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.

Label Review Training: Module 3: Special Issues, Page 23

EPA Pesticide Factsheets

This module further describes and provides strategies for reviewing some of the label parts introduced in Module 2 of the pesticide label training, such as precautionary statements, directions for use, worker protection labeling, and more.

Label Review Training: Module 3: Special Issues, Page 12

EPA Pesticide Factsheets

This module further describes and provides strategies for reviewing some of the label parts introduced in Module 2 of the pesticide label training, such as precautionary statements, directions for use, worker protection labeling, and more.

Label Review Training: Module 3: Special Issues, Page 3

EPA Pesticide Factsheets

This module further describes and provides strategies for reviewing some of the label parts introduced in Module 2 of the pesticide label training, such as precautionary statements, directions for use, worker protection labeling, and more.

Label Review Training: Module 3: Special Issues, Page 9

EPA Pesticide Factsheets

This module further describes and provides strategies for reviewing some of the label parts introduced in Module 2 of the pesticide label training, such as precautionary statements, directions for use, worker protection labeling, and more.

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

PubMed Central

Yoo, Sang Wook; Guevara, Pamela; Jeong, Yong; Yoo, Kwangsun; Shin, Joseph S.; Mangin, Jean-Francois; Seong, Joon-Kyung

2015-01-01

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively. PMID:26225419

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts.

PubMed

Yoo, Sang Wook; Guevara, Pamela; Jeong, Yong; Yoo, Kwangsun; Shin, Joseph S; Mangin, Jean-Francois; Seong, Joon-Kyung

2015-01-01

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

Human factors in labeling and training for home healthcare technology.

PubMed

Patterson, Patricia A

2010-01-01

In this article, Patricia A. Patterson, a contributor to the recently-released standard ANSI/AAMI HE75:2009 Human factors engineering-Design of medical devices, highlights information from the standard important to developing labeling and training for homecare devices. She also describes one approach to developing labeling and training materials.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi

Machine learning with naturally labeled data for identifying abbreviation definitions.

PubMed

Yeganova, Lana; Comeau, Donald C; Wilbur, W John

2011-06-09

The rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. Most existing approaches for the abbreviation definition identification task employ rule-based methods. While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns. Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem. However, they require manually labeled training data. In this work, we develop a machine learning algorithm for abbreviation definition identification in text which makes use of what we term naturally labeled data. Positive training examples are naturally occurring potential abbreviation-definition pairs in text. Negative training examples are generated by randomly mixing potential abbreviations with unrelated potential definitions. The machine learner is trained to distinguish between these two sets of examples. Then, the learned feature weights are used to identify the abbreviation full form. This approach does not require manually labeled training data. We evaluate the performance of our algorithm on the Ab3P, BIOADI and Medstract corpora. Our system demonstrated results that compare favourably to the existing Ab3P and BIOADI systems. We achieve an F-measure of 91.36% on Ab3P corpus, and an F-measure of 87.13% on BIOADI corpus which are superior to the results reported by Ab3P and BIOADI systems. Moreover, we outperform these systems in terms of recall, which is one of our goals.

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

NASA Astrophysics Data System (ADS)

Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Timo; Kim, Hyojin

2016-03-01

Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.

Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol.

PubMed

Boccardi, Marina; Bocchetta, Martina; Morency, Félix C; Collins, D Louis; Nishikawa, Masami; Ganzola, Rossana; Grothe, Michel J; Wolf, Dominik; Redolfi, Alberto; Pievani, Michela; Antelmi, Luigi; Fellgiebel, Andreas; Matsuda, Hiroshi; Teipel, Stefan; Duchesne, Simon; Jack, Clifford R; Frisoni, Giovanni B

2015-02-01

The European Alzheimer's Disease Consortium and Alzheimer's Disease Neuroimaging Initiative (ADNI) Harmonized Protocol (HarP) is a Delphi definition of manual hippocampal segmentation from magnetic resonance imaging (MRI) that can be used as the standard of truth to train new tracers, and to validate automated segmentation algorithms. Training requires large and representative data sets of segmented hippocampi. This work aims to produce a set of HarP labels for the proper training and certification of tracers and algorithms. Sixty-eight 1.5 T and 67 3 T volumetric structural ADNI scans from different subjects, balanced by age, medial temporal atrophy, and scanner manufacturer, were segmented by five qualified HarP tracers whose absolute interrater intraclass correlation coefficients were 0.953 and 0.975 (left and right). Labels were validated as HarP compliant through centralized quality check and correction. Hippocampal volumes (mm(3)) were as follows: controls: left = 3060 (standard deviation [SD], 502), right = 3120 (SD, 897); mild cognitive impairment (MCI): left = 2596 (SD, 447), right = 2686 (SD, 473); and Alzheimer's disease (AD): left = 2301 (SD, 492), right = 2445 (SD, 525). Volumes significantly correlated with atrophy severity at Scheltens' scale (Spearman's ρ = <-0.468, P = <.0005). Cerebrospinal fluid spaces (mm(3)) were as follows: controls: left = 23 (32), right = 25 (25); MCI: left = 15 (13), right = 22 (16); and AD: left = 11 (13), right = 20 (25). Five subjects (3.7%) presented with unusual anatomy. This work provides reference hippocampal labels for the training and certification of automated segmentation algorithms. The publicly released labels will allow the widespread implementation of the standard segmentation protocol. Copyright © 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Classification without labels: learning from mixed samples in high energy physics

NASA Astrophysics Data System (ADS)

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-01

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

Classification without labels: learning from mixed samples in high energy physics

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

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Classification without labels: learning from mixed samples in high energy physics

DOE PAGES

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-25

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Isolating the Effects of Training Using Simple Regression Analysis: An Example of the Procedure.

ERIC Educational Resources Information Center

Waugh, C. Keith

This paper provides a case example of simple regression analysis, a forecasting procedure used to isolate the effects of training from an identified extraneous variable. This case example focuses on results of a three-day sales training program to improve bank loan officers' knowledge, skill-level, and attitude regarding solicitation and sale of…

Cues-Pause-Point Language Training: Teaching Echolalics Functional Use of Their Verbal Labeling Repertoires.

ERIC Educational Resources Information Center

McMorrow, Martin J.; And Others

1987-01-01

A cues-pause-point procedure was used to train two severely retarded females to remain quiet before, during, and briefly after the presentation of questions and then to verbalize on the basis of environmental cues whose labels represented the correct responses. Echolalia was rapidly replaced by correct responding on the trained stimuli. (Author/JW)

Learning with imperfectly labeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Cues-pause-point language training: teaching echolalics functional use of their verbal labeling repertoires.

PubMed Central

McMorrow, M J; Foxx, R M; Faw, G D; Bittle, R G

1987-01-01

We evaluated the direct and generalized effects of cues-pause-point language training procedures on immediate echolalia and correct responding in two severely retarded females. Two experiments were conducted with each subject in which the overall goal was to encourage them to remain quiet before, during, and briefly after the presentation of questions and then to verbalize on the basis of environmental cues whose labels represented the correct responses. Multiple baseline designs across question/response pairs (Experiment I) or question/response pairs and settings (Experiment II) demonstrated that echolalia was rapidly replaced by correct responding on the trained stimuli. More importantly, there were clear improvements in subjects' responding to untrained stimuli. Results demonstrated that the cues-pause-point procedures can be effective in teaching severely retarded or echolalic individuals functional use of their verbal labeling repertoires. PMID:3583962

Learning classification models with soft-label information.

PubMed

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2014-01-01

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

NASA Astrophysics Data System (ADS)

Gao, Yuan; Ma, Jiayi; Yuille, Alan L.

2017-05-01

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables such as bad lighting, wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem we propose a method called Semi-Supervised Sparse Representation based Classification (S$^3$RC). This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions, different glasses). The main idea is that (i) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework, then (ii) prototype face images are estimated as a gallery dictionary via a Gaussian Mixture Model (GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.

Sample Pesticide Label for Label Review Training

EPA Pesticide Factsheets

Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Use of doubly labeled water technique in soldiers training for jungle warfare

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

Forbes-Ewan, C.H.; Morrissey, B.L.; Gregg, G.C.

1989-07-01

The doubly labeled water method was used to estimate the energy expended by four members of an Australian Army platoon (34 soldiers) engaged in training for jungle warfare. Each subject received an oral isotope dose sufficient to raise isotope levels by 200-250 ({sup 18}O) and 100-120 ppm ({sup 2}H). The experimental period was 7 days. Concurrently, a factorial estimate of the energy expenditure of the platoon was conducted. Also, a food intake-energy balance study was conducted for the platoon. Mean daily energy expenditure by the doubly labeled water method was 4,750 kcal (range 4,152-5,394 kcal). The factorial estimate of meanmore » daily energy expenditure was 4,535 kcal. Because of inherent inaccuracies in the food intake-energy balance technique, we were able to conclude only that energy expenditure, as measured by this method, was greater than the estimated mean daily intake of 4,040 kcal. The doubly labeled water technique was well tolerated, is noninvasive, and appears to be suitable in a wide range of field applications.« less

Label Review Training - Resources

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Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Self-assessed performance improves statistical fusion of image labels

PubMed Central

Bryan, Frederick W.; Xu, Zhoubing; Asman, Andrew J.; Allen, Wade M.; Reich, Daniel S.; Landman, Bennett A.

2014-01-01

Purpose: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone to inter-individual differences. While fully automated methods have successfully targeted many anatomies, automated methods have not yet been developed for numerous essential structures (e.g., the internal structure of the spinal cord as seen on magnetic resonance imaging). Collaborative labeling is a new paradigm that offers a robust alternative that may realize both the throughput of automation and the guidance of experts. Yet, distributing manual labeling expertise across individuals and sites introduces potential human factors concerns (e.g., training, software usability) and statistical considerations (e.g., fusion of information, assessment of confidence, bias) that must be further explored. During the labeling process, it is simple to ask raters to self-assess the confidence of their labels, but this is rarely done and has not been previously quantitatively studied. Herein, the authors explore the utility of self-assessment in relation to automated assessment of rater performance in the context of statistical fusion. Methods: The authors conducted a study of 66 volumes manually labeled by 75 minimally trained human raters recruited from the university undergraduate population. Raters were given 15 min of training during which they were shown examples of correct segmentation, and the online segmentation tool was demonstrated. The volumes were labeled 2D slice-wise, and the slices were unordered. A self-assessed quality metric was produced by raters for each slice by marking a confidence bar superimposed on the slice. Volumes produced by both voting and statistical fusion algorithms were compared against a set of expert segmentations of the same volumes. Results: Labels for 8825 distinct slices were obtained. Simple majority voting resulted in statistically poorer performance than voting weighted by self-assessed performance

Robust Statistical Fusion of Image Labels

PubMed Central

Landman, Bennett A.; Asman, Andrew J.; Scoggins, Andrew G.; Bogovic, John A.; Xing, Fangxu; Prince, Jerry L.

2011-01-01

Image labeling and parcellation (i.e. assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability. PMID:22010145

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

Label Review Training: Module 1: Label Basics, Page 8

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Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human he

Less label, more free: approaches in label-free quantitative mass spectrometry.

PubMed

Neilson, Karlie A; Ali, Naveid A; Muralidharan, Sridevi; Mirzaei, Mehdi; Mariani, Michael; Assadourian, Gariné; Lee, Albert; van Sluyter, Steven C; Haynes, Paul A

2011-02-01

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Label Review Training: Module 1: Label Basics, Page 2

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Label Review Training: Module 1: Label Basics, Page 9

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Label Review Training: Module 1: Label Basics, Page 5

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Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Label Review Training: Module 1: Label Basics, Page 4

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Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Label Review Training: Module 1: Label Basics, Page 3

EPA Pesticide Factsheets

Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Artificial intelligence in sports on the example of weight training.

PubMed

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Artificial Intelligence in Sports on the Example of Weight Training

PubMed Central

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Label Review Training: Module 1: Label Basics, Page 6

EPA Pesticide Factsheets

Page 6, Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment

30 CFR 47.42 - Label contents.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Label contents. 47.42 Section 47.42 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.42 Label contents. When an operator must make a label, the label must— (a) Be...

30 CFR 47.42 - Label contents.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Label contents. 47.42 Section 47.42 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.42 Label contents. When an operator must make a label, the label must— (a) Be...

30 CFR 47.42 - Label contents.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Label contents. 47.42 Section 47.42 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.42 Label contents. When an operator must make a label, the label must— (a) Be...

30 CFR 47.42 - Label contents.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Label contents. 47.42 Section 47.42 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.42 Label contents. When an operator must make a label, the label must— (a) Be...

30 CFR 47.42 - Label contents.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Label contents. 47.42 Section 47.42 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR EDUCATION AND TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.42 Label contents. When an operator must make a label, the label must— (a) Be...

