a value between 250 Hz and 1 kHz. Network addresses can be programmed by the user.

Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review

PubMed Central

Saw, Anna E; Main, Luana C; Gastin, Paul B

2016-01-01

Background Monitoring athlete well-being is essential to guide training and to detect any progression towards negative health outcomes and associated poor performance. Objective (performance, physiological, biochemical) and subjective measures are all options for athlete monitoring. Objective We systematically reviewed objective and subjective measures of athlete well-being. Objective measures, including those taken at rest (eg, blood markers, heart rate) and during exercise (eg, oxygen consumption, heart rate response), were compared against subjective measures (eg, mood, perceived stress). All measures were also evaluated for their response to acute and chronic training load. Methods The databases Academic search complete, MEDLINE, PsycINFO, SPORTDiscus and PubMed were searched in May 2014. Fifty-six original studies reported concurrent subjective and objective measures of athlete well-being. The quality and strength of findings of each study were evaluated to determine overall levels of evidence. Results Subjective and objective measures of athlete well-being generally did not correlate. Subjective measures reflected acute and chronic training loads with superior sensitivity and consistency than objective measures. Subjective well-being was typically impaired with an acute increase in training load, and also with chronic training, while an acute decrease in training load improved subjective well-being. Summary This review provides further support for practitioners to use subjective measures to monitor changes in athlete well-being in response to training. Subjective measures may stand alone, or be incorporated into a mixed methods approach to athlete monitoring, as is current practice in many sport settings. PMID:26423706

Update of the Polar SWIFT model for polar stratospheric ozone loss (Polar SWIFT version 2)

NASA Astrophysics Data System (ADS)

Wohltmann, Ingo; Lehmann, Ralph; Rex, Markus

2017-07-01

The Polar SWIFT model is a fast scheme for calculating the chemistry of stratospheric ozone depletion in polar winter. It is intended for use in global climate models (GCMs) and Earth system models (ESMs) to enable the simulation of mutual interactions between the ozone layer and climate. To date, climate models often use prescribed ozone fields, since a full stratospheric chemistry scheme is computationally very expensive. Polar SWIFT is based on a set of coupled differential equations, which simulate the polar vortex-averaged mixing ratios of the key species involved in polar ozone depletion on a given vertical level. These species are O3, chemically active chlorine (ClOx), HCl, ClONO2 and HNO3. The only external input parameters that drive the model are the fraction of the polar vortex in sunlight and the fraction of the polar vortex below the temperatures necessary for the formation of polar stratospheric clouds. Here, we present an update of the Polar SWIFT model introducing several improvements over the original model formulation. In particular, the model is now trained on vortex-averaged reaction rates of the ATLAS Chemistry and Transport Model, which enables a detailed look at individual processes and an independent validation of the different parameterizations contained in the differential equations. The training of the original Polar SWIFT model was based on fitting complete model runs to satellite observations and did not allow for this. A revised formulation of the system of differential equations is developed, which closely fits vortex-averaged reaction rates from ATLAS that represent the main chemical processes influencing ozone. In addition, a parameterization for the HNO3 change by denitrification is included. The rates of change of the concentrations of the chemical species of the Polar SWIFT model are purely chemical rates of change in the new version, whereas in the original Polar SWIFT model, they included a transport effect caused by the original training on satellite data. Hence, the new version allows for an implementation into climate models in combination with an existing stratospheric transport scheme. Finally, the model is now formulated on several vertical levels encompassing the vertical range in which polar ozone depletion is observed. The results of the Polar SWIFT model are validated with independent Microwave Limb Sounder (MLS) satellite observations and output from the original detailed chemistry model of ATLAS.

Going the distance: spatial scale of athletic experience affects the accuracy of path integration.

PubMed

Smith, Alastair D; Howard, Christina J; Alcock, Niall; Cater, Kirsten

2010-09-01

Evidence suggests that athletically trained individuals are more accurate than untrained individuals in updating their spatial position through idiothetic cues. We assessed whether training at different spatial scales affects the accuracy of path integration. Groups of rugby players (large-scale training) and martial artists (small-scale training) participated in a triangle-completion task: they were led (blindfolded) along two sides of a right-angled triangle and were required to complete the hypotenuse by returning to the origin. The groups did not differ in their assessment of the distance to the origin, but rugby players were more accurate than martial artists in assessing the correct angle to turn (heading), and landed significantly closer to the origin. These data support evidence that distance and heading components can be dissociated. Furthermore, they suggest that the spatial scale at which an individual is trained may affect the accuracy of one component of path integration but not the other.

Establishing Fire Safety Skills Using Behavioral Skills Training

ERIC Educational Resources Information Center

Houvouras, Andrew J., IV; Harvey, Mark T.

2014-01-01

The use of behavioral skills training (BST) to educate 3 adolescent boys on the risks of lighters and fire setting was evaluated using in situ assessment in a school setting. Two participants had a history of fire setting. After training, all participants adhered to established rules: (a) avoid a deactivated lighter, (b) leave the training area,…

A practical model for the train-set utilization: The case of Beijing-Tianjin passenger dedicated line in China

PubMed Central

Li, Xiaomeng; Yang, Zhuo

2017-01-01

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. PMID:28489933

Comprehensive simulation-enhanced training curriculum for an advanced minimally invasive procedure: a randomized controlled trial.

PubMed

Zevin, Boris; Dedy, Nicolas J; Bonrath, Esther M; Grantcharov, Teodor P

2017-05-01

There is no comprehensive simulation-enhanced training curriculum to address cognitive, psychomotor, and nontechnical skills for an advanced minimally invasive procedure. 1) To develop and provide evidence of validity for a comprehensive simulation-enhanced training (SET) curriculum for an advanced minimally invasive procedure; (2) to demonstrate transfer of acquired psychomotor skills from a simulation laboratory to live porcine model; and (3) to compare training outcomes of SET curriculum group and chief resident group. University. This prospective single-blinded, randomized, controlled trial allocated 20 intermediate-level surgery residents to receive either conventional training (control) or SET curriculum training (intervention). The SET curriculum consisted of cognitive, psychomotor, and nontechnical training modules. Psychomotor skills in a live anesthetized porcine model in the OR was the primary outcome. Knowledge of advanced minimally invasive and bariatric surgery and nontechnical skills in a simulated OR crisis scenario were the secondary outcomes. Residents in the SET curriculum group went on to perform a laparoscopic jejunojejunostomy in the OR. Cognitive, psychomotor, and nontechnical skills of SET curriculum group were also compared to a group of 12 chief surgery residents. SET curriculum group demonstrated superior psychomotor skills in a live porcine model (56 [47-62] versus 44 [38-53], P<.05) and superior nontechnical skills (41 [38-45] versus 31 [24-40], P<.01) compared with conventional training group. SET curriculum group and conventional training group demonstrated equivalent knowledge (14 [12-15] versus 13 [11-15], P = 0.47). SET curriculum group demonstrated equivalent psychomotor skills in the live porcine model and in the OR in a human patient (56 [47-62] versus 63 [61-68]; P = .21). SET curriculum group demonstrated inferior knowledge (13 [11-15] versus 16 [14-16]; P<.05), equivalent psychomotor skill (63 [61-68] versus 68 [62-74]; P = .50), and superior nontechnical skills (41 [38-45] versus 34 [27-35], P<.01) compared with chief resident group. Completion of the SET curriculum resulted in superior training outcomes, compared with conventional surgery training. Implementation of the SET curriculum can standardize training for an advanced minimally invasive procedure and can ensure that comprehensive proficiency milestones are met before exposure to patient care. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

DTIC Science & Technology

2010-01-01

classi- fication algorithms: simple random resampling (RRS), equal-instance random resampling (ERS), and network cross-validation ( NCV ). The first two... NCV procedure that eliminates overlap between test sets altogether. The procedure samples for k disjoint test sets that will be used for evaluation...propLabeled ∗ S) nodes from train Pool in f erenceSet =network − trainSet F = F ∪ < trainSet, test Set, in f erenceSet > end for output: F NCV addresses

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

PubMed

González-Recio, O; Jiménez-Montero, J A; Alenda, R

2013-01-01

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy and bias. This modification may be used to speed the calculus of genome-assisted evaluation in large data sets such us those obtained from consortiums. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Reduction of metal artifacts in x-ray CT images using a convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Yanbo; Chu, Ying; Yu, Hengyong

2017-09-01

Patients usually contain various metallic implants (e.g. dental fillings, prostheses), causing severe artifacts in the x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past four decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based MAR framework, which combines the information from the original and corrected images to suppress artifacts. Before the MAR, we generate a group of data and train a CNN. First, we numerically simulate various metal artifacts cases and build a dataset, which includes metal-free images (used as references), metal-inserted images and various MAR methods corrected images. Then, ten thousands patches are extracted from the databased to train the metal artifact reduction CNN. In the MAR stage, the original image and two corrected images are stacked as a three-channel input image for CNN, and a CNN image is generated with less artifacts. The water equivalent regions in the CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal affected projections, followed by the FBP reconstruction. Experimental results demonstrate the superior metal artifact reduction capability of the proposed method to its competitors.

Smartphone-Based System for Learning and Inferring Hearing Aid Settings.

PubMed

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J

2016-10-01

Previous research has shown that hearing aid wearers can successfully self-train their instruments' gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the "untrained system," that is, the manufacturer's algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The "trained system" first learned each individual's preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. An experimental within-participants study. Participants used a prototype hearing system-comprising two hearing aids, Android smartphone, and body-worn gateway device-for ∼6 weeks. Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Participants were fitted and instructed to perform daily comparisons of settings ("listening evaluations") through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone-including environmental sound classification, sound level, and location-to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system ("trained settings") to those suggested by the hearing aids' untrained system ("untrained settings"). We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. American Academy of Audiology

How well does multiple OCR error correction generalize?

NASA Astrophysics Data System (ADS)

Lund, William B.; Ringger, Eric K.; Walker, Daniel D.

2013-12-01

As the digitization of historical documents, such as newspapers, becomes more common, the need of the archive patron for accurate digital text from those documents increases. Building on our earlier work, the contributions of this paper are: 1. in demonstrating the applicability of novel methods for correcting optical character recognition (OCR) on disparate data sets, including a new synthetic training set, 2. enhancing the correction algorithm with novel features, and 3. assessing the data requirements of the correction learning method. First, we correct errors using conditional random fields (CRF) trained on synthetic training data sets in order to demonstrate the applicability of the methodology to unrelated test sets. Second, we show the strength of lexical features from the training sets on two unrelated test sets, yielding a relative reduction in word error rate on the test sets of 6.52%. New features capture the recurrence of hypothesis tokens and yield an additional relative reduction in WER of 2.30%. Further, we show that only 2.0% of the full training corpus of over 500,000 feature cases is needed to achieve correction results comparable to those using the entire training corpus, effectively reducing both the complexity of the training process and the learned correction model.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

PubMed

Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

2013-01-01

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

Dissociable effects of game elements on motivation and cognition in a task-switching training in middle childhood

PubMed Central

Dörrenbächer, Sandra; Müller, Philipp M.; Tröger, Johannes; Kray, Jutta

2014-01-01

Although motivational reinforcers are often used to enhance the attractiveness of trainings of cognitive control in children, little is known about how such motivational manipulations of the setting contribute to separate gains in motivation and cognitive-control performance. Here we provide a framework for systematically investigating the impact of a motivational video-game setting on the training motivation, the task performance, and the transfer success in a task-switching training in middle-aged children (8–11 years of age). We manipulated both the type of training (low-demanding/single-task training vs. high-demanding/task-switching training) as well as the motivational setting (low-motivational/without video-game elements vs. high-motivational/with video-game elements) separately from another. The results indicated that the addition of game elements to a training setting enhanced the intrinsic interest in task practice, independently of the cognitive demands placed by the training type. In the task-switching group, the high-motivational training setting led to an additional enhancement of task and switching performance during the training phase right from the outset. These motivation-induced benefits projected onto the switching performance in a switching situation different from the trained one (near-transfer measurement). However, in structurally dissimilar cognitive tasks (far-transfer measurement), the motivational gains only transferred to the response dynamics (speed of processing). Hence, the motivational setting clearly had a positive impact on the training motivation and on the paradigm-specific task-switching abilities; it did not, however, consistently generalize on broad cognitive processes. These findings shed new light on the conflation of motivation and cognition in childhood and may help to refine guidelines for designing adequate training interventions. PMID:25431564

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Accuracy of genomic prediction using deregressed breeding values estimated from purebred and crossbred offspring phenotypes in pigs.

PubMed

Hidalgo, A M; Bastiaansen, J W M; Lopes, M S; Veroneze, R; Groenen, M A M; de Koning, D-J

2015-07-01

Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

The UXO Classification Demonstration at San Luis Obispo, CA

DTIC Science & Technology

2010-09-01

Set ................................45  2.17.2  Active Learning Training and Test Set ..........................................47  2.17.3  Extended...optimized algorithm by applying it to only the unlabeled data in the test set. 2.17.2 Active Learning Training and Test Set SIG also used active ... learning [12]. Active learning , an alternative approach for constructing a training set, is used in conjunction with either supervised or semi

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Teaching Health Center Graduate Medical Education Locations Predominantly Located in Federally Designated Underserved Areas.

PubMed

Barclift, Songhai C; Brown, Elizabeth J; Finnegan, Sean C; Cohen, Elena R; Klink, Kathleen

2016-05-01

Background The Teaching Health Center Graduate Medical Education (THCGME) program is an Affordable Care Act funding initiative designed to expand primary care residency training in community-based ambulatory settings. Statute suggests, but does not require, training in underserved settings. Residents who train in underserved settings are more likely to go on to practice in similar settings, and graduates more often than not practice near where they have trained. Objective The objective of this study was to describe and quantify federally designated clinical continuity training sites of the THCGME program. Methods Geographic locations of the training sites were collected and characterized as Health Professional Shortage Area, Medically Underserved Area, Population, or rural areas, and were compared with the distribution of Centers for Medicare and Medicaid Services (CMS)-funded training positions. Results More than half of the teaching health centers (57%) are located in states that are in the 4 quintiles with the lowest CMS-funded resident-to-population ratio. Of the 109 training sites identified, more than 70% are located in federally designated high-need areas. Conclusions The THCGME program is a model that funds residency training in community-based ambulatory settings. Statute suggests, but does not explicitly require, that training take place in underserved settings. Because the majority of the 109 clinical training sites of the 60 funded programs in 2014-2015 are located in federally designated underserved locations, the THCGME program deserves further study as a model to improve primary care distribution into high-need communities.

Language, culture and international exchange of virtual patients.

PubMed

Muntean, Valentin; Calinici, Tudor; Tigan, Stefan; Fors, Uno G H

2013-02-11

Language and cultural differences could be a limiting factor for the international exchange of Virtual Patients (VPs), especially for small countries and languages of limited circulation. Our research evaluated whether it would be feasible to develop a VP based educational program in our Romanian institution, with cases in English and developed in a non-Romanian setting. The participants in the research comprised 4th year Romanian medical students from the Faculty of Medicine in Cluj-Napoca, Romania, with previous training exclusively in Romanian, good English proficiency and no experience with VPs. The students worked on eight VPs in two identical versions, Romanian and English. The first group (2010) of 136 students worked with four VPs developed in Cluj and the second group (2011) of 144 students with four VPs originally developed at an US University. Every student was randomly assigned two different VPs, one in Romanian and another in English. Student activity throughout the case, the diagnosis, therapeutic plan and diagnosis justification were recorded. We also compared student performance on the two VPs versions, Romanian and English and the student performance on the two sets of cases, originally developed in Romania, respectively USA. We found no significant differences between the students' performance on the Romanian vs. English version of VPs. Regarding the students' performance on the two sets of cases, in those originally developed in Romania, respectively in the USA, we found a number of statistically significant differences in the students' activity through the cases. There were no statistically significant differences in the students' ability to reach the correct diagnosis and therapeutic plan. The development of our program with VPs in English would be feasible, cost-effective and in accordance with the globalization of medical education.

A four-component model of the action potential in mouse detrusor smooth muscle cell

PubMed Central

Brain, Keith L.; Young, John S.; Manchanda, Rohit

2018-01-01

Background and hypothesis Detrusor smooth muscle cells (DSMCs) of the urinary bladder are electrically connected to one another via gap junctions and form a three dimensional syncytium. DSMCs exhibit spontaneous electrical activity, including passive depolarizations and action potentials. The shapes of spontaneous action potentials (sAPs) observed from a single DSM cell can vary widely. The biophysical origins of this variability, and the precise components which contribute to the complex shapes observed are not known. To address these questions, the basic components which constitute the sAPs were investigated. We hypothesized that linear combinations of scaled versions of these basic components can produce sAP shapes observed in the syncytium. Methods and results The basic components were identified as spontaneous evoked junction potentials (sEJP), native AP (nAP), slow after hyperpolarization (sAHP) and very slow after hyperpolarization (vsAHP). The experimental recordings were grouped into two sets: a training data set and a testing data set. A training set was used to estimate the components, and a test set to evaluate the efficiency of the estimated components. We found that a linear combination of the identified components when appropriately amplified and time shifted replicated various AP shapes to a high degree of similarity, as quantified by the root mean square error (RMSE) measure. Conclusions We conclude that the four basic components—sEJP, nAP, sAHP, and vsAHP—identified and isolated in this work are necessary and sufficient to replicate all varieties of the sAPs recorded experimentally in DSMCs. This model has the potential to generate testable hypotheses that can help identify the physiological processes underlying various features of the sAPs. Further, this model also provides a means to classify the sAPs into various shape classes. PMID:29351282

A four-component model of the action potential in mouse detrusor smooth muscle cell.

PubMed

Padmakumar, Mithun; Brain, Keith L; Young, John S; Manchanda, Rohit

2018-01-01

Detrusor smooth muscle cells (DSMCs) of the urinary bladder are electrically connected to one another via gap junctions and form a three dimensional syncytium. DSMCs exhibit spontaneous electrical activity, including passive depolarizations and action potentials. The shapes of spontaneous action potentials (sAPs) observed from a single DSM cell can vary widely. The biophysical origins of this variability, and the precise components which contribute to the complex shapes observed are not known. To address these questions, the basic components which constitute the sAPs were investigated. We hypothesized that linear combinations of scaled versions of these basic components can produce sAP shapes observed in the syncytium. The basic components were identified as spontaneous evoked junction potentials (sEJP), native AP (nAP), slow after hyperpolarization (sAHP) and very slow after hyperpolarization (vsAHP). The experimental recordings were grouped into two sets: a training data set and a testing data set. A training set was used to estimate the components, and a test set to evaluate the efficiency of the estimated components. We found that a linear combination of the identified components when appropriately amplified and time shifted replicated various AP shapes to a high degree of similarity, as quantified by the root mean square error (RMSE) measure. We conclude that the four basic components-sEJP, nAP, sAHP, and vsAHP-identified and isolated in this work are necessary and sufficient to replicate all varieties of the sAPs recorded experimentally in DSMCs. This model has the potential to generate testable hypotheses that can help identify the physiological processes underlying various features of the sAPs. Further, this model also provides a means to classify the sAPs into various shape classes.

DNA Everywhere. A Guide for Simplified Environmental Genomic DNA Extraction Suitable for Use in Remote Areas

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

Gabrielle N. Pecora; Francine C. Reid; Lauren M. Tom

2016-05-01

Collecting field samples from remote or geographically distant areas can be a financially and logistically challenging. With participation of a local organization where the samples are originated from, gDNA samples can be extracted from the field and shipped to a research institution for further processing and analysis. The ability to set up gDNA extraction capabilities in the field can drastically reduce cost and time when running long-term microbial studies with a large sample set. The method outlined here has developed a compact and affordable method for setting up a “laboratory” and extracting and shipping gDNA samples from anywhere in themore » world. This white paper explains the process of setting up the “laboratory”, choosing and training individuals with no prior scientific experience how to perform gDNA extractions and safe methods for shipping extracts to any research institution. All methods have been validated by the Andersen group at Lawrence Berkeley National Laboratory using the Berkeley Lab PhyloChip.« less

32 CFR 2001.11 - Original classification authority.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Original classification authority. 2001.11... Classification § 2001.11 Original classification authority. (a) General. Agencies shall establish a training program for original classifiers in accordance with subpart G of this part. (b) Requests for original...

STACCATO: a novel solution to supernova photometric classification with biased training sets

NASA Astrophysics Data System (ADS)

Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

2018-01-01

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

Recruitment, training outcomes, retention, and performance of community health advisors in two tobacco control interventions for Latinos.

PubMed

Woodruff, Susan I; Candelaria, Jeanette I; Elder, John P

2010-04-01

Community Health Advisors (CHAs) are indigenous lay health advisors who, with training, can create health awareness, disseminate health information and support behavior change in their communities. Little data are available that describe the characteristics, recruitment, training, retention, and performance of CHAs. The present study described the characteristics, recruitment process, training outcomes, retention activities, and performance of two sets of CHAs who delivered tobacco-related interventions in the local Latino community. The Tobacco Control in Latino Communities (TCLC) Center trained 35 CHAs to conduct either a smoking cessation program for Spanish-speaking adult smokers or a behavioral problem-solving intervention to reduce environmental tobacco smoke (ETS) exposure among low-income Latino children. Theoretical psychosocial constructs related to behavior change, general self-esteem, general self-efficacy, and demographics were collected from CHAs before and after training. Additional measures captured the level of professionalism exercised and effort undertaken by the CHAs, as well actual outcomes of their efforts. Of the 33 women and 2 men CHAs recruited, 86% were originally from Mexico, most had a high school education, most were married, and the average monthly household income was $1,100-$1,400. Mean participant age was 42 years, and level of acculturation was relatively low. There were changes in the desired direction pre-to-post training for both ETS and smoking cessation program CHAs for most of the psychosocial constructs. Expert ratings of CHA performance were good, and recipients of the CHAs' efforts showed positive changes in behavior. This information may aid in planning for recruitment and evaluation of CHAs for future tobacco control programs.

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

PubMed

Samarasinghe, S; Ling, H

In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced parameters and protein concentrations similar to the original RNN system. Results thus demonstrated the reliability of the proposed RNN method for modelling relatively large networks by modularisation for practical settings. Advantages of the method are its ability to represent accurate continuous system dynamics and ease of: parameter estimation through training with data from a practical setting, model analysis (40% faster than ODE), fine tuning parameters when more data are available, sub-model extension when new elements and/or interactions come to light and model expansion with addition of sub-models. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Child health in low-resource settings: pathways through UK paediatric training.

PubMed

Goenka, Anu; Magnus, Dan; Rehman, Tanya; Williams, Bhanu; Long, Andrew; Allen, Steve J

2013-11-01

UK doctors training in paediatrics benefit from experience of child health in low-resource settings. Institutions in low-resource settings reciprocally benefit from hosting UK trainees. A wide variety of opportunities exist for trainees working in low-resource settings including clinical work, research and the development of transferable skills in management, education and training. This article explores a range of pathways for UK trainees to develop experience in low-resource settings. It is important for trainees to start planning a robust rationale early for global child health activities via established pathways, in the interests of their own professional development as well as UK service provision. In the future, run-through paediatric training may include core elements of global child health, as well as designated 'tracks' for those wishing to develop their career in global child health further. Hands-on experience in low-resource settings is a critical component of these training initiatives.

Online learning from input versus offline memory evolution in adult word learning: effects of neighborhood density and phonologically related practice.

PubMed

Storkel, Holly L; Bontempo, Daniel E; Pak, Natalie S

2014-10-01

In this study, the authors investigated adult word learning to determine how neighborhood density and practice across phonologically related training sets influence online learning from input during training versus offline memory evolution during no-training gaps. Sixty-one adults were randomly assigned to learn low- or high-density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second-set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high-density and second-set words than for low-density and first-set words. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate online learning from input but seems to impede offline memory evolution.

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.

38 CFR 21.6072 - Extending the duration of a vocational training program.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Extending the duration of... Vocational Training for Certain New Pension Recipients Duration of Training § 21.6072 Extending the duration... training, the originally planned period of training may be extended to a total period consisting of the...

38 CFR 21.6072 - Extending the duration of a vocational training program.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Extending the duration of... Vocational Training for Certain New Pension Recipients Duration of Training § 21.6072 Extending the duration... training, the originally planned period of training may be extended to a total period consisting of the...

38 CFR 21.6072 - Extending the duration of a vocational training program.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Extending the duration of... Vocational Training for Certain New Pension Recipients Duration of Training § 21.6072 Extending the duration... training, the originally planned period of training may be extended to a total period consisting of the...

Machine Tool Technology. Automatic Screw Machine Troubleshooting & Set-Up Training Outlines [and] Basic Operator's Skills Set List.

ERIC Educational Resources Information Center

Anoka-Hennepin Technical Coll., Minneapolis, MN.

This set of two training outlines and one basic skills set list are designed for a machine tool technology program developed during a project to retrain defense industry workers at risk of job loss or dislocation because of conversion of the defense industry. The first troubleshooting training outline lists the categories of problems that develop…

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

PubMed

Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant

2013-01-01

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

PubMed Central

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J.

2017-01-01

Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. Conclusions The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. PMID:27718350

Leveling the Playing Field: Teacher Perception of Integrated STEM, Engineering, and Engineering Practices

NASA Astrophysics Data System (ADS)

Fincher, Bridgette Ann

The purpose of this study was to describe the perceptions and approaches of 14 third-through-fifth grade Arkansan elementary teachers towards integrative engineering and engineering practices during 80 hours of integrated STEM professional development training in the summer and fall of 2014. This training was known as Project Flight. The purpose of the professional development was to learn integrated STEM content related to aviation and to write grade level curriculum units using Wiggins and McTighe's Understanding by Design curriculum framework. The current study builds upon on the original research. Using a mixed method exploratory, embedded QUAL[quan] case study design and a non-experimental convenience sample derived from original 20 participants of Project Flight, this research sought to answer the following question: Does professional development influence elementary teachers' perceptions of the curriculum and instruction of integrated STEM engineering and engineering practices in a 3-to-5 grade level setting? A series of six qualitative and one quantitative sub-questions informed the research of the mixed method question. Hermeneutic content analysis was applied to archival and current qualitative data sets while descriptive statistics, independent t-tests, and repeated measures ANOVA tests were performed on the quantitative data. Broad themes in the teachers' perceptions and understanding of the nature of integrated engineering and engineering practices emerged through triangulation. After the professional development and the teaching of the integrated STEM units, all 14 teachers sustained higher perceptions of personal self-efficacy in their understanding of Next Generation Science Standards (NGSS). The teachers gained understanding of engineering and engineering practices, excluding engineering habits of mind, throughout the professional development training and unit teaching. The research resulted in four major findings specific to elementary engineering, which included engineering as student social agency and empowerment and the emergence of the engineering design loop as a new heuristic, and three more general non-engineering specific findings. All seven, however, have implications for future elementary engineering professional development as teachers in adopting states start to transition into using the NGSS standards.

Collaboration in a competitive healthcare system: negotiation 101 for clinicians.

PubMed

Clay-Williams, Robyn; Johnson, Andrew; Lane, Paul; Li, Zhicheng; Camilleri, Lauren; Winata, Teresa; Klug, Michael

2018-04-09

Purpose The purpose of this paper is to evaluate the effectiveness of negotiation training delivered to senior clinicians, managers and executives, by exploring whether staff members implemented negotiation skills in their workplace following the training, and if so, how and when. Design/methodology/approach This is a qualitative study involving face-to-face interviews with 18 senior clinicians, managers and executives who completed a two-day intensive negotiation skills training course. Interviews were transcribed verbatim, and inductive interpretive analysis techniques were used to identify common themes. Research setting was a large tertiary care hospital and health service in regional Australia. Findings Participants generally reported positive affective and utility reactions to the training, and attempted to implement at least some of the skills in the workplace. The main enabler was provision of a Negotiation Toolkit to assist in preparing and conducting negotiations. The main barrier was lack of time to reflect on the principles and prepare for upcoming negotiations. Participants reported that ongoing skill development and retention were not adequately addressed; suggestions for improving sustainability included provision of refresher training and mentoring. Research limitations/implications Limitations include self-reported data, and interview questions positively elicited examples of training translation. Practical implications The training was well matched to participant needs, with negotiation a common and daily activity for most healthcare professionals. Implementation of the skills showed potential for improving collaboration and problem solving in the workplace. Practical examples of how the skills were used in the workplace are provided. Originality/value To the authors' knowledge, this is the first international study aimed at evaluating the effectiveness of an integrative bargaining negotiation training program targeting executives, senior clinicians and management staff in a large healthcare organization.

Issues in the Development and Evaluation of Cross-Cultural Training in a Business Setting.

ERIC Educational Resources Information Center

Broadbooks, Wendy J.

Issues in the development and evaluation of cross-cultural training in a business setting were investigated. Cross-cultural training and cross-cultural evaluation were defined as training and evaluation of training that involve the interaction of participants from two or more different countries. Two evaluations of a management development-type…

Same But Different: An Intercultural Training Workshop Manual.

ERIC Educational Resources Information Center

Ching, Judith; And Others

This manual is a product of the Intercultural Training Program (ICT) Workshops which were originally designed to provide experiential training to Hawaii's teachers, students, and administrators. The training, however, may be useful to anyone interested in acquiring the experience and knowledge necessary to communicate effectively with varied…

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

A Giant in the Shadows: Major General Benjamin Foulois and the Rise of the Army Air Service in World War I

DTIC Science & Technology

2013-05-01

he was 28 years old. As one of America’s original military aviators, he flew the Army’s first dirigible balloon and its first airplane, learning to...training gratis from the Wrights, as the original contract only paid for the training of two pilots, and he received 54 minutes of student flight...first en- listment, Foulois asked everyone to call him Ben. No one asked him about the origin of his nickname, and he never volunteered the information

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

Influence of tumor microenvironment on prognosis in colorectal cancer: Tissue architecture-dependent signature of endosialin (TEM-1) and associated proteins

PubMed Central

O'Shannessy, Daniel J.; Somers, Elizabeth B.; Chandrasekaran, Lakshmi K.; Nicolaides, Nicholas C.; Bordeaux, Jennifer; Gustavson, Mark D.

2014-01-01

Tumor survival is influenced by interactions between tumor cells and the stromal microenvironment. One example is Endosialin (Tumor Endothelial Marker-1 (TEM-1) or CD248), which is expressed primarily by cells of mesenchymal origin and some tumor cells. The expression, as a function of architectural masking, of TEM-1 and its pathway-associated proteins was quantified and examined for association with five-year disease-specific survival on a colorectal cancer (CRC) cohort divided into training (n=330) and validation (n=164) sets. Although stromal expression of TEM-1 had prognostic value, a more significant prognostic signature was obtained through linear combination of five compartment-specific expression scores (TEM-1 Stroma, TEM-1 Tumor Vessel, HIF2α Stromal Vessel, Collagen IV Tumor, and Fibronectin Stroma). This resulted in a single continuous risk score (TAPPS: TEM-1 Associated Pathway Prognostic Signature) which was significantly associated with decreased survival on both the training set [HR=1.76 (95%CI: 1.44-2.15); p<0.001] and validation set [HR=1.38 (95%CI: 1.02-1.88); p=0.04]. Importantly, since prognosis is a critical clinical question in Stage II patients, the TAPPS score also significantly predicted survival in the Stage II patient (n=126) cohort [HR=1.75 (95%CI: 1.22-2.52); p=0.002] suggesting the potential of using the TAPPS score to assess overall risk in CRC patients, and specifically in Stage II patients. PMID:24980818

Using Google Glass in Surgical Settings: Systematic Review.

PubMed

Wei, Nancy J; Dougherty, Bryn; Myers, Aundria; Badawy, Sherif M

2018-03-06

In recent years, wearable devices have become increasingly attractive and the health care industry has been especially drawn to Google Glass because of its ability to serve as a head-mounted wearable device. The use of Google Glass in surgical settings is of particular interest due to the hands-free device potential to streamline workflow and maintain sterile conditions in an operating room environment. The aim is to conduct a systematic evaluation of the literature on the feasibility and acceptability of using Google Glass in surgical settings and to assess the potential benefits and limitations of its application. The literature was searched for articles published between January 2013 and May 2017. The search included the following databases: PubMed MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO (EBSCO), and IEEE Xplore. Two reviewers independently screened titles and abstracts and assessed full-text articles. Original research articles that evaluated the feasibility, usability, or acceptability of using Google Glass in surgical settings were included. This review was completed following the Preferred Reporting Results of Systematic Reviews and Meta-Analyses guidelines. Of the 520 records obtained, 31 met all predefined criteria and were included in this review. Google Glass was used in various surgical specialties. Most studies were in the United States (23/31, 74%) and all were conducted in hospital settings: 29 in adult hospitals (29/31, 94%) and two in children's hospitals (2/31, 7%). Sample sizes of participants who wore Google Glass ranged from 1 to 40. Of the 31 studies, 25 (81%) were conducted under real-time conditions or actual clinical care settings, whereas the other six (19%) were conducted under simulated environment. Twenty-six studies were pilot or feasibility studies (84%), three were case studies (10%), and two were randomized controlled trials (6%). The majority of studies examined the potential use of Google Glass as an intraoperative intervention (27/31, 87%), whereas others observed its potential use in preoperative (4/31, 13%) and postoperative settings (5/31, 16%). Google Glass was utilized as a videography and photography device (21/31, 68%), a vital sign monitor (6/31, 19%), a surgical navigation display (5/31, 16%), and as a videoconferencing tool to communicate with remote surgeons intraoperatively (5/31, 16%). Most studies reported moderate or high acceptability of using Google Glass in surgical settings. The main reported limitations of using Google Glass utilization were short battery life (8/31, 26%) and difficulty with hands-free features (5/31, 16%). There are promising feasibility and usability data of using Google Glass in surgical settings with particular benefits for surgical education and training. Despite existing technical limitations, Google Glass was generally well received and several studies in surgical settings acknowledged its potential for training, consultation, patient monitoring, and audiovisual recording. ©Nancy J Wei, Bryn Dougherty, Aundria Myers, Sherif M Badawy. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.03.2018.