GEO Label: User and Producer Perspectives on a Label for Geospatial Data

NASA Astrophysics Data System (ADS)

Lush, V.; Lumsden, J.; Masó, J.; Díaz, P.; McCallum, I.

2012-04-01

One of the aims of the Science and Technology Committee (STC) of the Group on Earth Observations (GEO) was to establish a GEO Label- a label to certify geospatial datasets and their quality. As proposed, the GEO Label will be used as a value indicator for geospatial data and datasets accessible through the Global Earth Observation System of Systems (GEOSS). It is suggested that the development of such a label will significantly improve user recognition of the quality of geospatial datasets and that its use will help promote trust in datasets that carry the established GEO Label. Furthermore, the GEO Label is seen as an incentive to data providers. At the moment GEOSS contains a large amount of data and is constantly growing. Taking this into account, a GEO Label could assist in searching by providing users with visual cues of dataset quality and possibly relevance; a GEO Label could effectively stand as a decision support mechanism for dataset selection. Currently our project - GeoViQua, - together with EGIDA and ID-03 is undertaking research to define and evaluate the concept of a GEO Label. The development and evaluation process will be carried out in three phases. In phase I we have conducted an online survey (GEO Label Questionnaire) to identify the initial user and producer views on a GEO Label or its potential role. In phase II we will conduct a further study presenting some GEO Label examples that will be based on Phase I. We will elicit feedback on these examples under controlled conditions. In phase III we will create physical prototypes which will be used in a human subject study. The most successful prototypes will then be put forward as potential GEO Label options. At the moment we are in phase I, where we developed an online questionnaire to collect the initial GEO Label requirements and to identify the role that a GEO Label should serve from the user and producer standpoint. The GEO Label Questionnaire consists of generic questions to identify whether

Competencies in Training at the Graduate Student Level: Example of a Pediatric Psychology Seminar Course

PubMed Central

Ievers-Landis, Carolyn E.; Hazen, Rebecca A.; Fehr, Karla K.

2015-01-01

The recently developed competencies in pediatric psychology from the Society of Pediatric Psychology (SPP) Task Force on Competencies and Best Training Practices in Pediatric Psychology provide a benchmark to evaluate training program practices and student progress toward training in level-specific competency goals. Graduate-level training presents a unique challenge for addressing the breadth of competencies required in pediatric psychology while maintaining development of broader clinical psychology training goals. We describe a recurring graduate-level pediatric psychology seminar course that addresses training in a number of the competency cluster areas. The structure of the seminar, examples of classroom topics that correspond with competency cluster areas as well as benchmarks used to evaluate each student’s development in the competency area are provided. Specific challenges in developing and maintaining the seminar in this format are identified, and possible solutions are offered. This training format could serve as a model for established pediatric psychology programs to expand their didactic training goals or for programs without formal pediatric psychology training to address competencies outside of clinical placements. PMID:26900536

Diagnostic labels assigned to patients with orthopedic conditions and the influence of the label on selection of interventions: a qualitative study of orthopaedic clinical specialists.

PubMed

Miller-Spoto, Marcia; Gombatto, Sara P

2014-06-01

A variety of diagnostic classification systems are used by physical therapists, but little information about how therapists assign diagnostic labels and how the labels are used to direct intervention is available. The purposes of this study were: (1) to examine the diagnostic labels assigned to patient problems by physical therapists who are board-certified Orthopaedic Clinical Specialists (OCSs) and (2) to determine whether the label influences selection of interventions. A cross-sectional survey was conducted. Two written cases were developed for patients with low back and shoulder pain. A survey was used to evaluate the diagnostic label assigned and the interventions considered important for each case. The cases and survey were sent to therapists who are board-certified OCSs. Respondents assigned a diagnostic label and rated the importance of intervention categories for each case. Each diagnostic label was coded based on the construct it represented. Percentage responses for each diagnostic label code and intervention category were calculated. Relative importance of intervention category based on diagnostic label was examined. For the low back pain and shoulder pain cases, respectively, "Combination" (48.5%, 34.9%) and "Pathology/Pathophysiology" (32.7%, 57.3%) diagnostic labels were most common. Strengthening (85.9%, 98.1%), stretching (86.8%, 84.9%), neuromuscular re-education (87.6%, 93.4%), functional training (91.4%, 88.6%), and mobilization/manipulation (85.1%, 86.8%) were considered the most important interventions. Relative importance of interventions did not differ based on diagnostic label (χ2=0.050-1.263, P=.261-.824). The low response rate may limit the generalizability of the findings. Also, examples provided for labels may have influenced responses, and some of the label codes may have represented overlapping constructs. There is little consistency with which OCS therapists assign diagnostic labels, and the label does not seem to influence

Collision activity during training increases total energy expenditure measured via doubly labelled water.

PubMed

Costello, Nessan; Deighton, Kevin; Preston, Thomas; Matu, Jamie; Rowe, Joshua; Sawczuk, Thomas; Halkier, Matt; Read, Dale B; Weaving, Daniel; Jones, Ben

2018-06-01

Collision sports are characterised by frequent high-intensity collisions that induce substantial muscle damage, potentially increasing the energetic cost of recovery. Therefore, this study investigated the energetic cost of collision-based activity for the first time across any sport. Using a randomised crossover design, six professional young male rugby league players completed two different 5-day pre-season training microcycles. Players completed either a collision (COLL; 20 competitive one-on-one collisions) or non-collision (nCOLL; matched for kinematic demands, excluding collisions) training session on the first day of each microcycle, exactly 7 days apart. All remaining training sessions were matched and did not involve any collision-based activity. Total energy expenditure was measured using doubly labelled water, the literature gold standard. Collisions resulted in a very likely higher (4.96 ± 0.97 MJ; ES = 0.30 ± 0.07; p = 0.0021) total energy expenditure across the 5-day COLL training microcycle (95.07 ± 16.66 MJ) compared with the nCOLL training microcycle (90.34 ± 16.97 MJ). The COLL training session also resulted in a very likely higher (200 ± 102 AU; ES = 1.43 ± 0.74; p = 0.007) session rating of perceived exertion and a very likely greater (- 14.6 ± 3.3%; ES = - 1.60 ± 0.51; p = 0.002) decrease in wellbeing 24 h later. A single collision training session considerably increased total energy expenditure. This may explain the large energy expenditures of collision-sport athletes, which appear to exceed kinematic training and match demands. These findings suggest fuelling professional collision-sport athletes appropriately for the "muscle damage caused" alongside the kinematic "work required".

Training Self-Regulated Learning Skills with Video Modeling Examples: Do Task-Selection Skills Transfer?

ERIC Educational Resources Information Center

Raaijmakers, Steven F.; Baars, Martine; Schaap, Lydia; Paas, Fred; van Merriënboer, Jeroen; van Gog, Tamara

2018-01-01

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and…

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

PubMed

Xu, Yonghui; Min, Huaqing; Song, Hengjie; Wu, Qingyao

2016-08-01

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms. Copyright © 2016 Elsevier Ltd. All rights reserved.

101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol

PubMed Central

Klein, Arno; Tourville, Jason

2012-01-01

We introduce the Mindboggle-101 dataset, the largest and most complete set of free, publicly accessible, manually labeled human brain images. To manually label the macroscopic anatomy in magnetic resonance images of 101 healthy participants, we created a new cortical labeling protocol that relies on robust anatomical landmarks and minimal manual edits after initialization with automated labels. The “Desikan–Killiany–Tourville” (DKT) protocol is intended to improve the ease, consistency, and accuracy of labeling human cortical areas. Given how difficult it is to label brains, the Mindboggle-101 dataset is intended to serve as brain atlases for use in labeling other brains, as a normative dataset to establish morphometric variation in a healthy population for comparison against clinical populations, and contribute to the development, training, testing, and evaluation of automated registration and labeling algorithms. To this end, we also introduce benchmarks for the evaluation of such algorithms by comparing our manual labels with labels automatically generated by probabilistic and multi-atlas registration-based approaches. All data and related software and updated information are available on the http://mindboggle.info/data website. PMID:23227001

Label Review Training - Table of Contents

EPA Pesticide Factsheets

Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

Classification with asymmetric label noise: Consistency and maximal denoising

DOE PAGES

Blanchard, Gilles; Flaska, Marek; Handy, Gregory; ...

2016-09-20

In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that amore » majority of the observed labels are correct and that the true class-conditional distributions are “mutually irreducible,” a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to “mixture proportion estimation,” which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach. MSC 2010 subject classifications: Primary 62H30; secondary 68T10. Keywords and phrases: Classification, label noise, mixture proportion estimation, surrogate loss, consistency.« less

Classification with asymmetric label noise: Consistency and maximal denoising

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

Blanchard, Gilles; Flaska, Marek; Handy, Gregory

In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that amore » majority of the observed labels are correct and that the true class-conditional distributions are “mutually irreducible,” a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to “mixture proportion estimation,” which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach. MSC 2010 subject classifications: Primary 62H30; secondary 68T10. Keywords and phrases: Classification, label noise, mixture proportion estimation, surrogate loss, consistency.« less

Track-before-detect labeled multi-bernoulli particle filter with label switching

NASA Astrophysics Data System (ADS)

Garcia-Fernandez, Angel F.

2016-10-01

This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.

30 CFR 47.41 - Requirement for container labels.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Requirement for container labels. 47.41 Section... TRAINING HAZARD COMMUNICATION (HazCom) Container Labels and Other Forms of Warning § 47.41 Requirement for container labels. (a) The operator must ensure that each container of a hazardous chemical has a label. If a...

Effects of Labeling and Teacher Certification Type on Recall and Conflict Resolution

ERIC Educational Resources Information Center

Ayers, Jane M.; Krueger, Lacy E.; Jones, Beth A.

2015-01-01

Understanding how labels and prior training affect teachers of students with a disability is a step toward creating effective educational environments. Two goals of the present study were to examine how teacher training (special education vs. general education training) and labeling of students (either as having attention deficit hyperactivity…

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.…

Training Requirements in OSHA Standards and Training Guidelines.

ERIC Educational Resources Information Center

Occupational Safety and Health Administration, Washington, DC.

This booklet contains Occupational Safety and Health Administration (OSHA) training requirements, excerpted from OSHA standards. The booklet is designed to help employers, safety and health professionals, training directors, and others who need to know training requirements. (Requirements for posting information, warning signs, labels, and the…

Joint learning of labels and distance metric.

PubMed

Liu, Bo; Wang, Meng; Hong, Richang; Zha, Zhengjun; Hua, Xian-Sheng

2010-06-01

Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.

7 CFR 201.31a - Labeling treated seed.

Code of Federal Regulations, 2011 CFR

2011-01-01

... been treated shall be labeled in type no smaller than 8 point to indicate that the seed has been... labeling all types of mercurials. Examples of commonly accepted abbreviated chemical names are: BHC (1, 2... the size of the type used for information required to be on the label under paragraph (a) and shall...

Collaborative labeling of malignant glioma with WebMILL: a first look

NASA Astrophysics Data System (ADS)

Singh, Eesha; Asman, Andrew J.; Xu, Zhoubing; Chambless, Lola; Thompson, Reid; Landman, Bennett A.

2012-02-01

Malignant gliomas are the most common form of primary neoplasm in the central nervous system, and one of the most rapidly fatal of all human malignancies. They are treated by maximal surgical resection followed by radiation and chemotherapy. Herein, we seek to improve the methods available to quantify the extent of tumors using newly presented, collaborative labeling techniques on magnetic resonance imaging. Traditionally, labeling medical images has entailed that expert raters operate on one image at a time, which is resource intensive and not practical for very large datasets. Using many, minimally trained raters to label images has the possibility of minimizing laboratory requirements and allowing high degrees of parallelism. A successful effort also has the possibility of reducing overall cost. This potentially transformative technology presents a new set of problems, because one must pose the labeling challenge in a manner accessible to people with little or no background in labeling medical images and raters cannot be expected to read detailed instructions. Hence, a different training method has to be employed. The training must appeal to all types of learners and have the same concepts presented in multiple ways to ensure that all the subjects understand the basics of labeling. Our overall objective is to demonstrate the feasibility of studying malignant glioma morphometry through statistical analysis of the collaborative efforts of many, minimally-trained raters. This study presents preliminary results on optimization of the WebMILL framework for neoplasm labeling and investigates the initial contributions of 78 raters labeling 98 whole-brain datasets.

49 CFR 172.411 - EXPLOSIVE 1.1, 1.2, 1.3, 1.4, 1.5 and 1.6 labels, and EXPLOSIVE Subsidiary label.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 2 2011-10-01 2011-10-01 false EXPLOSIVE 1.1, 1.2, 1.3, 1.4, 1.5 and 1.6 labels..., EMERGENCY RESPONSE INFORMATION, TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.411 EXPLOSIVE 1.1, 1.2, 1.3, 1.4, 1.5 and 1.6 labels, and EXPLOSIVE Subsidiary label. (a) Except for size and color...

A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

PubMed

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2016-09-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling

PubMed Central

Wang, Sheng; Sun, Siqi

2017-01-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC. PMID:28884168

HCP: A Flexible CNN Framework for Multi-label Image Classification.

PubMed

Wei, Yunchao; Xia, Wei; Lin, Min; Huang, Junshi; Ni, Bingbing; Dong, Jian; Zhao, Yao; Yan, Shuicheng

2015-10-26

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [44] based on hand-crafted features on the VOC 2012 dataset.

Instance annotation for multi-instance multi-label learning

Treesearch

F. Briggs; X.Z. Fern; R. Raich; Q. Lou

2013-01-01

Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior work on MIML has focused on predicting label sets for previously unseen...

49 CFR 172.404 - Labels for mixed and consolidated packaging.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 2 2012-10-01 2012-10-01 false Labels for mixed and consolidated packaging. 172..., TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.404 Labels for mixed and consolidated packaging. (a) Mixed packaging. When compatible hazardous materials having different hazard classes are packed...

49 CFR 172.404 - Labels for mixed and consolidated packaging.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 2 2014-10-01 2014-10-01 false Labels for mixed and consolidated packaging. 172..., TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.404 Labels for mixed and consolidated packaging. (a) Mixed packaging. When compatible hazardous materials having different hazard classes are packed...

49 CFR 172.404 - Labels for mixed and consolidated packaging.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 2 2011-10-01 2011-10-01 false Labels for mixed and consolidated packaging. 172..., TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.404 Labels for mixed and consolidated packaging. (a) Mixed packaging. When compatible hazardous materials having different hazard classes are packed...

49 CFR 172.404 - Labels for mixed and consolidated packaging.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 2 2013-10-01 2013-10-01 false Labels for mixed and consolidated packaging. 172..., TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.404 Labels for mixed and consolidated packaging. (a) Mixed packaging. When compatible hazardous materials having different hazard classes are packed...

Attitudes and experiences of community pharmacists towards paediatric off-label prescribing: a prospective survey

PubMed Central

Stewart, Derek; Rouf, Abdul; Snaith, Ailsa; Elliott, Kathleen; Helms, Peter J; McLay, James S

2007-01-01

What is already known about this subject There are increasing concerns about the safety and efficacy of paediatric off-label medicines. In the UK, each year 26% of children receive an off-label prescription from their general practitioner. The community pharmacist is the final and key professional in the chain, with the responsibility to ensure that medicines are both prescribed and dispensed appropriately. What this study adds The majority of community pharmacists are aware of off-label prescribing, but through work experience rather than undergraduate or postgraduate training or professional development. Community pharmacists, like UK general practitioners, underestimate the levels of paediatric off-label prescribing, and appear unclear as to the most common reasons for a prescription being off label. Most community pharmacists stated that they should inform the prescriber that a medicine was off label; however, when given specific practical examples, less than half would actually appear to do so. The majority of community pharmacists have been asked by the public to sell over-the-counter medicines for paediatric off-label use. Aim To identify community pharmacist experiences of, and attitudes towards paediatric off-label prescribing. Methods A prospective questionnaire-based study, with a 21-item questionnaire issued to 1500 randomly selected community pharmacies throughout the UK during 2005 on three separate occasions. Results Four hundred and eighty-two (32.1%) completed questionnaires were returned. Over 70% of respondents were familiar with the concept of off-label prescribing, primarily through dispensing experience rather than education, although only 40% were aware of having dispensed a paediatric off-label prescription within the previous month. The reasons given for a prescription being off label were younger age than recommended (84.6%, 297/351), primarily for antihistamines, analgesics and β2-agonists, and higher (73.9%, 229/310) or lower than (41

If training data appears to be mislabeled, should we relabel it? Improving supervised learning algorithms for threat detection in ground penetrating radar data

NASA Astrophysics Data System (ADS)

Reichman, Daniël.; Collins, Leslie M.; Malof, Jordan M.

2018-04-01

This work focuses on the development of automatic buried threat detection (BTD) algorithms using ground penetrating radar (GPR) data. Buried threats tend to exhibit unique characteristics in GPR imagery, such as high energy hyperbolic shapes, which can be leveraged for detection. Many recent BTD algorithms are supervised, and therefore they require training with exemplars of GPR data collected over non-threat locations and threat locations, respectively. Frequently, data from non-threat GPR examples will exhibit high energy hyperbolic patterns, similar to those observed from a buried threat. Is it still useful therefore, to include such examples during algorithm training, and encourage an algorithm to label such data as a non-threat? Similarly, some true buried threat examples exhibit very little distinctive threat-like patterns. We investigate whether it is beneficial to treat such GPR data examples as mislabeled, and either (i) relabel them, or (ii) remove them from training. We study this problem using two algorithms to automatically identify mislabeled examples, if they are present, and examine the impact of removing or relabeling them for training. We conduct these experiments on a large collection of GPR data with several state-of-the-art GPR-based BTD algorithms.

Virtual Training and Coaching of Health Behavior: Example from Mindfulness Meditation Training

PubMed Central

Hudlicka, Eva

2014-01-01

Objective Computer-based virtual coaches are increasingly being explored for patient education, counseling, and health behavior training and coaching. The objective of this research was to develop and evaluate a Virtual Mindfulness Coach for training and coaching in mindfulness meditation. Method The coach was implemented as an embodied conversational character, providing mindfulness training and coaching via mixed initiative, text-based, natural language dialogue with the student, and emphasizing affect-adaptive interaction. (The term ‘mixed initiative dialog’ refers to a human-machine dialogue where either can initiate a conversation or a change in the conversation topic.) Results Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping students establish a regular practice than self-administered training using written and audio materials. The coached group also appeared to be in more advanced stages of change in terms of the transtheoretical model, and have a higher sense of self-efficacy regarding establishment of a regular mindfulness practice. Conclusion These results suggest that virtual coach-based training of mindfulness is both feasible, and potentially more effective, than a self-administered program. Of particular interest is the identification of the specific coach features that contribute to its effectiveness. Practice Implications Virtual coaches could provide easily-accessible and cost-effective customized training for a range of health behaviors. The affect-adaptive aspect of these coaches is particularly relevant for helping patients establish long-term behavior changes. PMID:23809167

Virtual training and coaching of health behavior: example from mindfulness meditation training.

PubMed

Hudlicka, Eva

2013-08-01

Computer-based virtual coaches are increasingly being explored for patient education, counseling, and health behavior training and coaching. The objective of this research was to develop and evaluate a Virtual Mindfulness Coach for training and coaching in mindfulness meditation. The coach was implemented as an embodied conversational character, providing mindfulness training and coaching via mixed initiative, text-based, natural language dialog with the student, and emphasizing affect-adaptive interaction. (The term 'mixed initiative dialog' refers to a human-machine dialog where either can initiate a conversation or a change in the conversation topic.) Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping students establish a regular practice than self-administered training using written and audio materials. The coached group also appeared to be in more advanced stages of change in terms of the transtheoretical model, and have a higher sense of self-efficacy regarding establishment of a regular mindfulness practice. These results suggest that virtual coach-based training of mindfulness is both feasible, and potentially more effective, than a self-administered program. Of particular interest is the identification of the specific coach features that contribute to its effectiveness. Virtual coaches could provide easily accessible and cost-effective customized training for a range of health behaviors. The affect-adaptive aspect of these coaches is particularly relevant for helping patients establish long-term behavior changes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

49 CFR 172.404 - Labels for mixed and consolidated packaging.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false Labels for mixed and consolidated packaging. 172..., TRAINING REQUIREMENTS, AND SECURITY PLANS Labeling § 172.404 Labels for mixed and consolidated packaging. (a) Mixed packaging. When hazardous materials having different hazard classes are packed within the...

Bladder cancer staging in CT urography: effect of stage labels on statistical modeling of a decision support system

NASA Astrophysics Data System (ADS)

Gandikota, Dhanuj; Hadjiiski, Lubomir; Cha, Kenny H.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Alva, Ajjai; Paramagul, Chintana; Wei, Jun; Zhou, Chuan

2018-02-01

In bladder cancer, stage T2 is an important threshold in the decision of administering neoadjuvant chemotherapy. Our long-term goal is to develop a quantitative computerized decision support system (CDSS-S) to aid clinicians in accurate staging. In this study, we examined the effect of stage labels of the training samples on modeling such a system. We used a data set of 84 bladder cancers imaged with CT Urography (CTU). At clinical staging prior to treatment, 43 lesions were staged as below stage T2 and 41 were stage T2 or above. After cystectomy and pathological staging that is considered the gold standard, 10 of the lesions were upstaged to stage T2 or above. After correcting the stage labels, 33 lesions were below stage T2, and 51 were stage T2 or above. For the CDSS-S, the lesions were segmented using our AI-CALS method and radiomic features were extracted. We trained a linear discriminant analysis (LDA) classifier with leave-one-case-out cross validation to distinguish between bladder lesions of stage T2 or above and those below stage T2. The CDSS-S was trained and tested with the corrected post-cystectomy labels, and as a comparison, CDSS-S was also trained with understaged pre-treatment labels and tested on lesions with corrected labels. The test AUC for the CDSS-S trained with corrected labels was 0.89 +/- 0.04. For the CDSS-S trained with understaged pre-treatment labels and tested on the lesions with corrected labels, the test AUC was 0.86 +/- 0.04. The likelihood of stage T2 or above for 9 out of the 10 understaged lesions was correctly increased for the CDSS-S trained with corrected labels. The CDSS-S is sensitive to the accuracy of stage labeling. The CDSS-S trained with correct labels shows promise in prediction of the bladder cancer stage.

Multi-View Budgeted Learning under Label and Feature Constraints Using Label-Guided Graph-Based Regularization

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

Symons, Christopher T; Arel, Itamar

2011-01-01

Budgeted learning under constraints on both the amount of labeled information and the availability of features at test time pertains to a large number of real world problems. Ideas from multi-view learning, semi-supervised learning, and even active learning have applicability, but a common framework whose assumptions fit these problem spaces is non-trivial to construct. We leverage ideas from these fields based on graph regularizers to construct a robust framework for learning from labeled and unlabeled samples in multiple views that are non-independent and include features that are inaccessible at the time the model would need to be applied. We describemore » examples of applications that fit this scenario, and we provide experimental results to demonstrate the effectiveness of knowledge carryover from training-only views. As learning algorithms are applied to more complex applications, relevant information can be found in a wider variety of forms, and the relationships between these information sources are often quite complex. The assumptions that underlie most learning algorithms do not readily or realistically permit the incorporation of many of the data sources that are available, despite an implicit understanding that useful information exists in these sources. When multiple information sources are available, they are often partially redundant, highly interdependent, and contain noise as well as other information that is irrelevant to the problem under study. In this paper, we are focused on a framework whose assumptions match this reality, as well as the reality that labeled information is usually sparse. Most significantly, we are interested in a framework that can also leverage information in scenarios where many features that would be useful for learning a model are not available when the resulting model will be applied. As with constraints on labels, there are many practical limitations on the acquisition of potentially useful features. A key

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

PubMed

Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2017-09-01

labelers. We also compared the performance of the passive and active learning models when using the consensus label. The AL methods: produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p=0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275-0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers' different models during the training phase, compared to the variance of the induced models' AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p=0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p=0.29), as was the difference between the Combination_XA and Exploitation methods (p=0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC

Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

PubMed Central

Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2018-01-01

using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. Results The AL methods produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p = 0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275 to 0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers’ different models during the training phase, compared to the variance of the induced models’ AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods. The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p = 0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p = 0.29), as was the difference between the Combination_XA and Exploitation methods (p = 0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired

SODA In Train Swarm Example

NASA Image and Video Library

2017-07-13

SODA, Swarm Orbital Dynamics Advisor, a tool that provides the orbital maneuvers required to achieve a desired type of relative swarm motion for satellite missions. For the in-train swarm type, the objective is to phase the satellites ahead and behind one another to achieve a string-of-pearls relative position configuration. SODA maneuvers each satellite by performing a two-impulse elliptical transfer orbit from and back to the same orbit, known as a phasing maneuver.

Label Review Training: Module 5: Course Final Quiz

EPA Pesticide Factsheets

Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

PRN 2000-5: Guidance for Mandatory and Advisory Labeling Statements

EPA Pesticide Factsheets

This notice provides guidance for improving the clarity of labeling statements in order to avoid confusing directions and precautions and to prevent the misuse of pesticides. It includes definitions and examples for mandatory and advisory label statements.

Audit of manufactured products: use of allergen advisory labels and identification of labeling ambiguities.