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.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation

NASA Astrophysics Data System (ADS)

Xie, Xiaomin; Wang, Tingting

2017-02-01

In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.

rPM6 parameters for phosphorous and sulphur-containing open-shell molecules

NASA Astrophysics Data System (ADS)

Saito, Toru; Takano, Yu

2018-03-01

In this article, we have introduced a reparameterisation of PM6 (rPM6) for phosphorus and sulphur to achieve a better description of open-shell species containing the two elements. Two sets of the parameters have been optimised separately using our training sets. The performance of the spin-unrestricted rPM6 (UrPM6) method with the optimised parameters is evaluated against 14 radical species, which contain either phosphorus or sulphur atom, comparing with the original UPM6 and the spin-unrestricted density functional theory (UDFT) methods. The standard UPM6 calculations fail to describe the adiabatic singlet-triplet energy gaps correctly, and may cause significant structural mismatches with UDFT-optimised geometries. Leaving aside three difficult cases, tests on 11 open-shell molecules strongly indicate the superior performance of UrPM6, which provides much better agreement with the results of UDFT methods for geometric and electronic properties.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Effects of interset whole-body vibration on bench press resistance training in trained and untrained individuals.

PubMed

Timon, Rafael; Collado-Mateo, Daniel; Olcina, Guillermo; Gusi, Narcis

2016-03-01

Previous studies have demonstrated positive effects of acute vibration exercise on concentric strength and power, but few have observed the effects of vibration exposure on resistance training. The aim of this study was to verify the effects of whole body vibration applied to the chest via hands on bench press resistance training in trained and untrained individuals. Nineteen participants (10 recreationally trained bodybuilders and 9 untrained students) performed two randomized sessions of resistance training on separate days. Each strength session consisted of 3 bench press sets with a load of 75% 1RM to failure in each set, with 2 minutes' rest between sets. All subjects performed the same strength training with either, vibration exposure (12 Hz, 4 mm) of 30 seconds immediately before each bench press set or without vibration. Number of total repetitions, kinematic parameters, blood lactate and perceived exertion were analyzed. In the untrained group, vibration exposure caused a significant increase in the mean velocity (from 0.36±0.02 to 0.39±0.03 m/s) and acceleration (from 0.75±0.10 to 0.86±0.09 m/s2), as well as a decrease in perceived effort (from 8±0.57 to 7.35±0.47) in the first bench press set, but no change was observed in the third bench press set. In the recreationally trained bodybuilders, vibration exposure did not cause any improvement on the performance of bench press resistance training. These results suggest that vibration exposure applied just before the bench press exercise could be a good practice to be implemented by untrained individuals in resistance training.

The effectiveness of three sets of school-based instructional materials and community training on the acquisition and generalization of community laundry skills by students with severe handicaps.

PubMed

Morrow, S A; Bates, P E

1987-01-01

This study examined the effectiveness of three sets of school-based instructional materials and community training on acquisition and generalization of a community laundry skill by nine students with severe handicaps. School-based instruction involved artificial materials (pictures), simulated materials (cardboard replica of a community washing machine), and natural materials (modified home model washing machine). Generalization assessments were conducted at two different community laundromats, on two machines represented fully by the school-based instructional materials and two machines not represented fully by these materials. After three phases of school-based instruction, the students were provided ten community training trials in one laundromat setting and a final assessment was conducted in both the trained and untrained community settings. A multiple probe design across students was used to evaluate the effectiveness of the three types of school instruction and community training. After systematic training, most of the students increased their laundry performance with all three sets of school-based materials; however, generalization of these acquired skills was limited in the two community settings. Direct training in one of the community settings resulted in more efficient acquisition of the laundry skills and enhanced generalization to the untrained laundromat setting for most of the students. Results of this study are discussed in regard to the issue of school versus community-based instruction and recommendations are made for future research in this area.

Effect of slice thickness on brain magnetic resonance image texture analysis

PubMed Central

2010-01-01

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue. PMID:20955567

The Effects of Transfer in Teaching Vocabulary to School Children: An Analysis of the Dependencies between Lists of Trained and Non-Trained Words

ERIC Educational Resources Information Center

Frost, Jørgen; Ottem, Ernst; Hagtvet, Bente E.; Snow, Catherine E.

2016-01-01

In the present study, 81 Norwegian students were taught the meaning of words by the Word Generation (WG) method and 51 Norwegian students were taught by an approach inspired by the Thinking Schools (TS) concept. Two sets of words were used: a set of words to be trained and a set of non-trained control words. The two teaching methods yielded no…

Preventing a Relapse or Setting Goals? Elucidating the Impact of Post-Training Transfer Interventions on Training Transfer Performance

ERIC Educational Resources Information Center

Rahyuda, Agoes Ganesha; Soltani, Ebrahim; Syed, Jawad

2018-01-01

Based on a review of the literature on post-training transfer interventions, this paper offers a conceptual model that elucidates potential mechanisms through which two types of post-training transfer intervention (relapse prevention and proximal plus distal goal setting) influence the transfer of training. We explain how the application of…

Robot-assisted walking training for individuals with Parkinson’s disease: a pilot randomized controlled trial

PubMed Central

2013-01-01

Background Over the last years, the introduction of robotic technologies into Parkinson’s disease rehabilitation settings has progressed from concept to reality. However, the benefit of robotic training remains elusive. This pilot randomized controlled observer trial is aimed at investigating the feasibility, the effectiveness and the efficacy of new end-effector robot training in people with mild Parkinson’s disease. Methods Design. Pilot randomized controlled trial. Setting. Robot assisted gait training (EG) compared to treadmill training (CG). Participants. Twenty cognitively intact participants with mild Parkinson’s disease and gait disturbance. Interventions. The EG underwent a rehabilitation programme of robot assisted walking for 40 minutes, 5 times a week for 4 weeks. The CG received a treadmill training programme for 40 minutes, 5 times a week for 4 weeks. Main outcome measures. The outcome measure of efficacy was recorded by gait analysis laboratory. The assessments were performed at the beginning (T0) and at the end of the treatment (T1). The main outcome was the change in velocity. The feasibility of the intervention was assessed by recording exercise adherence and acceptability by specific test. Results Robot training was feasible, acceptable, safe, and the participants completed 100% of the prescribed training sessions. A statistically significant improvement in gait index was found in favour of the EG (T0 versus T1). In particular, the statistical analysis of primary outcome (gait speed) using the Friedman test showed statistically significant improvements for the EG (p = 0,0195). The statistical analysis performed by Friedman test of Step length left (p = 0,0195) and right (p = 0,0195) and Stride length left (p = 0,0078) and right (p = 0,0195) showed a significant statistical gain. No statistically significant improvements on the CG were found. Conclusions Robot training is a feasible and safe form of rehabilitative exercise for cognitively intact people with mild PD. This original approach can contribute to increase a short time lower limb motor recovery in idiopathic PD patients. The focus on the gait recovery is a further characteristic that makes this research relevant to clinical practice. On the whole, the simplicity of treatment, the lack of side effects, and the positive results from patients support the recommendation to extend the use of this treatment. Further investigation regarding the long-time effectiveness of robot training is warranted. Trial registration ClinicalTrials.gov NCT01668407 PMID:23706025

Comparison of molecular breeding values based on within- and across-breed training in beef cattle.

PubMed

Kachman, Stephen D; Spangler, Matthew L; Bennett, Gary L; Hanford, Kathryn J; Kuehn, Larry A; Snelling, Warren M; Thallman, R Mark; Saatchi, Mahdi; Garrick, Dorian J; Schnabel, Robert D; Taylor, Jeremy F; Pollak, E John

2013-08-16

Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.

Effects of draught load exercise and training on calcium homeostasis in horses.

PubMed

Vervuert, I; Coenen, M; Zamhöfer, J

2005-01-01

This study was conducted to investigate the effects of draught load exercise on calcium (Ca) homeostasis in young horses. Five 2-year-old untrained Standardbred horses were studied in a 4-month training programme. All exercise workouts were performed on a treadmill at a 6% incline and with a constant draught load of 40 kg (0.44 kN). The training programme started with a standardized exercise test (SET 1; six incremental steps of 5 min duration each, first step 1.38 m/s, stepwise increase by 0.56 m/s). A training programme was then initiated which consisted of low-speed exercise sessions (LSE; constant velocity at 1.67 m/s for 60 min, 48 training sessions in total). After the 16th and 48th LSE sessions, SETs (SET 2: middle of training period, SET 3: finishing training period) were performed again under the identical test protocol of SET 1. Blood samples for blood lactate, plasma total Ca, blood ionized calcium (Ca(2+)), blood pH, plasma inorganic phosphorus (P(i)) and plasma intact parathyroid hormone (PTH) were collected before, during and after SETs, and before and after the first, 16th, 32nd and 48th LSE sessions. During SETs there was a decrease in ionized Ca(2+) and a rise in lactate, P(i) and intact PTH. The LSEs resulted in an increase in pH and P(i), whereas lactate, ionized Ca(2+), total Ca and intact PTH were not affected. No changes in Ca metabolism were detected in the course of training. Results of this study suggest that the type of exercise influences Ca homeostasis and intact PTH response, but that these effects are not influenced in the course of the training period.

Methods Beyond Methods: A Model for Africana Graduate Methods Training

PubMed Central

Best, Latrica E.; Byrd, W. Carson

2018-01-01

A holistic graduate education can impart not just tools and knowledge, but critical positioning to fulfill many of the original missions of Africana Studies programs set forth in the 1960s and 1970s. As an interdisciplinary field with many approaches to examining the African Diaspora, the methodological training of graduate students can vary across graduate programs. Although taking qualitative methods courses are often required of graduate students in Africana Studies programs, and these programs offer such courses, rarely if ever are graduate students in these programs required to take quantitative methods courses, let alone have these courses offered in-house. These courses can offer Africana Studies graduate students new tools for their own research, but more importantly, improve their knowledge of quantitative research of diasporic communities. These tools and knowledge can assist with identifying flawed arguments about African-descended communities and their members. This article explores the importance of requiring and offering critical quantitative methods courses in graduate programs in Africana Studies, and discusses the methods requirements of one graduate program in the field as an example of more rigorous training that other programs could offer graduate students. PMID:29710883

Construction of an isokinetic eccentric cycle ergometer for research and training.

PubMed

Elmer, Steven J; Martin, James C

2013-08-01

Eccentric cycling serves a useful exercise modality in clinical, research, and sport training settings. However, several constraints can make it difficult to use commercially available eccentric cycle ergometers. In this technical note, we describe the process by which we built an isokinetic eccentric cycle ergometer using exercise equipment modified with commonly available industrial parts. Specifically, we started with a used recumbent cycle ergometer and removed all the original parts leaving only the frame and seat. A 2.2 kW electric motor was attached to a transmission system that was then joined with the ergometer. The motor was controlled using a variable frequency drive, which allowed for control of a wide range of pedaling rates. The ergometer was also equipped with a power measurement device that quantified work, power, and pedaling rate and provided feedback to the individual performing the exercise. With these parts along with some custom fabrication, we were able to construct an isokinetic eccentric cycle ergometer suitable for research and training. This paper offers a guide for those individuals who plan to use eccentric cycle ergometry as an exercise modality and wish to construct their own ergometer.

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

PubMed

McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S

2006-09-01

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.

Transitions of Young Migrants to Initial Vocational Education and Training in Germany: The Significance of Social Origin and Gender

ERIC Educational Resources Information Center

Beicht, Ursula; Walden, Günter

2017-01-01

The topic of the present paper is how successful young people from a migration background in Germany are in making the transition to initial vocational education and training (VET). Particular emphasis is placed on interactions with social origin and gender. The analyses are based on the 2011 BIBB Transitional Study, a representative survey of…

Quarry identification of historical building materials by means of laser induced breakdown spectroscopy, X-ray fluorescence and chemometric analysis

NASA Astrophysics Data System (ADS)

Colao, F.; Fantoni, R.; Ortiz, P.; Vazquez, M. A.; Martin, J. M.; Ortiz, R.; Idris, N.

2010-08-01

To characterize historical building materials according to the geographic origin of the quarries from which they have been mined, the relative content of major and trace elements were determined by means of Laser Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) techniques. 48 different specimens were studied and the entire samples' set was divided in two different groups: the first, used as reference set, was composed by samples mined from eight different quarries located in Seville province; the second group was composed by specimens of unknown provenance collected in several historical buildings and churches in the city of Seville. Data reduction and analysis on laser induced breakdown spectroscopy and X-ray fluorescence measurements was performed using multivariate statistical approach, namely the Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). A clear separation among reference sample materials mined from different quarries was observed in Principal Components (PC) score plots, then a supervised soft independent modeling of class analogy classification was trained and run, aiming to assess the provenance of unknown samples according to their elemental content. The obtained results were compared with the provenance assignments made on the basis of petrographical description. This work gives experimental evidence that laser induced breakdown spectroscopy measurements on a relatively small set of elements is a fast and effective method for the purpose of origin identification.

Team Training and Retention of Skills Acquired Above Real Time Training on a Flight Simulator

NASA Technical Reports Server (NTRS)

Ali, Syed Friasat; Guckenberger, Dutch; Crane, Peter; Rossi, Marcia; Williams, Mayard; Williams, Jason; Archer, Matt

2000-01-01

Above Real-Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments related to training of pilots performed by NASA engineers (Kolf in 1973, Hoey in 1976) and others (Guckenberger, Crane and their associates in the nineties) have shown that in comparison with the real time training (RTT), ARTT provides the following benefits: increased rate of skill acquisition, reduced simulator and aircraft training time, and more effective training for emergency procedures. Two sets of experiments have been performed; they are reported in professional conferences and the respective papers are included in this report. The retention of effects of ARTT has been studied in the first set of experiments and the use of ARTT as top-off training has been examined in the second set of experiments. In ARTT, the pace of events was 1.5 times the pace in RTT. In both sets of experiments, university students were trained to perform an aerial gunnery task. The training unit was equipped with a joystick and a throttle. The student acted as a nose gunner in a hypothetical two place attack aircraft. The flight simulation software was installed on a Universal Distributed Interactive Simulator platform supplied by ECC International of Orlando, Florida. In the first set of experiments, two training programs RTT or ART7 were used. Students were then tested in real time on more demanding scenarios: either immediately after training or two days later. The effects of ARTT did not decrease over a two day retention interval and ARTT was more time efficient than real time training. Therefore, equal test performance could be achieved with less clock-time spent in the simulator. In the second set of experiments three training programs RTT or ARTT or RARTT, were used. In RTT, students received 36 minutes of real time training. In ARTT, students received 36 minutes of above real time training. In RARTT, students received 18 minutes of real time training and 18 minutes of above real time training as top-off training. Students were then tested in real time on more demanding scenarios. The use of ARTT as top-off training after RTT offered better training than RTT alone or ARTT alone. It is, however, suggested that a similar experiment be conducted on a relatively more complex task with a larger sample of participants. Within the proposed duration of the research effort, the setting up of experiments and trial runs on using ARTT for team training were also scheduled but they could not be accomplished due to extra ordinary challenges faced in developing the required software configuration. Team training is, however, scheduled in a future study sponsored by NASA at Tuskegee University.

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

2016-03-01

Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

Critical capacity, travel time delays and travel time distribution of rapid mass transit systems

NASA Astrophysics Data System (ADS)

Legara, Erika Fille; Monterola, Christopher; Lee, Kee Khoon; Hung, Gih Guang

2014-07-01

We set up a mechanistic agent-based model of a rapid mass transit system. Using empirical data from Singapore's unidentifiable smart fare card, we validate our model by reconstructing actual travel demand and duration of travel statistics. We subsequently use this model to investigate two phenomena that are known to significantly affect the dynamics within the RTS: (1) overloading in trains and (2) overcrowding in the RTS platform. We demonstrate that by varying the loading capacity of trains, a tipping point emerges at which an exponential increase in the duration of travel time delays is observed. We also probe the impact on the rail system dynamics of three types of passenger growth distribution across stations: (i) Dirac delta, (ii) uniform and (iii) geometric, which is reminiscent of the effect of land use on transport. Under the assumption of a fixed loading capacity, we demonstrate the dependence of a given origin-destination (OD) pair on the flow volume of commuters in station platforms.

Systematic review of skills transfer after surgical simulation-based training.

PubMed

Dawe, S R; Pena, G N; Windsor, J A; Broeders, J A J L; Cregan, P C; Hewett, P J; Maddern, G J

2014-08-01

Simulation-based training assumes that skills are directly transferable to the patient-based setting, but few studies have correlated simulated performance with surgical performance. A systematic search strategy was undertaken to find studies published since the last systematic review, published in 2007. Inclusion of articles was determined using a predetermined protocol, independent assessment by two reviewers and a final consensus decision. Studies that reported on the use of surgical simulation-based training and assessed the transferability of the acquired skills to a patient-based setting were included. Twenty-seven randomized clinical trials and seven non-randomized comparative studies were included. Fourteen studies investigated laparoscopic procedures, 13 endoscopic procedures and seven other procedures. These studies provided strong evidence that participants who reached proficiency in simulation-based training performed better in the patient-based setting than their counterparts who did not have simulation-based training. Simulation-based training was equally as effective as patient-based training for colonoscopy, laparoscopic camera navigation and endoscopic sinus surgery in the patient-based setting. These studies strengthen the evidence that simulation-based training, as part of a structured programme and incorporating predetermined proficiency levels, results in skills transfer to the operative setting. © 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.

Posttraumatic stress in emergency settings outside North America and Europe: A review of the emic literature

PubMed Central

Rasmussen, Andrew; Keatley, Eva; Joscelyne, Amy

2014-01-01

Mental health professionals from North America and Europe have become common participants in postconflict and disaster relief efforts outside of North America and Europe. Consistent with their training, these practitioners focus primarily on posttraumatic stress disorder (PTSD) as their primary diagnostic concern. Most research that has accompanied humanitarian aid efforts has likewise originated in North America and Europe, has focused on PTSD, and in turn has reinforced practitioners’ assumptions about the universality of the diagnosis. In contrast, studies that have attempted to identify how local populations conceptualize posttrauma reactions portray a wide range of psychological states. We review this emic literature in order to examine differences and commonalities across local posttraumatic cultural concepts of distress (CCDs). We focus on symptoms to describe these constructs – i.e., using the dominant neo-Kraepelinian approach used in North American and European psychiatry – as opposed to focusing on explanatory models in order to examine whether positive comparisons of PTSD to CCDs meet criteria for face validity. Hierarchical clustering (Ward’s method) of symptoms within CCDs provides a portrait of the emic literature characterized by traumatic multifinality with several common themes. Global variety within the literature suggests that few disaster-affected populations have mental health nosologies that include PTSD-like syndromes. One reason for this seems to be the almost complete absence of avoidance as pathology. Many nosologies contain depression-like disorders. Relief efforts would benefit from mental health practitioners getting specific training in culture-bound posttrauma constructs when entering settings beyond the boundaries of the culture of their training and practice. PMID:24698712

Differences in Physiological Responses to Interval Training in Cyclists With and Without Interval Training Experience

PubMed Central

Hebisz, Rafal; Borkowski, Jacek; Zatoń, Marek

2016-01-01

Abstract The aim of this study was to determine differences in glycolytic metabolite concentrations and work output in response to an all-out interval training session in 23 cyclists with at least 2 years of interval training experience (E) and those inexperienced (IE) in this form of training. The intervention involved subsequent sets of maximal intensity exercise on a cycle ergometer. Each set comprised four 30 s repetitions interspersed with 90 s recovery periods; sets were repeated when blood pH returned to 7.3. Measurements of post-exercise hydrogen (H+) and lactate ion (LA-) concentrations and work output were taken. The experienced cyclists performed significantly more sets of maximal efforts than the inexperienced athletes (5.8 ± 1.2 vs. 4.3 ± 0.9 sets, respectively). Work output decreased in each subsequent set in the IE group and only in the last set in the E group. Distribution of power output changed only in the E group; power decreased in the initial repetitions of set only to increase in the final repetitions. H+ concentration decreased in the third, penultimate, and last sets in the E group and in each subsequent set in the IE group. LA- decreased in the last set in both groups. In conclusion, the experienced cyclists were able to repeatedly induce elevated levels of lactic acidosis. Power output distribution changed with decreased acid–base imbalance. In this way, this group could compensate for a decreased anaerobic metabolism. The above factors allowed cyclists experienced in interval training to perform more sets of maximal exercise without a decrease in power output compared with inexperienced cyclists. PMID:28149346

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

Unraveling Motivational Profiles of Health Care Professionals for Continuing Education: The Example of Pharmacists in the Netherlands.

PubMed

Tjin A Tsoi, Sharon L N M; de Boer, Anthonius; Croiset, Gerda; Koster, Andries S; Kusurkar, Rashmi A

2016-01-01

Continuing education (CE) can support health care professionals in maintaining and developing their knowledge and competencies. Although lack of motivation is one of the most important barriers of pharmacists' participation in CE, we know little about the quality or the quantity of motivation. We used the self-determination theory, which describes autonomous motivation (AM) as originating from within an individual and controlled motivation (CM) as originating from external factors, as a framework for this study. Our aim was to obtain insight into the quality and quantity of pharmacists' motivation for CE. The scores of 425 pharmacists on Academic Motivation Scale were subjected to K-means cluster analysis to generate motivational profiles. We unraveled four motivational profiles: (1) good quality with high AM/low CM, (2) high quantity with high AM/high CM, (3) poor quality with low AM/high CM, and (4) low quantity with low AM/low CM. Female pharmacists, pharmacists working in a hospital pharmacy, pharmacists working for more than 10 years, and pharmacists not in training were highly represented in the good-quality profile. Pharmacists working in a community pharmacy, pharmacists working for less than 10 years, and pharmacists in training were highly represented in the high-quantity profile. Male pharmacists were more or less equally distributed over the four profiles. The highest percentage of pharmacy owners was shown in the low-quantity profile, and the highest percentage of the nonowners was shown in the good-quality profile. Pharmacists exhibit different motivational profiles, which are associated with their background characteristics, such as gender, ownership of business, practice setting, and current training. Motivational profiles could be used to tailor CE courses for pharmacists.

Automotive Power Trains.

ERIC Educational Resources Information Center

Marine Corps Inst., Washington, DC.

This correspondence course, originally developed for the Marine Corps, is designed to provide mechanics with an understanding of the operation, maintenance, and troubleshooting of automotive power trains and certain auxiliary equipment. The course contains six study units covering basic power trains; clutch principles and operations; conventional…

Robot-assisted walking training for individuals with Parkinson's disease: a pilot randomized controlled trial.

PubMed

Sale, Patrizio; De Pandis, Maria Francesca; Le Pera, Domenica; Sova, Ivan; Cimolin, Veronica; Ancillao, Andrea; Albertini, Giorgio; Galli, Manuela; Stocchi, Fabrizio; Franceschini, Marco

2013-05-24

Over the last years, the introduction of robotic technologies into Parkinson's disease rehabilitation settings has progressed from concept to reality. However, the benefit of robotic training remains elusive. This pilot randomized controlled observer trial is aimed at investigating the feasibility, the effectiveness and the efficacy of new end-effector robot training in people with mild Parkinson's disease. Design. Pilot randomized controlled trial. Robot training was feasible, acceptable, safe, and the participants completed 100% of the prescribed training sessions. A statistically significant improvement in gait index was found in favour of the EG (T0 versus T1). In particular, the statistical analysis of primary outcome (gait speed) using the Friedman test showed statistically significant improvements for the EG (p = 0,0195). The statistical analysis performed by Friedman test of Step length left (p = 0,0195) and right (p = 0,0195) and Stride length left (p = 0,0078) and right (p = 0,0195) showed a significant statistical gain. No statistically significant improvements on the CG were found. Robot training is a feasible and safe form of rehabilitative exercise for cognitively intact people with mild PD. This original approach can contribute to increase a short time lower limb motor recovery in idiopathic PD patients. The focus on the gait recovery is a further characteristic that makes this research relevant to clinical practice. On the whole, the simplicity of treatment, the lack of side effects, and the positive results from patients support the recommendation to extend the use of this treatment. Further investigation regarding the long-time effectiveness of robot training is warranted. ClinicalTrials.gov NCT01668407.

Challenges of interprofessional team training: a qualitative analysis of residents' perceptions.

PubMed

van Schaik, Sandrijn; Plant, Jennifer; O'Brien, Bridget

2015-01-01

Simulation-based interprofessional team training is thought to improve patient care. Participating teams often consist of both experienced providers and trainees, which likely impacts team dynamics, particularly when a resident leads the team. Although similar team composition is found in real-life, debriefing after simulations puts a spotlight on team interactions and in particular on residents in the role of team leader. The goal of the current study was to explore residents' perceptions of simulation-based interprofessional team training. This was a secondary analysis of a study of residents in the pediatric residency training program at the University of California, San Francisco (United States) leading interprofessional teams in simulated resuscitations, followed by facilitated debriefing. Residents participated in individual, semi-structured, audio-recorded interviews within one month of the simulation. The original study aimed to examine residents' self-assessment of leadership skills, and during analysis we encountered numerous comments regarding the interprofessional nature of the simulation training. We therefore performed a secondary analysis of the interview transcripts. We followed an iterative process to create a coding scheme, and used interprofessional learning and practice as sensitizing concepts to extract relevant themes. 16 residents participated in the study. Residents felt that simulated resuscitations were helpful but anxiety provoking, largely due to interprofessional dynamics. They embraced the interprofessional training opportunity and appreciated hearing other healthcare providers' perspectives, but questioned the value of interprofessional debriefing. They identified the need to maintain positive relationships with colleagues in light of the teams' complex hierarchy as a barrier to candid feedback. Pediatric residents in our study appreciated the opportunity to participate in interprofessional team training but were conflicted about the value of feedback and debriefing in this setting. These data indicate that the optimal approach to such interprofessional education activities deserves further study.

Start-Up Training in Mississippi: Program Development Guide.

ERIC Educational Resources Information Center

Brooks, Kent; And Others

Due to recent industrial growth in Mississippi and the shortage of trained manpower in numerous occupations, start-up training programs have originated to provide a pretrained work force for new or expanding industry in the State. Each start-up training program is a joint effort between a new or expanding industry and a public educational…

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

A preliminary MTD-PLS study for androgen receptor binding of steroid compounds

NASA Astrophysics Data System (ADS)

Bora, Alina; Seclaman, E.; Kurunczi, L.; Funar-Timofei, Simona

The relative binding affinities (RBA) of a series of 30 steroids for Human Androgen Receptor (AR) were used to initiate a MTD-PLS study. The 3D structures of all the compounds were obtained through geometry optimization in the framework of AM1 semiempirical quantum chemical method. The MTD hypermolecule (HM) was constructed, superposing these structures on the AR-bonded dihydrotestosterone (DHT) skeleton obtained from PDB (AR complex, ID 1I37). The parameters characterizing the HM vertices were collected using: AM1 charges, XlogP fragmental values, calculated fragmental polarizabilities (from refractivities), volumes, and H-bond parameters (Raevsky's thermodynamic originated scale). The resulted QSAR data matrix was submitted to PCA (Principal Component Analysis) and PLS (Projections in Latent Structures) procedure (SIMCA P 9.0); five compounds were selected as test set, and the remaining 25 molecules were used as training set. In the PLS procedure supplementary chemical information was introduced, i.e. the steric effect was always considered detrimental, and the hydrophobic and van der Waals interactions were imposed to be beneficial. The initial PLS model using the entire training set has the following characteristics: R2Y = 0.584, Q2 = 0.344. Based on distances to the model criterions (DMODX and DMODY), five compounds were eliminated and the obtained final model had the following characteristics: R2Y D 0.891, Q2 D 0.591. For this the external predictivity on the test set was unsatisfactory. A tentative explanation for these behaviors is the weak information content of the input QSAR matrix for the present series comparatively with other successful MTD-PLS modeling published elsewhere.

Comparing classification methods for diffuse reflectance spectra to improve tissue specific laser surgery.

PubMed

Engelhardt, Alexander; Kanawade, Rajesh; Knipfer, Christian; Schmid, Matthias; Stelzle, Florian; Adler, Werner

2014-07-16

In the field of oral and maxillofacial surgery, newly developed laser scalpels have multiple advantages over traditional metal scalpels. However, they lack haptic feedback. This is dangerous near e.g. nerve tissue, which has to be preserved during surgery. One solution to this problem is to train an algorithm that analyzes the reflected light spectra during surgery and can classify these spectra into different tissue types, in order to ultimately send a warning or temporarily switch off the laser when critical tissue is about to be ablated. Various machine learning algorithms are available for this task, but a detailed analysis is needed to assess the most appropriate algorithm. In this study, a small data set is used to simulate many larger data sets according to a multivariate Gaussian distribution. Various machine learning algorithms are then trained and evaluated on these data sets. The algorithms' performance is subsequently evaluated and compared by averaged confusion matrices and ultimately by boxplots of misclassification rates. The results are validated on the smaller, experimental data set. Most classifiers have a median misclassification rate below 0.25 in the simulated data. The most notable performance was observed for the Penalized Discriminant Analysis, with a misclassifiaction rate of 0.00 in the simulated data, and an average misclassification rate of 0.02 in a 10-fold cross validation on the original data. The results suggest a Penalized Discriminant Analysis is the most promising approach, most probably because it considers the functional, correlated nature of the reflectance spectra.The results of this study improve the accuracy of real-time tissue discrimination and are an essential step towards improving the safety of oral laser surgery.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-07-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-04-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r-band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte-Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Data Programming: Creating Large Training Sets, Quickly.

PubMed

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2016-12-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions , which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

Data Programming: Creating Large Training Sets, Quickly

PubMed Central

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2018-01-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can “denoise” the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable. PMID:29872252

Task Analysis of Tactical Leadership Skills for Bradley Infantry Fighting Vehicle Leaders

DTIC Science & Technology

1986-10-01

The Bradley Leader Trainer is conceptualized as a device or set of de - vices that can be used to teach Bradley leaders to perform their full set of...experts. The task list was examined to de - termine critical training requirements, requirements for training device sup- port of this training, and...Functions/ j ITask | |Task | |Task | [Training j , To Further De - | ;Critical Train- | iTninir

[Training 5th-Year Clinical Pharmacy Students to Collect and Evaluate Information from Original Articles].

PubMed

Esumi, Satoru; Kawasaki, Yoichi; Ida, Hiromi; Kitamura, Yoshihisa; Sendo, Toshiaki

2018-01-01

　Pharmacists are required to contribute to evidence-based medicine (EBM) by providing drug information, which can be collected from various sources such as books, websites, and original articles. In particular, information from original articles is needed in some situations. For example, original articles by international researchers are used to aid the management of novel in-hospital preparations on which little knowledge is available. We introduced an information evaluation program, the Okayama University Hospital EBM Model, into the clinical training of 5th-year pharmacy students. It aims to enable students to evaluate the validity of novel in-hospital preparations using original articles. This program has improved students' knowledge of EBM, and the satisfaction level of those enrolled was high. In addition, customer satisfaction analysis revealed that the overall degree of student satisfaction was related to their understanding of the necessity for EBM and the difficulty of practical training. In addition, students' achievements were evaluated using rubrics, and that method allowed the achievements of each student to be assessed appropriately. We hope to revise this program with the aim of improving students' understanding of EBM.