PubMed

Pieretti, Mariah M; Chung, Danna; Pacenza, Robert; Slotkin, Todd; Sicherer, Scott H

2009-08-01

The Food Allergy Labeling and Consumer Protection Act became effective January 1, 2006, and mandates disclosure of the 8 major allergens in plain English and as a source of ingredients in the ingredient statement. It does not regulate advisory labels. We sought to determine the frequency and language used in voluntary advisory labels among commercially available products and to identify labeling ambiguities affecting consumers with allergy. Trained surveyors performed a supermarket survey of 20,241 unique manufactured food products (from an original assessment of 49,604 products) for use of advisory labels. A second detailed survey of 744 unique products evaluated additional labeling practices. Overall, 17% of 20,241 products surveyed contain advisory labels. Chocolate candy, cookies, and baking mixes were the 3 categories of 24 with the greatest frequency (> or = 40%). Categorically, advisory warnings included "may contain" (38%), "shared equipment" (33%), and "within plant" (29%). The subsurvey disclosed 25 different types of advisory terminology. Nonspecific terms, such as "natural flavors" and "spices," were found on 65% of products and were not linked to a specific ingredient for 83% of them. Additional ambiguities included unclear sources of soy (lecithin vs protein), nondisclosure of sources of gelatin and lecithin, and simultaneous disclosure of "contains" and "may contain" for the same allergen, among others. Numerous products have advisory labeling and ambiguities that present challenges to consumers with food allergy. Additional allergen labeling regulation could improve safety and quality of life for individuals with food allergy.

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

Enhanced labeling density and whole-cell 3D dSTORM imaging by repetitive labeling of target proteins.

PubMed

Venkataramani, Varun; Kardorff, Markus; Herrmannsdörfer, Frank; Wieneke, Ralph; Klein, Alina; Tampé, Robert; Heilemann, Mike; Kuner, Thomas

2018-04-03

With continuing advances in the resolving power of super-resolution microscopy, the inefficient labeling of proteins with suitable fluorophores becomes a limiting factor. For example, the low labeling density achieved with antibodies or small molecule tags limits attempts to reveal local protein nano-architecture of cellular compartments. On the other hand, high laser intensities cause photobleaching within and nearby an imaged region, thereby further reducing labeling density and impairing multi-plane whole-cell 3D super-resolution imaging. Here, we show that both labeling density and photobleaching can be addressed by repetitive application of trisNTA-fluorophore conjugates reversibly binding to a histidine-tagged protein by a novel approach called single-epitope repetitive imaging (SERI). For single-plane super-resolution microscopy, we demonstrate that, after multiple rounds of labeling and imaging, the signal density is increased. Using the same approach of repetitive imaging, washing and re-labeling, we demonstrate whole-cell 3D super-resolution imaging compensated for photobleaching above or below the imaging plane. This proof-of-principle study demonstrates that repetitive labeling of histidine-tagged proteins provides a versatile solution to break the 'labeling barrier' and to bypass photobleaching in multi-plane, whole-cell 3D experiments.

Improving labeling efficiency in automatic quality control of MRSI data.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

2017-12-01

To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

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

Criminalizing knowledge: the perverse implications of the intended use regulations of off-label promotion prosecutions.

PubMed

Gentry, Gregory

2009-01-01

Your company has spent months designing a compliance program and training your sales representatives. They know never to mention the off-label uses of your product. If they are asked about the off-label uses by the physician they are detailing, they know to forward those inquiries to the scientific liaisons at headquarters. But, could your company still be in legal jeopardy simply because it knows that the product is being used for an off-label purpose? This article attempts to track the Food and Drug Administration's (FDA's) shifting interpretation of its "intended use" regulations, from focusing entirely on the statements of the manufacturers to focusing on the knowledge of the industry, indeed, of the consumers of products, in determining the true intended use of a product. It will look at several recent attempts by FDA to use that new interpretation of the regulations to expand its power: to regulate tobacco and to require pediatric indications for any new drug. Finally, it will look at several recent examples of how this new interpretation has manifested in actions by FDA and the Department of Justice (DOJ).

[Basic psychosomatic care in ophthalmology. Relevance, training and case examples].

PubMed

Brumm, G; Schnell, S

2016-02-01

The incidence of psychosomatic disorders and their impact on society are increasing. Many patients suffer from psychosomatic symptoms. Medical studies and most notably medical training for ophthalmologists do not sufficiently cover these topics and do not adequately prepare doctors for dealing with patients suffering from psychosomatic disorders. Training in basic psychosomatic care can be absolved by all physicians irrespective of specialization. The structure, benefits and importance of this professional training are explained. The curriculum of the German Medical Association forms the basis of training in basic psychosomatic care. The personal experiences of the authors after completing the training as well as case studies are presented. Training in basic psychosomatic care conveys practical skills for dealing with patients with psychosomatic symptoms, which are often not acquired during medical training for ophthalmologists, where technical procedures predominate. Thus the professional ability is broadened with an immediate positive effect not only on the physician-patient relationship but also on the professional and private environment. Training in basic psychosomatic care should be obligatory in the specialist training of ophthalmologists.

Effectiveness of autism training programme: An example from Van, Turkey.

PubMed

Eray, Safak; Murat, Duygu

2017-11-01

To determine the knowledge and attitudes of family practitioners before and after their participation in a training programme. The study was conducted at Van Training and Research Hospital, Van, Turkey, from December 1to 15, 2016, and comprised family practitioners. Before the training, the practitioners were asked to fill out a questionnaire that was prepared by the researchers. Subsequently, the training course was presented by the child and adolescent psychiatrists. After the training, participants were asked to fill out the same questionnaire again. The results of survey were compared before and after training. Data was evaluated using SPSS 22.Descriptive analyses were used and baseline characteristics were compared between groups using McNemar's test and paired t-test. Of the 79 family practitioners who filled out the questionnaire,75(94.9%) were included. The mean age of the practitioners was 28.2±11.63, with 40(53%) being females. Moreover,26(34.7%) participants thought that they had sufficient information regarding autism spectrum disorder before training, and this number increased to 66(88%) after training. There was a significant difference between pre-training and post-training scores of the questionnaire (p<0.001). There was a deficiency in knowledge about autism symptoms, aetiology, prevalence and treatment among family practitioners. .

Use the Nutrition Facts Label

MedlinePlus

... Department of Health & Human Services Health Topics The Science Grants and Training News and Events About NHLBI Home » Health Information for the Public » Educational Campaigns & Programs » We Can! » Eat Right » Use the Nutrition Facts Label Ways to Enhance Children's Activity & Nutrition About ...

40 CFR 763.95 - Warning labels.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT ASBESTOS Asbestos-Containing Materials in Schools § 763.95 Warning labels. (a) The local education agency shall...: ASBESTOS. HAZARDOUS. DO NOT DISTURB WITHOUT PROPER TRAINING AND EQUIPMENT. ...

40 CFR 763.95 - Warning labels.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT ASBESTOS Asbestos-Containing Materials in Schools § 763.95 Warning labels. (a) The local education agency shall...: ASBESTOS. HAZARDOUS. DO NOT DISTURB WITHOUT PROPER TRAINING AND EQUIPMENT. ...

40 CFR 763.95 - Warning labels.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT ASBESTOS Asbestos-Containing Materials in Schools § 763.95 Warning labels. (a) The local education agency shall...: ASBESTOS. HAZARDOUS. DO NOT DISTURB WITHOUT PROPER TRAINING AND EQUIPMENT. ...

40 CFR 763.95 - Warning labels.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT ASBESTOS Asbestos-Containing Materials in Schools § 763.95 Warning labels. (a) The local education agency shall...: ASBESTOS. HAZARDOUS. DO NOT DISTURB WITHOUT PROPER TRAINING AND EQUIPMENT. ...

40 CFR 763.95 - Warning labels.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT ASBESTOS Asbestos-Containing Materials in Schools § 763.95 Warning labels. (a) The local education agency shall...: ASBESTOS. HAZARDOUS. DO NOT DISTURB WITHOUT PROPER TRAINING AND EQUIPMENT. ...

Information Measures of Degree Distributions with an Application to Labeled Graphs

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

Joslyn, Cliff A.; Purvine, Emilie AH

2016-01-11

The problem of describing the distribution of labels over a set of objects is relevant to many domains. For example: cyber security, social media, and protein interactions all care about the manner in which labels are distributed among different objects. In this paper we present three interacting statistical measures on label distributions, inspired by entropy and information theory. Labeled graphs are discussed as a specific case of labels distributed over a set of edges. We describe a use case in cyber security using a labeled directed multi-graph of IPFLOW. Finally we show how these measures respond when labels are updatedmore » in certain ways.« less

Obstacles to nutrition labeling in restaurants.

PubMed

Almanza, B A; Nelson, D; Chai, S

1997-02-01

This study determined the major obstacles that foodservices face regarding nutrition labeling. Survey questionnaire was conducted in May 1994. In addition to demographic questions, the directors were asked questions addressing willingness, current practices, and perceived obstacles related to nutrition labeling. Sixty-eight research and development directors of the largest foodservice corporations as shown in Restaurants & Institutions magazine's list of the top 400 largest foodservices (July 1993). P tests were used to determine significance within a group for the number of foodservices that were currently using nutrition labeling, perceived impact of nutrition labeling on sales, and perceived responsibility to add nutrition labels. Regression analysis was used to determine the importance of factors on willingness to label. Response rate was 45.3%. Most companies were neutral about their willingness to use nutrition labeling. Two thirds of the respondents were not currently using nutrition labels. Only one third thought that it was the foodservice's responsibility to provide such information. Several companies perceived that nutrition labeling would have a potentially negative effect on annual sales volume. Major obstacles were identified as menu or personnel related, rather than cost related. Menu-related obstacles included too many menu variations, limited space on the menu for labeling, and loss of flexibility in changing the menu. Personnel-related obstacles included difficulty in training employees to implement nutrition labeling, and not enough time for foodservice personnel to implement nutrition labeling. Numerous opportunities will be created for dietetics professionals in helping foodservices overcome these menu- or personnel-related obstacles.

Adapter reagents for protein site specific dye labeling.

PubMed

Thompson, Darren A; Evans, Eric G B; Kasza, Tomas; Millhauser, Glenn L; Dawson, Philip E

2014-05-01

Chemoselective protein labeling remains a significant challenge in chemical biology. Although many selective labeling chemistries have been reported, the practicalities of matching the reaction with appropriately functionalized proteins and labeling reagents is often a challenge. For example, we encountered the challenge of site specifically labeling the cellular form of the murine Prion protein with a fluorescent dye. To facilitate this labeling, a protein was expressed with site specific p-acetylphenylalanine. However, the utility of this acetophenone reactive group is hampered by the severe lack of commercially available aminooxy fluorophores. Here we outline a general strategy for the efficient solid phase synthesis of adapter reagents capable of converting maleimido-labels into aminooxy or azide functional groups that can be further tuned for desired length or solubility properties. The utility of the adapter strategy is demonstrated in the context of fluorescent labeling of the murine Prion protein through an adapted aminooxy-Alexa dye. © 2014 Wiley Periodicals, Inc.

Adapter Reagents for Protein Site Specific Dye Labeling

PubMed Central

Thompson, Darren A.; Evans, Eric G. B.; Kasza, Tomas; Millhauser, Glenn L.; Dawson, Philip E.

2016-01-01

Chemoselective protein labeling remains a significant challenge in chemical biology. Although many selective labeling chemistries have been reported, the practicalities of matching the reaction with appropriately functionalized proteins and labeling reagents is often a challenge. For example, we encountered the challenge of site specifically labeling the cellular form of the murine Prion protein with a fluorescent dye. To facilitate this labeling, a protein was expressed with site specific p-acetylphenylalanine. However, the utility of this aceto-phenone reactive group is hampered by the severe lack of commercially available aminooxy fluorophores. Here we outline a general strategy for the efficient solid phase synthesis of adapter reagents capable of converting maleimido-labels into aminooxy or azide functional groups that can be further tuned for desired length or solubility properties. The utility of the adapter strategy is demonstrated in the context of fluorescent labeling of the murine Prion protein through an adapted aminooxy-Alexa dye. PMID:24599728

Using partially labeled data for normal mixture identification with application to class definition

NASA Technical Reports Server (NTRS)

Shahshahani, Behzad M.; Landgrebe, David A.

1992-01-01

The problem of estimating the parameters of a normal mixture density when, in addition to the unlabeled samples, sets of partially labeled samples are available is addressed. The density of the multidimensional feature space is modeled with a normal mixture. It is assumed that the set of components of the mixture can be partitioned into several classes and that training samples are available from each class. Since for any training sample the class of origin is known but the exact component of origin within the corresponding class is unknown, the training samples as considered to be partially labeled. The EM iterative equations are derived for estimating the parameters of the normal mixture in the presence of partially labeled samples. These equations can be used to combine the supervised and nonsupervised learning processes.