Principles to Consider in Defining New Directions in Internal Medicine Training and Certification

PubMed Central

Turner, Barbara J; Centor, Robert M; Rosenthal, Gary E

2006-01-01

SGIM endoreses seven principles related to current thinking about internal medicine training: 1) internal medicine requires a full three years of residency training before subspecialization; 2) internal medicine residency programs must dramatically increase support for training in the ambulatory setting and offer equivalent opportunities for training in both inpatient and outpatient medicine; 3) in settings where adequate support and time are devoted to ambulatory training, the third year of residency could offer an opportunity to develop further expertise or mastery in a specific type or setting of care; 4) further certification in specific specialties within internal medicine requires the completion of an approved fellowship program; 5) areas of mastery in internal medicine can be demonstrated through modified board certification and recertification examinations; 6) certification processes throughout internal medicine should focus increasingly on demonstration of clinical competence through adherence to validated standards of care within and across practice settings; and 7) regardless of the setting in which General Internists practice, we should unite to promote the critical role that this specialty serves in patient care. PMID:16637826

Optimization of Training Sets for Neural-Net Processing of Characteristic Patterns from Vibrating Solids

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

2001-01-01

Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.

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.

Automatic detection of apical roots in oral radiographs

NASA Astrophysics Data System (ADS)

Wu, Yi; Xie, Fangfang; Yang, Jie; Cheng, Erkang; Megalooikonomou, Vasileios; Ling, Haibin

2012-03-01

The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases. In this paper we propose an automatic method for periapical root region detection by using the state-of-theart machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this problem, we boost the training set by including more root regions that are close to the annotated ones and decompose the original images to randomly generate negative samples. Based on these training samples, the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root detector. The learned detector usually generates a large amount of true and false positives. In order to reduce the number of false positives, a confidence score for each candidate detection result is calculated for further purification. We first merge the detected regions by combining tightly overlapped detected candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray images from 21 patients. The experimental results show that our approach can achieve promising detection accuracy.

Improved facial affect recognition in schizophrenia following an emotion intervention, but not training attention-to-facial-features or treatment-as-usual.

PubMed

Tsotsi, Stella; Kosmidis, Mary H; Bozikas, Vasilis P

2017-08-01

In schizophrenia, impaired facial affect recognition (FAR) has been associated with patients' overall social functioning. Interventions targeting attention or FAR per se have invariably yielded improved FAR performance in these patients. Here, we compared the effects of two interventions, one targeting FAR and one targeting attention-to-facial-features, with treatment-as-usual on patients' FAR performance. Thirty-nine outpatients with schizophrenia were randomly assigned to one of three groups: FAR intervention (training to recognize emotional information, conveyed by changes in facial features), attention-to-facial-features intervention (training to detect changes in facial features), and treatment-as-usual. Also, 24 healthy controls, matched for age and education, were assigned to one of the two interventions. Two FAR measurements, baseline and post-intervention, were conducted using an original experimental procedure with alternative sets of stimuli. We found improved FAR performance following the intervention targeting FAR in comparison to the other patient groups, which in fact was comparable to the pre-intervention performance of healthy controls in the corresponding intervention group. This improvement was more pronounced in recognizing fear. Our findings suggest that compared to interventions targeting attention, and treatment-as-usual, training programs targeting FAR can be more effective in improving FAR in patients with schizophrenia, particularly assisting them in perceiving threat-related information more accurately. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Large scale analysis of protein-binding cavities using self-organizing maps and wavelet-based surface patches to describe functional properties, selectivity discrimination, and putative cross-reactivity.

PubMed

Kupas, Katrin; Ultsch, Alfred; Klebe, Gerhard

2008-05-15

A new method to discover similar substructures in protein binding pockets, independently of sequence and folding patterns or secondary structure elements, is introduced. The solvent-accessible surface of a binding pocket, automatically detected as a depression on the protein surface, is divided into a set of surface patches. Each surface patch is characterized by its shape as well as by its physicochemical characteristics. Wavelets defined on surfaces are used for the description of the shape, as they have the great advantage of allowing a comparison at different resolutions. The number of coefficients to describe the wavelets can be chosen with respect to the size of the considered data set. The physicochemical characteristics of the patches are described by the assignment of the exposed amino acid residues to one or more of five different properties determinant for molecular recognition. A self-organizing neural network is used to project the high-dimensional feature vectors onto a two-dimensional layer of neurons, called a map. To find similarities between the binding pockets, in both geometrical and physicochemical features, a clustering of the projected feature vector is performed using an automatic distance- and density-based clustering algorithm. The method was validated with a small training data set of 109 binding cavities originating from a set of enzymes covering 12 different EC numbers. A second test data set of 1378 binding cavities, extracted from enzymes of 13 different EC numbers, was then used to prove the discriminating power of the algorithm and to demonstrate its applicability to large scale analyses. In all cases, members of the data set with the same EC number were placed into coherent regions on the map, with small distances between them. Different EC numbers are separated by large distances between the feature vectors. A third data set comprising three subfamilies of endopeptidases is used to demonstrate the ability of the algorithm to detect similar substructures between functionally related active sites. The algorithm can also be used to predict the function of novel proteins not considered in training data set. 2007 Wiley-Liss, Inc.

Augmenting Outpatient Alcohol Treatment as Usual With Online Alcohol Avoidance Training: Protocol for a Double-Blind Randomized Controlled Trial.

PubMed

Bratti-van der Werf, Marleen Kj; Laurens, Melissa C; Postel, Marloes G; Pieterse, Marcel E; Ben Allouch, Somaya; Wiers, Reinout W; Bohlmeijer, Ernst T; Salemink, Elske

2018-03-01

Recent theoretical models emphasize the role of impulsive processes in alcohol addiction, which can be retrained with computerized Cognitive Bias Modification (CBM) training. In this study, the focus is on action tendencies that are activated relatively automatically. The aim of the study is to examine the effectiveness of online CBM Alcohol Avoidance Training using an adapted Approach-Avoidance Task as a supplement to treatment as usual (TAU) in an outpatient treatment setting. The effectiveness of 8 online sessions of CBM Alcohol Avoidance Training added to TAU is tested in a double-blind, randomized controlled trial with pre- and postassessments, plus follow-up assessments after 3 and 6 months. Participants are adult patients (age 18 years or over) currently following Web-based or face-to-face TAU to reduce or stop drinking. These patients are randomly assigned to a CBM Alcohol Avoidance or a placebo training. The primary outcome measure is a reduction in alcohol consumption. We hypothesize that TAU + CBM will result in up to a 13-percentage point incremental effect in the number of patients reaching the safe drinking guidelines compared to TAU + placebo CBM. Secondary outcome measures include an improvement in health status and a decrease in depression, anxiety, stress, and possible mediation by the change in approach bias. Finally, patients' adherence, acceptability, and credibility will be examined. The trial was funded in 2014 and is currently in the active participant recruitment phase (since May 2015). Enrolment will be completed in 2019. First results are expected to be submitted for publication in 2020. The main purpose of this study is to increase our knowledge about the added value of online Alcohol Avoidance Training as a supplement to TAU in an outpatient treatment setting. If the added effectiveness of the training is proven, the next step could be to incorporate the intervention into current treatment. Netherlands Trial Register NTR5087; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5087 (Archived at WebCite http://www.webcitation.org/6wuS4i1tH). ©Marleen KJ Bratti-van der Werf, Melissa C Laurens, Marloes G Postel, Marcel E Pieterse, Somaya Ben Allouch, Reinout W Wiers, Ernst T Bohlmeijer, Elske Salemink. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 01.03.2018.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

Clinical pathology accreditation: standards for the medical laboratory

PubMed Central

Burnett, D; Blair, C; Haeney, M R; Jeffcoate, S L; Scott, K W M; Williams, D L

2002-01-01

This article describes a new set of revised standards for the medical laboratory, which have been produced by Clinical Pathology Accreditation (UK) Ltd (CPA). The original standards have been in use since 1992 and it was recognised that extensive revision was required. A standards revision group was established by CPA and this group used several international standards as source references, so that the resulting new standards are compatible with the most recent international reference sources. The aim is to make the assessment of medical laboratories as objective as possible in the future. CPA plans to introduce these standards in the UK in 2003 following extensive consultation with professional bodies, piloting in selected laboratories, and training of assessors. PMID:12354795

Comparison of molecular breeding values based on within- and across-breed training in beef cattle

PubMed Central

2013-01-01

Background Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Methods Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. Results With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Conclusions Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set. PMID:23953034

Behavioral Skills Training in Portuguese Children With School Failure Problems

PubMed Central

Galindo, Edgar; Candeias, Adelinda A.; Pires, Heldemerina S.; Grácio, Luísa; Stück, Marcus

2018-01-01

This paper postulates that psychology can make an important contribution at an individual level to help children with school failure problems in a context where too little applied research has been conducted on the instructional needs of these children. Some data are analyzed, revealing that, despite some progress, school failure is still a main educational problem in many countries. In this study, Behavioral Skills Training (BST) was applied in Portugal to train children with school failure difficulties. BST is a method based on Applied Behavior Analysis, a teaching package consisting of a combination of behavioral techniques: instructions, modeling, rehearsal, and feedback. Two empirical studies are presented. Their main purpose was to develop behavioral diagnostic and training techniques to teach lacking skills. School success was defined in terms of a set of skills proposed by teachers and school failure as a lack of one or more of these skills. The main instrument was a package of training programs to be applied in three areas: basic behavior (precurrents), academic behavior, or social behavior. The second instrument is a package of check-lists, aimed to determine the level of performance of the child in an area. This check-list was applied before (pre-test) and after (post-test) training. In the first study, 16, 7- to 8-year old children were trained. They were attending the second or third grades and having academic difficulties of different origins. The effects of the training programs are evaluated in terms of percentage of attained objectives, comparing a pre- and a post-test. The results showed an increase in correct responses after training in all cases. To provide a sounder demonstration of the efficacy of the training programs, a second study was carried out using a quasi-experimental design. A multiple baseline design was applied to three 10- to 11-year-old children, referred by teachers because of learning difficulties in the fourth grade. Results showed few performance changes without training. Increases in behavior following BST were evident in all cases, indicating that training generated improvement in all three children. In both studies, comparable results occurred across students, demonstrating replication of the effects of the training programs. PMID:29896134

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.

Effects of Spaced Training in Creative Imagination and Delayed Posttesting on Originality.

ERIC Educational Resources Information Center

Masten, William G.; Hairston, Susan A.

While there is increasing evidence that creativity can be improved through training, the spacing of the training has not been studied. This study assessed the effect of spaced training on the use of creative imagination in 110 undergraduate students. The research design was a randomized delayed posttest-only design. The independent variable was…

A Local Industry Solves Its Training Needs: A Cooperative Training Venture that Works.

ERIC Educational Resources Information Center

Cantor, Jeffrey A.

The Maritime Trades Program is a cooperative training program that was established through the joint efforts of 14 shipyards in the Tidewater area of Virginia. Established in 1980, the program originally operated under the guidelines imposed by the Comprehensive Employment and Training Act (CETA). Now, however, the program operates in accordance…

Modeling an aquatic ecosystem: application of an evolutionary algorithm with genetic doping to reduce prediction uncertainty

NASA Astrophysics Data System (ADS)

Friedel, Michael; Buscema, Massimo

2016-04-01

Aquatic ecosystem models can potentially be used to understand the influence of stresses on catchment resource quality. Given that catchment responses are functions of natural and anthropogenic stresses reflected in sparse and spatiotemporal biological, physical, and chemical measurements, an ecosystem is difficult to model using statistical or numerical methods. We propose an artificial adaptive systems approach to model ecosystems. First, an unsupervised machine-learning (ML) network is trained using the set of available sparse and disparate data variables. Second, an evolutionary algorithm with genetic doping is applied to reduce the number of ecosystem variables to an optimal set. Third, the optimal set of ecosystem variables is used to retrain the ML network. Fourth, a stochastic cross-validation approach is applied to quantify and compare the nonlinear uncertainty in selected predictions of the original and reduced models. Results are presented for aquatic ecosystems (tens of thousands of square kilometers) undergoing landscape change in the USA: Upper Illinois River Basin and Central Colorado Assessment Project Area, and Southland region, NZ.

Questions and Answers in Extreme Energy Cosmic Rays - a guide to explore the data set of the Pierre Auger Observatory

NASA Astrophysics Data System (ADS)

Abreu, P.; Andringa, S.; Diogo, F.; Espírito Santo, M. C.; Pierre Auger Collaboration

2016-04-01

The Pierre Auger Observatory is the largest extensive air shower detector, covering 3000 km2 in Argentina. The Observatory makes available, for educational and outreach purposes, 1% of its cosmic ray data set, corresponding after 10 years of running to more than 35 000 cosmic ray events. Several different proposals of educational activities have been developed within the collaboration and are available. We will focus on the activity guide we developed with the aim of exploring the rich education and outreach potential of cosmic rays with Portuguese high school students. In this guide we use the Auger public data set as a starting point to introduce open questions on the origin, nature and spectrum of high energy cosmic rays. To address them, the students learn about the air-shower cascade development, data reconstruction and its statistical analysis. The guide has been used both in the context of student summer internships at research labs and directly in schools, under the supervision of trained teachers and in close collaboration with Auger researchers. It is now available in Portuguese, English and Spanish.

Detecting and preventing error propagation via competitive learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2013-05-01

Semisupervised learning is a machine learning approach which is able to employ both labeled and unlabeled samples in the training process. It is an important mechanism for autonomous systems due to the ability of exploiting the already acquired information and for exploring the new knowledge in the learning space at the same time. In these cases, the reliability of the labels is a crucial factor, because mislabeled samples may propagate wrong labels to a portion of or even the entire data set. This paper has the objective of addressing the error propagation problem originated by these mislabeled samples by presenting a mechanism embedded in a network-based (graph-based) semisupervised learning method. Such a procedure is based on a combined random-preferential walk of particles in a network constructed from the input data set. The particles of the same class cooperate among them, while the particles of different classes compete with each other to propagate class labels to the whole network. Computer simulations conducted on synthetic and real-world data sets reveal the effectiveness of the model. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

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

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

PubMed Central

Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J.

2018-01-01

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. PMID:29538331

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

PubMed

Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

2018-03-14

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

PubMed

Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

2016-04-19

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.

Coping with challenging behaviours of children with autism: effectiveness of brief training workshop for frontline staff in special education settings.

PubMed

Ling, C Y M; Mak, W W S

2012-03-01

The present study examined the effectiveness of three staff training elements: psychoeducation (PE) on autism, introduction of functional behavioural analysis (FBA) and emotional management (EM), on the reaction of challenging behaviours for frontline staff towards children with autism in Hong Kong special education settings. A sample of 311 frontline staff in educational settings was recruited to one of the three conditions: control, PE-FBA and PE-FBA-EM groups. A total of 175 participants completed all three sets of questionnaires during pre-training, immediate post-training and 1-month follow-up. Findings showed that the one-session staff training workshop increased staff knowledge of autism and perceived efficacy but decrease helping behavioural intention. In spite of the limited effectiveness of a one-session staff training workshop, continued staff training is still necessary for the improvement of service quality. Further exploration on how to change emotion response of staff is important. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

PubMed

Kalderstam, Jonas; Edén, Patrik; Bendahl, Pär-Ola; Strand, Carina; Fernö, Mårten; Ohlsson, Mattias

2013-06-01

The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of training set selection on pain recognition via facial expressions

NASA Astrophysics Data System (ADS)

Shier, Warren A.; Yanushkevich, Svetlana N.

2016-07-01

This paper presents an approach to pain expression classification based on Gabor energy filters with Support Vector Machines (SVMs), followed by analyzing the effects of training set variations on the systems classification rate. This approach is tested on the UNBC-McMaster Shoulder Pain Archive, which consists of spontaneous pain images, hand labelled using the Prkachin and Solomon Pain Intensity scale. In this paper, the subjects pain intensity level has been quantized into three disjoint groups: no pain, weak pain and strong pain. The results of experiments show that Gabor energy filters with SVMs provide comparable or better results to previous filter- based pain recognition methods, with precision rates of 74%, 30% and 78% for no pain, weak pain and strong pain, respectively. The study of effects of intra-class skew, or changing the number of images per subject, show that both completely removing and over-representing poor quality subjects in the training set has little effect on the overall accuracy of the system. This result suggests that poor quality subjects could be removed from the training set to save offline training time and that SVM is robust not only to outliers in training data, but also to significant amounts of poor quality data mixed into the training sets.

International standards for programmes of training in intensive care medicine in Europe.

PubMed

2011-03-01

To develop internationally harmonised standards for programmes of training in intensive care medicine (ICM). Standards were developed by using consensus techniques. A nine-member nominal group of European intensive care experts developed a preliminary set of standards. These were revised and refined through a modified Delphi process involving 28 European national coordinators representing national training organisations using a combination of moderated discussion meetings, email, and a Web-based tool for determining the level of agreement with each proposed standard, and whether the standard could be achieved in the respondent's country. The nominal group developed an initial set of 52 possible standards which underwent four iterations to achieve maximal consensus. All national coordinators approved a final set of 29 standards in four domains: training centres, training programmes, selection of trainees, and trainers' profiles. Only three standards were considered immediately achievable by all countries, demonstrating a willingness to aspire to quality rather than merely setting a minimum level. Nine proposed standards which did not achieve full consensus were identified as potential candidates for future review. This preliminary set of clearly defined and agreed standards provides a transparent framework for assuring the quality of training programmes, and a foundation for international harmonisation and quality improvement of training in ICM.

Acute effects of verbal feedback on upper-body performance in elite athletes.

PubMed

Argus, Christos K; Gill, Nicholas D; Keogh, Justin Wl; Hopkins, Will G

2011-12-01

Argus, CK, Gill, ND, Keogh, JWL, and Hopkins, WG. Acute effects of verbal feedback on upper-body performance in elite athletes. J Strength Cond Res 25(12): 3282-3287, 2011-Improved training quality has the potential to enhance training adaptations. Previous research suggests that receiving feedback improves single-effort maximal strength and power tasks, but whether quality of a training session with repeated efforts can be improved remains unclear. The purpose of this investigation was to determine the effects of verbal feedback on upper-body performance in a resistance training session consisting of multiple sets and repetitions in well-trained athletes. Nine elite rugby union athletes were assessed using the bench throw exercise on 4 separate occasions each separated by 7 days. Each athlete completed 2 sessions consisting of 3 sets of 4 repetitions of the bench throw with feedback provided after each repetition and 2 identical sessions where no feedback was provided after each repetition. When feedback was received, there was a small increase of 1.8% (90% confidence limits, ±2.7%) and 1.3% (±0.7%) in mean peak power and velocity when averaged over the 3 sets. When individual sets were compared, there was a tendency toward the improvements in mean peak power being greater in the second and third sets. These results indicate that providing verbal feedback produced acute improvements in upper-body power output of well-trained athletes. The benefits of feedback may be greatest in the latter sets of training and could improve training quality and result in greater long-term adaptation.

Viterbi sparse spike detection and a compositional origin to ultralow-velocity zones

NASA Astrophysics Data System (ADS)

Brown, Samuel Paul

Accurate interpretation of seismic travel times and amplitudes in both the exploration and global scales is complicated by the band-limited nature of seismic data. We present a stochastic method, Viterbi sparse spike detection (VSSD), to reduce a seismic waveform into a most probable constituent spike train. Model waveforms are constructed from a set of candidate spike trains convolved with a source wavelet estimate. For each model waveform, a profile hidden Markov model (HMM) is constructed to represent the waveform as a stochastic generative model with a linear topology corresponding to a sequence of samples. The Viterbi algorithm is employed to simultaneously find the optimal nonlinear alignment between a model waveform and the seismic data, and to assign a score to each candidate spike train. The most probable travel times and amplitudes are inferred from the alignments of the highest scoring models. Our analyses show that the method can resolve closely spaced arrivals below traditional resolution limits and that travel time estimates are robust in the presence of random noise and source wavelet errors. We applied the VSSD method to constrain the elastic properties of a ultralow- velocity zone (ULVZ) at the core-mantle boundary beneath the Coral Sea. We analyzed vertical component short period ScP waveforms for 16 earthquakes occurring in the Tonga-Fiji trench recorded at the Alice Springs Array (ASAR) in central Australia. These waveforms show strong pre and postcursory seismic arrivals consistent with ULVZ layering. We used the VSSD method to measure differential travel-times and amplitudes of the post-cursor arrival ScSP and the precursor arrival SPcP relative to ScP. We compare our measurements to a database of approximately 340,000 synthetic seismograms finding that these data are best fit by a ULVZ model with an S-wave velocity reduction of 24%, a P-wave velocity reduction of 23%, a thickness of 8.5 km, and a density increase of 6%. We simultaneously constrain both P- and S-wave velocity reductions as a 1:1 ratio inside this ULVZ. This 1:1 ratio is not consistent with a partial melt origin to ULVZs. Rather, we demonstrate that a compositional origin is more likely.

Effect of creatine supplementation and drop-set resistance training in untrained aging adults.

PubMed

Johannsmeyer, Sarah; Candow, Darren G; Brahms, C Markus; Michel, Deborah; Zello, Gordon A

2016-10-01

To investigate the effects of creatine supplementation and drop-set resistance training in untrained aging adults. Participants were randomized to one of two groups: Creatine (CR: n=14, 7 females, 7 males; 58.0±3.0yrs, 0.1g/kg/day of creatine+0.1g/kg/day of maltodextrin) or Placebo (PLA: n=17, 7 females, 10 males; age: 57.6±5.0yrs, 0.2g/kg/day of maltodextrin) during 12weeks of drop-set resistance training (3days/week; 2 sets of leg press, chest press, hack squat and lat pull-down exercises performed to muscle fatigue at 80% baseline 1-repetition maximum [1-RM] immediately followed by repetitions to muscle fatigue at 30% baseline 1-RM). Prior to and following training and supplementation, assessments were made for body composition, muscle strength, muscle endurance, tasks of functionality, muscle protein catabolism and diet. Drop-set resistance training improved muscle mass, muscle strength, muscle endurance and tasks of functionality (p<0.05). The addition of creatine to drop-set resistance training significantly increased body mass (p=0.002) and muscle mass (p=0.007) compared to placebo. Males on creatine increased muscle strength (lat pull-down only) to a greater extent than females on creatine (p=0.005). Creatine enabled males to resistance train at a greater capacity over time compared to males on placebo (p=0.049) and females on creatine (p=0.012). Males on creatine (p=0.019) and females on placebo (p=0.014) decreased 3-MH compared to females on creatine. The addition of creatine to drop-set resistance training augments the gains in muscle mass from resistance training alone. Creatine is more effective in untrained aging males compared to untrained aging females. Copyright © 2016 Elsevier Inc. All rights reserved.

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

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.

20 CFR 667.268 - What prohibitions apply to the use of WIA title I funds to encourage business relocation?

Code of Federal Regulations, 2010 CFR

2010-04-01

... her job at the original location; (2) Customized training, skill training, or on-the-job training or company specific assessments of job applicants or employees of a business or a part of a business that has...

19 CFR Appendix to Part 181 - Rules of Origin Regulations

Code of Federal Regulations, 2010 CFR

2010-04-01

... and after-sales service personnel; (d) recruiting and training of sales promotion, marketing and after-sales service personnel, and after-sales training of customers' employees, where such costs are... similar agreements that can be related to specific services such as (a) personnel training, without regard...

Long-Term Abstract Learning of Attentional Set

ERIC Educational Resources Information Center

Leber, Andrew B.; Kawahara, Jun-Ichiro; Gabari, Yuji

2009-01-01

How does past experience influence visual search strategy (i.e., attentional set)? Recent reports have shown that, when given the option to use 1 of 2 attentional sets, observers persist with the set previously required in a training phase. Here, 2 related questions are addressed. First, does the training effect result only from perseveration with…

Geropsychology Training in a VA Nursing Home Setting

ERIC Educational Resources Information Center

Karel, Michele J.; Moye, Jennifer

2005-01-01

There is a growing need for professional psychology training in nursing home settings, and nursing homes provide a rich environment for teaching geropsychology competencies. We describe the nursing home training component of our Department of Veterans Affairs (VA) Predoctoral Internship and Geropsychology Postdoctoral Fellowship programs. Our…

Measures which host countries and countries of origin could adopt to promote the return of migrants.

PubMed

Debart, M H

1986-03-01

The immigration wave in the 1960s and 1970s brought scores of migrants to Europe. Most intended to work a few years in a foreign country and return to their homeland; however, poor economies in their own countries discouraged their return. At the same time, jobs became scarcer in their host countries. Several European countries today are resorting to measures designed to promote the return of migrants to their countries of origin. This paper outlines the two major options open to governments in their reintegration efforts. Option 1 requires instituting a definite reintegration policy. Public aid to promote reintegration may be provided. For example, the French give aid contingent upon the return of foreign workers in the labor force to the country of origin and not just upon their departure from the host country. Classical methods pay conpensation to the foreign worker; the problem then is to determine at what point to limit the funds. It must be decided whether or not unemployment benefits should be capitalized and whether or not to reimburse social security and old age contributions. It is also desirable for foreign workers to have access to a specialized organization which is able to advise them on setting up a project or business on their return; ideally, this organization should finance the project. Perhaps the best solution is to enlist participation of the governments of the countries of origin to make job openings known to their nationals desiring to return. Option 2 requires that reintegration be introduced into other economic and social programs. Returning foreign workers would be included as a factor in overall policy planning. Vocational training for return migrants could be proposed to job seekers as well as to dismissed workers. A portion of money used to finance housing projects could be earmarked for construction or reservation of housing in the country of origin. Bilateral vocational training programs can be addressed to nationals who want to return home. A portion of bilateral public development aid may also be used in support of reintegration projects. Finally, it should be possible to propose small development projects in the country of origin for nationals desiring to return.

Exercise order affects the total training volume and the ratings of perceived exertion in response to a super-set resistance training session

PubMed Central

Balsamo, Sandor; Tibana, Ramires Alsamir; Nascimento, Dahan da Cunha; de Farias, Gleyverton Landim; Petruccelli, Zeno; de Santana, Frederico dos Santos; Martins, Otávio Vanni; de Aguiar, Fernando; Pereira, Guilherme Borges; de Souza, Jéssica Cardoso; Prestes, Jonato

2012-01-01

The super-set is a widely used resistance training method consisting of exercises for agonist and antagonist muscles with limited or no rest interval between them – for example, bench press followed by bent-over rows. In this sense, the aim of the present study was to compare the effects of different super-set exercise sequences on the total training volume. A secondary aim was to evaluate the ratings of perceived exertion and fatigue index in response to different exercise order. On separate testing days, twelve resistance-trained men, aged 23.0 ± 4.3 years, height 174.8 ± 6.75 cm, body mass 77.8 ± 13.27 kg, body fat 12.0% ± 4.7%, were submitted to a super-set method by using two different exercise orders: quadriceps (leg extension) + hamstrings (leg curl) (QH) or hamstrings (leg curl) + quadriceps (leg extension) (HQ). Sessions consisted of three sets with a ten-repetition maximum load with 90 seconds rest between sets. Results revealed that the total training volume was higher for the HQ exercise order (P = 0.02) with lower perceived exertion than the inverse order (P = 0.04). These results suggest that HQ exercise order involving lower limbs may benefit practitioners interested in reaching a higher total training volume with lower ratings of perceived exertion compared with the leg extension plus leg curl order. PMID:22371654

How large a training set is needed to develop a classifier for microarray data?

PubMed

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Canine neuroanatomy: Development of a 3D reconstruction and interactive application for undergraduate veterinary education

PubMed Central

Raffan, Hazel; Guevar, Julien; Poyade, Matthieu; Rea, Paul M.

2017-01-01

Current methods used to communicate and present the complex arrangement of vasculature related to the brain and spinal cord is limited in undergraduate veterinary neuroanatomy training. Traditionally it is taught with 2-dimensional (2D) diagrams, photographs and medical imaging scans which show a fixed viewpoint. 2D representations of 3-dimensional (3D) objects however lead to loss of spatial information, which can present problems when translating this to the patient. Computer-assisted learning packages with interactive 3D anatomical models have become established in medical training, yet equivalent resources are scarce in veterinary education. For this reason, we set out to develop a workflow methodology creating an interactive model depicting the vasculature of the canine brain that could be used in undergraduate education. Using MR images of a dog and several commonly available software programs, we set out to show how combining image editing, segmentation and surface generation, 3D modeling and texturing can result in the creation of a fully interactive application for veterinary training. In addition to clearly identifying a workflow methodology for the creation of this dataset, we have also demonstrated how an interactive tutorial and self-assessment tool can be incorporated into this. In conclusion, we present a workflow which has been successful in developing a 3D reconstruction of the canine brain and associated vasculature through segmentation, surface generation and post-processing of readily available medical imaging data. The reconstructed model was implemented into an interactive application for veterinary education that has been designed to target the problems associated with learning neuroanatomy, primarily the inability to visualise complex spatial arrangements from 2D resources. The lack of similar resources in this field suggests this workflow is original within a veterinary context. There is great potential to explore this method, and introduce a new dimension into veterinary education and training. PMID:28192461

Canine neuroanatomy: Development of a 3D reconstruction and interactive application for undergraduate veterinary education.

PubMed

Raffan, Hazel; Guevar, Julien; Poyade, Matthieu; Rea, Paul M

2017-01-01

Current methods used to communicate and present the complex arrangement of vasculature related to the brain and spinal cord is limited in undergraduate veterinary neuroanatomy training. Traditionally it is taught with 2-dimensional (2D) diagrams, photographs and medical imaging scans which show a fixed viewpoint. 2D representations of 3-dimensional (3D) objects however lead to loss of spatial information, which can present problems when translating this to the patient. Computer-assisted learning packages with interactive 3D anatomical models have become established in medical training, yet equivalent resources are scarce in veterinary education. For this reason, we set out to develop a workflow methodology creating an interactive model depicting the vasculature of the canine brain that could be used in undergraduate education. Using MR images of a dog and several commonly available software programs, we set out to show how combining image editing, segmentation and surface generation, 3D modeling and texturing can result in the creation of a fully interactive application for veterinary training. In addition to clearly identifying a workflow methodology for the creation of this dataset, we have also demonstrated how an interactive tutorial and self-assessment tool can be incorporated into this. In conclusion, we present a workflow which has been successful in developing a 3D reconstruction of the canine brain and associated vasculature through segmentation, surface generation and post-processing of readily available medical imaging data. The reconstructed model was implemented into an interactive application for veterinary education that has been designed to target the problems associated with learning neuroanatomy, primarily the inability to visualise complex spatial arrangements from 2D resources. The lack of similar resources in this field suggests this workflow is original within a veterinary context. There is great potential to explore this method, and introduce a new dimension into veterinary education and training.

The effect of providing a USB syllabus on resident reading of landmark articles

PubMed Central

Chahla, Mayy; Eberlein, Michael; Wright, Scott

2010-01-01

Background The acquisition of new knowledge is a primary goal of residency training. Retrieving and retaining influential primary and secondary medical literature can be challenging for house officers. We set out to investigate the effect of a Universal Serial Bus (USB) drive loaded with landmark scientific articles on housestaff education in a pilot study. Methods We created a USB syllabus that contains 187 primary scientific research articles. The electronic syllabus had links to the full-text articles and was organized using an html webpage with a table of contents according to medical subspecialties. We performed a prospective cohort study of 53 house officers in the internal medicine residency program who received the USB syllabus. We evaluated the impact of the USB syllabus on resident education with surveys at the beginning and conclusion of the nine-month study period. Results All 50 respondents (100%) reported to have used the USB syllabus. The self-reported number of original articles read each month was higher at the end of the nine-month study period compared to baseline. Housestaff rated original articles as being a more valuable educational resource after the intervention. Conclusions An electronic syllabus with landmark scientific articles placed on a USB drive was widely utilized by housestaff, increased the self-reported reading of original scientific articles and seemed to have positively influenced residents' attitude toward original medical literature. PMID:20165697

Facial Attractiveness Assessment using Illustrated Questionnairers

PubMed Central

MESAROS, ANCA; CORNEA, DANIELA; CIOARA, LIVIU; DUDEA, DIANA; MESAROS, MICHAELA; BADEA, MINDRA

2015-01-01

Introduction. An attractive facial appearance is considered nowadays to be a decisive factor in establishing successful interactions between humans. In relation to this topic, scientific literature states that some of the facial features have more impact then others, and important authors revealed that certain proportions between different anthropometrical landmarks are mandatory for an attractive facial appearance. Aim. Our study aims to assess if certain facial features count differently in people’s opinion while assessing facial attractiveness in correlation with factors such as age, gender, specific training and culture. Material and methods. A 5-item multiple choice illustrated questionnaire was presented to 236 dental students. The Photoshop CS3 software was used in order to obtain the sets of images for the illustrated questions. The original image was handpicked from the internet by a panel of young dentists from a series of 15 pictures of people considered to have attractive faces. For each of the questions, the images presented were simulating deviations from the ideally symmetric and proportionate face. The sets of images consisted in multiple variations of deviations mixed with the original photo. Junior and sophomore year students from our dental medical school, having different nationalities were required to participate in our questionnaire. Simple descriptive statistics were used to interpret the data. Results. Assessing the results obtained from the questionnaire it was observed that a majority of students considered as unattractive the overdevelopment of the lower third, while the initial image with perfect symmetry and proportion was considered as the most attractive by only 38.9% of the subjects. Likewise, regarding the symmetry 36.86% considered unattractive the canting of the inter-commissural line. The interviewed subjects considered that for a face to be attractive it needs to have harmonious proportions between the different facial elements. Conclusions. Considering an evaluation of facial attractiveness it is important to keep in mind that such assessment is subjective and influenced by multiple factors, among which the most important are cultural background and specific training. PMID:26528052

Infrastructure for Training and Partnershipes: California Water and Coastal Ocean Resources

NASA Technical Reports Server (NTRS)

Siegel, David A.; Dozier, Jeffrey; Gautier, Catherine; Davis, Frank; Dickey, Tommy; Dunne, Thomas; Frew, James; Keller, Arturo; MacIntyre, Sally; Melack, John

2000-01-01

The purpose of this project was to advance the existing ICESS/Bren School computing infrastructure to allow scientists, students, and research trainees the opportunity to interact with environmental data and simulations in near-real time. Improvements made with the funding from this project have helped to strengthen the research efforts within both units, fostered graduate research training, and helped fortify partnerships with government and industry. With this funding, we were able to expand our computational environment in which computer resources, software, and data sets are shared by ICESS/Bren School faculty researchers in all areas of Earth system science. All of the graduate and undergraduate students associated with the Donald Bren School of Environmental Science and Management and the Institute for Computational Earth System Science have benefited from the infrastructure upgrades accomplished by this project. Additionally, the upgrades fostered a significant number of research projects (attached is a list of the projects that benefited from the upgrades). As originally proposed, funding for this project provided the following infrastructure upgrades: 1) a modem file management system capable of interoperating UNIX and NT file systems that can scale to 6.7 TB, 2) a Qualstar 40-slot tape library with two AIT tape drives and Legato Networker backup/archive software, 3) previously unavailable import/export capability for data sets on Zip, Jaz, DAT, 8mm, CD, and DLT media in addition to a 622Mb/s Internet 2 connection, 4) network switches capable of 100 Mbps to 128 desktop workstations, 5) Portable Batch System (PBS) computational task scheduler, and vi) two Compaq/Digital Alpha XP1000 compute servers each with 1.5 GB of RAM along with an SGI Origin 2000 (purchased partially using funds from this project along with funding from various other sources) to be used for very large computations, as required for simulation of mesoscale meteorology or climate.