Controlling off-label medication use.

PubMed

Gillick, Muriel R

2009-03-03

Off-label prescribing may lead to innovative new uses of old medications, is essential in such fields as pediatrics, and avoids the lengthy and expensive process of modifying U.S. Food and Drug Administration (FDA) drug labeling. Using medications for unapproved indications, however, raises concerns about patient safety when the drugs have a high potential for toxicity and generates economic concerns when their cost is high. A possible means of controlling the use of off-label drugs is to focus on medications used off-label that are both expensive and potentially risky. These are principally biotechnology drugs, such as recombinant enzymes, cytokines, and monoclonal antibodies. This article suggests a 2-step process for controlling use of such drugs, analogous to that used for devices. Once a drug is FDA approved, it would undergo scrutiny using the Centers for Medicare & Medicaid Services (CMS) National Coverage Determination method if its cost exceeds a specified benchmark-for example, $12 000, which is the average cost of a pacemaker. The CMS would pay only for off-label uses for which there is adequate evidence in its National Coverage Determination process. Other insurance companies would probably adopt the recommendations of CMS.

United States Food and Drug Administration Product Label Changes.

PubMed

Kircik, Leon; Sung, Julie C; Stein-Gold, Linda; Goldenberg, Gary

2017-02-01

Once a drug has been approved by the United States Food and Drug Administration and is on the market, the Food and Drug Administration communicates new safety information through product label changes. Most of these label changes occur after a spontaneous report to either the drug manufacturing companies or the Food and Drug Administration MedWatch program. As a result, 400 to 500 label changes occur every year. Actinic keratosis treatments exemplify the commonality of label changes throughout the postmarket course of a drug. Diclofenac gel, 5-fluorouracil cream, imiquimod, and ingenol mebutate are examples of actinic keratosis treatments that have all undergone at least one label revision. With the current system of spontaneous reports leading to numerous label changes, each occurrence does not necessarily signify a radical change in the safety of a drug.

Harnessing Innovative Technologies to Advance Children’s Mental Health: Behavioral Parent Training As an Example

PubMed Central

Jones, Deborah J.; Forehand, Rex; Cuellar, Jessica; Kincaid, Carlye; Parent, Justin; Fenton, Nicole; Goodrum, Nada

2012-01-01

Disruptive behaviors of childhood are among the most common reasons for referral of children to mental health professionals. Behavioral parent training (BPT) is the most efficacious intervention for these problem behaviors, yet BPT is substantially underutilized beyond university research and clinic settings. With the aim of addressing this research-to-practice gap, this article highlights the considerable, but largely unrealized, potential for technology to overcome the two most pressing challenges hindering the diffusion of BPT: (1). The dearth of BPT training and supervision opportunities for therapists who work with families of children with disruptive behaviors and; (2). The failure to engage and retain families in BPT services when services are available. To this end, this review presents a theoretical framework to guide technological innovations in BPT and highlights examples of how technology is currently being harnessed to overcome these challenges. This review also discusses recommendations for using technology as a delivery vehicle to further advance the field of BPT and the potential implications of technological innovations in BPT for other areas of children’s mental health are discussed. PMID:23313761

Effect of physical training on the oxidation of an oral glucose load at rest: a naturally labeled 13C-glucose study.

PubMed

Krzentowski, G; Pirnay, F; Luyckx, A S; Lacroix, M; Mosora, F; Lefebvre, P J

1983-01-01

This study aimed at investigating, in six healthy, non obese, young (25 +/- 1 years) male volunteers, with strictly normal oral glucose tolerance, the influence of a six week physical training period (60 min bicycling 5 days/week at 30-40% of their individual VO2 max) on the hormonal and metabolic response to a 100 g oral 13C-naturally labeled glucose load given at rest before and 36 h after the last training session. Exogenous glucose oxidation was derived from 13CO2 measurements on expired air. Training resulted in: a 29% increase in VO2 max (2 p less than 0.002), a 27% decrease in plasma triglycerides (2 p less than 0.02). No changes were observed concerning weight, total body K, skinfold tolerance, which was strictly normal before training, remained unchanged, but the insulin response to the oral glucose load decreased by 24% (2 p less than 0.025). Exogenous glucose oxidation was similar before and after training, averaging 35.9 +/- 2.1 and 37.4 +/- 2.0 g/7 h respectively. a 6 week training period, performed on strictly healthy young males, studied at rest, induced an increase in VO2 max, a decrease in plasma triglycerides and a lower insulin response to oral glucose while glucose tolerance and exogenous glucose oxidation remained unchanged.

What's in a Label? Careers in Integrated Early Childhood Programs.

ERIC Educational Resources Information Center

Gorelick, Molly C.

The paper, given by the director of a project to train teachers for early childhood education programs which integrate handicapped and normal children, focuses on the effects of labeling on teacher-child interaction. The author recounts her own experience with teaching handicapped children and the historical tendency to label and segregate various…

An algorithm for optimal fusion of atlases with different labeling protocols

PubMed Central

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Aganj, Iman; Bhatt, Priyanka; Casillas, Christen; Salat, David; Boxer, Adam; Fischl, Bruce; Van Leemput, Koen

2014-01-01

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as “atlases”). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures. PMID:25463466

Showing Parents How to Talk to Their Kids about the Nutrition Facts Label

MedlinePlus

... How to Talk to Their Kids about the Nutrition Facts Label Training for Health Educators and Community ... leaders unite with the goal of using the Nutrition Facts Label as their everyday tool for making ...

United States Food and Drug Administration Product Label Changes

PubMed Central

Sung, Julie C.; Stein-Gold, Linda; Goldenberg, Gary

2017-01-01

Once a drug has been approved by the United States Food and Drug Administration and is on the market, the Food and Drug Administration communicates new safety information through product label changes. Most of these label changes occur after a spontaneous report to either the drug manufacturing companies or the Food and Drug Administration MedWatch program. As a result, 400 to 500 label changes occur every year. Actinic keratosis treatments exemplify the commonality of label changes throughout the postmarket course of a drug. Diclofenac gel, 5-fluorouracil cream, imiquimod, and ingenol mebutate are examples of actinic keratosis treatments that have all undergone at least one label revision. With the current system of spontaneous reports leading to numerous label changes, each occurrence does not necessarily signify a radical change in the safety of a drug. PMID:28367259

United States Food and Drug Administration Product Label Changes

PubMed Central

Sung, Julie C.; Stein-Gold, Linda; Goldenberg, Gary

2016-01-01

Once a drug has been approved by the United States Food and Drug Administration and is on the market, the Food and Drug Administration communicates new safety information through product label changes. Most of these label changes occur after a spontaneous report to either the drug manufacturing companies or the Food and Drug Administration MedWatch program. As a result, 400 to 500 label changes occur every year. Actinic keratosis treatments exemplify the commonality of label changes throughout the postmarket course of a drug. Diclofenac gel, 5-fluorouracil cream, imiquimod, and ingenol mebutate are examples of actinic keratosis treatments that have all undergone at least one label revision. With the current system of spontaneous reports leading to numerous label changes, each occurrence does not necessarily signify a radical change in the safety of a drug. PMID:26962391

Homogeneous Biosensing Based on Magnetic Particle Labels

PubMed Central

Schrittwieser, Stefan; Pelaz, Beatriz; Parak, Wolfgang J.; Lentijo-Mozo, Sergio; Soulantica, Katerina; Dieckhoff, Jan; Ludwig, Frank; Guenther, Annegret; Tschöpe, Andreas; Schotter, Joerg

2016-01-01

The growing availability of biomarker panels for molecular diagnostics is leading to an increasing need for fast and sensitive biosensing technologies that are applicable to point-of-care testing. In that regard, homogeneous measurement principles are especially relevant as they usually do not require extensive sample preparation procedures, thus reducing the total analysis time and maximizing ease-of-use. In this review, we focus on homogeneous biosensors for the in vitro detection of biomarkers. Within this broad range of biosensors, we concentrate on methods that apply magnetic particle labels. The advantage of such methods lies in the added possibility to manipulate the particle labels by applied magnetic fields, which can be exploited, for example, to decrease incubation times or to enhance the signal-to-noise-ratio of the measurement signal by applying frequency-selective detection. In our review, we discriminate the corresponding methods based on the nature of the acquired measurement signal, which can either be based on magnetic or optical detection. The underlying measurement principles of the different techniques are discussed, and biosensing examples for all techniques are reported, thereby demonstrating the broad applicability of homogeneous in vitro biosensing based on magnetic particle label actuation. PMID:27275824

Snorkel: Rapid Training Data Creation with Weak Supervision.

PubMed

Ratner, Alexander; Bach, Stephen H; Ehrenberg, Henry; Fries, Jason; Wu, Sen; Ré, Christopher

2017-11-01

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8× faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8× speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

Snorkel: Rapid Training Data Creation with Weak Supervision

PubMed Central

Ratner, Alexander; Bach, Stephen H.; Ehrenberg, Henry; Fries, Jason; Wu, Sen; Ré, Christopher

2018-01-01

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8× faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8× speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets. PMID:29770249

Hyperspectral Sulfur Detection Using an SVM with Extreme Minority Positive Examples Onboard EO-1

NASA Astrophysics Data System (ADS)

Mandrake, Lukas; Wagstaff, Kiri L.; Gleeson, Damhnait; Rebbapragada, Umaa; Tran, Daniel; Castano, Rebecca; Chien, Steven; Pappalardo, Robert T.

2009-09-01

Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher- resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard the EO-1 spacecraft. This system consists of naturally occurring sulfur-rich springs emerging from glacial ice, which are a known environment for microbial life. The biological activity of the spring is associated with sulfur compounds that can be detected remotely via spectral analysis. This system may offer an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Unfortunately, spacecraft processing power and memory is severely limited which places strong constraints on the algorithms available. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. Of key interest is our assessment of the classifier's behavior on non-sulfur-bearing imagery far from the training region. Selection of the most relevant spectral bands and parameters for the SVM are also explored.

Volume-labeled nanoparticles and methods of preparation

DOEpatents

Wang, Wei; Gu, Baohua; Retterer, Scott T; Doktycz, Mitchel J

2015-04-21

Compositions comprising nanosized objects (i.e., nanoparticles) in which at least one observable marker, such as a radioisotope or fluorophore, is incorporated within the nanosized object. The nanosized objects include, for example, metal or semi-metal oxide (e.g., silica), quantum dot, noble metal, magnetic metal oxide, organic polymer, metal salt, and core-shell nanoparticles, wherein the label is incorporated within the nanoparticle or selectively in a metal oxide shell of a core-shell nanoparticle. Methods of preparing the volume-labeled nanoparticles are also described.

Example Owner/Operator Certification for Painter Training Under National Emission Standards for Hazardous Air Pollutant (NESHAP): Area Source Standards for Nine Metal Fabrication and Finishing Source Categories 40 CFR 60 Subpart XXXXXX

EPA Pesticide Factsheets

This example training certification format and any attached training documentation may be used to demonstrate, document and certify successful completion of required training topics under 40 CFR 63.11515(d)(6) for personnel, who spray apply surface coating

Challenges in Measuring Benefit of Clinical Research Training Programs--the ASH Clinical Research Training Institute Example.

PubMed

Sung, Lillian; Crowther, Mark; Byrd, John; Gitlin, Scott D; Basso, Joe; Burns, Linda

2015-12-01

The American Society of Hematology developed the Clinical Research Training Institute (CRTI) to address the lack of training in patient-oriented research among hematologists. As the program continues, we need to consider metrics for measuring the benefits of such a training program. This article addresses the benefits of clinical research training programs. The fundamental and key components are education and mentorship. However, there are several other benefits including promotion of collaboration, job and advancement opportunities, and promotion of work-life balance. The benefits of clinical research training programs need to be measured so that funders and society can judge if they are worth the investment in time and resources. Identification of elements that are important to program benefit is essential to measuring the benefit of the program as well as program planning. Future work should focus on the constructs which contribute to benefits of clinical research training programs such as CRTI.

77 FR 38177 - TRICARE; Off-Label Uses of Devices; Partial List of Examples of Unproven Drugs, Devices, Medical...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-27

... medical literature, national organizations, or technology assessment bodies that the off-label use is safe... medical literature, national organizations, or technology assessment bodies that the off-label use is safe.... Due to the rapid and extensive changes in medical technology it is not feasible to maintain this list...

Category labels versus feature labels: category labels polarize inferential predictions.

PubMed

Yamauchi, Takashi; Yu, Na-Yung

2008-04-01

What makes category labels different from feature labels in predictive inference? This study suggests that category labels tend to make inductive reasoning polarized and homogeneous. In two experiments, participants were shown two schematic pictures of insects side by side and predicted the value of a hidden feature of one insect on the basis of the other insect. Arbitrary verbal labels were shown above the two pictures, and the meanings of the labels were manipulated in the instructions. In one condition, the labels represented the category membership of the insects, and in the other conditions, the same labels represented attributes of the insects. When the labels represented category membership, participants' responses became substantially polarized and homogeneous, indicating that the mere reference to category membership can modify reasoning processes.

Cross-Age Peer Tutors in Asynchronous Discussion Groups: Studying the Impact of Tutors Labelling Their Interventions

ERIC Educational Resources Information Center

De Smet, M.; Van Keer, H.; Valcke, M.

2008-01-01

Cross-age tutors were randomly assigned to one of the three tutor training conditions distinguished for the current study: (1) the labelling experimental condition, characterized by requirements to label their tutor interventions, based on the e-moderating model of Salmon; (2) the non-labelling experimental condition, focusing on tutor's acting…

Estradiol treatment in preadolescent females enhances adolescent spatial memory and differentially modulates hippocampal region-specific phosphorylated ERK labeling.

PubMed

Wartman, Brianne C; Keeley, Robin J; Holahan, Matthew R

2012-10-24

Estrogen levels in rats are positively correlated with enhanced memory function and hippocampal dendritic spine density. There is much less work on the long-term effects of estradiol manipulation in preadolescent rats. The present work examined how injections of estradiol during postnatal days 19-22 (p19-22; preadolescence) affected water maze performance and hippocampal phosphorylated ERK labeling. To investigate this, half of the estradiol- and vehicle-treated female rats were trained on a water maze task 24h after the end of estradiol treatment (p23-27) while the other half was not trained. All female rats were tested on the water maze from p40 to p44 (adolescence) and hippocampal pERK1/2 labeling was assessed as a putative marker of neuronal plasticity. During adolescence, preadolescent-trained groups showed lower latencies than groups without preadolescent training. Retention data revealed lower latencies in both estradiol groups, whether preadolescent trained or not. Immunohistochemical detection of hippocampal pERK1/2 revealed elevations in granule cell labeling associated with the preadolescent trained groups and reductions in CA1 labeling associated with estradiol treatment. These results show a latent beneficial effect of preadolescent estradiol treatment on adolescent spatial performance and suggest an organizational effect of prepubescent exogenously applied estradiol. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning

PubMed Central

Goerner-Potvin, Patricia; Morin, Andreanne; Shao, Xiaojian; Pastinen, Tomi

2017-01-01

Motivation: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks. The main idea is to manually label a small subset of the genome, and then learn a model that makes consistent peak predictions on the rest of the genome. Results: We created 7 new histone mark datasets with 12 826 visually determined labels, and analyzed 3 existing transcription factor datasets. We observed that default peak detection parameters yield high false positive rates, which can be reduced by learning parameters using a relatively small training set of labeled data from the same experiment type. We also observed that labels from different people are highly consistent. Overall, these data indicate that our supervised labeling method is useful for quantitatively training and testing peak detection algorithms. Availability and Implementation: Labeled histone mark data http://cbio.ensmp.fr/~thocking/chip-seq-chunk-db/, R package to compute the label error of predicted peaks https://github.com/tdhock/PeakError Contacts: toby.hocking@mail.mcgill.ca or guil.bourque@mcgill.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27797775

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

PubMed

Hocking, Toby Dylan; Goerner-Potvin, Patricia; Morin, Andreanne; Shao, Xiaojian; Pastinen, Tomi; Bourque, Guillaume

2017-02-15

Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks. The main idea is to manually label a small subset of the genome, and then learn a model that makes consistent peak predictions on the rest of the genome. We created 7 new histone mark datasets with 12 826 visually determined labels, and analyzed 3 existing transcription factor datasets. We observed that default peak detection parameters yield high false positive rates, which can be reduced by learning parameters using a relatively small training set of labeled data from the same experiment type. We also observed that labels from different people are highly consistent. Overall, these data indicate that our supervised labeling method is useful for quantitatively training and testing peak detection algorithms. Labeled histone mark data http://cbio.ensmp.fr/~thocking/chip-seq-chunk-db/ , R package to compute the label error of predicted peaks https://github.com/tdhock/PeakError. toby.hocking@mail.mcgill.ca or guil.bourque@mcgill.ca. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

PubMed

Cocos, Anne; Fiks, Alexander G; Masino, Aaron J

2017-07-01

Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system

NASA Astrophysics Data System (ADS)

Rajwa, Bartek; Dundar, M. Murat; Akova, Ferit; Patsekin, Valery; Bae, Euiwon; Tang, Yanjie; Dietz, J. Eric; Hirleman, E. Daniel; Robinson, J. Paul; Bhunia, Arun K.

2011-06-01

The majority of tools for pathogen sensing and recognition are based on physiological or genetic properties of microorganisms. However, there is enormous interest in devising label-free and reagentless biosensors that would operate utilizing the biophysical signatures of samples without the need for labeling and reporting biochemistry. Optical biosensors are closest to realizing this goal and vibrational spectroscopies are examples of well-established optical label-free biosensing techniques. A recently introduced forward-scatter phenotyping (FSP) also belongs to the broad class of optical sensors. However, in contrast to spectroscopies, the remarkable specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and/or serovar. Both FSP technology and spectroscopies rely on statistical machine learning to perform recognition and classification. However, the commonly used methods utilize either simplistic unsupervised learning or traditional supervised techniques that assume completeness of training libraries. This restrictive assumption is known to be false for real-life conditions, resulting in unsatisfactory levels of accuracy, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates preliminary studies on the use of FSP system to classify selected serotypes of non-O157 Shiga toxin-producing E. coli in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated and distributed detection of unknown bacterial classes.

The EuroSprite2005 Observational Campaign: an example of training and outreach opportunities for CAL young scientists

NASA Astrophysics Data System (ADS)

Chanrion, O.; Crosby, N. B.; Arnone, E.; Boberg, F.; van der Velde, O.; Odzimek, A.; Mika, Á.; Enell, C.-F.; Berg, P.; Ignaccolo, M.; Steiner, R. J.; Laursen, S.; Neubert, T.

2007-07-01

The four year "Coupling of Atmospheric Layers (CAL)" EU FP5 Research Training Network project studied unanswered questions related to transient luminous events (sprites, jets and elves) in the upper atmosphere. Consisting of ten scientific work-packages CAL also included intensive training and outreach programmes for the young scientists hired. Educational activities were based on the following elements: national PhD programmes, activities at CAL and other meetings, a dedicated summer school, and two European sprite observational campaigns. The young scientists were strongly involved in the latter and, as an example, the "EuroSprite2005" observational campaign is presented in detail. Some of the young scientists participated in the instrument set-up, others in the campaign logistics, some coordinated the observations, and others gathered the results to build a catalogue. During the four-month duration of this campaign, all of them took turns in operating the system and making their own night observations. The ongoing campaign activities were constantly advertised and communicated via an Internet blog. In summary the campaign required all the CAL young scientists to embark on experimental work, to develop their organisational skills, and to enhance their ability to communicate their activities. The campaign was a unique opportunity to train and strengthen skills that will be an asset to their future careers and, overall, was most successful.

Labeling strategies to overcome the problem of niche markets for sustainable milk products: The example of pasture-raised milk.

PubMed

Kühl, S; Gassler, B; Spiller, A

2017-06-01

Pasture-raised milk is gaining in importance in some European countries and in the United States. The production of pasture-raised milk is linked to higher costs, as the milk is normally collected and processed separately from conventional barn milk. This could hinder the production of sustainable milk products. We discuss alternative labeling strategies that allow the mixing of pasture-raised (sustainable) and conventional milk to reduce costs and break free from the current niche market. The lower price would allow for more pasture-raised milk to be produced and enter the mainstream market. The aim of this study was to analyze consumers' willingness to pay for alternative labeling types using a discrete choice experiment with 1,065 German milk buyers. The 2 alternative labels, besides the classical labeling approach, are based on the mass balance approach (at least 50% pasture-raised milk in a package) and cause-related marketing (support of farmers who keep their cows on pasture). The discrete choice experiment was combined with a cluster analysis to get a deeper understanding of the buying behavior of the diverse consumer segments for milk. We found that all consumer groups prefer the classical label where products are segregated but also understand the benefits of cause-related marketing. The average consumer was willing to pay €0.50 more for pasture-raised milk certified with the classical label and €0.38 more for pasture-raised milk labeled with a cause-related marketing claim. However, differences between the clusters are strong: The smallest cluster of ethically involved consumers (15%) is willing to pay the highest premiums, especially for the classical label. Cause-related marketing is an interesting alternative for involved buyers under price pressure (41%), whereas the mass balance approach is little understood and thus less valued by consumers. From our results we concluded that cause-related marketing (in our case, the support of pasturing of

Protein specific fluorescent microspheres for labelling a protein

NASA Technical Reports Server (NTRS)

Rembaum, Alan (Inventor)

1982-01-01

Highly fluorescent, stable and biocompatible microspheres are obtained by copolymerizing an acrylic monomer containing a covalent bonding group such as hydroxyl, amine or carboxyl, for example, hydroxyethylmethacrylate, with an addition polymerizable fluorescent comonomer such as dansyl allyl amine. A lectin or antibody is bound to the covalent site to provide cell specificity. When the microspheres are added to a cell suspension the marked microspheres will specifically label a cell membrane by binding to a specific receptor site thereon. The labeled membrane can then be detected by fluorescence of the fluorescent monomer.

Learning communication from erroneous video-based examples: A double-blind randomised controlled trial.

PubMed

Schmitz, Felix Michael; Schnabel, Kai Philipp; Stricker, Daniel; Fischer, Martin Rudolf; Guttormsen, Sissel

2017-06-01

Appropriate training strategies are required to equip undergraduate healthcare students to benefit from communication training with simulated patients. This study examines the learning effects of different formats of video-based worked examples on initial communication skills. First-year nursing students (N=36) were randomly assigned to one of two experimental groups (correct v. erroneous examples) or to the control group (no examples). All the groups were provided an identical introduction to learning materials on breaking bad news; the experimental groups also received a set of video-based worked examples. Each example was accompanied by a self-explanation prompt (considering the example's correctness) and elaborated feedback (the true explanation). Participants presented with erroneous examples broke bad news to a simulated patient significantly more appropriately than students in the control group. Additionally, they tended to outperform participants who had correct examples, while participants presented with correct examples tended to outperform the control group. The worked example effect was successfully adapted for learning in the provider-patient communication domain. Implementing video-based worked examples with self-explanation prompts and feedback can be an effective strategy to prepare students for their training with simulated patients, especially when examples are erroneous. Copyright © 2017 Elsevier B.V. All rights reserved.

Joint Labeling Of Multiple Regions of Interest (Rois) By Enhanced Auto Context Models.

PubMed

Kim, Minjeong; Wu, Guorong; Guo, Yanrong; Shen, Dinggang

2015-04-01

Accurate segmentation of a set of regions of interest (ROIs) in the brain images is a key step in many neuroscience studies. Due to the complexity of image patterns, many learning-based segmentation methods have been proposed, including auto context model (ACM) that can capture high-level contextual information for guiding segmentation. However, since current ACM can only handle one ROI at a time, neighboring ROIs have to be labeled separately with different ACMs that are trained independently without communicating each other. To address this, we enhance the current single-ROI learning ACM to multi-ROI learning ACM for joint labeling of multiple neighboring ROIs (called e ACM). First, we extend current independently-trained single-ROI ACMs to a set of jointly-trained cross-ROI ACMs, by simultaneous training of ACMs for all spatially-connected ROIs to let them to share their respective intermediate outputs for coordinated labeling of each image point. Then, the context features in each ACM can capture the cross-ROI dependence information from the outputs of other ACMs that are designed for neighboring ROIs. Second, we upgrade the output labeling map of each ACM with the multi-scale representation, thus both local and global context information can be effectively used to increase the robustness in characterizing geometric relationship among neighboring ROIs. Third, we integrate ACM into a multi-atlases segmentation paradigm, for encompassing high variations among subjects. Experiments on LONI LPBA40 dataset show much better performance by our e ACM, compared to the conventional ACM.

ALE: automated label extraction from GEO metadata.

PubMed

Giles, Cory B; Brown, Chase A; Ripperger, Michael; Dennis, Zane; Roopnarinesingh, Xiavan; Porter, Hunter; Perz, Aleksandra; Wren, Jonathan D

2017-12-28

NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.

Development of a general methodology for labelling peptide-morpholino oligonucleotide conjugates using alkyne-azide click chemistry.

PubMed

Shabanpoor, Fazel; Gait, Michael J

2013-11-11

We describe a general methodology for fluorescent labelling of peptide conjugates of phosphorodiamidate morpholino oligonucleotides (PMOs) by alkyne functionalization of peptides, subsequent conjugation to PMOs and labelling with a fluorescent compound (Cy5-azide). Two peptide-PMO (PPMO) examples are shown. No detrimental effect of such labelled PMOs was seen in a biological assay.