28 CFR 345.35 - Assignments to FPI.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., color, religion, ethnic origin, age, or disability. (b) The SOI ordinarily makes assignments based on... course in pre-industrial training or on-the-job training (as available) before promotion to pay grade four. (2) An inmate who has not successfully completed pre-industrial or on-the-job training remains at...

Training as a Social Purpose: Are Economic and Social Benefits Delivered?

ERIC Educational Resources Information Center

Butler, Allan; Lobley, Matt

2016-01-01

This paper reports original research which measures the social and economic impact of training and skills development on individuals who participated in training provided by social purpose, nonprofit organizations. An implicit policy assumption is that such organizations contribute to social and economic regeneration. Examining the costs and…

Improving Training in Methodology Enriches the Science of Psychology

ERIC Educational Resources Information Center

Aiken, Leona S.; West, Stephen G.; Millsap, Roger E.

2009-01-01

Replies to the comment Ramifications of increased training in quantitative methodology by Herbet Zimiles on the current authors original article "Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America". The…

78 FR 14875 - Petition for Waiver of Compliance

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-07

... certain provisions of the Federal railroad safety regulations contained at 49 CFR part 232--Brake System Safety Standards for Freight and Other Non-Passenger Trains and Equipment, End-of-Train Devices. FRA... 232.207(a) for certain Bakken-oil unit trains that originate at refineries in North Dakota. These...

Training for Community Development Personnel in India.

ERIC Educational Resources Information Center

Makhija, H. R.

The book traces the development of training schemes in India for community development workers. It is divided into four parts which deal with: origin and growth of the Community Development Training Programme; problems encountered and the process of solutions through trial and error; major reorganization of the initial program and the research…

Performance Measures for Adaptive Decisioning Systems

DTIC Science & Technology

1991-09-11

set to hypothesis space mapping best approximates the known map. Two assumptions, a sufficiently representative training set and the ability of the...successful prediction of LINEXT performance. The LINEXT algorithm above performs the decision space mapping on the training-set ele- ments exactly. For a

Training creative cognition: adolescence as a flexible period for improving creativity

PubMed Central

Stevenson, Claire E.; Kleibeuker, Sietske W.; de Dreu, Carsten K. W.; Crone, Eveline A.

2014-01-01

Creativity commonly refers to the ability to generate ideas, solutions, or insights that are novel yet feasible. The ability to generate creative ideas appears to develop and change from childhood to adulthood. Prior research, although inconsistent, generally indicates that adults perform better than adolescents on the alternative uses task (AUT), a commonly used index of creative ideation. The focus of this study was whether performance could be improved by practicing alternative uses generation. We examined the effectiveness of creative ideation training in adolescents (13–16 years, N = 71) and adults (23–30 years, N = 61). Participants followed one of three types of training, each comprising eight 20-min practice sessions within 2 week time: (1) alternative uses generation (experimental condition: creative ideation); (2) object characteristic generation (control condition: general ideation); (3) rule-switching (control condition: rule-switching). Progression in fluency, flexibility, originality of creative ideation was compared between age-groups and training conditions. Participants improved in creative ideation and cognitive flexibility, but not in general ideation. Participants in all three training conditions became better in fluency and originality on the AUT. With regard to originality, adolescents benefitted more from training than adults, although this was not specific for the creative ideation training condition. These results are interpreted in relation to (a) the different underlying processes targeted in the three conditions and (b) developmental differences in brain plasticity with increased sensitivity to training in adolescents. In sum, the results show that improvement can be made in creative ideation and supports the hypothesis that adolescence is a developmental stage of increased flexibility optimized for learning and explorative behavior. PMID:25400565

Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

NASA Astrophysics Data System (ADS)

Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

2016-02-01

Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This capability will allow ecologists to identify trends in the distribution of difficult to sample organisms in their data.

Coordinating a national rangeland monitoring training program: Success and lessons learned

USDA-ARS?s Scientific Manuscript database

One of the best ways to ensure quality of information gathered in a rangeland monitoring program is through a strong and uniform set of trainings. Curriculum development and delivery of monitoring trainings poses unique challenges that are not seen in academic settings. Participants come from a rang...

Manual cleaning of hospital mattresses: an observational study comparing high- and low-resource settings.

PubMed

Hopman, J; Hakizimana, B; Meintjes, W A J; Nillessen, M; de Both, E; Voss, A; Mehtar, S

2016-01-01

Hospital-associated infections (HAIs) are more frequently encountered in low- than in high-resource settings. There is a need to identify and implement feasible and sustainable approaches to strengthen HAI prevention in low-resource settings. To evaluate the biological contamination of routinely cleaned mattresses in both high- and low-resource settings. In this two-stage observational study, routine manual bed cleaning was evaluated at two university hospitals using adenosine triphosphate (ATP). Standardized training of cleaning personnel was achieved in both high- and low-resource settings. Qualitative analysis of the cleaning process was performed to identify predictors of cleaning outcome in low-resource settings. Mattresses in low-resource settings were highly contaminated prior to cleaning. Cleaning significantly reduced biological contamination of mattresses in low-resource settings (P < 0.0001). After training, the contamination observed after cleaning in both the high- and low-resource settings seemed comparable. Cleaning with appropriate type of cleaning materials reduced the contamination of mattresses adequately. Predictors for mattresses that remained contaminated in a low-resource setting included: type of product used, type of ward, training, and the level of contamination prior to cleaning. In low-resource settings mattresses were highly contaminated as noted by ATP levels. Routine manual cleaning by trained staff can be as effective in a low-resource setting as in a high-resource setting. We recommend a multi-modal cleaning strategy that consists of training of domestic services staff, availability of adequate time to clean beds between patients, and application of the correct type of cleaning products. Copyright © 2015 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Teaching between-class generalization of toy play behavior to handicapped children.

PubMed Central

Haring, T G

1985-01-01

In this study, young children with severe and moderate handicaps were taught to generalize play responses. A multiple baseline across responses design, replicated with four children, was used to assess the effects of generalization training within four sets of toys on generalization to untrained toys from four other sets. The responses taught were unique for each set of toys. Across the four participants, training to generalize within-toy sets resulted in complete between-class generalization in 11 sets, partial generalization in 3 sets, and no generalization in 2 sets. No generalization occurred to another class of toys that differed from the previous sets in that they produced a reaction to the play movement (e.g., pianos). Implications for conducting research using strategies based on class interrelationships in training contexts are discussed. PMID:4019349

[Current status on management and needs related to education and training programs set for new employees at the provincial Centers for Disease Control and Prevention, in China].

PubMed

Ma, J; Meng, X D; Luo, H M; Zhou, H C; Qu, S L; Liu, X T; Dai, Z

2016-06-01

In order to understand the current management status on education/training and needs for training among new employees working at the provincial CDC in China during 2012-2014, so as to provide basis for setting up related programs at the CDC levels. Based on data gathered through questionnaire surveys run by CDCs from 32 provincial and 5 specifically-designated cities, microsoft excel was used to analyze the current status on management of education and training, for new employees. There were 156 management staff members working on education and training programs in 36 CDCs, with 70% of them having received intermediate or higher levels of education. Large differences were seen on equipment of training hardware in different regions. There were 1 214 teaching staff with 66 percent in the fields or related professional areas on public health, in 2014. 5084 new employees conducted pre/post training programs, from 2012 to 2014 with funding as 750 thousand RMB Yuan. 99.5% of the new employees expressed the needs for further training while. 74% of the new staff members expecting a 2-5 day training program to be implemented. 79% of the new staff members claimed that practice as the most appropriate method for training. Institutional programs set for education and training at the CDCs need to be clarified, with management team organized. It is important to provide more financial support on both hardware, software and human resources related to training programs which are set for new stuff members at all levels of CDCs.

Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems

PubMed Central

Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

2016-01-01

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system. PMID:27598390

Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

PubMed

Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

2016-01-01

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

Baryonic effects in cosmic shear tomography: PCA parametrization and importance of extreme baryonic models

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

Mohammed, Irshad; Gnedin, Nickolay Y.

Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and addressed two critical aspects. Firstly, we performed the analysis by separating the simulations into training and test sets, computing a minimal set of principle components from the training set and examining the fits on the test set. We found that using only four parameters, corresponding to the four largest eigenmodes of the training set, the test sets can be fitted thoroughly with an RMSmore » $$\\sim 0.0011$$. Secondly, we explored the significance of outliers, the most exotic/extreme baryonic scenarios, in this method. We found that excluding the outliers from the training set results in a relatively bad fit and degraded the RMS by nearly a factor of 3. Therefore, for a direct employment of this method to the tomographic analysis of the weak lensing data, the principle components should be derived from a training set that comprises adequately exotic but reasonable models such that the reality is included inside the parameter domain sampled by the training set. The baryonic effects can be parameterized as the coefficients of these principle components and should be marginalized over the cosmological parameter space.« less

Reinforced Adversarial Neural Computer for de Novo Molecular Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Ivanenkov, Yan; Aladinskiy, Vladimir; Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán; Zhavoronkov, Alex

2018-06-12

In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL). As a generator RANC uses a differentiable neural computer (DNC), a category of neural networks, with increased generation capabilities due to the addition of an explicit memory bank, which can mitigate common problems found in adversarial settings. The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms its first DNN-based counterpart ORGANIC by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters (MCFs), Muegge criteria, and high QED scores. RANC is able to generate structures that match the distributions of the key chemical features/descriptors (e.g., MW, logP, TPSA) and lengths of the SMILES strings in the training data set. Therefore, RANC can be reasonably regarded as a promising starting point to develop novel molecules with activity against different biological targets or pathways. In addition, this approach allows scientists to save time and covers a broad chemical space populated with novel and diverse compounds.

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

PubMed

Kyathanahally, Sreenath P; Mocioiu, Victor; Pedrosa de Barros, Nuno; Slotboom, Johannes; Wright, Alan J; Julià-Sapé, Margarida; Arús, Carles; Kreis, Roland

2018-05-01

To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

PubMed

Plagianakos, V P; Magoulas, G D; Vrahatis, M N

2006-03-01

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

Monitoring training response in young Friesian dressage horses using two different standardised exercise tests (SETs).

PubMed

de Bruijn, Cornelis Marinus; Houterman, Willem; Ploeg, Margreet; Ducro, Bart; Boshuizen, Berit; Goethals, Klaartje; Verdegaal, Elisabeth-Lidwien; Delesalle, Catherine

2017-02-14

Most Friesian horses reach their anaerobic threshold during a standardized exercise test (SET) which requires lower intensity exercise than daily routine training. to study strengths and weaknesses of an alternative SET-protocol. Two different SETs (SETA and SETB) were applied during a 2 month training period of 9 young Friesian dressage horses. SETB alternated short episodes of canter with trot and walk, lacking long episodes of cantering, as applied in SETA. Following parameters were monitored: blood lactic acid (BLA) after cantering, average heart rate (HR) in trot and maximum HR in canter. HR and BLA of SETA and SETB were analyzed using a paired two-sided T-test and Spearman Correlation-coefficient (p* < 0.05). BLA after cantering was significantly higher in SETA compared to SETB and maximum HR in canter was significantly higher in SETA compared to SETB. The majority of horses showed a significant training response based upon longitudinal follow-up of BLA. Horses with the lowest fitness at start, displayed the largest training response. BLA was significantly lower in week 8 compared to week 0, in both SETA and SETB. A significantly decreased BLA level after cantering was noticeable in week 6 in SETA, whereas in SETB only as of week 8. In SETA a very strong correlation for BLA and average HR at trot was found throughout the entire training period, not for canter. Young Friesian horses do reach their anaerobic threshold during a SET which requires lower intensity than daily routine training. Therefore close monitoring throughout training is warranted. Longitudinal follow up of BLA and not of HR is suitable to assess training response. In the current study, horses that started with the lowest fitness level, showed the largest training response. During training monitoring HR in trot rather than in canter is advised. SETB is best suited as a template for daily training in the aerobic window.

Training clinicians in how to use patient-reported outcome measures in routine clinical practice.

PubMed

Santana, Maria J; Haverman, Lotte; Absolom, Kate; Takeuchi, Elena; Feeny, David; Grootenhuis, Martha; Velikova, Galina

2015-07-01

Patient-reported outcome measures (PROs) were originally developed for comparing groups of people in clinical trials and population studies, and the results were used to support treatment recommendations or inform health policy, but there was not direct benefit for the participants providing PROs data. However, as the experience in using those measures increased, it became obvious the clinical value in using individual patient PROs profiles in daily practice to identify/monitor symptoms, evaluate treatment outcomes and support shared decision-making. A key issue limiting successful implementation is clinicians' lack of knowledge on how to effectively utilize PROs data in their clinical encounters. Using a change management theoretical framework, this paper describes the development and implementation of three programs for training clinicians to effectively use PRO data in routine practice. The training programs are in three diverse clinical areas (adult oncology, lung transplant and paediatrics), in three countries with different healthcare systems, thus providing a rare opportunity to pull out common approaches whilst recognizing specific settings. For each program, we describe the clinical and organizational setting, the program planning and development, the content of the training session with supporting material, subsequent monitoring of PROs use and evidence of adoption. The common successful components and practical steps are identified, leading to discussion and future recommendations. The results of the three training programs are described as the implementation. In the oncology program, PRO data have been developed and are currently evaluated; in the lung transplant program, PRO data are used in daily practice and the integration with electronic patient records is under development; and in the paediatric program, PRO data are fully implemented with around 7,600 consultations since the start of the implementation. Adult learning programs teaching clinicians how to use and act on PROs in clinical practice are a key steps in supporting patient engagement and participation in shared decision-making. Researchers and clinicians from different clinical areas should collaborate to share ideas, develop guidelines and promote good practice in patient-centred care.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

PubMed

Yu, Jingkai; Finley, Russell L

2009-01-01

High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space. We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

The Confusion Assessment Method (CAM): A Systematic Review of Current Usage

PubMed Central

Wei, Leslie A.; Fearing, Michael A.; Sternberg, Eliezer J.; Inouye, Sharon K.

2008-01-01

Objectives To examine the psychometric properties, adaptations, translations, and applications of the Confusion Assessment Method (CAM), a widely-used instrument and diagnostic algorithm for identification of delirium. Design Systematic literature review Setting NA Measurements Electronic searches of PubMED, EMBASE, PsychINFO, CINAHL, Ageline, and Google Scholar, augmented by reviews of reference listings, were conducted to identify original English-language articles utilizing the CAM from January 1, 1991 to December 31, 2006. Two reviewers independently abstracted key information from each article. Participants NA Results Of 239 original articles, 10 (4%) were categorized as validation studies, 16 (7%) as adaptations; 12 (5%) as translations, and 222 (93%) as applications. Validation studies evaluated performance of the CAM against a reference standard. Results were combined across 7 high quality studies (n=1071), demonstrating an overall sensitivity of 94% (95% confidence interval, CI, 91–97%), and specificity of 89% (95% CI, 85–94%). CAM has been adapted for use in ICU, emergency, and institutional settings, and for scoring severity and subsyndromal delirium. CAM has been translated into 10 languages where published articles are available. In application studies, CAM-rated delirium is most commonly used as a risk factor or outcome, but also as an intervention or reference standard. Conclusions The CAM has helped to improve identification of delirium in clinical and research settings. To optimize performance, the CAM should be scored based on observations made during formal cognitive testing, and training is recommended. Future action is needed to optimize use of the CAM and to improve the recognition and management of delirium. PMID:18384586

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.

Timbre influences chord discrimination in black-capped chickadees (Poecile atricapillus) but not humans (Homo sapiens).

PubMed

Hoeschele, Marisa; Cook, Robert G; Guillette, Lauren M; Hahn, Allison H; Sturdy, Christopher B

2014-11-01

Timbre is an important attribute of sound both in music and nature. Previously, using an operant conditioning paradigm, we found that black-capped chickadees and humans show similar response patterns in discriminating triadic chords of the same timbre and transferred this discrimination to a novel key center (novel absolute pitch). The current study examined how varying the timbre of the chords influenced discrimination. Using a similar operant conditioning procedure, we trained humans (Experiment 1) and chickadees (Experiments 2 and 3) to discriminate a major chord from 6 other chord types that had semitone deviations from the major chord. The pattern of errors of the 2 species replicated our previous findings. We then tested participants with novel timbres. We found that humans readily transferred their discrimination to novel timbres, suggesting they were attending to triadic pitch relations. The chickadees failed to transfer to novel timbres, suggesting they were using a different strategy to perform the original chord discrimination. We conducted an acoustic analysis examining frequency ranges that are biologically relevant to chickadees. We found that the relative intensity within each chord of the frequencies used in black-capped chickadee song significantly correlated with chickadees' percent response during probe testing. In Experiment 3, we trained a new set of chickadees by including either expanded pitch or timbre training before testing. Although chickadees showed some transfer to novel chords following this expanded training, we found that neither type of expanded training helped the chickadees when probe tested with novel stimuli. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2003-01-01

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Using Google Glass in Nonsurgical Medical Settings: Systematic Review.

PubMed

Dougherty, Bryn; Badawy, Sherif M

2017-10-19

Wearable technologies provide users hands-free access to computer functions and are becoming increasingly popular on both the consumer market and in various industries. The medical industry has pioneered research and implementation of head-mounted wearable devices, such as Google Glass. Most of this research has focused on surgical interventions; however, other medical fields have begun to explore the potential of this technology to support both patients and clinicians. Our aim was to systematically evaluate the feasibility, usability, and acceptability of using Google Glass in nonsurgical medical settings and to determine the benefits, limitations, and future directions of its application. This review covers literature published between January 2013 and May 2017. Searches included PubMed MEDLINE, Embase, INSPEC (Ebsco), Cochrane Central Register of Controlled Trials (CENTRAL), IEEE Explore, Web of Science, Scopus, and Compendex. The search strategy sought all articles on Google Glass. Two reviewers independently screened titles and abstracts, assessed full-text articles, and extracted data from articles that met all predefined criteria. Any disagreements were resolved by discussion or consultation by the senior author. Included studies were original research articles that evaluated the feasibility, usability, or acceptability of Google Glass in nonsurgical medical settings. The preferred reporting results of systematic reviews and meta-analyses (PRISMA) guidelines were followed for reporting of results. Of the 852 records examined, 51 met all predefined criteria, including patient-centered (n=21) and clinician-centered studies (n=30). Patient-centered studies explored the utility of Google Glass in supporting patients with motor impairments (n=8), visual impairments (n=5), developmental and psychiatric disorders (n=2), weight management concerns (n=3), allergies (n=1), or other health concerns (n=2). Clinician-centered studies explored the utility of Google Glass in student training (n=9), disaster relief (n=4), diagnostics (n=2), nursing (n=1), autopsy and postmortem examination (n=1), wound care (n=1), behavioral sciences (n=1), and various medical subspecialties, including, cardiology (n=3), radiology (n=3), neurology (n=1), anesthesiology (n=1), pulmonology (n=1), toxicology (n=1), and dermatology (n=1). Most of the studies were conducted in the United States (40/51, 78%), did not report specific age information for participants (38/51, 75%), had sample size <30 participants (29/51, 57%), and were pilot or feasibility studies (31/51, 61%). Most patient-centered studies (19/21, 90%) demonstrated feasibility with high satisfaction and acceptability among participants, despite a few technical challenges with the device. A number of clinician-centered studies (11/30, 37%) reported low to moderate satisfaction among participants, with the most promising results being in the area of student training. Studies varied in sample size, approach for implementation of Google Glass, and outcomes assessment. The use of Google Glass in nonsurgical medical settings varied. More promising results regarding the feasibility, usability, and acceptability of using Google Glass were seen in patient-centered studies and student training settings. Further research evaluating the efficacy and cost-effectiveness of Google Glass as an intervention to improve important clinical outcomes is warranted. ©Bryn Dougherty, Sherif M Badawy. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 19.10.2017.

Differential Effects of Heavy Versus Moderate Loads on Measures of Strength and Hypertrophy in Resistance-Trained Men.

PubMed

Schoenfeld, Brad J; Contreras, Bret; Vigotsky, Andrew D; Peterson, Mark

2016-12-01

The purpose of the present study was to evaluate muscular adaptations between heavy- and moderate-load resistance training (RT) with all other variables controlled between conditions. Nineteen resistance-trained men were randomly assigned to either a strength-type RT routine (HEAVY) that trained in a loading range of 2-4 repetitions per set (n = 10) or a hypertrophy-type RT routine (MODERATE) that trained in a loading range of 8-12 repetitions per set (n = 9). Training was carried out 3 days a week for 8 weeks. Both groups performed 3 sets of 7 exercises for the major muscle groups of the upper and lower body. Subjects were tested pre- and post-study for: 1 repetition maximum (RM) strength in the bench press and squat, upper body muscle endurance, and muscle thickness of the elbow flexors, elbow extensors, and lateral thigh. Results showed statistically greater increases in 1RM squat strength favoring HEAVY compared to MODERATE. Alternatively, statistically greater increases in lateral thigh muscle thickness were noted for MODERATE versus HEAVY. These findings indicate that heavy load training is superior for maximal strength goals while moderate load training is more suited to hypertrophy-related goals when an equal number of sets are performed between conditions.

Programming "loose training" as a strategy to facilitate language generalization.

PubMed Central

Campbell, C R; Stremel-Campbell, K

1982-01-01

This study investigated the generalization of spontaneous complex language behavior across a nontraining setting and the durability of generalization as a result of programming and "loose training" strategy. A within-subject, across-behaviors multiple-baseline design was used to examine the performance of two moderately retarded students in the use of is/are across three syntactic structures (i.e., "wh" questions, "yes/no" reversal questions, and statements). The language training procedure used in this study represented a functional example of programming "loose training." The procedure involved conducting concurrent language training within the context of an academic training task, and establishing a functional reduction in stimulus control by permitting the student to initiate a language response based on a wide array of naturally occurring stimulus events. Concurrent probes were conducted in the free play setting to assess the immediate generalization and the durability of the language behaviors. The results demonstrated that "loose training" was effective in establishing a specific set of language responses with the participants of this investigation. Further, both students demonstrated spontaneous use of the language behavior in the free play generalization setting and a trend was clearly evident for generalization to continue across time. Thus, the methods used appear to be successful for training the use of is/are in three syntactic structures. PMID:7118759

Development of knowledge tests for multi-disciplinary emergency training: a review and an example.

PubMed

Sørensen, J L; Thellesen, L; Strandbygaard, J; Svendsen, K D; Christensen, K B; Johansen, M; Langhoff-Roos, P; Ekelund, K; Ottesen, B; Van Der Vleuten, C

2015-01-01

The literature is sparse on written test development in a post-graduate multi-disciplinary setting. Developing and evaluating knowledge tests for use in multi-disciplinary post-graduate training is challenging. The objective of this study was to describe the process of developing and evaluating a multiple-choice question (MCQ) test for use in a multi-disciplinary training program in obstetric-anesthesia emergencies. A multi-disciplinary working committee with 12 members representing six professional healthcare groups and another 28 participants were involved. Recurrent revisions of the MCQ items were undertaken followed by a statistical analysis. The MCQ items were developed stepwise, including decisions on aims and content, followed by testing for face and content validity, construct validity, item-total correlation, and reliability. To obtain acceptable content validity, 40 out of originally 50 items were included in the final MCQ test. The MCQ test was able to distinguish between levels of competence, and good construct validity was indicated by a significant difference in the mean score between consultants and first-year trainees, as well as between first-year trainees and medical and midwifery students. Evaluation of the item-total correlation analysis in the 40 items set revealed that 11 items needed re-evaluation, four of which addressed content issues in local clinical guidelines. A Cronbach's alpha of 0.83 for reliability was found, which is acceptable. Content and construct validity and reliability were acceptable. The presented template for the development of this MCQ test could be useful to others when developing knowledge tests and may enhance the overall quality of test development. © 2014 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Analysis of precision and accuracy in a simple model of machine learning

NASA Astrophysics Data System (ADS)

Lee, Julian

2017-12-01

Machine learning is a procedure where a model for the world is constructed from a training set of examples. It is important that the model should capture relevant features of the training set, and at the same time make correct prediction for examples not included in the training set. I consider the polynomial regression, the simplest method of learning, and analyze the accuracy and precision for different levels of the model complexity.

HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.

PubMed

Nunes, Kelly; Zheng, Xiuwen; Torres, Margareth; Moraes, Maria Elisa; Piovezan, Bruno Z; Pontes, Gerlandia N; Kimura, Lilian; Carnavalli, Juliana E P; Mingroni Netto, Regina C; Meyer, Diogo

2016-03-01

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. Copyright © 2016 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

The efficacy of a whole body sprint-interval training intervention in an office setting: A feasibility study.

PubMed

Gurd, Brendon J; Patel, Jugal; Edgett, Brittany A; Scribbans, Trisha D; Quadrilatero, Joe; Fischer, Steven L

2018-05-28

Whole body sprint-interval training (WB-SIT) represents a mode of exercise training that is both time-efficient and does not require access to an exercise facility. The current study examined the feasibility of implementing a WB-SIT intervention in a workplace setting. A total of 747 employees from a large office building were invited to participate with 31 individuals being enrolled in the study. Anthropometrics, aerobic fitness, core and upper body strength, and lower body mobility were assessed before and after a 12-week exercise intervention consisting of 2-4 training sessions per week. Each training session required participants to complete 8, 20-second intervals (separated by 10 seconds of rest) of whole body exercise. Proportion of participation was 4.2% while the response rate was 35% (11/31 participants completed post training testing). In responders, compliance to prescribed training was 83±17%, and significant (p <  0.05) improvements were observed for aerobic fitness, push-up performance and lower body mobility. These results demonstrate the efficacy of WB-FIT for improving fitness and mobility in an office setting, but highlight the difficulties in achieving high rates of participation and response in this setting.

Education and Training for Clinical Neuropsychologists in Integrated Care Settings.

PubMed

Roper, Brad L; Block, Cady K; Osborn, Katie; Ready, Rebecca E

2018-05-01

The increasing importance of integrated care necessitates that education and training experiences prepare clinical neuropsychologists for competent practice in integrated care settings, which includes (a) general competence related to an integrated/interdisciplinary approach and (b) competence specific to the setting. Formal neuropsychology training prepares neuropsychologists with a wide range of knowledge and skills in assessment, intervention, teaching/supervision, and research that are relevant to such settings. However, less attention has been paid to the knowledge and skills that directly address functioning within integrated teams, such as the ability to develop, maintain, and expand collaboration across disciplines, bidirectional clinical-research translation and implementation in integrated team settings, and how such collaboration contributes to clinical and research activities. Foundational knowledge and skills relevant to interdisciplinary systems have been articulated as part of competencies for entry into clinical neuropsychology, but their emphasis in education and training programs is unclear. Recommendations and resources are provided regarding how competencies relevant to integrated care can be provided across the continuum of education and training (i.e., doctoral, internship, postdoctoral, and post-licensure).

Effects of Modification of Cognitive Style on Creative Behavior

ERIC Educational Resources Information Center

Renner, Vivian

1970-01-01

Suggests that training can increase the preference for more complex types of art, that such training will transfer to music, and that verbal originality will also increase. Bibliography and tables. (RW)

24 CFR 7.5 - EEO Alternative Dispute Resolution Program.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Opportunity Without Regard to Race, Color Religion, Sex, National Origin, Age, Disability or Reprisal General...) ADR training. Training and education on the EEO ADR Program will be provided to all Department...

An interactive media program for managing psychosocial problems on long-duration spaceflights.

PubMed

Carter, James A; Buckey, Jay C; Greenhalgh, Leonard; Holland, Albert W; Hegel, Mark T

2005-06-01

Space crews must be self-reliant to complete long-duration missions successfully. This project involves the development and evaluation of a network of self-guided interactive multimedia programs to train and assist long-duration flyers in the prevention, assessment, and management of psychosocial problems that can arise on extended missions. The system is currently under development and is intended for use both during training and on orbit. A virtual space station 3-dimensional graphic was created to serve as a portal to multimedia-based training, assessment, and intervention resources. Additionally, original content on interpersonal conflict and depression is being developed for the system. Input on the best practices for managing conflict and depression on extended missions was obtained from 13 veteran long-duration flyers, as well as from clinical experts. Formative evaluation of a prototype of the system will be conducted with 10 members of the astronaut corps. Subsequently, the content on conflict and depression will be completed, and the depression self-treatment portion will be evaluated in a randomized controlled trial. Although this study involves developing countermeasures to assist long-duration flyers, it also provides a model that could be applied in many Earthbound settings, both in operational environments and in everyday life.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

NASA Astrophysics Data System (ADS)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

Potential benefits of exercise on blood pressure and vascular function.

PubMed

Pal, Sebely; Radavelli-Bagatini, Simone; Ho, Suleen

2013-01-01

Physical activity seems to enhance cardiovascular fitness during the course of the lifecycle, improve blood pressure, and is associated with decreased prevalence of hypertension and coronary heart disease. It may also delay or prevent age-related increases in arterial stiffness. It is unclear if specific exercise types (aerobic, resistance, or combination) have a better effect on blood pressure and vascular function. This review was written based on previous original articles, systematic reviews, and meta-analyses indexed on PubMed from years 1975 to 2012 to identify studies on different types of exercise and the associations or effects on blood pressure and vascular function. In summary, aerobic exercise (30 to 40 minutes of training at 60% to 85% of predicted maximal heart rate, most days of the week) appears to significantly improve blood pressure and reduce augmentation index. Resistance training (three to four sets of eight to 12 repetitions at 10 repetition maximum, 3 days a week) appears to significantly improve blood pressure, whereas combination exercise training (15 minutes of aerobic and 15 minutes of resistance, 5 days a week) is beneficial to vascular function, but at a lower scale. Aerobic exercise seems to better benefit blood pressure and vascular function. Copyright © 2013 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

A cascade model of information processing and encoding for retinal prosthesis.

PubMed

Pei, Zhi-Jun; Gao, Guan-Xin; Hao, Bo; Qiao, Qing-Li; Ai, Hui-Jian

2016-04-01

Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases. Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue. Some retinal models have been presented, ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena. However, Most of these focus on stimulus image compression, edge detection and reconstruction, but do not generate spike trains corresponding to visual image. In this study, based on state-of-the-art retinal physiological mechanism, including effective visual information extraction, static nonlinear rectification of biological systems and neurons Poisson coding, a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward, which integrates both anatomic connections and functional computations of retina. Using MATLAB software, spike trains corresponding to stimulus image were numerically computed by four steps: linear spatiotemporal filtering, static nonlinear rectification, radial sampling and then Poisson spike generation. The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina, which is helpful in developing artificial retina for the retinally blind.