18F-Labeling of Sensitive Biomolecules for Positron Emission Tomography

PubMed Central

Krishnan, Hema S.; Ma, Longle; Vasdev, Neil; Liang, Steven H.

2017-01-01

Positron emission tomography (PET) imaging study of fluorine-18 labeled biomolecules is an emerging and rapidly growing area for preclinical and clinical research. The present review focuses on recent advances in radiochemical methods for incorporating fluorine-18 into biomolecules via ‘direct’ or ‘indirect’ bioconjugation. Recently developed prosthetic groups and pre-targeting strategies, as well as representative examples in 18F-labeling of biomolecules in PET imaging research studies are highlighted. PMID:28704575

Label-free functional nucleic acid sensors for detecting target agents

DOEpatents

Lu, Yi; Xiang, Yu

2015-01-13

A general methodology to design label-free fluorescent functional nucleic acid sensors using a vacant site approach and an abasic site approach is described. In one example, a method for designing label-free fluorescent functional nucleic acid sensors (e.g., those that include a DNAzyme, aptamer or aptazyme) that have a tunable dynamic range through the introduction of an abasic site (e.g., dSpacer) or a vacant site into the functional nucleic acids. Also provided is a general method for designing label-free fluorescent aptamer sensors based on the regulation of malachite green (MG) fluorescence. A general method for designing label-free fluorescent catalytic and molecular beacons (CAMBs) is also provided. The methods demonstrated here can be used to design many other label-free fluorescent sensors to detect a wide range of analytes. Sensors and methods of using the disclosed sensors are also provided.

Putting the "th" in Tenths: Providing Place-Value Labels Helps Reveal the Structure of Our Base-10 Numeral System

ERIC Educational Resources Information Center

Loehr, Abbey M.; Rittle-Johnson, Bethany

2017-01-01

Research has demonstrated that providing labels helps children notice key features of examples. Much less is known about how different labels impact children's ability to make inferences about the structure underlying mathematical notation. We tested the impact of labeling decimals such as 0.34 using formal place-value labels ("3 tenths and 4…

Statistical label fusion with hierarchical performance models

PubMed Central

Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

2014-01-01

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

99mTc: Labeling Chemistry and Labeled Compounds

NASA Astrophysics Data System (ADS)

Alberto, R.; Abram, U.

This chapter reviews the radiopharmaceutical chemistry of technetium related to the synthesis of perfusion agents and to the labeling of receptor-binding biomolecules. To understand the limitations of technetium chemistry imposed by future application of the complexes in nuclear medicine, an introductory section analyzes the compulsory requirements to be considered when facing the incentive of introducing a novel radiopharmaceutical into the market. Requirements from chemistry, routine application, and market are discussed. In a subsequent section, commercially available 99mTc-based radiopharmaceuticals are treated. It covers the complexes in use for imaging the most important target organs such as heart, brain, or kidney. The commercially available radiopharmaceuticals fulfill the requirements outlined earlier and are discussed with this background. In a following section, the properties and perspectives of the different generations of radiopharmaceuticals are described in a general way, covering characteristics for perfusion agents and for receptor-specific molecules. Technetium chemistry for the synthesis of perfusion agents and the different labeling approaches for target-specific biomolecules are summarized. The review comprises a general introduction to the common approaches currently in use, employing the N x S4-x , [3+1] and 2-hydrazino-nicotinicacid (HYNIC) method as well as more recent strategies such as the carbonyl and the TcN approach. Direct labeling without the need of a bifunctional chelator is briefly reviewed as well. More particularly, recent developments in the labeling of concrete targeting molecules, the second generation of radiopharmaceuticals, is then discussed and prominent examples with antibodies/peptides, neuroreceptor targeting small molecules, myocardial imaging agents, vitamins, thymidine, and complexes relevant to multidrug resistance are given. In addition, a new approach toward peptide drug development is described. The section

D Semantic Labeling of ALS Data Based on Domain Adaption by Transferring and Fusing Random Forest Models

NASA Astrophysics Data System (ADS)

Wu, J.; Yao, W.; Zhang, J.; Li, Y.

2018-04-01

Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

PubMed Central

Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

2015-01-01

For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

Rapid Training of Information Extraction with Local and Global Data Views

DTIC Science & Technology

2012-05-01

56 xiii 4.1 An example of words and their bit string representations. Bold ones are transliterated Arabic words...Natural Language Processing ( NLP ) community faces new tasks and new domains all the time. Without enough labeled data of a new task or a new domain to...conduct supervised learning, semi-supervised learning is particularly attractive to NLP researchers since it only requires a handful of labeled examples

Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. PMID:19888348

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Spine labeling in MRI via regularized distribution matching.

PubMed

Hojjat, Seyed-Parsa; Ayed, Ismail; Garvin, Gregory J; Punithakumar, Kumaradevan

2017-11-01

This study investigates an efficient (nearly real-time) two-stage spine labeling algorithm that removes the need for an external training while being applicable to different types of MRI data and acquisition protocols. Based solely on the image being labeled (i.e., we do not use training data), the first stage aims at detecting potential vertebra candidates following the optimization of a functional containing two terms: (i) a distribution-matching term that encodes contextual information about the vertebrae via a density model learned from a very simple user input, which amounts to a point (mouse click) on a predefined vertebra; and (ii) a regularization constraint, which penalizes isolated candidates in the solution. The second stage removes false positives and identifies all vertebrae and discs by optimizing a geometric constraint, which embeds generic anatomical information on the interconnections between neighboring structures. Based on generic knowledge, our geometric constraint does not require external training. We performed quantitative evaluations of the algorithm over a data set of 90 mid-sagittal MRI images of the lumbar spine acquired from 45 different subjects. To assess the flexibility of the algorithm, we used both T1- and T2-weighted images for each subject. A total of 990 structures were automatically detected/labeled and compared to ground-truth annotations by an expert. On the T2-weighted data, we obtained an accuracy of 91.6% for the vertebrae and 89.2% for the discs. On the T1-weighted data, we obtained an accuracy of 90.7% for the vertebrae and 88.1% for the discs. Our algorithm removes the need for external training while being applicable to different types of MRI data and acquisition protocols. Based on the current testing data, a subject-specific model density and generic anatomical information, our method can achieve competitive performances when applied to T1- and T2-weighted MRI images.

18 F-Labeling of Sensitive Biomolecules for Positron Emission Tomography.

PubMed

Krishnan, Hema S; Ma, Longle; Vasdev, Neil; Liang, Steven H

2017-11-07

Positron emission tomography (PET) imaging study of fluorine-18 labeled biomolecules is an emerging and rapidly growing area for preclinical and clinical research. The present review focuses on recent advances in radiochemical methods for incorporating fluorine-18 into biomolecules via "direct" or "indirect" bioconjugation. Recently developed prosthetic groups and pre-targeting strategies, as well as representative examples in 18 F-labeling of biomolecules in PET imaging research studies are highlighted. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Fact Sheet on Training and Exam Options for Pesticide Applicators

EPA Pesticide Factsheets

New pesticide label requirements for training protect applicators, other fumigant handlers and bystanders from soil fumigant exposures. Find criteria details and content check list for approval of training programs.

A Label Propagation Approach for Detecting Buried Objects in Handheld GPR Data

DTIC Science & Technology

2016-04-17

regions of interest that correspond to locations with anomalous signatures. Second, a classifier (or an ensemble of classifiers ) is used to assign a...investigated for almost two decades and several classifiers have been developed. Most of these methods are based on the supervised learning paradigm where...labeled target and clutter signatures are needed to train a classifier to discriminate between the two classes. Typically, large and diverse labeled

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.

Team Training (Training at Own Facility) versus Individual Surgeon's Training (Training at Trainer's Facility) When Implementing a New Surgical Technique: Example from the ONSTEP Inguinal Hernia Repair

PubMed Central

Laursen, Jannie

2014-01-01

Background. When implementing a new surgical technique, the best method for didactic learning has not been settled. There are basically two scenarios: the trainee goes to the teacher's clinic and learns the new technique hands-on, or the teacher goes to the trainee's clinic and performs the teaching there. Methods. An informal literature review was conducted to provide a basis for discussing pros and cons. We also wanted to discuss how many surgeons can be trained in a day and the importance of the demand for a new surgical procedure to ensure a high adoption rate and finally to apply these issues on a discussion of barriers for adoption of the new ONSTEP technique for inguinal hernia repair after initial training. Results and Conclusions. The optimal training method would include moving the teacher to the trainee's department to obtain team-training effects simultaneous with surgical technical training of the trainee surgeon. The training should also include a theoretical presentation and discussion along with the practical training. Importantly, the training visit should probably be followed by a scheduled visit to clear misunderstandings and fine-tune the technique after an initial self-learning period. PMID:25506078

Fusing Continuous-Valued Medical Labels Using a Bayesian Model.

PubMed

Zhu, Tingting; Dunkley, Nic; Behar, Joachim; Clifton, David A; Clifford, Gari D

2015-12-01

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator (BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78 ± 0.63 ms, significantly outperforming the best Challenge entry (15.37 ± 2.13 ms) as well as the EM, mean, and median voting strategies (14.76 ± 0.52, 17.61 ± 0.55, and 14.43 ± 0.57 ms respectively with p < 0.0001). The BCLA could therefore provide accurate estimation for medical continuous-valued label tasks in an unsupervised manner even when the ground truth is not available.

Low-cost training technology

NASA Technical Reports Server (NTRS)

Lee, A. T.

1984-01-01

The differences between flight training technology and flight simulation technology are highlighted. Examples of training technologies are provided, including the Navy's training system and the interactive cockpit training device. Training problems that might arise in the near future are discussed. These challenges follow from the increased amount and variety of information that a pilot must have access to in the cockpit.

Effect of Worked Examples and Cognitive Tutor Training on Constructing Equations

ERIC Educational Resources Information Center

Reed, Stephen K.; Corbett, Albert; Hoffman, Bob; Wagner, Angela; MacLaren, Ben

2013-01-01

Algebra students studied either static-table, static-graphics, or interactive-graphics instructional worked examples that alternated with Algebra Cognitive Tutor practice problems. A control group did not study worked examples but solved both the instructional and practice problems on the Cognitive Tutor (CT). Students in the control group…

Structure of the knowledge base for an expert labeling system

NASA Technical Reports Server (NTRS)

Rajaram, N. S.

1981-01-01

One of the principal objectives of the NASA AgRISTARS program is the inventory of global crop resources using remotely sensed data gathered by Land Satellites (LANDSAT). A central problem in any such crop inventory procedure is the interpretation of LANDSAT images and identification of parts of each image which are covered by a particular crop of interest. This task of labeling is largely a manual one done by trained human analysts and consequently presents obstacles to the development of totally automated crop inventory systems. However, development in knowledge engineering as well as widespread availability of inexpensive hardware and software for artificial intelligence work offers possibilities for developing expert systems for labeling of crops. Such a knowledge based approach to labeling is presented.

The Cannon: A data-driven approach to Stellar Label Determination

NASA Astrophysics Data System (ADS)

Ness, M.; Hogg, David W.; Rix, H.-W.; Ho, Anna. Y. Q.; Zasowski, G.

2015-07-01

New spectroscopic surveys offer the promise of stellar parameters and abundances (“stellar labels”) for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the “known” labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with {T}{eff}, {log} g, and [{Fe}/{{H}}] as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.

Online Video-Based Training in the Use of Hydrologic Models: A Case Example Using SWAT

NASA Astrophysics Data System (ADS)

Frankenberger, J.