The experimental teaching reform in biochemistry and molecular biology for undergraduate students in Peking University Health Science Center.

PubMed

Yang, Xiaohan; Sun, Luyang; Zhao, Ying; Yi, Xia; Zhu, Bin; Wang, Pu; Lin, Hong; Ni, Juhua

2015-01-01

Since 2010, second-year undergraduate students of an eight-year training program leading to a Doctor of Medicine degree or Doctor of Philosophy degree in Peking University Health Science Center (PKUHSC) have been required to enter the "Innovative talent training project." During that time, the students joined a research lab and participated in some original research work. There is a critical educational need to prepare these students for the increasing accessibility of research experience. The redesigned experimental curriculum of biochemistry and molecular biology was developed to fulfill such a requirement, which keeps two original biochemistry experiments (Gel filtration and Enzyme kinetics) and adds a new two-experiment component called "Analysis of anti-tumor drug induced apoptosis." The additional component, also known as the "project-oriented experiment" or the "comprehensive experiment," consists of Western blotting and a DNA laddering assay to assess the effects of etoposide (VP16) on the apoptosis signaling pathways. This reformed laboratory teaching system aims to enhance the participating students overall understanding of important biological research techniques and the instrumentation involved, and to foster a better understanding of the research process all within a classroom setting. Student feedback indicated that the updated curriculum helped them improve their operational and self-learning capability, and helped to increase their understanding of theoretical knowledge and actual research processes, which laid the groundwork for their future research work. © 2015 The International Union of Biochemistry and Molecular Biology.

Re-Conceptualization of Modified Angoff Standard Setting: Unified Statistical, Measurement, Cognitive, and Social Psychological Theories

ERIC Educational Resources Information Center

Iyioke, Ifeoma Chika

2013-01-01

This dissertation describes a design for training, in accordance with probability judgment heuristics principles, for the Angoff standard setting method. The new training with instruction, practice, and feedback tailored to the probability judgment heuristics principles was called the Heuristic training and the prevailing Angoff method training…

Replacing Maladaptive Speech with Verbal Labeling Responses: An Analysis of Generalized Responding.

ERIC Educational Resources Information Center

Foxx, R. M.; And Others

1988-01-01

Three mentally handicapped students (aged 13, 36, and 40) with maladaptive speech received training to answer questions with verbal labels. The results of their cues-pause-point training showed that the students replaced their maladaptive speech with correct labels (answers) to questions in the training setting and three generalization settings.…

A Model for Teaching Rational Behavior Therapy in a Public School Setting.

ERIC Educational Resources Information Center

Patton, Patricia L.

A training model for the use of rational behavior therapy (RBT) with emotionally disturbed adolescents in a school setting is presented, including a structured, didactic format consisting of five basic RBT training techniques. The training sessions, lasting 10 weeks each, are described. Also presented is the organization for the actual classroom…

Sentiment Analysis of Health Care Tweets: Review of the Methods Used.

PubMed

Gohil, Sunir; Vuik, Sabine; Darzi, Ara

2018-04-23

Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first. ©Sunir Gohil, Sabine Vuik, Ara Darzi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 23.04.2018.

A novel classifier based on three preoperative tumor markers predicting the cancer-specific survival of gastric cancer (CEA, CA19-9 and CA72-4).

PubMed

Guo, Jing; Chen, Shangxiang; Li, Shun; Sun, Xiaowei; Li, Wei; Zhou, Zhiwei; Chen, Yingbo; Xu, Dazhi

2018-01-12

Several studies have highlighted the prognostic value of the individual and the various combinations of the tumor markers for gastric cancer (GC). Our study was designed to assess establish a new novel model incorporating carcino-embryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 72-4 (CA72-4). A total of 1,566 GC patients (Primary cohort) between Jan 2000 and July 2013 were analyzed. The Primary cohort was randomly divided into Training set (n=783) and Validation set (n=783). A three-tumor marker classifier was developed in the Training set and validated in the Validation set by multivariate regression and risk-score analysis. We have identified a three-tumor marker classifier (including CEA, CA19-9 and CA72-4) for the cancer specific survival (CSS) of GC (p<0.001). Consistent results were obtained in the both Training set and Validation set. Multivariate analysis showed that the classifier was an independent predictor of GC (All p value <0.001 in the Training set, Validation set and Primary cohort). Furthermore, when the leave-one-out approach was performed, the classifier showed superior predictive value to the individual or two of them (with the highest AUC (Area Under Curve); 0.618 for the Training set, and 0.625 for the Validation set), which ascertained its predictive value. Our three-tumor marker classifier is closely associated with the CSS of GC and may serve as a novel model for future decisions concerning treatments.

Application of the Repetitions in Reserve-Based Rating of Perceived Exertion Scale for Resistance Training

PubMed Central

Cronin, John; Storey, Adam; Zourdos, Michael C.

2016-01-01

ABSTRACT RATINGS OF PERCEIVED EXERTION ARE A VALID METHOD OF ESTIMATING THE INTENSITY OF A RESISTANCE TRAINING EXERCISE OR SESSION. SCORES ARE GIVEN AFTER COMPLETION OF AN EXERCISE OR TRAINING SESSION FOR THE PURPOSES OF ATHLETE MONITORING. HOWEVER, A NEWLY DEVELOPED SCALE BASED ON HOW MANY REPETITIONS ARE REMAINING AT THE COMPLETION OF A SET MAY BE A MORE PRECISE TOOL. THIS APPROACH ADJUSTS LOADS AUTOMATICALLY TO MATCH ATHLETE CAPABILITIES ON A SET-TO-SET BASIS AND MAY MORE ACCURATELY GAUGE INTENSITY AT NEAR-LIMIT LOADS. THIS ARTICLE OUTLINES HOW TO INCORPORATE THIS NOVEL SCALE INTO A TRAINING PLAN. PMID:27531969

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Pruning Neural Networks with Distribution Estimation Algorithms

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

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

NASA Technical Reports Server (NTRS)

Das, Santanu; Oza, Nikunj C.

2011-01-01

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

Tourism. Leonardo da Vinci Series: Good Practices.

ERIC Educational Resources Information Center

Commission of the European Communities, Brussels (Belgium). Directorate-General for Education and Culture.

This brochure, part of a series about good practices in vocational training in the European Union, describes 10 projects that have promoted investment in human resources through training in the tourism sector to promote sustainable, or responsible, tourism. The projects and their countries of origin are as follows: (1) BEEFT, training of mobility…

Toilet Training Individuals with Autism and Other Developmental Disabilities: A Critical Review

ERIC Educational Resources Information Center

Kroeger, K. A.; Sorensen-Burnworth, Rena

2009-01-01

The following article reviews the current literature addressing toilet training individuals with autism and other developmental disabilities. The review addresses programs typical to toilet training the developmental disability population, most of which are modeled after the original Foxx and Azrin [Azrin, N. H., & Foxx, R. M. (1971). A rapid…

75 FR 65023 - Notice of Issuance of Final Determination Concerning Certain Heating Boilers

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-21

... of the heat exchanger, the gas train, electronics and controls, and the combustion fan. Assembly of... the heat exchanger are of U.S. origin. The gas train assembly requires fitting the components together... stage. The sub-assembly stage has three processes: the gas train, electronics and controls, and the...

Meeting the Needs of Children and Families: Opportunities and Challenges for School Psychology Training Programs.

ERIC Educational Resources Information Center

Curtis, Michael J.; Batsche, George M.

1991-01-01

Notes that graduate training programs face challenges, as well as opportunities, in fulfillment of their responsibilities to prepare school psychologists for entry into professional practice. Examines nature and origins of potential changes facing school psychology and discusses adequacy of current training programs. Discusses future implications…

The Effect of Anxiety Management Training on College Students' General, Overt, and Covert Anxiety.

ERIC Educational Resources Information Center

Vinson, Michael L.

The effect on anxiety of a behaviorally-oriented treatment, Anxiety Management Training (AMT), was investigated with a sample of college students (N=23). The treatment was based upon the techniques originally used by Richardson, Suinn, and Meichenbaum, and consisted of three principal elements: relaxation training, cognitive-restructuring, and…

The Training and Employment of Area Specialists in the Military

DTIC Science & Technology

1989-06-01

Unit Acca =o N I1. Title (Include Security Classification) The Training and Employment of Area Specialists in the Military 12 Personal Author(s) Randy P...administering, and analyzing a survey that accounts for about half of the information to come. This work does not answer all the ques- tions that exist...original Academic Associate and co-author of the area studies curriculum at NPS, that these factors accounted for the original decision by the Army to begin

Comparing two methods to promote generalization of receptive identification in children with autism spectrum disorders.

PubMed

Dufour, Marie-Michèle; Lanovaz, Marc J

2017-11-01

The purpose of our study was to compare the effects of serial and concurrent training on the generalization of receptive identification in children with autism spectrum disorders (ASD). We taught one to three pairs of stimulus sets to nine children with ASD between the ages of three and six. One stimulus set within each pair was taught using concurrent training and the other using serial training. We alternated the training sessions within a multielement design and staggered the introduction of subsequent pairs for each participant as in a multiple baseline design. Overall, six participants generalized at least one stimulus set more rapidly with concurrent training whereas two participants showed generalization more rapidly with serial training. Our results differ from other comparison studies on the topic and indicate that practitioners should consider assessing the effects of both procedures prior to teaching receptive identification to children with ASD.

"Functional" Inspiratory and Core Muscle Training Enhances Running Performance and Economy.

PubMed

Tong, Tomas K; McConnell, Alison K; Lin, Hua; Nie, Jinlei; Zhang, Haifeng; Wang, Jiayuan

2016-10-01

Tong, TK, McConnell, AK, Lin, H, Nie, J, Zhang, H, and Wang, J. "Functional" inspiratory and core muscle training enhances running performance and economy. J Strength Cond Res 30(10): 2942-2951, 2016-We compared the effects of two 6-week high-intensity interval training interventions. Under the control condition (CON), only interval training was undertaken, whereas under the intervention condition (ICT), interval training sessions were followed immediately by core training, which was combined with simultaneous inspiratory muscle training (IMT)-"functional" IMT. Sixteen recreational runners were allocated to either ICT or CON groups. Before the intervention phase, both groups undertook a 4-week program of "foundation" IMT to control for the known ergogenic effect of IMT (30 inspiratory efforts at 50% maximal static inspiratory pressure [P0] per set, 2 sets per day, 6 days per week). The subsequent 6-week interval running training phase consisted of 3-4 sessions per week. In addition, the ICT group undertook 4 inspiratory-loaded core exercises (10 repetitions per set, 2 sets per day, inspiratory load set at 50% post-IMT P0) immediately after each interval training session. The CON group received neither core training nor functional IMT. After the intervention phase, global inspiratory and core muscle functions increased in both groups (p ≤ 0.05), as evidenced by P0 and a sport-specific endurance plank test (SEPT) performance, respectively. Compared with CON, the ICT group showed larger improvements in SEPT, running economy at the speed of the onset of blood lactate accumulation, and 1-hour running performance (3.04% vs. 1.57%, p ≤ 0.05). The changes in these variables were interindividually correlated (r ≥ 0.57, n = 16, p ≤ 0.05). Such findings suggest that the addition of inspiratory-loaded core conditioning into a high-intensity interval training program augments the influence of the interval program on endurance running performance and that this may be underpinned by an improvement in running economy.

Training transfer: a systematic review of the impact of inner setting factors.

PubMed

Jackson, Carrie B; Brabson, Laurel A; Quetsch, Lauren B; Herschell, Amy D

2018-06-19

Consistent with Baldwin and Ford's model (Pers Psychol 41(1):63-105, 1988), training transfer is defined as the generalization of learning from a training to everyday practice in the workplace. The purpose of this review was to examine the influence of work-environment factors, one component of the model hypothesized to influence training transfer within behavioral health. An electronic literature search guided by the Consolidated Framework for Implementation Research's inner setting domain was conducted was conducted on Medline OVID, Medline EMBASE, and PsycINFO databases. Of 9184 unique articles, 169 full-text versions of articles were screened for eligibility, yielding 26 articles meeting inclusion criteria. Results from the 26 studies revealed that overall, having more positive networks and communication, culture, implementation climate, and readiness for implementation can facilitate training transfer. Although few studies have examined the impact of inner setting factors on training transfer, these results suggest organizational context is important to consider with training efforts. These findings have important implications for individuals in the broader health professions educational field.

Exercise order in resistance training.

PubMed

Simão, Roberto; de Salles, Belmiro Freitas; Figueiredo, Tiago; Dias, Ingrid; Willardson, Jeffrey M

2012-03-01

Resistance training (RT) is now an integral component of a well rounded exercise programme. For a correct training prescription, it is of the utmost importance to understand the interaction among training variables, such as the load, volume, rest interval between sets and exercises, frequency of sessions, exercise modality, repetition velocity and, finally, exercise order. Sports medicine research has indicated that exercise order is an important variable that affects both acute responses and chronic adaptations to RT programmes. Therefore, the purpose of this review was to analyse and discuss exercise order with relevance to acute responses (e.g. repetition performance) and also the expression of chronic adaptable characteristics (e.g. maximal strength and hypertrophy). To accomplish this purpose, the Scielo, Science Citation Index, National Library of Medicine, MEDLINE, Scopus, SPORTDiscus™ and CINAHL® databases were accessed to locate previously conducted original scientific investigations. The studies reviewed examined both acute responses and chronic adaptations with exercise order as the experimental variable. Generally, with relevance to acute responses, a key finding was that exercise order affects repetition performance over multiple sets, indicating that the total repetitions, and thus the volume, is greater when an exercise is placed at the beginning of an RT session, regardless of the relative amount of muscle mass involved. The pre-exhaustion method might not be an effective technique to increase the extent of neuromuscular recruitment for larger muscle groups (e.g. pectoralis major for the bench press) when preceded by a single-joint movement (e.g. pec-deck fly). With relevance to localized muscular endurance performance, oxygen consumption and ratings of perceived exertion, the limited amount of research conducted thus far indicates that exercise order does not appear to impact the acute expression of these variables. In terms of chronic adaptations, greater strength increases were evident by untrained subjects for the first exercise of a given sequence, while strength increases were inhibited for the last exercise of a given sequence. Additionally, based on strength and hypertrophy (i.e. muscle thickness and volume) effect-size data, the research suggests that exercises be ordered based on priority of importance as dictated by the training goal of a programme, irrespective of whether the exercise involves a relatively large or small muscle group. In summary, exercise order is an important variable that should receive greater attention in RT prescription. When prescribed appropriately with other key prescriptive variables (i.e. load, volume, rest interval between sets and exercises), the exercise order can influence the efficiency, safety and ultimate effectiveness of an RT programme.

High Intensity High Volume Interval Training Improves Endurance Performance and Induces a Nearly Complete Slow-to-Fast Fiber Transformation on the mRNA Level.

PubMed

Eigendorf, Julian; May, Marcus; Friedrich, Jan; Engeli, Stefan; Maassen, Norbert; Gros, Gerolf; Meissner, Joachim D

2018-01-01

We present here a longitudinal study determining the effects of two 3 week-periods of high intensity high volume interval training (HIHVT) (90 intervals of 6 s cycling at 250% maximum power, P max /24 s) on a cycle ergometer. HIHVT was evaluated by comparing performance tests before and after the entire training (baseline, BSL, and endpoint, END) and between the two training sets (intermediate, INT). The mRNA expression levels of myosin heavy chain (MHC) isoforms and markers of energy metabolism were analyzed in M. vastus lateralis biopsies by quantitative real-time PCR. In incremental tests peak power (P peak ) was increased, whereas V ˙ O 2peak was unaltered. Prolonged time-to-exhaustion was found in endurance tests with 65 and 80% P max at INT and END. No changes in blood levels of lipid metabolites were detected. Training-induced decreases of hematocrit indicate hypervolemia. A shift from slow MHCI/β to fast MHCIIa mRNA expression occurred after the first and second training set. The mRNA expression of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α), a master regulator of oxidative energy metabolism, decreased after the second training set. In agreement, a significant decrease was also found for citrate synthase mRNA after the second training set, indicating reduced oxidative capacity. However, mRNA expression levels of glycolytic marker enzyme glyceraldehyde-3-phosphate dehydrogenase did not change after the first and second training set. HIHVT induced a nearly complete slow-to-fast fiber type transformation on the mRNA level, which, however, cannot account for the improvements of performance parameters. The latter might be explained by the well-known effects of hypervolemia on exercise performance.

Rural origin and exposure drives Ghanaian midwives reported future practice.

PubMed

Lori, Jody R; Livingston, Laura; Eagle, Megan; Rominski, Sarah; Nakua, Emmanuel Kweku; Agyei-Baffour, Peter

2014-09-01

A primary cause of Ghana's higher than global average maternal mortality rate is limited access to maternal care in rural areas. To date, few studies have examined how rural background/training of midwives impacts their future willingness to work in remote areas. The purpose of this paper is to describe the relationship between Ghanaian student midwife place of origin and rural training on their willingness to choose a future rural practice location. A cross-sectional computer-based survey was completed by 238 final year Ghanaian midwifery students from two public midwifery training schools located in urban Ghana between October and December 2009. The relationship between rural exposure and willingness to work in rural Ghana was analyzed using independent t-test, chi-square, and bivariate logistic regression. Participants who experienced a rural rotation (OR: 1.51, 95% CI: 0.71, 3.22) and those born in a rural area (OR: 2.24, 95% CI: 0.74, 6.75) resulted in greater odds ratio to choose rural practice following graduation. This study indicates an association between midwifery students' place of origin and training and their willingness to practice in a rural area after graduation.

Learning in fully recurrent neural networks by approaching tangent planes to constraint surfaces.

PubMed

May, P; Zhou, E; Lee, C W

2012-10-01

In this paper we present a new variant of the online real time recurrent learning algorithm proposed by Williams and Zipser (1989). Whilst the original algorithm utilises gradient information to guide the search towards the minimum training error, it is very slow in most applications and often gets stuck in local minima of the search space. It is also sensitive to the choice of learning rate and requires careful tuning. The new variant adjusts weights by moving to the tangent planes to constraint surfaces. It is simple to implement and requires no parameters to be set manually. Experimental results show that this new algorithm gives significantly faster convergence whilst avoiding problems like local minima. Copyright © 2012 Elsevier Ltd. All rights reserved.

Vector rogue waves and dark-bright boomeronic solitons in autonomous and nonautonomous settings.

PubMed

Mareeswaran, R Babu; Charalampidis, E G; Kanna, T; Kevrekidis, P G; Frantzeskakis, D J

2014-10-01

In this work we consider the dynamics of vector rogue waves and dark-bright solitons in two-component nonlinear Schrödinger equations with various physically motivated time-dependent nonlinearity coefficients, as well as spatiotemporally dependent potentials. A similarity transformation is utilized to convert the system into the integrable Manakov system and subsequently the vector rogue and dark-bright boomeronlike soliton solutions of the latter are converted back into ones of the original nonautonomous model. Using direct numerical simulations we find that, in most cases, the rogue wave formation is rapidly followed by a modulational instability that leads to the emergence of an expanding soliton train. Scenarios different than this generic phenomenology are also reported.

Vector excitation speech or audio coder for transmission or storage

NASA Technical Reports Server (NTRS)

Davidson, Grant (Inventor); Gersho, Allen (Inventor)

1989-01-01

A vector excitation coder compresses vectors by using an optimum codebook designed off line, using an initial arbitrary codebook and a set of speech training vectors exploiting codevector sparsity (i.e., by making zero all but a selected number of samples of lowest amplitude in each of N codebook vectors). A fast-search method selects a number N.sub.c of good excitation vectors from the codebook, where N.sub.c is much smaller tha ORIGIN OF INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) under which the inventors were granted a request to retain title.

Training practices and ergogenic aids used by male bodybuilders.

PubMed

Hackett, Daniel A; Johnson, Nathan A; Chow, Chin-Moi

2013-06-01

Bodybuilding involves performing a series of poses on stage where the competitor is judged on aesthetic muscular appearance. The purpose of this study was to describe training practices and ergogenic aids used by competitive bodybuilders and to determine whether training practices comply with current recommendations for muscular hypertrophy. A web-based survey was completed by 127 competitive male bodybuilders. The results showed that during the off-season phase of training (OFF), the majority of respondents performed 3-6 sets per exercise (95.3%), 7-12 repetition maximum (RM) per set (77.0%), and 61- to 120-seconds recovery between sets and exercises (68.6%). However, training practices changed 6 weeks before competition (PRE), where there was an increased number of respondents who reported undertaking 3-4 sets per exercise at the expense of 5-6 sets per exercise (p < 0.001), an increase in the number reporting 10-15RM per set from 7-9RM per set (p < 0.001), and an increase in the number reporting 30-60 seconds vs. 61-180 seconds recovery between sets and exercises (p < 0.001). Anabolic steroid use was high among respondents competing in amateur competitions (56 of 73 respondents), whereas dietary supplementation was used by all respondents. The findings of this study demonstrate that competitive bodybuilders comply with current resistance exercise recommendations for muscular hypertrophy; however, these changed before competition during which there is a reduction resistance training volume and intensity. This alteration, in addition to an increase in aerobic exercise volume, is purportedly used to increase muscle definition. However, these practices may increase the risk of muscle mass loss in natural compared with amateur bodybuilders who reportedly use drugs known to preserve muscle mass.

Importance of eccentric actions in performance adaptations to resistance training

NASA Technical Reports Server (NTRS)

Dudley, Gary A.; Miller, Bruce J.; Buchanan, Paul; Tesch, Per A.

1991-01-01

The importance of eccentric (ecc) muscle actions in resistance training for the maintenance of muscle strength and mass in hypogravity was investigated in experiments in which human subjects, divided into three groups, were asked to perform four-five sets of 6 to 12 repetitions (rep) per set of three leg press and leg extension exercises, 2 days each weeks for 19 weeks. One group, labeled 'con', performed each rep with only concentric (con) actions, while group con/ecc with performed each rep with only ecc actions; the third group, con/con, performed twice as many sets with only con actions. Control subjects did not train. It was found that resistance training wih both con and ecc actions induced greater increases in muscle strength than did training with only con actions.

Teaching adolescents with severe disabilities to use the public telephone.

PubMed

Test, D W; Spooner, F; Keul, P K; Grossi, T

1990-04-01

Two adolescents with severe disabilities served as participants in a study conducted to train in the use of the public telephone to call home. Participants were trained to complete a 17-step task analysis using a training package which consisted of total task presentation in conjunction with a four-level prompting procedure (i.e., independent, verbal, verbal + gesture, verbal + guidance). All instruction took place in a public setting (e.g., a shopping mall) with generalization probes taken in two alternative settings (e.g., a movie theater and a convenience store). A multiple probe across individuals design demonstrated the training package was successful in teaching participants to use the telephone to call home. In addition, newly acquired skills generalized to the two untrained settings. Implications for community-based training are discussed.

19 CFR 10.770 - Originating goods.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Rules of Origin § 10.770 Originating goods. (a) General. A good will be considered an originating good... provided for in a heading or subheading of the HTSUS that is not covered by the product-specific rules set... the product-specific rules set forth in General Note 27(h), HTSUS, and: (i)(A) Each of the non...

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

PubMed

Weng, Ziqing; Wolc, Anna; Shen, Xia; Fernando, Rohan L; Dekkers, Jack C M; Arango, Jesus; Settar, Petek; Fulton, Janet E; O'Sullivan, Neil P; Garrick, Dorian J

2016-03-19

Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

Lack of training threatening drilling talent supply

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

Von Flatern, R.

When oil prices crashed in the mid-1980s, the industry tightened budgets. Among the austerity measures taken to survive the consequences of low product prices was an end to expensive, long-term investment training of drilling engineers. In the absence of traditional sources of trained drilling talent, forward-looking contractors are creating their own training programs. The paper describes the activities of some companies who are setting up their own training programs, and an alliance being set up by Chevron and Amoco for training. The paper also discusses training drilling managers, third-party trainers, and the consequences for the industry that does not renewmore » its inventory of people.« less

Training in interprofessional collaboration: pedagogic innovation in family medicine units.

PubMed

Paré, Line; Maziade, Jean; Pelletier, Francine; Houle, Nathalie; Iloko-Fundi, Maximilien

2012-04-01

A number of agencies that accredit university health sciences programs recently added standards for the acquisition of knowledge and skills with respect to interprofessional collaboration. Within primary care settings there are no practical training programs that allow students from different disciplines to develop competencies in this area. The training program was developed within family medicine units affiliated with Université Laval in Quebec for family medicine residents and trainees from various disciplines to develop competencies in patient-centred, interprofessional collaborative practice in primary care. Based on adult learning theories, the program was divided into 3 phases--preparing family medicine unit professionals, training preceptors, and training the residents and trainees. The program's pedagogic strategies allowed participants to learn with, from, and about one another while preparing them to engage in contemporary primary care practices. A combination of quantitative and qualitative methods was used to evaluate the implementation process and the immediate results of the training program. The training program had a positive effect on both the clinical settings and the students. Preparation of clinical settings is an important issue that must be considered when planning practical interprofessional training.

Impact of Web-based Case Conferencing on Cancer Genetics Training Outcomes for Community-based Clinicians

PubMed Central

Blazer, Kathleen R.; Christie, Christina; Uman, Gwen; Weitzel, Jeffrey N.

2013-01-01

Introduction Technology and market forces are driving the demand for cancer risk assessment services in the community setting, where few clinicians are trained to order and interpret predictive genetic tests. City of Hope conducts a three-phase course in genetic cancer risk assessment (GCRA) for community-based clinicians, comprised of distance didactics, face-to-face workshops and 12 months of professional development. As designed, the course cannot meet increasing demands for GCRA training. Action research identified face-to-face workshops as a barrier to increasing course capacity. This study compared the learning effectiveness of Web-based case conferencing to face-to-face training. Methods A quasi-experimental design compared pre-post knowledge, skills and professional self-efficacy outcomes from 2009-2010 course cohorts (n=96). The intervention group (n=52) engaged in Web-based case conferences during distance learning; the comparison group (n=44) participated in the course as originally designed. Results Both groups and all practice disciplines demonstrated significant pre-to-post increases on all measures. Knowledge increases were higher for the intervention group (p < .015); skills and self-efficacy increases were comparable between groups (p < .33 and p < .30, respectively). Discussion Findings support the learning utility of Web-based case conferencing. Further studies may inform the development of tools to assess the impact of Web-based case conferencing on practice change and patient outcomes, in alignment with the highest standards of continuing professional development. PMID:22328115

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Brain-Compatible Learning: Principles and Applications in Athletic Training

PubMed Central

2003-01-01

Objective: To discuss the principles of brain-compatible learning research and provide insights into how this research may be applied in athletic training education to benefit the profession. Background: In the past decade, new brain-imaging techniques have allowed us to observe the brain while it is learning. The field of neuroscience has produced a body of empirical data that provides a new understanding of how we learn. This body of data has implications in education, although the direct study of these implications is in its infancy. Description: An overview of how the brain learns at a cellular level is provided, followed by a discussion of the principles of brain-compatible learning. Applications of these principles and implications for the field of athletic training education are also offered. Application: Many educational-reform fads have garnered attention in the past. Brain-compatible learning will not likely be one of those, as its origin is in neuroscience, not education. Brain-compatible learning is not an educational-reform movement. It does not prescribe how to run your classroom or offer specific techniques to use. Rather, it provides empirical data about how the brain learns and suggests guidelines to be considered while preparing lessons for your students. These guidelines may be incorporated into every educational setting, with every type of curriculum and every age group. The field of athletic training lends itself well to many of the basic principles of brain-compatible learning. PMID:16558681

Organizing for teamwork in healthcare: an alternative to team training?

PubMed

Rydenfält, Christofer; Odenrick, Per; Larsson, Per Anders

2017-05-15

Purpose The purpose of this paper is to explore how organizational design could support teamwork and to identify organizational design principles that promote successful teamwork. Design/methodology/approach Since traditional team training sessions take resources away from production, the alternative approach pursued here explores the promotion of teamwork by means of organizational design. A wide and pragmatic definition of teamwork is applied: a team is considered to be a group of people that are set to work together on a task, and teamwork is then what they do in relation to their task. The input - process - output model of teamwork provides structure to the investigation. Findings Six teamwork enablers from the healthcare team literature - cohesion, collaboration, communication, conflict resolution, coordination, and leadership - are discussed, and the organizational design measures required to implement them are identified. Three organizational principles are argued to facilitate the teamwork enablers: team stability, occasions for communication, and a participative and adaptive approach to leadership. Research limitations/implications The findings could be used as a foundation for intervention studies to improve team performance or as a framework for evaluation of existing organizations. Practical implications By implementing these organizational principles, it is possible to achieve many of the organizational traits associated with good teamwork. Thus, thoughtful organization for teamwork can be used as an alternative or complement to the traditional team training approach. Originality/value With regards to the vast literature on team training, this paper offers an alternative perspective on how to improve team performance in healthcare.

Interprofessional Emergency Training Leads to Changes in the Workplace.

PubMed

Eisenmann, Dorothea; Stroben, Fabian; Gerken, Jan D; Exadaktylos, Aristomenis K; Machner, Mareen; Hautz, Wolf E

2018-01-01

Preventable mistakes occur frequently and can lead to patient harm and death. The emergency department (ED) is notoriously prone to such errors, and evidence suggests that improving teamwork is a key aspect to reduce the rate of error in acute care settings. Only a few strategies are in place to train team skills and communication in interprofessional situations. Our goal was to conceptualize, implement, and evaluate a training module for students of three professions involved in emergency care. The objective was to sensitize participants to barriers for their team skills and communication across professional borders. We developed a longitudinal simulation-enhanced training format for interprofessional teams, consisting of final-year medical students, advanced trainees of emergency nursing and student paramedics. The training format consisted of several one-day training modules, which took place twice in 2016 and 2017. Each training module started with an introduction to share one's roles, professional self-concepts, common misconceptions, and communication barriers. Next, we conducted different simulated cases. Each case consisted of a prehospital section (for paramedics and medical students), a handover (everyone), and an ED section (medical students and emergency nurses). After each training module, we assessed participants' "Commitment to Change." In this questionnaire, students were anonymously asked to state up to three changes that they wished to implement as a result of the course, as well as the strength of their commitment to these changes. In total, 64 of 80 participants (80.0%) made at least one commitment to change after participating in the training modules. The total of 123 commitments was evenly distributed over four emerging categories: communication , behavior , knowledge and attitude . Roughly one third of behavior- and attitude-related commitments were directly related to interprofessional topics (e.g., "acknowledge other professions' work"), and these were equally distributed among professions. At the two-month follow-up, 32 participants (50%) provided written feedback on their original commitments: 57 of 62 (91.9%) commitments were at least partly realized at the follow-up, and only five (8.1%) commitments lacked realization entirely. A structured simulation-enhanced intervention was successful in promoting change to the practice of emergency care, while training teamwork and communication skills jointly.

Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order

NASA Astrophysics Data System (ADS)

Nahlawi, Layan; Goncalves, Caroline; Imani, Farhad; Gaed, Mena; Gomez, Jose A.; Moussa, Madeleine; Gibson, Eli; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin; Shatkay, Hagit

2017-03-01

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.

Emergence of Tacts following Mand Training in Young Children with Autism

ERIC Educational Resources Information Center

Egan, Claire E.; Barnes-Holmes, Dermot

2009-01-01

This study sought to examine the effects of training mands on the emergence of tacts with the same response forms. Results indicated that training adjective sets as mands resulted in the emergence of adjective sets as tacts under modified, but not standard, antecedent conditions. The findings suggested that the apparent functional independence of…

Using a Process Social Skills Training Approach with Adolescents with Mild Intellectual Disabilities in a High School Setting.

ERIC Educational Resources Information Center

O'Reilly, Mark F.; Glynn, Dawn

1995-01-01

A process social skills training approach was implemented and evaluated with two high school students having mild intellectual disabilities and social skills deficits. The intervention package was successful in promoting generalization of targeted social skills from the training setting to the classroom for both students. Participants had…

Bringing the Science of Team Training to School-Based Teams

ERIC Educational Resources Information Center

Benishek, Lauren E.; Gregory, Megan E.; Hodges, Karin; Newell, Markeda; Hughes, Ashley M.; Marlow, Shannon; Lacerenza, Christina; Rosenfield, Sylvia; Salas, Eduardo

2016-01-01

Teams are ubiquitous in schools in the 21st Century; yet training for effective teaming within these settings has lagged behind. The authors of this article developed 5 modules, grounded in the science of team training and adapted from an evidence-based curriculum used in medical settings called TeamSTEPPS®, to prepare instructional and…

Training a Retarded Client's Mother and Teacher through Sequenced Instructions to Establish Self-Feeding.

ERIC Educational Resources Information Center

Kissel, Robert C.; And Others

1980-01-01

A parent and teacher were trained in home and school settings to administer a self-feeding program to a profoundly retarded adult woman. During training, an increase in both the parent and teacher's appropriate use of instruction and attention occurred, and a high stable rate of self-feeding responses developed across settings. (Author)

Assessing and Improving Performance: A Longitudinal Evaluation of Priority Setting and Resource Allocation in a Canadian Health Region.

PubMed

Hall, William; Smith, Neale; Mitton, Craig; Urquhart, Bonnie; Bryan, Stirling

2017-08-22

In order to meet the challenges presented by increasing demand and scarcity of resources, healthcare organizations are faced with difficult decisions related to resource allocation. Tools to facilitate evaluation and improvement of these processes could enable greater transparency and more optimal distribution of resources. The Resource Allocation Performance Assessment Tool (RAPAT) was implemented in a healthcare organization in British Columbia, Canada. Recommendations for improvement were delivered, and a follow up evaluation exercise was conducted to assess the trajectory of the organization's priority setting and resource allocation (PSRA) process 2 years post the original evaluation. Implementation of RAPAT in the pilot organization identified strengths and weaknesses of the organization's PSRA process at the time of the original evaluation. Strengths included the use of criteria and evidence, an ability to reallocate resources, and the involvement of frontline staff in the process. Weaknesses included training, communication, and lack of program budgeting. Although the follow up revealed a regression from a more formal PSRA process, a legacy of explicit resource allocation was reported to be providing ongoing benefit for the organization. While past studies have taken a cross-sectional approach, this paper introduces the first longitudinal evaluation of PSRA in a healthcare organization. By including the strengths, weaknesses, and evolution of one organization's journey, the authors' intend that this paper will assist other healthcare leaders in meeting the challenges of allocating scarce resources. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Origin and outcome of multiple pregnancies in Bern, Switzerland, 1995-2006 and the current proposal of the Swiss parliament to revise the Swiss law of reproductive medicine: Switzerland quo vadis?

PubMed

Wunder, Dorothea; Neurohr, Eva-Maria; Faouzi, Mohamed; Birkhäuser, Martin H

2013-09-19

Infertility treatments are a major source of the increase in multiple pregnancies (MPs). The aims of the present study were (1.) to investigate the origin and maternal/neonatal outcomes of MP and (2.) to review the different measures that can be adopted to reduce these serious complications. The study included all women with multiple births between 1 January 1995 and 31 December 2006 at the University Hospital of Bern, Switzerland. The outcomes associated with the various origins of MP (natural conception, ovarian stimulation [OS]--in-vitro fertilisation [IVF-ICSI]) were analysed using a multinomial logistic regression model. An analysis of the Swiss law on reproductive medicine and its current proposed revision, as well as a literature review using Pubmed, was carried out. A total of 592 MP were registered, 91% (n = 537) resulted in live births. There was significantly more neonatal/maternal morbidity in MP after OS compared with natural conception and even with the IVF-ICSI group. With a policy of elective single embryo transfer (eSET), twin rates after IVF-ICSI can be reduced to <5% and triplets to <1%. After OS, more triplets are found and the outcome of MP is worse. MP is known to be associated with morbidity, mortality, and economic and social risks. To counteract these complications (1.) better training for physicians performing OS should be encouraged and (2.) the Swiss law on reproductive medicine needs to be changed, with the introduction of eSET policies. This would lead to a dramatic decrease in neonatal and maternal morbidity/mortality as well as significant cost reductions for the Swiss healthcare system.

Decimated Input Ensembles for Improved Generalization

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Norvig, Peter (Technical Monitor)

1999-01-01

Recently, many researchers have demonstrated that using classifier ensembles (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the ensemble performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the ensemble.

Developing Canadian physician: the quest for leadership effectiveness.

PubMed

Comber, Scott; Wilson, Lisette; Crawford, Kyle C

2016-07-04

Purpose The purpose of this study is to discern the physicians' perception of leadership effectiveness in their clinical and non-clinical roles (leadership) by identifying their political skill levels. Design/methodology/approach A sample of 209 Canadian physicians was surveyed using the Political Skills Inventory (PSI) during the period 2012-2014. The PSI was chosen because it assesses leadership effectiveness on four dimensions: social astuteness, interpersonal influence, networking ability and apparent authenticity. Findings Physicians in clinical roles' PSI scores were significantly lower in all four PSI dimensions when compared to all other physicians in non-clinical roles, with the principal difference being in their networking abilities. Practical implications More emphasis is needed on educating and training physicians, specifically in the areas of political skills, in current clinical roles if they are to assume leadership roles and be effective. Originality/value Although this study is located in Canada, the study design and associated findings may have implications to other areas and countries wanting to increase physician leadership effectiveness. Further, replication of this study in other settings may provide insight into the future design of physician leadership training curriculum.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

An incremental approach to genetic-algorithms-based classification.

PubMed

Guan, Sheng-Uei; Zhu, Fangming

2005-04-01

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

Effect of core stability training on throwing velocity in female handball players.

PubMed

Saeterbakken, Atle H; van den Tillaar, Roland; Seiler, Stephen

2011-03-01

The purpose was to study the effect of a sling exercise training (SET)-based core stability program on maximal throwing velocity among female handball players. Twenty-four female high-school handball players (16.6 ± 0.3 years, 63 ± 6 kg, and 169 ± 7 cm) participated and were initially divided into a SET training group (n = 14) and a control group (CON, n = 10). Both groups performed their regular handball training for 6 weeks. In addition, twice a week, the SET group performed a progressive core stability-training program consisting of 6 unstable closed kinetic chain exercises. Maximal throwing velocity was measured before and after the training period using photocells. Maximal throwing velocity significantly increased 4.9% from 17.9 ± 0.5 to 18.8 ± 0.4 m·s in the SET group after the training period (p < 0.01), but was unchanged in the control group (17.1 ± 0.4 vs. 16.9 ± 0.4 m·s). These results suggest that core stability training using unstable, closed kinetic chain movements can significantly improve maximal throwing velocity. A stronger and more stable lumbopelvic-hip complex may contribute to higher rotational velocity in multisegmental movements. Strength coaches can incorporate exercises exposing the joints for destabilization force during training in closed kinetic chain exercises. This may encourage an effective neuromuscular pattern and increase force production and can improve a highly specific performance task such as throwing.

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.

Validity and validation of expert (Q)SAR systems.

PubMed

Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

2005-08-01

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.

PubMed

Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan; Liu, Jingjing

2018-01-18

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33-100%, and ELM, with an accuracy rate of 98.01-100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016-0.3494, lower than the error of 0.5-1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

Multiscale 3D Shape Analysis using Spherical Wavelets

PubMed Central

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2013-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data. PMID:16685992

Multiscale 3D shape analysis using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen R

2005-01-01

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

A review of factors affecting the transfer of sexual and reproductive health training into practice in low and lower-middle income country humanitarian settings.

PubMed

Beek, Kristen; Dawson, Angela; Whelan, Anna

2017-01-01

A lack of access to sexual and reproductive health (SRH) care is the leading cause of morbidity and mortality among displaced women and girls of reproductive age. Efforts to address this public health emergency in humanitarian settings have included the widespread delivery of training programmes to address gaps in health worker capacity for SRH. There remains a lack of data on the factors which may affect the ability of health workers to apply SRH knowledge and skills gained through training programmes in humanitarian contexts. We searched four electronic databases and ten key organizations' websites to locate literature on SRH training for humanitarian settings in low and lower-middle income countries. Papers were examined using content analysis to identify factors which contribute to health workers' capacity to transfer SRH knowledge, skills and attitudes learned in training into practice in humanitarian settings. Seven studies were included in this review. Six research papers focused on the response stage of humanitarian crises and five papers featured the disaster context of conflict. A range of SRH components were addressed including maternal, newborn health and sexual violence. The review identified factors, including appropriate resourcing, organisational support and confidence in health care workers that were found to facilitate the transfer of learning. The findings suggest the presence of factors that moderate the transfer of training at the individual, training, organisational, socio-cultural, political and health system levels. Supportive strategies are necessary to best assist trainees to apply newly acquired knowledge and skills in their work settings. These interventions must address factors that moderate the success of learning transfer. Findings from this review suggest that these are related to the individual trainee, the training program itself and the workplace as well as the broader environmental context. Organisations which provide SRH training for humanitarian emergencies should work to identify the system of moderating factors that affect training transfer in their setting and employ evidence-based strategies to ameliorate these.

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

Chen, X; Wang, J; Hu, W

Purpose: The Varian RapidPlan™ is a commercial knowledge-based optimization process which uses a set of clinically used treatment plans to train a model that can predict individualized dose-volume objectives. The purpose of this study is to evaluate the performance of RapidPlan to generate intensity modulated radiation therapy (IMRT) plans for cervical cancer. Methods: Totally 70 IMRT plans for cervical cancer with varying clinical and physiological indications were enrolled in this study. These patients were all previously treated in our institution. There were two prescription levels usually used in our institution: 45Gy/25 fractions and 50.4Gy/28 fractions. 50 of these plans weremore » selected to train the RapidPlan model for predicting dose-volume constraints. After model training, this model was validated with 10 plans from training pool(internal validation) and additional other 20 new plans(external validation). All plans used for the validation were re-optimized with the original beam configuration and the generated priorities from RapidPlan were manually adjusted to ensure that re-optimized DVH located in the range of the model prediction. DVH quantitative analysis was performed to compare the RapidPlan generated and the original manual optimized plans. Results: For all the validation cases, RapidPlan based plans (RapidPlan) showed similar or superior results compared to the manual optimized ones. RapidPlan increased the result of D98% and homogeneity in both two validations. For organs at risk, the RapidPlan decreased mean doses of bladder by 1.25Gy/1.13Gy (internal/external validation) on average, with p=0.12/p<0.01. The mean dose of rectum and bowel were also decreased by an average of 2.64Gy/0.83Gy and 0.66Gy/1.05Gy,with p<0.01/ p<0.01and p=0.04/<0.01 for the internal/external validation, respectively. Conclusion: The RapidPlan model based cervical cancer plans shows ability to systematically improve the IMRT plan quality. It suggests that RapidPlan has great potential to make the treatment planning process more efficient.« less

Assessing the Biological Safety Profession's Evaluation and Control of Risks Associated with the Field Collection of Potentially Infectious Specimens.

PubMed

Patlovich, Scott J; Emery, Robert J; Whitehead, Lawrence W; Brown, Eric L; Flores, Rene

2015-03-01

Because the origins of the biological safety profession are rooted in the control and prevention of laboratory-associated infections, the vocation focuses primarily on the safe handling of specimens within the laboratory. But in many cases, the specimens and samples handled in the lab are originally collected in the field where a broader set of possible exposure considerations may be present, each with varying degrees of controllability. The failure to adequately control the risks associated with collecting biological specimens in the field may result in illness or injury, and could have a direct impact on laboratory safety, if infectious specimens were packaged or transported inappropriately, for example. This study developed a web-based survey distributed to practicing biological safety professionals to determine the prevalence of and extent to which biological safety programs consider and evaluate field collection activities. In cases where such issues were considered, the data collected characterize the types of controls and methods of oversight at the institutional level that are employed. Sixty-one percent (61%) of the survey respondents indicated that research involving the field collection of biological specimens is conducted at their institutions. A majority (79%) of these field collection activities occur at academic institutions. Twenty-seven percent (27%) of respondents indicated that their safety committees do not consider issues related to biological specimens collected in the field, and only 25% with an oversight committee charged to review field collection protocols have generated a field research-specific risk assessment form to facilitate the assembly of pertinent information for a project risk assessment review. The results also indicated that most biosafety professionals (73% overall; 71% from institutions conducting field collection activities) have not been formally trained on the topic, but many (64% overall; 87% from institutions conducting field collection activities) indicated that training on field research safety issues would be helpful, and even more (71% overall; 93% from institutions conducting field collection activities) would consider participation in such a training course. Results obtained from this study can be used to develop a field research safety toolkit and associated training curricula specifically targeted to biological safety professionals.

Social Scientists, Historians and Super Patriots: The Origins of Civic Education in the United States.

ERIC Educational Resources Information Center

Shermis, Samuel

1991-01-01

Discusses social studies' evolution as a discipline from its 1890s origins through the twentieth century. Examines the objectives of historians, sociologists, and "super patriots" (proponents of the Americanism movement) in advancing citizenship training. Concludes that the failure to achieve some of the original goals of social studies…

Expertise in musical improvisation and creativity: the mediation of idea evaluation.

PubMed

Kleinmintz, Oded M; Goldstein, Pavel; Mayseless, Naama; Abecasis, Donna; Shamay-Tsoory, Simone G

2014-01-01

The current study explored the influence of musical expertise, and specifically training in improvisation on creativity, using the framework of the twofold model, according to which creativity involves a process of idea generation and idea evaluation. Based on the hypothesis that a strict evaluation phase may have an inhibiting effect over the generation phase, we predicted that training in improvisation may have a "releasing effect" on the evaluation system, leading to greater creativity. To examine this hypothesis, we compared performance among three groups--musicians trained in improvisation, musicians not trained in improvisation, and non-musicians--on divergent thinking tasks and on their evaluation of creativity. The improvisation group scored higher on fluency and originality compared to the other two groups. Among the musicians, evaluation of creativity mediated how experience in improvisation was related to originality and fluency scores. It is concluded that deliberate practice of improvisation may have a "releasing effect" on creativity.

Single versus multiple sets of resistance exercise: a meta-regression.

PubMed

Krieger, James W

2009-09-01

There has been considerable debate over the optimal number of sets per exercise to improve musculoskeletal strength during a resistance exercise program. The purpose of this study was to use hierarchical, random-effects meta-regression to compare the effects of single and multiple sets per exercise on dynamic strength. English-language studies comparing single with multiple sets per exercise, while controlling for other variables, were considered eligible for inclusion. The analysis comprised 92 effect sizes (ESs) nested within 30 treatment groups and 14 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.26 +/- 0.05; confidence interval [CI]: 0.15, 0.37; p < 0.0001). In a dose-response model, 2 to 3 sets per exercise were associated with a significantly greater ES than 1 set (difference = 0.25 +/- 0.06; CI: 0.14, 0.37; p = 0.0001). There was no significant difference between 1 set per exercise and 4 to 6 sets per exercise (difference = 0.35 +/- 0.25; CI: -0.05, 0.74; p = 0.17) or between 2 to 3 sets per exercise and 4 to 6 sets per exercise (difference = 0.09 +/- 0.20; CI: -0.31, 0.50; p = 0.64). There were no interactions between set volume and training program duration, subject training status, or whether the upper or lower body was trained. Sensitivity analysis revealed no highly influential studies, and no evidence of publication bias was observed. In conclusion, 2 to 3 sets per exercise are associated with 46% greater strength gains than 1 set, in both trained and untrained subjects.

Residents' perceived needs in communication skills training across in- and outpatient clinical settings.

PubMed

Junod Perron, Noelle; Sommer, Johanna; Hudelson, Patricia; Demaurex, Florence; Luthy, Christophe; Louis-Simonet, Martine; Nendaz, Mathieu; De Grave, Willem; Dolmans, Diana; Van der Vleuten, Cees

2009-05-01

Residents' perceived needs in communication skills training are important to identify before designing context-specific training programmes, since learrners' perceived needs can influence the effectiveness of training. To explore residents' perceptions of their training needs and training experiences around communication skills, and whether these differ between residents training in inpatient and outpatient clinical settings. Four focus groups (FG) and a self-administered questionnaire were conducted with residents working in in- and outpatient medical service settings at a Swiss University Hospital. Focus groups explored residents' perceptions of their communication needs, their past training experiences and suggestions for future training programmes in communication skills. Transcripts were analysed in a thematic way using qualitative analytic approaches. All residents from both settings were asked to complete a questionnaire that queried their sociodemographics and amount of prior training in communication skills. In focus groups, outpatient residents felt that communication skills were especially useful in addressing chronic diseases and social issues. In contrast, inpatient residents emphasized the importance of good communication skills for dealing with family conflicts and end-of-life issues. Felt needs reflected residents' differing service priorities: outpatient residents saw the need for skills to structure the consultation and explore patients' perspectives in order to build therapeutic alliances, whereas inpatient residents wanted techniques to help them break bad news, provide information and increase their own well-being. The survey's overall response rate was 56%. Its data showed that outpatient residents received more training in communication skills and more of them than inpatient residents considered communication skills training to be useful (100% vs 74%). Outpatient residents' perceived needs in communication skills were more patient-centered than the needs perceived by inpatient residents. Residents' perceived needs for communication skills may differ not only because of their differing service priorities but also because of differences in their previous experiences with communication skills training. These factors should be taken into account when designing a training programme in communication skills.

Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function

PubMed Central

2010-01-01

Background Comparative genomics methods such as phylogenetic profiling can mine powerful inferences from inherently noisy biological data sets. We introduce Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL), a method that applies the Partial Phylogenetic Profiling (PPP) approach locally within a protein sequence to discover short sequence signatures associated with functional sites. The approach is based on the basic scoring mechanism employed by PPP, namely the use of binomial distribution statistics to optimize sequence similarity cutoffs during searches of partitioned training sets. Results Here we illustrate and validate the ability of the SIMBAL method to find functionally relevant short sequence signatures by application to two well-characterized protein families. In the first example, we partitioned a family of ABC permeases using a metabolic background property (urea utilization). Thus, the TRUE set for this family comprised members whose genome of origin encoded a urea utilization system. By moving a sliding window across the sequence of a permease, and searching each subsequence in turn against the full set of partitioned proteins, the method found which local sequence signatures best correlated with the urea utilization trait. Mapping of SIMBAL "hot spots" onto crystal structures of homologous permeases reveals that the significant sites are gating determinants on the cytosolic face rather than, say, docking sites for the substrate-binding protein on the extracellular face. In the second example, we partitioned a protein methyltransferase family using gene proximity as a criterion. In this case, the TRUE set comprised those methyltransferases encoded near the gene for the substrate RF-1. SIMBAL identifies sequence regions that map onto the substrate-binding interface while ignoring regions involved in the methyltransferase reaction mechanism in general. Neither method for training set construction requires any prior experimental characterization. Conclusions SIMBAL shows that, in functionally divergent protein families, selected short sequences often significantly outperform their full-length parent sequence for making functional predictions by sequence similarity, suggesting avenues for improved functional classifiers. When combined with structural data, SIMBAL affords the ability to localize and model functional sites. PMID:20102603

An Analysis of Training Focused on Improving SMART Goal Setting for Specific Employee Groups

ERIC Educational Resources Information Center

Worden, Jeannie M.

2014-01-01

This quantitative study examined the proficiency of employee SMART goal setting following the intervention of employee SMART goal setting training. Current challenges in higher education substantiate the need for employees to align their performance with the mission, vision, and strategic directions of the organization. A performance management…

Educational Preparation and Experiences in the Industrial-Occupational Setting: A Qualitative Study of Athletic Training Graduates' Perspectives

ERIC Educational Resources Information Center

Schilling, Jim F.

2011-01-01

Context: The industrial-occupational setting provides a workplace of substantial potential for the athletic training graduate. Acquiring input from entry-level athletic trainers (ATs) pertaining to experiences, knowledge, and skills necessary to be successful in the industrial-occupational setting is critical information for future Athletic…

The effects of training with loads that maximise power output and individualised repetitions vs. traditional power training

PubMed Central

Moya-Ramón, M.; Hernández-Davó, J. L.; Fernandez-Fernandez, J.; Sabido, R.

2017-01-01

Background It has been suggested that strength training effects (i.e. neural or structural) vary, depending on the total repetitions performed and velocity loss in each training set. Purpose The aim of this study is to compare the effects of two training programmes (i.e. one with loads that maximise power output and individualised repetitions, and the other following traditional power training). Methods Twenty-five males were divided into three groups (optimum power [OP = 10], traditional training [TT = 9] and control group [CG = 6]). The training load used for OP was individualised using loads that maximised power output (41.7% ± 5.8 of one repetition maximum [1RM]) and repetitions at maximum power (4 to 9 repetitions, or ‘reps’). Volume (sets x repetitions) was the same for both experimental groups, while intensity for TT was that needed to perform only 50% of the maximum number of possible repetitions (i.e. 61.1%–66.6% of 1RM). The training programme ran over 11 weeks (2 sessions per week; 4–5 sets per session; 3-minute rests between sets), with pre-, intermediate and post-tests which included: anthropometry, 1RM, peak power output (PPO) with 30%, 40% and 50% of 1RM in the bench press throw, and salivary testosterone (ST) and cortisol (SC) concentrations. Rate of perceived exertion (RPE) and power output were recorded in all sessions. Results Following the intermediate test, PPO was increased in the OP group for each load (10.9%–13.2%). Following the post-test, both experimental groups had increased 1RM (11.8%–13.8%) and PPO for each load (14.1%–19.6%). Significant decreases in PPO were found for the TT group during all sets (4.9%–15.4%), along with significantly higher RPE (37%). Conclusion OP appears to be a more efficient method of training, with less neuromuscular fatigue and lower RPE. PMID:29053725

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

PubMed

Wu, X; Lund, M S; Sun, D; Zhang, Q; Su, G

2015-10-01

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction. © 2015 Blackwell Verlag GmbH.

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Wagstaff, Kiri L.

2011-01-01

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

The Impact of Emotional Intelligence on Conditions of Trust among Leaders at the Kentucky Department for Public Health

PubMed Central

Knight, Jennifer Redmond; Bush, Heather M.; Mase, William A.; Riddell, Martha Cornwell; Liu, Meng; Holsinger, James W.

2015-01-01

There has been limited leadership research on emotional intelligence and trust in governmental public health settings. The purpose of this study was to identify and seek to understand the relationship between trust and elements of emotional intelligence, including stress management, at the Kentucky Department for Public Health (KDPH). The KDPH serves as Kentucky’s state governmental health department. KDPH is led by a Commissioner and composed of seven primary divisions and 25 branches within those divisions. The study was a non-randomized cross-sectional study utilizing electronic surveys that evaluated conditions of trust among staff members and emotional intelligence among supervisors. Pearson correlation coefficients and corresponding p-values are presented to provide the association between emotional intelligence scales and the conditions of trust. Significant positive correlations were observed between supervisors’ stress management and the staff members’ trust or perception of supervisors’ loyalty (r = 0.6, p = 0.01), integrity (r = 0.5, p = 0.03), receptivity (r = 0.6, p = 0.02), promise fulfillment (r = 0.6, p = 0.02), and availability (r = 0.5, p = 0.07). This research lays the foundation for emotional intelligence and trust research and leadership training in other governmental public health settings, such as local, other state, national, or international organizations. This original research provides metrics to assess the public health workforce with attention to organizational management and leadership constructs. The survey tools could be used in other governmental public health settings in order to develop tailored training opportunities related to emotional intelligence and trust organizations. PMID:25821778

A cost-effective smartphone-based antimicrobial susceptibility test reader for drug resistance testing (Conference Presentation)

NASA Astrophysics Data System (ADS)

Feng, Steve W.; Tseng, Derek; Di Carlo, Dino; Garner, Omai B.; Ozcan, Aydogan

2017-03-01

Antimicrobial susceptibility testing (AST) is commonly used for determining microbial drug resistance, but routine testing, which can significantly reduce the spread of multi-drug resistant organisms, is not regularly performed in resource-limited and field-settings due to technological challenges and lack of trained diagnosticians. We developed a portable cost-effective smartphone-based colorimetric 96-well microtiter plate (MTP) reader capable of automated AST without the need for a trained diagnostician. This system is composed of a smartphone used in conjunction with a 3D-printed opto-mechanical attachment, which holds a set of inexpensive light-emitting-diodes and fiber-optic cables coupled to the 96-well MTP for enabling the capture of the transmitted light through each well by the smartphone camera. Images of the MTP plate are captured at multiple exposures and uploaded to a local or remote server (e.g., a laptop) for automated processing/analysis of the results using a custom-designed smartphone application. Each set of images are combined to generate a high dynamic-range image and analyzed for well turbidity (indicative of bacterial growth), followed by interpretative analysis per plate to determine minimum inhibitory concentration (MIC) and drug susceptibility for the specific bacterium. Results are returned to the originating device within 1 minute and shown to the user in tabular form. We demonstrated the capability of this platform using MTPs prepared with 17 antibiotic drugs targeting Gram-negative bacteria and tested 82 patient isolate MTPs of Klebsiella pneumoniae, achieving well turbidity accuracy of 98.19%, MIC accuracy of 95.15%, and drug susceptibility interpretation accuracy of 99.06%, meeting the FDA defined criteria for AST.

Leapfrog diagnostics: Demonstration of a broad spectrum pathogen identification platform in a resource-limited setting

PubMed Central

2012-01-01

Background Resource-limited tropical countries are home to numerous infectious pathogens of both human and zoonotic origin. A capability for early detection to allow rapid outbreak containment and prevent spread to non-endemic regions is severely impaired by inadequate diagnostic laboratory capacity, the absence of a “cold chain” and the lack of highly trained personnel. Building up detection capacity in these countries by direct replication of the systems existing in developed countries is not a feasible approach and instead requires “leapfrogging” to the deployment of the newest diagnostic systems that do not have the infrastructure requirements of systems used in developed countries. Methods A laboratory for molecular diagnostics of infectious agents was established in Bo, Sierra Leone with a hybrid solar/diesel/battery system to ensure stable power supply and a satellite modem to enable efficient communication. An array of room temperature stabilization and refrigeration technologies for reliable transport and storage of reagents and biological samples were also tested to ensure sustainable laboratory supplies for diagnostic assays. Results The laboratory demonstrated its operational proficiency by conducting an investigation of a suspected avian influenza outbreak at a commercial poultry farm at Bo using broad range resequencing microarrays and real time RT-PCR. The results of the investigation excluded influenza viruses as a possible cause of the outbreak and indicated a link between the outbreak and the presence of Klebsiella pneumoniae. Conclusions This study demonstrated that by application of a carefully selected set of technologies and sufficient personnel training, it is feasible to deploy and effectively use a broad-range infectious pathogen detection technology in a severely resource-limited setting. PMID:22759725

Modification of the Clinical Global Impressions (CGI) Scale for use in bipolar illness (BP): the CGI-BP.

PubMed

Spearing, M K; Post, R M; Leverich, G S; Brandt, D; Nolen, W

1997-12-05

The Clinical Global Impressions Scale (CGI) was modified specifically for use in assessing global illness severity and change in patients with bipolar disorder. Criticisms of the original CGI were addressed by correcting inconsistencies in scaling, identifying time frames for comparison, clarifying definitions of illness severity and change, and separating out assessment of treatment side effects from illness improvement during treatment. A Detailed User's Guide was developed to train clinicians in the use of the new CGI-Bipolar Version (CGI-BP) for rating severity of manic and depressive episodes and the degree of change from the immediately preceding phase and from the worst phase of illness. The revised scale and manual provide a focused set of instructions to facilitate the reliability of these ratings of mania, depression, and overall bipolar illness during treatment of an acute episode or in longer-term illness prophylaxis. Interrater reliability of the scale was demonstrated in preliminary analyses. Thus, the modified CGI-BP is anticipated to be more useful than the original CGI in studies of bipolar disorder.

Training Decisions Technology Analysis

DTIC Science & Technology

1992-06-01

4.5.1 Relational Data Base Management 69 4.5.2 TASCS Data Content 69 4.5.3 Relationships with TDS 69 4.6 Other Air Force Modeling R&D 70 4.6.1 Time ...executive decision making were first developed by M. S. Scott Morton in the early 1970’s who, at that time , termed them " management decision systems" (Scott...Allocations to Training Settings o Managers ’ Preferences for Task Allocations to Training Settings o Times Required to Training Tasks in Various

Ongoing training of community health workers in low-income and middle-income countries: a systematic scoping review of the literature

PubMed Central

O’Donovan, Charles; Kuhn, Isla; Sachs, Sonia Ehrlich

2018-01-01

Objectives Understanding the current landscape of ongoing training for community health workers (CHWs) in low-income and middle-income countries (LMICs) is important both for organisations responsible for their training, as well as researchers and policy makers. This scoping review explores this under-researched area by mapping the current delivery implementation and evaluation of ongoing training provision for CHWs in LMICs. Design Systematic scoping review. Data sources MEDLINE, Embase, AMED, Global Health, Web of Science, Scopus, ASSIA, LILACS, BEI and ERIC. Study selection Original studies focusing on the provision of ongoing training for CHWs working in a country defined as low income and middle income according to World Bank Group 2012 classification of economies. Results The scoping review found 35 original studies that met the inclusion criteria. Ongoing training activities for CHWs were described as supervision (n=19), inservice or refresher training (n=13) or a mixture of both (n=3). Although the majority of studies emphasised the importance of providing ongoing training, several studies reported no impact of ongoing training on performance indicators. The majority of ongoing training was delivered inperson; however, four studies reported the use of mobile technologies to support training delivery. The outcomes from ongoing training activities were measured and reported in different ways, including changes in behaviour, attitudes and practice measured in a quantitative manner (n=16), knowledge and skills (n=6), qualitative assessments (n=5) or a mixed methods approach combining one of the aforementioned modalities (n=8). Conclusions This scoping review highlights the diverse range of ongoing training for CHWs in LMICs. Given the expansion of CHW programmes globally, more attention should be given to the design, delivery, monitoring and sustainability of ongoing training from a health systems strengthening perspective. PMID:29705769

Training instructional skills with paraprofessional service providers at a community-based habilitation setting.

PubMed

Wood, Amanda L; Luiselli, James K; Harchik, Alan E

2007-11-01

The present study evaluates a training program with paraprofessional service providers at a community-based habilitation setting. Four staff were taught to implement alternative and augmentative communication instruction with an adult who had autism and mental retardation through a combination of instruction, demonstration, behavior rehearsal, and performance feedback. Training was conducted under natural conditions at the adult's group home residence. Three of the four staff were able to maintain near-100% instructional accuracy following initial training. The results add to the limited research literature concerning community-based training of direct-care personnel.

Autoshaping, random control, and omission training in the rat1

PubMed Central

Locurto, Charles; Terrace, H. S.; Gibbon, John

1976-01-01

The role of the stimulus-reinforcer contingency in the development and maintenance of lever contact responding was studied in hooded rats. In Experiment I, three groups of experimentally naive rats were trained either on autoshaping, omission training, or a random-control procedure. Subjects trained by the autoshaping procedure responded more consistently than did either random-control or omission-trained subjects. The probability of at least one lever contact per trial was slightly higher in subjects trained by the omission procedure than by the random-control procedure. However, these differences were not maintained during extended training, nor were they evident in total lever-contact frequencies. When omission and random-control subjects were switched to the autoshaping condition, lever contacts increased in all animals, but a pronounced retardation was observed in omission subjects relative to the random-control subjects. In addition, subjects originally exposed to the random-control procedure, and later switched to autoshaping, acquired more rapidly than naive subjects that were exposed only on the autoshaping procedure. In Experiment II, subjects originally trained by an autoshaping procedure were exposed either to an omission, a random-control, or an extinction procedure. No differences were observed among the groups either in the rate at which lever contacts decreased or in the frequency of lever contacts at the end of training. These data implicate prior experience in the interpretation of omission-training effects and suggest limitations in the influence of stimulus-reinforcer relations in autoshaping. PMID:16811960

Autoshaping, random control, and omission training in the rat.

PubMed

Locurto, C; Terrace, H S; Gibbon, J

1976-11-01

The role of the stimulus-reinforcer contingency in the development and maintenance of lever contact responding was studied in hooded rats. In Experiment I, three groups of experimentally naive rats were trained either on autoshaping, omission training, or a random-control procedure. Subjects trained by the autoshaping procedure responded more consistently than did either random-control or omission-trained subjects. The probability of at least one lever contact per trial was slightly higher in subjects trained by the omission procedure than by the random-control procedure. However, these differences were not maintained during extended training, nor were they evident in total lever-contact frequencies. When omission and random-control subjects were switched to the autoshaping condition, lever contacts increased in all animals, but a pronounced retardation was observed in omission subjects relative to the random-control subjects. In addition, subjects originally exposed to the random-control procedure, and later switched to autoshaping, acquired more rapidly than naive subjects that were exposed only on the autoshaping procedure. In Experiment II, subjects originally trained by an autoshaping procedure were exposed either to an omission, a random-control, or an extinction procedure. No differences were observed among the groups either in the rate at which lever contacts decreased or in the frequency of lever contacts at the end of training. These data implicate prior experience in the interpretation of omission-training effects and suggest limitations in the influence of stimulus-reinforcer relations in autoshaping.

Effects of a Modified German Volume Training Program on Muscular Hypertrophy and Strength.

PubMed

Amirthalingam, Theban; Mavros, Yorgi; Wilson, Guy C; Clarke, Jillian L; Mitchell, Lachlan; Hackett, Daniel A

2017-11-01

Amirthalingam, T, Mavros, Y, Wilson, GC, Clarke, JL, Mitchell, L, and Hackett, DA. Effects of a modified German volume training program on muscular hypertrophy and strength. J Strength Cond Res 31(11): 3109-3119, 2017-German Volume Training (GVT), or the 10 sets method, has been used for decades by weightlifters to increase muscle mass. To date, no study has directly examined the training adaptations after GVT. The purpose of this study was to investigate the effect of a modified GVT intervention on muscular hypertrophy and strength. Nineteen healthy men were randomly assign to 6 weeks of 10 or 5 sets of 10 repetitions for specific compound resistance exercises included in a split routine performed 3 times per week. Total and regional lean body mass, muscle thickness, and muscle strength were measured before and after the training program. Across groups, there were significant increases in lean body mass measures, however, greater increases in trunk (p = 0.043; effect size [ES] = -0.21) and arm (p = 0.083; ES = -0.25) lean body mass favored the 5-SET group. No significant increases were found for leg lean body mass or measures of muscle thickness across groups. Significant increases were found across groups for muscular strength, with greater increases in the 5-SET group for bench press (p = 0.014; ES = -0.43) and lat pull-down (p = 0.003; ES = -0.54). It seems that the modified GVT program is no more effective than performing 5 sets per exercise for increasing muscle hypertrophy and strength. To maximize hypertrophic training effects, it is recommended that 4-6 sets per exercise be performed, as it seems gains will plateau beyond this set range and may even regress due to overtraining.

3D active shape models of human brain structures: application to patient-specific mesh generation

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Castro-Mateos, Isaac; Pozo, Jose M.; Frangi, Alejandro F.; Taylor, Zeike A.

2015-03-01

The use of biomechanics-based numerical simulations has attracted growing interest in recent years for computer-aided diagnosis and treatment planning. With this in mind, a method for automatic mesh generation of brain structures of interest, using statistical models of shape (SSM) and appearance (SAM), for personalised computational modelling is presented. SSMs are constructed as point distribution models (PDMs) while SAMs are trained using intensity profiles sampled from a training set of T1-weighted magnetic resonance images. The brain structures of interest are, the cortical surface (cerebrum, cerebellum & brainstem), lateral ventricles and falx-cerebri membrane. Two methods for establishing correspondences across the training set of shapes are investigated and compared (based on SSM quality): the Coherent Point Drift (CPD) point-set registration method and B-spline mesh-to-mesh registration method. The MNI-305 (Montreal Neurological Institute) average brain atlas is used to generate the template mesh, which is deformed and registered to each training case, to establish correspondence over the training set of shapes. 18 healthy patients' T1-weightedMRimages form the training set used to generate the SSM and SAM. Both model-training and model-fitting are performed over multiple brain structures simultaneously. Compactness and generalisation errors of the BSpline-SSM and CPD-SSM are evaluated and used to quantitatively compare the SSMs. Leave-one-out cross validation is used to evaluate SSM quality in terms of these measures. The mesh-based SSM is found to generalise better and is more compact, relative to the CPD-based SSM. Quality of the best-fit model instance from the trained SSMs, to test cases are evaluated using the Hausdorff distance (HD) and mean absolute surface distance (MASD) metrics.

The "global surgeon": is it time for modifications in the American surgical training paradigm?

PubMed

Ginwalla, Rashna F; Rustin, Rudolph B

2015-01-01

"Global surgery" is becoming an increasingly popular concept not only for new trainees, but also for established surgeons. The need to provide surgical care in low-resource settings is laudable, but the American surgical training system currently does not impart the breadth of skills required to provide quality care. We propose one possible model for a surgical fellowship program that provides those trainees who desire to practice in these settings a comprehensive experience that encompasses not only broad technical skills but also the opportunity to engage in policy and programmatic development and implementation. This is a descriptive commentary based on personal experience and a review of the literature. The proposed model is 2 years long, and can either be done after general surgery training as an additional "global surgery" fellowship or as part of a 3 + 2 general surgery + global surgery system. It would incorporate training in general surgery as well as orthopedics, urology, obstetrics & gynecology, neurosurgery, plastics & reconstructive surgery, as well as dedicated time for health systems training. Incorporating such training in a low-resource setting would be a requirement of such a program, in order to obtain field experience. Global surgery is a key word these days in attracting young trainees to academic surgical residency programs, yet they are subsequently inadequately trained to provide the required surgical services in these low-resource settings. Dedicated programmatic changes are required to allow those who choose to practice in these settings to obtain the full breadth of training needed to become safe, competent surgeons in such environments. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Interoceptive conditioning in rats: effects of using a single training dose or a set of 5 different doses of nicotine.

PubMed

Pittenger, Steven T; Bevins, Rick A

2013-12-01

Interoceptive conditioning contributes to the tenacity of nicotine dependence. Previous research investigating nicotine as an interoceptive stimulus has typically employed administration of a single training dose of nicotine over an extended time. This approach has allowed for careful study of the nicotine stimulus. In humans, the nicotine stimulus is unlikely to be fixed across learning episodes. Thus, from a translational perspective, systematic variation of nicotine dose in training might better approximate interoceptive conditioning in humans. Notably, training with a class or set of discrete exteroceptive stimuli (e.g., different pictures of cars) produces interesting behavioral differences relative to training with a single stimulus. The present study sought to determine whether similar differences would occur if a set of nicotine stimuli were used in place of a single dose. To investigate this question, one group of male Sprague-Dawley rats was trained on a discriminated goal-tracking task with a set of nicotine doses (0.05, 0.125, 0.2, 0.275, and 0.35mg/kg). A second group received the standard protocol of training with a single nicotine dose (0.2mg/kg). On each nicotine session, there was intermittent access to liquid sucrose (26%) in a conditioning chamber. On intermixed saline sessions, sucrose was withheld. We examined acquisition, subsequent extinction, transfer of extinction, nicotine generalization, and mecamylamine blockade. Both groups reliably discriminated between nicotine and saline sessions, were sensitive to non-reinforcement, displayed transfer of extinction, demonstrated dose-dependent nicotine generalization, and responding was blocked by mecamylamine. There were no significant differences between the two groups. The unique nature of an interoceptive pharmacological stimulus and the challenges posed for studying the impact of training with a set of interoceptive stimuli are discussed. © 2013.

Effect of Single Setting versus Multiple Setting Training on Learning to Shop in a Department Store.

ERIC Educational Resources Information Center

Westling, David L.; And Others

1990-01-01

Fifteen students, age 13-21, with moderate to profound mental retardation received shopping skills training in either 1 or 3 department stores. A study of operational behaviors, social behaviors, number of settings in which criterion performance was achieved, and number of sessions required to achieve criterion found no significant differences…

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Original Research: ACE2 activator associated with physical exercise potentiates the reduction of pulmonary fibrosis

PubMed Central

Prata, Luana O; Rodrigues, Carolina R; Martins, Jéssica M; Vasconcelos, Paula C; Oliveira, Fabrício Marcus S; Ferreira, Anderson J; Rodrigues-Machado, Maria da Glória

2016-01-01

The interstitial lung diseases are poorly understood and there are currently no studies evaluating the association of physical exercise with an ACE2 activator (DIZE) as a possible treatment for this group of diseases. We evaluate the effects of pharmacological treatment with an angiotensin-converting enzyme 2 activator drug, associated with exercise, on the pulmonary lesions induced by bleomycin. From the 96 male Balb/c mice used in the experiment, only 49 received 8 U/kg of bleomycin (BLM, intratracheally). The mice were divided into control (C) and bleomycin (BLM) groups, sedentary and trained (C-SED, C-EXE, BLM-SED, BLM-EXE), control and bleomycin and also sedentary and trained treated with diminazene (C-SED/E, C-EXE/E, BLM-SED/E, BLM-EXE/E). The animals were trained five days/week, 1 h/day with 60% of the maximum load obtained in a functional capacity test, for four weeks. Diminazene groups were treated (1 mg/kg, by gavage) daily until the end of the experiment. The lungs were collected 48 h after the training program, set in buffered formalin and investigated by Gomori’s trichrome, immunohistochemistry of collagen type I, TGF-β1, beta-prolyl-4-hydroxylase, MMP-1 and -2. The BLM-EXE/E group obtained a significant increase in functional capacity, reduced amount of fibrosis and type I collagen, decreased expression of TGF-β1 and beta-prolyl-4-hydroxylase and an increase of metalloproteinase −1, −2 when compared with the other groups. The present research shows, for the first time, that exercise training associated with the activation of ACE2 potentially reduces pulmonary fibrosis. PMID:27550926

Otolaryngology Resident Education and the Accreditation Council for Graduate Medical Education Core Competencies: A Systematic Review.

PubMed

Faucett, Erynne A; Barry, Jonnae Y; McCrary, Hilary C; Saleh, Ahlam A; Erman, Audrey B; Ishman, Stacey L

2018-04-01

To date, there have been no reports in the current literature regarding the use of the Accreditation Council for Graduate Medical Education (ACGME) core competencies in otolaryngology residency training. An evaluation may help educators address these core competencies in the training curriculum. To examine the quantity and nature of otolaryngology residency training literature through a systematic review and to evaluate whether this literature aligns with the 6 core competencies. A medical librarian assisted in a search of all indexed years of the PubMed, Embase, Education Resources Information Center (via EBSCOhost), Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Cochrane Methodology Register), Thomson Reuters Web of Science (Science Citation Index Expanded, Social Sciences Citation Index Expanded, Conference Proceedings Citation Index-Science, and Conference Proceedings Citation Index-Social Science and Humanities), Elsevier Scopus, and ClinicalTrials.gov databases to identify relevant English-language studies. Included studies contained original human data and focused on otolaryngology resident education. Data regarding study design, setting, and ACGME core competencies addressed were extracted from each article. Initial searches were performed on May 20, 2015, and updated on October 4, 2016. In this systematic review of 104 unique studies, interpersonal communication skills were reported 15 times; medical knowledge, 48 times; patient care, 44 times; practice-based learning and improvement, 31 times; professionalism, 15 times; and systems-based practices, 10 times. Multiple studies addressed more than 1 core competency at once, and 6 addressed all 6 core competencies. Increased emphasis on nonclinical core competencies is needed, including professionalism, interpersonal and communication skills, and systems-based practices in the otolaryngology residency training curriculum. A formal curriculum addressing nonclinical core competencies should be integrated into otolaryngology residency training.

The French Advanced Course for Deployment Surgery (ACDS) called Cours Avancé de Chirurgie en Mission Extérieure (CACHIRMEX): history of its development and future prospects.

PubMed

Bonnet, Stéphane; Gonzalez, F; Mathieu, L; Boddaert, G; Hornez, E; Bertani, A; Avaro, J-P; Durand, X; Rongieras, F; Balandraud, P; Rigal, S; Pons, F

2016-10-01

The composition of a French Forward Surgical Team (FST) has remained constant since its creation in the early 1950s: 12 personnel, including a general and an orthopaedic surgeon. The training of military surgeons, however, has had to evolve to adapt to the growing complexities of modern warfare injuries in the context of increasing subspecialisation within surgery. The Advanced Course for Deployment Surgery (ACDS)-called Cours Avancé de Chirurgie en Mission Extérieure (CACHIRMEX)-has been designed to extend, reinforce and adapt the surgical skill set of the FST that will be deployed. Created in 2007 by the French Military Health Service Academy (Ecole du Val-de-Grâce), this annual course is composed of five modules. The surgical knowledge and skills necessary to manage complex military trauma and give medical support to populations during deployment are provided through a combination of didactic lectures, deployment experience reports and hands-on workshops. The course is now a compulsory component of initial surgical training for junior military surgeons and part of the Continuous Medical Education programme for senior military surgeons. From 2012, the standardised content of the ACDS paved the way for the development of two more team-training courses: the FST and the Special Operation Surgical Team training. The content of this French military original war surgery course is described, emphasising its practical implications and future prospects. The military surgical training needs to be regularly assessed to deliver the best quality of care in an context of evolving modern warfare casualties. 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 Effect of Different Resistance Training Load Schemes on Strength and Body Composition in Trained Men

PubMed Central

Lopes, Charles Ricardo; Aoki, Marcelo Saldanha; Crisp, Alex Harley; de Mattos, Renê Scarpari; Lins, Miguel Alves; da Mota, Gustavo Ribeiro; Schoenfeld, Brad Jon; Marchetti, Paulo Henrique

2017-01-01

Abstract The purpose of this study was to evaluate the impact of moderate-load (10 RM) and low-load (20 RM) resistance training schemes on maximal strength and body composition. Sixteen resistance-trained men were randomly assigned to 1 of 2 groups: a moderate-load group (n = 8) or a low-load group (n = 8). The resistance training schemes consisted of 8 exercises performed 4 times per week for 6 weeks. In order to equate the number of repetitions performed by each group, the moderate load group performed 6 sets of 10 RM, while the low load group performed 3 sets of 20 RM. Between-group differences were evaluated using a 2-way ANOVA and independent t-tests. There was no difference in the weekly total load lifted (sets × reps × kg) between the 2 groups. Both groups equally improved maximal strength and measures of body composition after 6 weeks of resistance training, with no significant between-group differences detected. In conclusion, both moderate-load and low-load resistance training schemes, similar for the total load lifted, induced a similar improvement in maximal strength and body composition in resistance-trained men. PMID:28828088

Training in interprofessional collaboration

PubMed Central

Paré, Line; Maziade, Jean; Pelletier, Francine; Houle, Nathalie; Iloko-Fundi, Maximilien

2012-01-01

Abstract Problem addressed A number of agencies that accredit university health sciences programs recently added standards for the acquisition of knowledge and skills with respect to interprofessional collaboration. Within primary care settings there are no practical training programs that allow students from different disciplines to develop competencies in this area. Objective of the program The training program was developed within family medicine units affiliated with Université Laval in Quebec for family medicine residents and trainees from various disciplines to develop competencies in patient-centred, interprofessional collaborative practice in primary care. Program description Based on adult learning theories, the program was divided into 3 phases—preparing family medicine unit professionals, training preceptors, and training the residents and trainees. The program’s pedagogic strategies allowed participants to learn with, from, and about one another while preparing them to engage in contemporary primary care practices. A combination of quantitative and qualitative methods was used to evaluate the implementation process and the immediate results of the training program. Conclusion The training program had a positive effect on both the clinical settings and the students. Preparation of clinical settings is an important issue that must be considered when planning practical interprofessional training. PMID:22611607

Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance.

PubMed

Asadi, Abbas; Ramírez-Campillo, Rodrigo

2016-01-01

The aim of this study was to compare the effects of 6-week cluster versus traditional plyometric training sets on jumping ability, sprint and agility performance. Thirteen college students were assigned to a cluster sets group (N=6) or traditional sets group (N=7). Both training groups completed the same training program. The traditional group completed five sets of 20 repetitions with 2min of rest between sets each session, while the cluster group completed five sets of 20 [2×10] repetitions with 30/90-s rest each session. Subjects were evaluated for countermovement jump (CMJ), standing long jump (SLJ), t test, 20-m and 40-m sprint test performance before and after the intervention. Both groups had similar improvements (P<0.05) in CMJ, SLJ, t test, 20-m, and 40-m sprint. However, the magnitude of improvement in CMJ, SLJ and t test was greater for the cluster group (effect size [ES]=1.24, 0.81 and 1.38, respectively) compared to the traditional group (ES=0.84, 0.60 and 0.55). Conversely, the magnitude of improvement in 20-m and 40-m sprint test was greater for the traditional group (ES=1.59 and 0.96, respectively) compared to the cluster group (ES=0.94 and 0.75, respectively). Although both plyometric training methods improved lower body maximal-intensity exercise performance, the traditional sets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations. Copyright © 2016 The Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Oak Glen: California Youth Conservation and Training Program, November 1, 1963 - May 31, 1965.

ERIC Educational Resources Information Center

California State Dept. of Conservation, Sacramento. Div. of Forestry.

A description of the origin and development of the California Youth Conservation and Training and its relationship to federal legislation and actions is the focus of this report. The program, authorized by the state legislature in 1963, trained out-of-school, unemployed youth, 16-21 years old for a period of six months. Emphasis was on good…

"Too Many Actors and Too Few Jobs": A Case for Curriculum Extension in UK Vocational Actor Training

ERIC Educational Resources Information Center

Wilkie, Ian

2015-01-01

This article questions the current situation for vocational acting training (VAT) in the UK. It aims to provide an update on the report into burgeoning provision of acting training (and the attempt to address subsequent high rates of actor unemployment) that was originally undertaken by the Calouste Gulbenkian Foundation (CGF, 1975) in their…

The Origins and Development of the National Training Center, 1976-1984. TRADOC Historical Monograph Series.

ERIC Educational Resources Information Center

Chapman, Anne W.

Focusing on the development of the United States Army's National Training Center (NTC) from conceptualization and initial implementation in 1981 to the end of the first phase of development in 1984, this monograph provides a documented historical analysis of how and why the landmark event in army training was launched and examines attendant policy…

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Abe, Osamu; Kiryu, Shigeru

2018-03-01

Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. © RSNA, 2017 Online supplemental material is available for this article.

Automatic NMR-Based Identification of Chemical Reaction Types in Mixtures of Co-Occurring Reactions

PubMed Central

Latino, Diogo A. R. S.; Aires-de-Sousa, João

2014-01-01

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the 1H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the 1H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of the molecules in the mixtures. PMID:24551112

Automatic NMR-based identification of chemical reaction types in mixtures of co-occurring reactions.

PubMed

Latino, Diogo A R S; Aires-de-Sousa, João

2014-01-01

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the (1)H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the (1)H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of the molecules in the mixtures.

Using the epigenetic field defect to detect prostate cancer in biopsy negative patients.

PubMed

Truong, Matthew; Yang, Bing; Livermore, Andrew; Wagner, Jennifer; Weeratunga, Puspha; Huang, Wei; Dhir, Rajiv; Nelson, Joel; Lin, Daniel W; Jarrard, David F

2013-06-01

We determined whether a novel combination of field defect DNA methylation markers could predict the presence of prostate cancer using histologically normal transrectal ultrasound guided biopsy cores. Methylation was assessed using quantitative Pyrosequencing® in a training set consisting of 65 nontumor and tumor associated prostate tissues from University of Wisconsin. A multiplex model was generated using multivariate logistic regression and externally validated in blinded fashion in a set of 47 nontumor and tumor associated biopsy specimens from University of Washington. We observed robust methylation differences in all genes at all CpGs assayed (p <0.0001). Regression models incorporating individual genes (EVX1, CAV1 and FGF1) and a gene combination (EVX1 and FGF1) discriminated nontumor from tumor associated tissues in the original training set (AUC 0.796-0.898, p <0.001). On external validation uniplex models incorporating EVX1, CAV1 or FGF1 discriminated tumor from nontumor associated biopsy negative specimens (AUC 0.702, 0.696 and 0.658, respectively, p <0.05). A multiplex model (EVX1 and FGF1) identified patients with prostate cancer (AUC 0.774, p = 0.001) and had a negative predictive value of 0.909. Comparison between 2 separate cores in patients in this validation set revealed similar methylation defects, indicating detection of a widespread field defect. A widespread epigenetic field defect can be used to detect prostate cancer in patients with histologically negative biopsies. To our knowledge this assay is unique, in that it detects alterations in nontumor cells. With further validation this marker combination (EVX1 and FGF1) has the potential to decrease the need for repeat prostate biopsies, a procedure associated with cost and complications. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Effect of train type on annoyance and acoustic features of the rolling noise.

PubMed

Kasess, Christian H; Noll, Anton; Majdak, Piotr; Waubke, Holger

2013-08-01

This study investigated the annoyance associated with the rolling noise of different railway stock. Passbys of nine train types (passenger and freight trains) equipped with different braking systems were recorded. Acoustic features showed a clear distinction of the braking system with the A-weighted energy equivalent sound level (LAeq) showing a difference in the range of 10 dB between cast-iron braked trains and trains with disk or K-block brakes. Further, annoyance was evaluated in a psychoacoustic experiment where listeners rated the relative annoyance of the rolling noise for the different train types. Stimuli with and without the original LAeq differences were tested. For the original LAeq differences, the braking system significantly affected the annoyance with cast-iron brakes being most annoying, most likely as a consequence of the increased wheel roughness causing an increased LAeq. Contribution of the acoustic features to the annoyance was investigated revealing that the LAeq explained up to 94% of the variance. For the stimuli without differences in the LAeq, cast-iron braked train types were significantly less annoying and the spectral features explained up to 60% of the variance in the annoyance. The effect of these spectral features on the annoyance of the rolling noise is discussed.

Postdoctoral training in posttraumatic stress disorder research.

PubMed

Sloan, Denise M; Vogt, Dawne; Wisco, Blair E; Keane, Terence M

2015-03-01

Postdoctoral training is increasingly common in the field of psychology. Although many individuals pursue postdoctoral training in psychology, guidelines for research training programs at this level do not exist. The rapid advances in the field, particularly with respect to genetics, neuroimaging, and data analytic approaches, require clinical scientists to possess knowledge and expertise across a broad array of areas. Postdoctoral training is often needed to acquire such a skill set. This paper describes a postdoctoral training program designed for individuals pursuing academic careers in traumatic stress disorders research. In this paper, we describe the structure of our training program, challenges we have faced during the 15 years of its existence, and how we have addressed these challenges. We conclude with a presentation of outcome data for the training program and a discussion of how training programs in other settings might be structured. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting

PubMed Central

Haynos, Ann F.; Fruzzetti, Alan E.; Anderson, Calli; Briggs, David; Walenta, Jason

2017-01-01

Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff (n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge (p = .007) and decreased staff personal (p = .02) and work (p = .03) burnout and stigma towards BPD patients (p = .02). Burnout indices and BPD stigma were highly correlated at both time points (p < .001); however, while pre-training BPD stigma significantly predicted post-training client burnout (p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout. PMID:28751925

Effects of dialectical behavior therapy skills training on outcomes for mental health staff in a child and adolescent residential setting.

PubMed

Haynos, Ann F; Fruzzetti, Alan E; Anderson, Calli; Briggs, David; Walenta, Jason

2016-04-01

Training in Dialectical Behavior Therapy (DBT) skills coaching is desirable for staff in psychiatric settings, due to the efficacy of DBT in treating difficult patient populations. In such settings, training resources are typically limited, and staff turnover is high, necessitating brief training. This study evaluated the effects of a brief training in DBT skills coaching for nursing staff working in a child and adolescent psychiatric residential program. Nursing staff ( n = 22) completed assessments of DBT skill knowledge, burnout, and stigma towards patients with borderline personality disorder (BPD) before and after a six-week DBT skills coaching training. Repeated measure ANOVAs were conducted to examine changes on all measures from pre- to post- treatment and hierarchical linear regressions to examine relationships between pre- training DBT knowledge, burnout, and BPD stigma and these same measures post-training. The brief DBT skill coaching training significantly increased DBT knowledge ( p = .007) and decreased staff personal ( p = .02) and work ( p = .03) burnout and stigma towards BPD patients ( p = .02). Burnout indices and BPD stigma were highly correlated at both time points ( p < .001); however, while pre-training BPD stigma significantly predicted post-training client burnout ( p = .04), pre-training burnout did not predict post-training BPD stigma. These findings suggest that brief training of psychiatric nursing staff in DBT skills and coaching techniques can result in significant benefits, including reduced staff burnout and stigma toward patients with BPD-related problems, and that reducing BPD stigma may particularly promote lower burnout.

Origins of Competency-Based Training

ERIC Educational Resources Information Center

McCowan, Richard J.

1998-01-01

This paper describes the theories and social factors that contributed to the development of competency-based training (CBT). These include behaviorism (Edward L. Thorndike), scientific management (Frederick Taylor), progressive education (John Dewey), and derivative theories including operant conditioning (B.F. Skinner), objectives-based…

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.

Traditional and pyramidal resistance training systems improve muscle quality and metabolic biomarkers in older women: A randomized crossover study.

PubMed

Ribeiro, Alex S; Schoenfeld, Brad J; Souza, Mariana F; Tomeleri, Crisieli M; Venturini, Danielle; Barbosa, Décio S; Cyrino, Edilson S

2016-06-15

The purpose of this study was to compare the effect of RT performed in a pyramid (PR) and traditional (TD) straight set training system on muscle quality and metabolic biomarkers in older women. Twenty-five physically independent older women (67.6±5.1years, 65.9±11.1kg, 154.7±5.8cm) performed a RT program in TD and PR training systems in a balanced crossover design. Measurements of muscle quality, serum levels of C-reactive protein (CRP), glucose (GLU), total cholesterol, high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and triglycerides (TG) were obtained at different moments. The TD program consisted of 3 sets of 8-12 repetitions maximum (RM) with a constant weight for the 3 sets, whereas the PR training consisted of 3 sets of 12/10/8 RM with incremental weight for each set. The training was performed in 2 phases of 8weeks each, with a 12-week washout period between phases. Significant (P<0.05) improvements were observed in both groups for muscle quality (TD=+8.6% vs. PR=+6.8%), GLU (TD=-4.5% vs. PR=-1.9%), TG (TD=-18.0% vs. PR=-11.7%), HDL-C (TD=+10.6 vs. PR=+7.8%), LDL-C (TD=-23.3% vs. PR=-21.0%), and CRP (TD=-19.4% vs. PR=-14.3%) with no differences between training systems. These results suggest that RT improves muscle quality and metabolic biomarkers of older women independently of the training system. Copyright © 2016 Elsevier Inc. All rights reserved.

Parent Training on Generalized Use of Behavior Analytic Strategies for Decreasing the Problem Behavior of Children with Autism Spectrum Disorder: A Data-Based Case Study

ERIC Educational Resources Information Center

Crone, Regina M.; Mehta, Smita Shukla

2016-01-01

Setting variables such as location of parent training, programming with common stimuli, generalization of discrete responses to non-trained settings, and subsequent reduction in child problem behavior may influence the effectiveness of interventions. The purpose of this study was to evaluate the effectiveness of home-versus clinic-based training…

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

PubMed

Ngo, Tuan Anh; Lu, Zhi; Carneiro, Gustavo

2017-01-01

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Training for cervical cancer prevention programs in low-resource settings: focus on visual inspection with acetic acid and cryotherapy.

PubMed

Blumenthal, P D; Lauterbach, M; Sellors, J W; Sankaranarayanan, R

2005-05-01

The modern approach to cervical cancer prevention, characterized by use of cytology and multiple visits for diagnosis and treatment, has frequently proven challenging and unworkable in low-resource settings. Because of this, the Alliance for Cervical Cancer Prevention (ACCP) has made it a priority to investigate and assess alternative approaches, particularly the use of visual screening methods, such as visual inspection with acetic acid (VIA) and visual inspection with Lugol's iodine (VILI), for precancer and cancer detection and the use of cryotherapy as a precancer treatment method. As a result of ACCP experience in providing training to nurses and doctors in these techniques, it is now widely agreed that training should be competency based, combining both didactic and hands-on approaches, and should be done in a clinical setting that resembles the service-delivery conditions at the program site. This article reviews ACCP experiences and perceptions about the essentials of training in visual inspection and cryotherapy and presents some lessons learned with regard to training in these techniques in low-resource settings.

NGFATOS : national guidelines for first aid training in occupational settings

DOT National Transportation Integrated Search

2002-05-01

NGFATOS is a course development guideline containing the essential elements of what can be considered safe, helpful and effective first aid training in occupational settings. This guide is intended for use by first aid program developers, institution...

Enhancing Care of Aged and Dying Prisoners: Is e-Learning a Feasible Approach?

PubMed

Loeb, Susan J; Penrod, Janice; Myers, Valerie H; Baney, Brenda L; Strickfaden, Sophia M; Kitt-Lewis, Erin; Wion, Rachel K

Prisons and jails are facing sharply increased demands in caring for aged and dying inmates. Our Toolkit for Enhancing End-of-life Care in Prisons effectively addressed end-of-life (EOL) care; however, geriatric content was limited, and the product was not formatted for broad dissemination. Prior research adapted best practices in EOL care and aging; but, delivery methods lacked emerging technology-focused learning and interactivity. Our purposes were to uncover current training approaches and preferences and to ascertain the technological capacity of correctional settings to deliver computer-based and other e-learning training. An environmental scan was conducted with 11 participants from U.S. prisons and jails to ensure proper fit, in terms of content and technology capacity, between an envisioned computer-based training product and correctional settings. Environmental scan findings focused on content of training, desirable qualities of training, prominence of "homegrown" products, and feasibility of commercial e-learning. This study identified qualities of training programs to adopt and pitfalls to avoid and revealed technology-related issues to be mindful of when designing computer-based training for correctional settings, and participants spontaneously expressed an interest in geriatrics and EOL training using this learning modality as long as training allowed for tailoring of materials.

European consensus on a competency-based virtual reality training program for basic endoscopic surgical psychomotor skills.

PubMed

van Dongen, Koen W; Ahlberg, Gunnar; Bonavina, Luigi; Carter, Fiona J; Grantcharov, Teodor P; Hyltander, Anders; Schijven, Marlies P; Stefani, Alessandro; van der Zee, David C; Broeders, Ivo A M J

2011-01-01

Virtual reality (VR) simulators have been demonstrated to improve basic psychomotor skills in endoscopic surgery. The exercise configuration settings used for validation in studies published so far are default settings or are based on the personal choice of the tutors. The purpose of this study was to establish consensus on exercise configurations and on a validated training program for a virtual reality simulator, based on the experience of international experts to set criterion levels to construct a proficiency-based training program. A consensus meeting was held with eight European teams, all extensively experienced in using the VR simulator. Construct validity of the training program was tested by 20 experts and 60 novices. The data were analyzed by using the t test for equality of means. Consensus was achieved on training designs, exercise configuration, and examination. Almost all exercises (7/8) showed construct validity. In total, 50 of 94 parameters (53%) showed significant difference. A European, multicenter, validated, training program was constructed according to the general consensus of a large international team with extended experience in virtual reality simulation. Therefore, a proficiency-based training program can be offered to training centers that use this simulator for training in basic psychomotor skills in endoscopic surgery.

Simulation-Optimization Model for Seawater Intrusion Management at Pingtung Coastal Area, Taiwan

NASA Astrophysics Data System (ADS)

Huang, P. S.; Chiu, Y.

2015-12-01

In 1970's, the agriculture and aquaculture were rapidly developed at Pingtung coastal area in southern Taiwan. The groundwater aquifers were over-pumped and caused the seawater intrusion. In order to remedy the contaminated groundwater and find the best strategies of groundwater usage, a management model to search the optimal groundwater operational strategies is developed in this study. The objective function is to minimize the total amount of injection water and a set of constraints are applied to ensure the groundwater levels and concentrations are satisfied. A three-dimension density-dependent flow and transport simulation model, called SEAWAT developed by U.S. Geological Survey, is selected to simulate the phenomenon of seawater intrusion. The simulation model is well calibrated by the field measurements and replaced by the surrogate model of trained artificial neural networks (ANNs) to reduce the computational time. The ANNs are embedded in the management model to link the simulation and optimization models, and the global optimizer of differential evolution (DE) is applied for solving the management model. The optimal results show that the fully trained ANNs could substitute the original simulation model and reduce much computational time. Under appropriate setting of objective function and constraints, DE can find the optimal injection rates at predefined barriers. The concentrations at the target locations could decrease more than 50 percent within the planning horizon of 20 years. Keywords : Seawater intrusion, groundwater management, numerical model, artificial neural networks, differential evolution

Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

PubMed

Toropov, Andrey A; Toropova, Alla P

2015-04-01

Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). Copyright © 2014 Elsevier Ltd. All rights reserved.

A Survey of Athletic Training Employers' Hiring Criteria

PubMed Central

Andrews, Lanna

2001-01-01

Objective: To identify athletic training employers' hiring criteria and to determine if the importance of individual hiring criteria vary by setting. Design and Setting: The Athletic Training Employer Needs Assessment Survey was mailed to athletic training employers advertising in the National Athletic Trainers' Association (NATA) placement vacancy notice between October 1996 and October 1998. Subjects: A total of 111 athletic training employers in NATA Districts 7, 8, and 10 were surveyed. Measurements: Employers rated the importance of hiring criteria on a 7-point Likert scale. Means and standard deviations were calculated for each criterion and compared these values to ascertain the importance of individual criteria. A principal component analysis was done to determine the underlying factors. Results: Hiring characteristics can be divided into 4 factors that include highly related criteria: (1) personal characteristics, (2) educational experience, (3) professional experience, and (4) work-related attributes. In addition, the hiring characteristics desired by employers varied among athletic training settings. Conclusions: When interviewing and presenting themselves for entry-level positions, athletic trainers should pay particular attention to the attributes within the 4 hiring criteria factors. Also, the desired hiring criteria of athletic training employers differed by setting. Applicants need to pay particular attention to these hiring criteria differences when constructing résumés, cover letters, and professional correspondence and when interviewing with prospective employers. PMID:12937484

Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

PubMed

Cheng, Phillip M; Tejura, Tapas K; Tran, Khoa N; Whang, Gilbert

2018-05-01

The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the majority classification was used as ground truth; 74 images were found to be consistent with small bowel obstruction. Images were rescaled and randomized, with 2210 images constituting the training set (39 with small bowel obstruction) and 1453 images constituting the test set (35 with small bowel obstruction). Weight parameters for the final classification layer of the Inception v3 convolutional neural network, previously trained on the 2014 Large Scale Visual Recognition Challenge dataset, were retrained on the training set. After training, the neural network achieved an AUC of 0.84 on the test set (95% CI 0.78-0.89). At the maximum Youden index (sensitivity + specificity-1), the sensitivity of the system for small bowel obstruction is 83.8%, with a specificity of 68.1%. The results demonstrate that transfer learning with convolutional neural networks, even with limited training data, may be used to train a detector for high-grade small bowel obstruction gas patterns on supine radiographs.

Training Costs with Reference to the Industrial Training Act.

ERIC Educational Resources Information Center

Garbutt, Douglas

Provisions and implications of the British Industrial Training Act of 1964 (including the system of training grants and levies) are set forth. Procedures for accounting and budgeting for training costs, routines for collecting training information, documents (budgets, cost sheets, control statements) for collecting and controlling costs, means of…

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

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

Polan, D; Brady, S; Kaufman, R

2016-06-15

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using anmore » anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).« less

Semi-supervised learning for photometric supernova classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.; Schafer, Chad M.; Poznanski, Dovi

2012-01-01

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template-based methods. Applied to supernova data simulated by Kessler et al. to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95 per cent Type Ia purity and 87 per cent Type Ia efficiency on the spectroscopic sample, but only 50 per cent Type Ia purity and 50 per cent efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine-learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that, despite collecting data on a smaller number of supernovae, deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44 per cent (1 per cent) increase in purity to 72 per cent (87 per cent) and 30 per cent (162 per cent) increase in efficiency to 65 per cent (84 per cent) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5 per cent improvement in Type Ia purity and 13 per cent improvement in Type Ia efficiency. A web service for the supernova classification method used in this paper can be found at .

An intervention to improve mental health care for conflict-affected forced migrants in low-resource primary care settings: a WHO MhGAP-based pilot study in Sri Lanka (COM-GAP study)

PubMed Central

2013-01-01

Background Inadequacy in mental health care in low and middle income countries has been an important contributor to the rising global burden of disease. The treatment gap is salient in resource-poor settings, especially when providing care for conflict-affected forced migrant populations. Primary care is often the only available service option for the majority of forced migrants, and integration of mental health into primary care is a difficult task. The proposed pilot study aims to explore the feasibility of integrating mental health care into primary care by providing training to primary care practitioners serving displaced populations, in order to improve identification, treatment, and referral of patients with common mental disorders via the World Health Organization Mental Health Gap Action Programme (mhGAP). Methods/Design This pilot randomized controlled trial will recruit 86 primary care practitioners (PCP) serving in the Puttalam and Mannar districts of Sri Lanka (with displaced and returning conflict-affected populations). The intervention arm will receive a structured training program based on the mhGAP intervention guide. Primary outcomes will be rates of correct identification, adequate management based on set criteria, and correct referrals of common mental disorders. A qualitative study exploring the attitudes, views, and perspectives of PCP on integrating mental health and primary care will be nested within the pilot study. An economic evaluation will be carried out by gathering service utilization information. Discussion In post-conflict Sri Lanka, an important need exists to provide adequate mental health care to conflict-affected internally displaced persons who are returning to their areas of origin after prolonged displacement. The proposed study will act as a local demonstration project, exploring the feasibility of formulating a larger-scale intervention study in the future, and is envisaged to provide information on engaging PCP, and data on training and evaluation including economic costs, patient recruitment, and acceptance and follow-up rates. The study should provide important information on the WHO mhGAP intervention guide to add to the growing evidence base of its implementation. Trial registration SLCTR/2013/025. PMID:24321171

An intervention to improve mental health care for conflict-affected forced migrants in low-resource primary care settings: a WHO MhGAP-based pilot study in Sri Lanka (COM-GAP study).

PubMed

Siriwardhana, Chesmal; Adikari, Anushka; Van Bortel, Tine; McCrone, Paul; Sumathipala, Athula

2013-12-09

Inadequacy in mental health care in low and middle income countries has been an important contributor to the rising global burden of disease. The treatment gap is salient in resource-poor settings, especially when providing care for conflict-affected forced migrant populations. Primary care is often the only available service option for the majority of forced migrants, and integration of mental health into primary care is a difficult task. The proposed pilot study aims to explore the feasibility of integrating mental health care into primary care by providing training to primary care practitioners serving displaced populations, in order to improve identification, treatment, and referral of patients with common mental disorders via the World Health Organization Mental Health Gap Action Programme (mhGAP). This pilot randomized controlled trial will recruit 86 primary care practitioners (PCP) serving in the Puttalam and Mannar districts of Sri Lanka (with displaced and returning conflict-affected populations). The intervention arm will receive a structured training program based on the mhGAP intervention guide. Primary outcomes will be rates of correct identification, adequate management based on set criteria, and correct referrals of common mental disorders. A qualitative study exploring the attitudes, views, and perspectives of PCP on integrating mental health and primary care will be nested within the pilot study. An economic evaluation will be carried out by gathering service utilization information. In post-conflict Sri Lanka, an important need exists to provide adequate mental health care to conflict-affected internally displaced persons who are returning to their areas of origin after prolonged displacement. The proposed study will act as a local demonstration project, exploring the feasibility of formulating a larger-scale intervention study in the future, and is envisaged to provide information on engaging PCP, and data on training and evaluation including economic costs, patient recruitment, and acceptance and follow-up rates. The study should provide important information on the WHO mhGAP intervention guide to add to the growing evidence base of its implementation. SLCTR/2013/025.

A web-based training program to support chronic kidney disease screening by community pharmacists.

PubMed

Gheewala, Pankti A; Peterson, Gregory M; Zaidi, Syed Tabish R; Bereznicki, Luke; Jose, Matthew D; Castelino, Ronald L

2016-10-01

Background Community pharmacists' role in screening of several chronic diseases has been widely explored. The global health burden of chronic kidney disease is high; however, the progression and adverse outcomes can be prevented or delayed by detecting and treating the disease in its initial stages 1-3. Therefore, a web-based training program was developed to enhance pharmacists' knowledge and skills required to perform a chronic kidney disease screening service in a community setting. Objective The aim of this study was to evaluate the impact of a web-based training program on community pharmacists' knowledge and skills associated with chronic kidney disease screening. As secondary aim, pharmacists' satisfaction with the training program was assessed. Setting Community pharmacy practice. Method A web-based training program was developed by four pharmacists and a nephrologist. Quantitative data was collected by employing a self-administered, web-based questionnaire, which comprised a set of five multiple-choice knowledge questions and one clinical vignette to assess skills. A nine-item Likert scale was used to determine pharmacists' satisfaction with the training program. Main outcome measure Pharmacists' knowledge and skills scores at pre and post-training, reliability of the Likert scale, and the proportion of responses to the individual nine items of the satisfaction survey. Results Fifty pharmacists participated in the pre-questionnaire and 38 pharmacists completed the web-based training and post-questionnaire. Significant differences were observed in the knowledge scores (p < 0.001) and skills scores (p < 0.001) at pre- and post-training. Cronbach's alpha for the nine-item satisfaction scale was 0.73 and the majority pharmacists (92.1-100 %) were satisfied with the various aspects of the training program. Conclusion The web-based training program positively enhanced pharmacists' knowledge and skills associated with chronic kidney disease screening. These findings support further development and widespread implementation of the training program to facilitate health promotion and early identification of chronic kidney disease in a community setting.

Factors for Preterm Births in Germany - An Analysis of Representative German Data (KiGGS).

PubMed

Weichert, A; Weichert, T M; Bergmann, R L; Henrich, W; Kalache, K D; Richter, R; Neymeyer, J; Bergmann, K E

2015-08-01

Introduction: Preterm birth is a global scourge, the leading cause of perinatal mortality and morbidity. This study set out to identify the principal risk factors for preterm birth, based on the German Health Interview and Examination Survey for Children and Adolescents (KiGGS). A range of possible factors influencing preterm birth were selected for inclusion in the questionnaire, covering factors such as gender, national origin, immigrant background, demography, living standard, family structure, parental education and vocational training. Methods: All data were taken from the aforementioned KiGGS survey conducted between 2003 and 2006. A total of 17 641 children and adolescents (8656 girls and 8985 boys) drawn from 167 German towns and municipalities deemed to be representative of the Federal Republic of Germany were included in the study. Gestational age at birth was available for 14 234 datasets. The questionnaire included questions from the following areas as possible factors influencing preterm birth: gender, national origins, immigrant background, demography, living standard, family structure, parental education and vocational training. Results: The preterm birth rate was 11.6 %, higher than that of other national statistical evaluations. Around 57.4 % of multiple pregnancies and 10 % of singleton pregnancies resulted in preterm delivery. Multiple pregnancy was found to be the most important risk factor (OR 13.116). With regard to national origins and immigration background, mothers from Turkey, the Middle East, and North Africa had a higher incidence of preterm birth. Preterm birth was more prevalent in cities and large towns than in small towns and villages. Conclusion: Risk factors associated with preterm birth were identified. These should help with the early identification of pregnant women at risk. The preterm birth rate in our survey was higher than that found in other national statistical evaluations based on process data. More than half of all multiple pregnancies ended in preterm birth.

Adaptations to Speed Endurance Training in Highly Trained Soccer Players.

PubMed

Nyberg, Michael; Fiorenza, Matteo; Lund, Anders; Christensen, Magnus; Rømer, Tue; Piil, Peter; Hostrup, Morten; Christensen, Peter M; Holbek, Simon; Ravnholt, Thomas; Gunnarsson, Thomas P; Bangsbo, Jens

2016-07-01

The present study examined whether a period of additional speed endurance training would improve intense intermittent exercise performance in highly trained soccer players during the season and whether the training changed aerobic metabolism and the level of oxidative enzymes in type I and type II muscle fibers. During the last 9 wk of the season, 13 semiprofessional soccer players performed additional speed endurance training sessions consisting of two to three sets of 8-10 repetitions of 30-m sprints with 10 s of passive recovery (SET). Before and after SET, subjects completed a double-step exercise protocol that included transitions from standing to moderate-intensity running (~75% HRmax), followed by transitions from moderate- to high-intensity running (~90% HRmax) in which pulmonary oxygen uptake (V˙O2) was determined. In addition, the yo-yo intermittent recovery test level 1 was performed, and a muscle biopsy was obtained at rest. The yo-yo intermittent recovery test level 1 performance was 11.6% ± 6.4% (mean ± SD) better (2803 ± 330 vs 3127 ± 383 m, P < 0.05) after SET compared with before SET. In the transition from standing to moderate-intensity running, phase II pulmonary V˙O2 kinetics was 11.4% ± 16.5% faster (P < 0.05), and the running economy at this intensity was 2.3% ± 3.0% better (P < 0.05). These improvements were apparent despite the content of muscle proteins regulating oxidative metabolism (3-hydroxyacyl CoA dehydrogenase, COX IV, and OXPHOS), and capillarization was reduced (P < 0.05). The content of 3-hydroxyacyl CoA dehydrogenase and citrate synthase in type I and type II fibers did not change. In highly trained soccer players, additional speed endurance training is associated with an improved ability to perform repeated high-intensity work. To what extent the training-induced changes in V˙O2 kinetics and mechanical efficiency in type I fibers caused the improvement in performance warrants further investigation.

Provider training to screen and initiate evidence-based pediatric obesity treatment in routine practice settings: A randomized pilot trial

PubMed Central

Kolko, Rachel P.; Kass, Andrea E.; Hayes, Jacqueline F.; Levine, Michele D.; Garbutt, Jane M.; Proctor, Enola K.; Wilfley, Denise E.

2016-01-01

Introduction This randomized pilot trial evaluated two training modalities for first-line, evidence-based pediatric obesity services (screening and goal-setting) among nursing students. Method Participants (N=63) were randomized to Live Interactive Training (Live) or Web-facilitated Self-study Training (Web). Pre-training, post-training, and one-month follow-up assessments evaluated training feasibility, acceptability, and impact (knowledge, and skill via simulation). Moderator (previous experience) and predictor (content engagement) analyses were conducted. Results Nearly-all (98%) participants completed assessments. Both trainings were acceptable, with higher ratings for Live and participants with previous experience (p’s<.05). Knowledge and skill improved from pre-training to post-training and follow-up in both conditions (p’s<.001). Live demonstrated greater content engagement (p’s<.01). Conclusions The training package was feasible, acceptable, and efficacious among nursing students. Given that Live had higher acceptability and engagement, and online training offers greater scalability, integrating interactive Live components within Web-based training may optimize outcomes, which may enhance practitioners’ delivery of pediatric obesity services. PMID:26873293

CGAT: a model for immersive personalized training in computational genomics

PubMed Central

Sims, David; Ponting, Chris P.

2016-01-01

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics. PMID:25981124

Dissemination and Implementation of Evidence-Based Practices: Training and Consultation as Implementation Strategies

PubMed Central

Edmunds, Julie M.; Beidas, Rinad S.; Kendall, Philip C.

2013-01-01

To provide effective treatment for individuals with mental health needs, there is a movement to deploy evidence-based practices (EBPs) developed in research settings into community settings. Training clinicians in EBPs is often used as the primary implementation strategy in these efforts, despite evidence suggesting that training alone does not change therapist behavior. A promising implementation strategy that can be combined with training is consultation, or ongoing support. This paper reviews the literature on consultation following initial training. A model of consultation is presented as well as preliminary findings regarding effective consultation techniques. Future directions are offered. PMID:24072959

Training Children's Self-Control: A Field Experiment in Self-Monitoring and Goal-Setting in the Classroom

ERIC Educational Resources Information Center

Sagotsky, Gerald; And Others

1978-01-01

Examined the effects of training in self-monitoring and goal setting skills on classroom study behavior and on the academic achievement of fifth and sixth grade children in an individualized mathematics program. (BD)

29 CFR 30.3 - Equal opportunity standards.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of race, color, religion, national origin, or sex; and (2) Uniformly apply rules and regulations... opportunity pledge: The recruitment, selection, employment, and training of apprentices during their apprenticeship, shall be without discrimination because of race, color, religion, national origin, or sex. The...

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

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

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

PubMed

Johnson, Justin A; Rodeberg, Nathan T; Wightman, R Mark

2016-03-16

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

[Application and case analysis on the problem-based teaching of Jingluo Shuxue Xue (Science of Meridian and Acupoint) in reference to the team oriented learning method].

PubMed

Ma, Ruijie; Lin, Xianming

2015-12-01

The problem based teaching (PBT) has been the main approach to the training in the universities o the world. Combined with the team oriented learning method, PBT will become the method available to the education in medical universities. In the paper, based on the common questions in teaching Jingluo Shuxue Xue (Science of Meridian and Acupoint), the concepts and characters of PBT and the team oriented learning method were analyzed. The implementation steps of PBT were set up in reference to the team oriented learning method. By quoting the original text in Beiji Qianjin Yaofang (Essential recipes for emergent use worth a thousand gold), the case analysis on "the thirteen devil points" was established with PBT.

Title IV Cash Management Life Cycle Training. Participant's Guide.

ERIC Educational Resources Information Center

Department of Education, Washington, DC.

This participant's guide includes: "Introduction: Welcome to Cash Management Life Cycle Training"; "Module 1: Review of Cash Management Principles" (cash management overview and activity); "Module 2: Common Origination and Disbursement (COD) System Overview" (e.g., full participants and phase-in participants, COD…

Global Health: Pediatric Neurology.

PubMed

Bearden, David R; Ciccone, Ornella; Patel, Archana A

2018-04-01

Neurologic disorders contribute significantly to both morbidity and mortality among children in resource-limited settings, but there are a few succinct studies summarizing the epidemiology of neurologic disorders in these settings. A review of available literature was performed to identify data on the prevalence, etiology, outcomes, and treatment of neurologic disorders in children in resource-limited settings. The burden of neurologic disorders in children is high in resource-limited settings. Barriers to optimal care include lack of trained personnel, limited access to diagnostic technology, and limited availability of drugs used to treat common conditions. Several solutions have been suggested to deal with these challenges including increased collaborations to train neurologists willing to practice in resource-limited settings and increased training of physician extenders or community health workers. Further studies are necessary to improve our understanding of the epidemiology of neurologic disorders in resource-limited settings. Future epidemiologic studies should incorporate multiple countries in resource-limited settings and utilize standardized definitions and methodologies to enable comparison across regions. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Forest Type and Above Ground Biomass Estimation Based on Sentinel-2A and WorldView-2 Data Evaluation of Predictor nd Data Suitability

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Enßle, Fabian; Zhang, Xiaoli; Koch, Barbara

2016-08-01

The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R pack- age (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.

PubMed

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-15

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications. Copyright © 2012 Elsevier B.V. All rights reserved.

A "Quiet Revolution"? The Impact of Training Schools on Initial Teacher Training Partnerships

ERIC Educational Resources Information Center

Brooks, Val

2006-01-01

This paper discusses the impact on initial teacher training of a new policy initiative in England: the introduction of Training Schools. First, the Training School project is set in context by exploring the evolution of a partnership approach to initial teacher training in England. Ways in which Training Schools represent a break with established…

Delivery and Evaluation of Training for School Nutrition Administrators and Managers on Meeting Special Food and Nutrition Needs of Students in the School Setting

ERIC Educational Resources Information Center

Oakley, Charlotte B.; Knight, Kathy; Hobbs, Margie; Dodd, Lacy M.; Cole, Janie

2011-01-01

Purpose/Objectives: The purpose of this investigation was to complete a formal evaluation of a project that provided specialized training for school nutrition (SN) administrators and managers on meeting children's special dietary needs in the school setting. Methods: The training was provided as part of the "Eating Good and Moving Like We…

Gear Tooth Wear Detection Algorithm

NASA Technical Reports Server (NTRS)

Delgado, Irebert R.

2015-01-01

Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

Replacing maladaptive speech with verbal labeling responses: an analysis of generalized responding.

PubMed Central

Foxx, R M; Faw, G D; McMorrow, M J; Kyle, M S; Bittle, R G

1988-01-01

We taught three mentally handicapped students to answer questions with verbal labels and evaluated the generalized effects of this training on their maladaptive speech (e.g., echolalia) and correct responding to untrained questions. The students received cues-pause-point training on an initial question set followed by generalization assessments on a different set in another setting. Probes were conducted on novel questions in three other settings to determine the strength and spread of the generalization effect. A multiple baseline across subjects design revealed that maladaptive speech was replaced with correct labels (answers) to questions in the training and all generalization settings. These results replicate and extend previous research that suggested that cues-pause-point procedures may be useful in replacing maladaptive speech patterns by teaching students to use their verbal labeling repertoires. PMID:3225258

Exploring the limit of accuracy for density functionals based on the generalized gradient approximation: Local, global hybrid, and range-separated hybrid functionals with and without dispersion corrections

DOE PAGES

Mardirossian, Narbe; Head-Gordon, Martin

2014-03-25

The limit of accuracy for semi-empirical generalized gradient approximation (GGA) density functionals is explored in this paper by parameterizing a variety of local, global hybrid, and range-separated hybrid functionals. The training methodology employed differs from conventional approaches in 2 main ways: (1) Instead of uniformly truncating the exchange, same-spin correlation, and opposite-spin correlation functional inhomogeneity correction factors, all possible fits up to fourth order are considered, and (2) Instead of selecting the optimal functionals based solely on their training set performance, the fits are validated on an independent test set and ranked based on their overall performance on the trainingmore » and test sets. The 3 different methods of accounting for exchange are trained both with and without dispersion corrections (DFT-D2 and VV10), resulting in a total of 491 508 candidate functionals. For each of the 9 functional classes considered, the results illustrate the trade-off between improved training set performance and diminished transferability. Since all 491 508 functionals are uniformly trained and tested, this methodology allows the relative strengths of each type of functional to be consistently compared and contrasted. Finally, the range-separated hybrid GGA functional paired with the VV10 nonlocal correlation functional emerges as the most accurate form for the present training and test sets, which span thermochemical energy differences, reaction barriers, and intermolecular interactions involving lighter main group elements.« less

Low- and High-Volume Water-Based Resistance Training Induces Similar Strength and Functional Capacity Improvements in Older Women: A Randomized Study.

PubMed

Reichert, Thaís; Delevatti, Rodrigo Sudatti; Prado, Alexandre Konig Garcia; Bagatini, Natália Carvalho; Simmer, Nicole Monticelli; Meinerz, Andressa Pellegrini; Barroso, Bruna Machado; Costa, Rochelle Rocha; Kanitz, Ana Carolina; Kruel, Luiz Fernando Martins

2018-03-27

Water-based resistance training (WRT) has been indicated to promote strength gains in elderly population. However, no study has compared different training strategies to identify the most efficient one. The aim of this study was to compare the effects of 3 WRT strategies on the strength and functional capacity of older women. In total, 36 women were randomly allocated to training groups: simple set of 30 seconds [1 × 30s; 66.41 (1.36) y; n = 12], multiple sets of 10 seconds [3 × 10s; 66.50 (1.43) y; n = 11], and simple set of 10 seconds [1 × 10s; 65.23 (1.09) y; n = 13]. Training lasted for 12 weeks. The maximal dynamic strength (in kilograms) and muscular endurance (number of repetitions) of knee extension, knee flexion, elbow flexion, and bench press, as well as functional capacity (number of repetitions), were evaluated. All types of training promoted similar gains in maximal dynamic strength of knee extension and flexion as well as elbow flexion. Only the 1 × 30s and 1 × 10s groups presented increments in bench press maximal strength. All 3 groups showed increases in muscular endurance in all exercises and functional capacity. WRT using long- or short-duration simple sets promotes the same gains in strength and functional capacity in older women as does WRT using multiple sets.

Educating residents in behavioral health care and collaboration: integrated clinical training of pediatric residents and psychology fellows.

PubMed

Pisani, Anthony R; leRoux, Pieter; Siegel, David M

2011-02-01

Pediatric residency practices face the challenge of providing both behavioral health (BH) training for pediatricians and psychosocial care for children. The University of Rochester School of Medicine and Dentistry and Rochester General Hospital developed a joint training program and continuity clinic infrastructure in which pediatric residents and postdoctoral psychology fellows train and practice together. The integrated program provides children access to BH care in a primary care setting and gives trainees the opportunity to integrate collaborative BH care into their regular practice routines. During 1998-2008, 48 pediatric residents and 8 psychology fellows trained in this integrated clinical environment. The program's accomplishments include longevity, faculty and fiscal stability, sustained support from pediatric leadership and community payers, the development in residents and faculty of greater comfort in addressing BH problems and collaborating with BH specialists, and replication of the model in two other primary care settings. In addition to quantitative program outcomes data, the authors present a case example that illustrates how the integrated program works and achieves its goals. They propose that educating residents and psychology trainees side by side in collaborative BH care is clinically and educationally valuable and potentially applicable to other settings. A companion report published in this issue provides results from a study comparing the perceptions of pediatric residents whose primary care continuity clinic took place in this integrated setting with those of residents from the same pediatric residency who had their continuity clinic training in a nonintegrated setting.

Building community resilience through mental health infrastructure and training in post-Katrina New Orleans.

PubMed

Springgate, Benjamin F; Wennerstrom, Ashley; Meyers, Diana; Allen, Charles E; Vannoy, Steven D; Bentham, Wayne; Wells, Kenneth B

2011-01-01

To describe a disaster recovery model focused on developing mental health services and capacity-building within a disparities-focused, community-academic participatory partnership framework. Community-based participatory, partnered training and services delivery intervention in a post-disaster setting. Post-Katrina Greater New Orleans community. More than 400 community providers from more than 70 health and social services agencies participated in the trainings. Partnered development of a training and services delivery program involving physicians, therapists, community health workers, and other clinical and non-clinical personnel to improve access and quality of care for mental health services in a post-disaster setting. Services delivery (outreach, education, screening, referral, direct treatment); training delivery; satisfaction and feedback related to training; partnered development of training products. Clinical services in the form of outreach, education, screening, referral and treatment were provided in excess of 110,000 service units. More than 400 trainees participated in training, and provided feedback that led to evolution of training curricula and training products, to meet evolving community needs over time. Participant satisfaction with training generally scored very highly. This paper describes a participatory, health-focused model of community recovery that began with addressing emerging, unmet mental health needs using a disparities-conscious partnership framework as one of the principle mechanisms for intervention. Population mental health needs were addressed by investment in infrastructure and services capacity among small and medium sized non-profit organizations working in disaster-impacted, low resource settings.

Disciplining Social Difference: Some Cultural Politics of Military Training in Public High Schools.

ERIC Educational Resources Information Center

Bartlett, Lesley; Lutz, Catherine

1998-01-01

Compares the sociopolitical context of the origin of the Junior Reserve Officer Training Corps (JROTC) to its 1990s expansion. JROTC is no longer thought of as job training for soldierly work roles, but as a life skills course that prepares students for any vocation and is promoted as a self-esteem generator, providing help especially for at-risk…

Transition to Glass: Pilot Training for High-Technology Transport Aircraft

DTIC Science & Technology

1999-05-01

training programs D. Training considerations STUDY METHODOLOGY A. Basic questions and premises B. Questionnaires: attitude scales, demography , and flying...product of many tributaries, including the original Continental ("Old Continental" as it is called by pilots), Pioneer , Texas International, Frontier...to the southwest and later Hawaii. In 1953 Continental acquired Pioneer Airlines, with 16 destinations in the west. Six moved the company from El

Assessing and Improving Performance: A Longitudinal Evaluation of Priority Setting and Resource Allocation in a Canadian Health Region

PubMed Central

Hall, William; Smith, Neale; Mitton, Craig; Urquhart, Bonnie; Bryan, Stirling

2018-01-01

Background: In order to meet the challenges presented by increasing demand and scarcity of resources, healthcare organizations are faced with difficult decisions related to resource allocation. Tools to facilitate evaluation and improvement of these processes could enable greater transparency and more optimal distribution of resources. Methods: The Resource Allocation Performance Assessment Tool (RAPAT) was implemented in a healthcare organization in British Columbia, Canada. Recommendations for improvement were delivered, and a follow up evaluation exercise was conducted to assess the trajectory of the organization’s priority setting and resource allocation (PSRA) process 2 years post the original evaluation. Results: Implementation of RAPAT in the pilot organization identified strengths and weaknesses of the organization’s PSRA process at the time of the original evaluation. Strengths included the use of criteria and evidence, an ability to reallocate resources, and the involvement of frontline staff in the process. Weaknesses included training, communication, and lack of program budgeting. Although the follow up revealed a regression from a more formal PSRA process, a legacy of explicit resource allocation was reported to be providing ongoing benefit for the organization. Conclusion: While past studies have taken a cross-sectional approach, this paper introduces the first longitudinal evaluation of PSRA in a healthcare organization. By including the strengths, weaknesses, and evolution of one organization’s journey, the authors’ intend that this paper will assist other healthcare leaders in meeting the challenges of allocating scarce resources. PMID:29626400

Novice teen driving : education and training administrative standards.

DOT National Transportation Integrated Search

2009-10-09

The Novice Teen Driver Education and Training Administrative Standards set forth in this document serve to guide all novice teen driver education and training programs in States striving to provide quality, consistent driver education and training. W...

Provider Training to Screen and Initiate Evidence-Based Pediatric Obesity Treatment in Routine Practice Settings: A Randomized Pilot Trial.

PubMed

Kolko, Rachel P; Kass, Andrea E; Hayes, Jacqueline F; Levine, Michele D; Garbutt, Jane M; Proctor, Enola K; Wilfley, Denise E

This randomized pilot trial evaluated two training modalities for first-line, evidence-based pediatric obesity services (screening and goal setting) among nursing students. Participants (N = 63) were randomized to live interactive training or Web-facilitated self-study training. Pretraining, post-training, and 1-month follow-up assessments evaluated training feasibility, acceptability, and impact (knowledge and skill via simulation). Moderator (previous experience) and predictor (content engagement) analyses were conducted. Nearly all participants (98%) completed assessments. Both types of training were acceptable, with higher ratings for live training and participants with previous experience (ps < .05). Knowledge and skill improved from pretraining to post-training and follow-up in both conditions (ps < .001). Live training demonstrated greater content engagement (p < .01). The training package was feasible, acceptable, and efficacious among nursing students. Given that live training had higher acceptability and engagement and online training offers greater scalability, integrating interactive live training components within Web-based training may optimize outcomes, which may enhance practitioners' delivery of pediatric obesity services. Copyright © 2016 National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.

Food hygiene training in small to medium-sized care settings.

PubMed

Seaman, Phillip; Eves, Anita

2008-10-01

Adoption of safe food handling practices is essential to effectively manage food safety. This study explores the impact of basic or foundation level food hygiene training on the attitudes and intentions of food handlers in care settings, using questionnaires based on the Theory of Planned Behaviour. Interviews were also conducted with food handlers and their managers to ascertain beliefs about the efficacy of, perceived barriers to, and relevance of food hygiene training. Most food handlers had undertaken formal food hygiene training; however, many who had not yet received training were preparing food, including high risk foods. Appropriate pre-training support and on-going supervision appeared to be lacking, thus limiting the effectiveness of training. Findings showed Subjective Norm to be the most significant influence on food handlers' intention to perform safe food handling practices, irrespective of training status, emphasising the role of important others in determining desirable behaviours.

Computer-aided detection of bladder masses in CT urography (CTU)

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Samala, Ravi K.

2017-03-01

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). We have previously developed methods for detection of bladder masses within the contrast-enhanced and the non-contrastenhanced regions of the bladder individually. In this study, we investigated methods for detection of bladder masses within the entire bladder. The bladder was segmented using our method that combined deep-learning convolutional neural network with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensity-projection-based method. The non-contrast region was smoothed and gray level threshold was applied to the contrast and non-contrast regions separately to extract the bladder wall and potential masses. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify lesion candidates in a prescreening step. The candidates were mapped back to the 3D CT volume and segmented using our auto-initialized cascaded level set (AI-CALS) segmentation method. Twenty-seven morphological features were extracted for each candidate. A data set of 57 patients with 71 biopsy-proven bladder lesions was used, which was split into independent training and test sets: 42 training cases with 52 lesions, and 15 test cases with 19 lesions. Using the training set, feature selection was performed and a linear discriminant (LDA) classifier was designed to merge the selected features for classification of bladder lesions and false positives. The trained classifier was evaluated with the test set. FROC analysis showed that the system achieved a sensitivity of 86.5% at 3.3 FPs/case for the training set, and 84.2% at 3.7 FPs/case for the test set.

The Organization and Management of Company Training.

ERIC Educational Resources Information Center

Turner, Barry T.

This document outlines the need for manpower training and discusses the components of adequate training as provided by the Industrial Training Act of March, 1964, in order to set guidelines and standards for industries involved in the training revolution in England. Besides training and what it entails, the document presents the philosophy of…

PREDICTING ER BINDING AFFINITY FOR EDC RANKING AND PRIORITIZATION: MODEL I

EPA Science Inventory

A Common Reactivity Pattern (COREPA) model, based on consideration of multiple energetically reasonable conformations of flexible chemicals was developed using a training set of 232 rat estrogen receptor (rER) relative binding affinity (RBA) measurements. The training set include...

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.

An easy-to-build, low-budget point-of-care ultrasound simulator: from Linux to a web-based solution.

PubMed

Damjanovic, Domagoj; Goebel, Ulrich; Fischer, Benedikt; Huth, Martin; Breger, Hartmut; Buerkle, Hartmut; Schmutz, Axel

2017-12-01

Hands-on training in point-of-care ultrasound (POC-US) should ideally comprise bedside teaching, as well as simulated clinical scenarios. High-fidelity phantoms and portable ultrasound simulation systems are commercially available, however, at considerable costs. This limits their suitability for medical schools. A Linux-based software for Emergency Department Ultrasound Simulation (edus2TM) was developed by Kulyk and Olszynski in 2011. Its feasibility for POC-US education has been well-documented, and shows good acceptance. An important limitation to an even more widespread use of edus2, however, may be due to the need for a virtual machine for WINDOWS ® systems. Our aim was to adapt the original software toward an HTML-based solution, thus making it affordable and applicable in any simulation setting. We created an HTML browser-based ultrasound simulation application, which reads the input of different sensors, triggering an ultrasound video to be displayed on a respective device. RFID tags, NFC tags, and QR Codes™ have been integrated into training phantoms or were attached to standardized patients. The RFID antenna was hidden in a mock ultrasound probe. The application is independent from the respective device. Our application was used successfully with different trigger/scanner combinations and mounted readily into simulated training scenarios. The application runs independently from operating systems or electronic devices. This low-cost, browser-based ultrasound simulator is easy-to-build, very adaptive, and independent from operating systems. It has the potential to facilitate POC-US training throughout the world, especially in resource-limited areas.