2009-12-01

Hydrologic models are increasingly important tools in public decision-making. For example, watershed models are used to develop Total Maximum Daily Load (TMDL) plans, quantify pollutant loads, and estimate the effects of watershed restoration efforts funded by the public. One widely-used tool is the Soil and Water Assessment Tool (SWAT), which has been applied by state and federal agencies, consultants, and university researchers to assess sources of nonpoint source pollution and the effects of potential solutions, and used in testimony in at least one lawsuit. The SWAT model has the capability to evaluate the relative effects of different management scenarios on water quality, sediment, and agricultural chemical yield at the watershed scale. As with all models, the model user and the decisions that s/he makes in the modeling process are important determinants of model performance. The SWAT model has an open structure, leaving most decisions up to the model user, which was especially appropriate when the model was primarily used in research by highly-experienced modelers. However, as the model has become more widely applied in planning and assessment, by people who may have limited hydrology background and modeling knowledge, the possibility that users may be using the model inconsistently or even incorrectly becomes a concern. Consistent training can lead to a minimum standard of knowledge that model users are expected to have, and therefore to higher use of best practices in modeling efforts. In addition, widespread availability of training can lead to better decisions about when and where using the model is appropriate, and what level of data needs to be available for confidence in predictions. Currently, most training in model use takes place in occasional face-to-face workshops, courses offered at a few universities, and a short tutorial available in the manual. Many new users simply acquire the model and learn from the manual, other users, trial and error

NEED FOR HARMONIZATION OF LABELING OF MEDICAL DEVICES: A REVIEW

PubMed Central

Songara, Raiendra K.; Sharma, Ganesh N.; Gupta, Vipul K.; Gupta, Promila

2010-01-01

Medical device labeling is any information associated with a device targeted to the patient or lay caregiver. It is intended to help assure that the device is used safely and effectively. Medical device labeling is supplied in many formats, for example, as patient brochures, patient leaflets, user manuals, and videotapes. The European commission has discussed a series of agreements with third countries, Australia, New Zealand, USA, Canada, Japan and Eastern European countries wishing to join the EU, concerning the mutual acceptance of inspection bodies, proof of conformity in connection with medical devices. Device labeling is exceedingly difficult for manufacturers for many reasons like regulations from government bodies to ensure compliance, increased competent authority surveillance, increased audits and language requirements. PMID:22247840

Label Information Guided Graph Construction for Semi-Supervised Learning.

PubMed

Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

2017-09-01

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Correlative fluorescence and electron microscopy of quantum dot labeled proteins on whole cells in liquid.

PubMed

Peckys, Diana B; Dukes, Madeline J; de Jonge, Niels

2014-01-01

Correlative fluorescence microscopy and scanning transmission electron microscopy (STEM) of cells fully immersed in liquid is a new methodology with many application areas. Proteins, in live cells immobilized on microchips, are labeled with fluorescent quantum dot (QD) nanoparticles. In this protocol, the epidermal growth factor receptor (EGFR) is labeled. The cells are fixed after a selected labeling time, for example, 5 min as needed to form EGFR dimers. The microchip with cells is then imaged with fluorescence microscopy. Thereafter, the microchip with the labeled cells and one with a spacer are assembled in a special microfluidic device and imaged with STEM.

Food for thought: obstacles to menu labelling in restaurants and cafeterias.

PubMed

Thomas, Erica

2016-08-01

Menu labelling is recommended as a policy intervention to reduce obesity and diet-related disease. The present commentary considers the many challenges the restaurant industry faces in providing nutrition information on its menus. Barriers include lack of nutrition expertise, time, cost, availability of nutrition information for exotic ingredients, ability to provide accurate nutrition information, libel risk, customer dissatisfaction, limited space on the menu, menu variations, loss of flexibility in changing the menu, staff training and resistance of employees to change current practice. Health promotion specialists and academics involved in fieldwork must help restaurateurs find solutions to these barriers for menu labelling interventions to be widely implemented and successful. Practical support for small independent restaurants such as free or subsidised nutrition analysis, nutrition training for staff and menu design may also be necessary to encourage voluntary participation.

Labeled Graph Kernel for Behavior Analysis.

PubMed

Zhao, Ruiqi; Martinez, Aleix M

2016-08-01

Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data.

Chemical biology-based approaches on fluorescent labeling of proteins in live cells.

PubMed

Jung, Deokho; Min, Kyoungmi; Jung, Juyeon; Jang, Wonhee; Kwon, Youngeun

2013-05-01

Recently, significant advances have been made in live cell imaging owing to the rapid development of selective labeling of proteins in vivo. Green fluorescent protein (GFP) was the first example of fluorescent reporters genetically introduced to protein of interest (POI). While GFP and various types of engineered fluorescent proteins (FPs) have been actively used for live cell imaging for many years, the size and the limited windows of fluorescent spectra of GFP and its variants set limits on possible applications. In order to complement FP-based labeling methods, alternative approaches that allow incorporation of synthetic fluorescent probes to target POIs were developed. Synthetic fluorescent probes are smaller than fluorescent proteins, often have improved photochemical properties, and offer a larger variety of colors. These synthetic probes can be introduced to POIs selectively by numerous approaches that can be largely categorized into chemical recognition-based labeling, which utilizes metal-chelating peptide tags and fluorophore-carrying metal complexes, and biological recognition-based labeling, such as (1) specific non-covalent binding between an enzyme tag and its fluorophore-carrying substrate, (2) self-modification of protein tags using substrate variants conjugated to fluorophores, (3) enzymatic reaction to generate a covalent binding between a small molecule substrate and a peptide tag, and (4) split-intein-based C-terminal labeling of target proteins. The chemical recognition-based labeling reaction often suffers from compromised selectivity of metal-ligand interaction in the cytosolic environment, consequently producing high background signals. Use of protein-substrate interactions or enzyme-mediated reactions generally shows improved specificity but each method has its limitations. Some examples are the presence of large linker protein, restriction on the choice of introducible probes due to the substrate specificity of enzymes, and competitive

Isotope labeling for studying RNA by solid-state NMR spectroscopy.

PubMed

Marchanka, Alexander; Kreutz, Christoph; Carlomagno, Teresa

2018-04-12

Nucleic acids play key roles in most biological processes, either in isolation or in complex with proteins. Often they are difficult targets for structural studies, due to their dynamic behavior and high molecular weight. Solid-state nuclear magnetic resonance spectroscopy (ssNMR) provides a unique opportunity to study large biomolecules in a non-crystalline state at atomic resolution. Application of ssNMR to RNA, however, is still at an early stage of development and presents considerable challenges due to broad resonances and poor dispersion. Isotope labeling, either as nucleotide-specific, atom-specific or segmental labeling, can resolve resonance overlaps and reduce the line width, thus allowing ssNMR studies of RNA domains as part of large biomolecules or complexes. In this review we discuss the methods for RNA production and purification as well as numerous approaches for isotope labeling of RNA. Furthermore, we give a few examples that emphasize the instrumental role of isotope labeling and ssNMR for studying RNA as part of large ribonucleoprotein complexes.

Flow-aggregated traffic-driven label mapping in label-switching networks

NASA Astrophysics Data System (ADS)

Nagami, Kenichi; Katsube, Yasuhiro; Esaki, Hiroshi; Nakamura, Osamu

1998-12-01

Label switching technology enables high performance, flexible, layer-3 packet forwarding based on the fixed length label information mapped to the layer-3 packet stream. A Label Switching Router (LSR) forwards layer-3 packets based on their label information mapped to the layer-3 address information as well as their layer-3 address information. This paper evaluates the required number of labels under traffic-driven label mapping policy using the real backbone traffic traces. The evaluation shows that the label mapping policy requires a large number of labels. In order to reduce the required number of labels, we propose a label mapping policy which is a traffic-driven label mapping for the traffic toward the same destination network. The evaluation shows that the proposed label mapping policy requires only about one tenth as many labels compared with the traffic-driven label mapping for the host-pair packet stream,and the topology-driven label mapping for the destination network packet stream.

Nutrition Label Viewing during a Food-Selection Task: Front-of-Package Labels vs Nutrition Facts Labels.

PubMed

Graham, Dan J; Heidrick, Charles; Hodgin, Katie

2015-10-01

Earlier research has identified consumer characteristics associated with viewing Nutrition Facts labels; however, little is known about those who view front-of-package nutrition labels. Front-of-package nutrition labels might appeal to more consumers than do Nutrition Facts labels, but it might be necessary to provide consumers with information about how to locate and use these labels. This study quantifies Nutrition Facts and front-of-package nutrition label viewing among American adult consumers. Attention to nutrition information was measured during a food-selection task. One hundred and twenty-three parents (mean age=38 years, mean body mass index [calculated as kg/m(2)]=28) and one of their children (aged 6 to 9 years) selected six foods from a university laboratory-turned-grocery aisle. Participants were randomized to conditions in which front-of-package nutrition labels were present or absent, and signage explaining front-of-package nutrition labels was present or absent. Adults' visual attention to Nutrition Facts labels and front-of-package nutrition labels was objectively measured via eye-tracking glasses. To examine whether there were significant differences in the percentages of participants who viewed Nutrition Facts labels vs front-of-package nutrition labels, McNemar's tests were conducted across all participants, as well as within various sociodemographic categories. To determine whether hypothesized factors, such as health literacy and education, had stronger relationships with front-of-package nutrition label vs Nutrition Facts label viewing, linear regression assessed the magnitude of relationships between theoretically and empirically derived factors and each type of label viewing. Overall, front-of-package nutrition labels were more likely to be viewed than Nutrition Facts labels; however, for all subgroups, higher rates of front-of-package nutrition label viewership occurred only when signage was present drawing attention to the presence and

The influence of false color infrared display on training field identification. [for crop inventories

NASA Technical Reports Server (NTRS)

Coberly, W. A.; Tubbs, J. D.; Odell, P. L.

1979-01-01

The overall success of large-scale crop inventories of agricultural regions using Landsat multispectral scanner data is highly dependent upon the labeling of training data by analyst/photointerpreters. The principal analyst tool in labeling training data is a false color infrared composite of Landsat bands 4, 5, and 7. In this paper, this color display is investigated and its influence upon classification errors is partially determined.

Relevant, irredundant feature selection and noisy example elimination.

PubMed

Lashkia, George V; Anthony, Laurence

2004-04-01

In many real-world situations, the method for computing the desired output from a set of inputs is unknown. One strategy for solving these types of problems is to learn the input-output functionality from examples in a training set. However, in many situations it is difficult to know what information is relevant to the task at hand. Subsequently, researchers have investigated ways to deal with the so-called problem of consistency of attributes, i.e., attributes that can distinguish examples from different classes. In this paper, we first prove that the notion of relevance of attributes is directly related to the consistency of attributes, and show how relevant, irredundant attributes can be selected. We then compare different relevant attribute selection algorithms, and show the superiority of algorithms that select irredundant attributes over those that select relevant attributes. We also show that searching for an "optimal" subset of attributes, which is considered to be the main purpose of attribute selection, is not the best way to improve the accuracy of classifiers. Employing sets of relevant, irredundant attributes improves classification accuracy in many more cases. Finally, we propose a new method for selecting relevant examples, which is based on filtering the so-called pattern frequency domain. By identifying examples that are nontypical in the determination of relevant, irredundant attributes, irrelevant examples can be eliminated prior to the learning process. Empirical results using artificial and real databases show the effectiveness of the proposed method in selecting relevant examples leading to improved performance even on greatly reduced training sets.

An Example for Portfolio Preparation in German Teacher Training

ERIC Educational Resources Information Center

Arak, Hüseyin

2017-01-01

In this study we are trying with the help of portfolio in teacher training and the diagnosis of the learning group concerning their skills in translation from German to Turkish, to show the documentation of the learning process. The portfolio provides a good overview about the performance of the students and it also prepares a basis for…

Labelling chronic illness in primary care: a good or a bad thing?

PubMed

Bedson, John; McCarney, Rob; Croft, Peter

2004-12-01

Traditionally the management of any chronic condition starts with its diagnosis. The labelling of disease can be beneficial in terms of defining appropriate treatment such as in coronary artery disease. However, sometimes it may be detrimental such as when x-rays are used to diagnose lumbar spondylosis leading to patients inappropriately limiting their activity. Chronic knee pain in the elderly is another example where applying labels is problematical. A common diagnosis in this situation is osteoarthritis, but this label can be applied in two ways: as a radiological diagnosis, or as a clinical one. The x-ray diagnosis, however, does not equate with the clinical syndrome, and vice versa. In addition, diagnosing knee pain as osteoarthritis does not necessarily help in management, since a patient's debility is more dependent upon their clinical signs and symptoms than the presence of radiographic osteoarthritis, and by the same token its clinical counterpart. GPs are consistent in their management of knee pain, but in attempting to diagnose the pain as osteoarthritis, these plans can alter and become more dependent on the actual diagnosis than the clinical picture. As a result management may well diverge from what the current best evidence supports. Diagnosis for diagnosis sake, should therefore be discouraged, and chronic knee pain gives us one example of why this is the case. GPs would be better placed to manage this condition if it was considered more as a regional pain syndrome, perhaps defining it simply as 'chronic knee pain in older people'. This example suggests that there is a pressing need in primary care to carefully consider in chronic disease when it is appropriate to be definitive in diagnosis such that when using disease specific labels, there is definite benefit for the patient and doctor.

Hazardous Materials Emergency Response Training: The Colorado Training Institute. Innovations.

ERIC Educational Resources Information Center

Cole, Leslie

The Colorado Training Institute (CTI), established in 1980, is a non-profit, instructional program devoted to promoting hazardous materials safety through education. It has trained over 3,000 emergency response personnel and industry officials and is a unique example of the private and public sectors working together to protect the public from…

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

A resource-saving collective approach to biomedical semantic role labeling

PubMed Central

2014-01-01

Background Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. Results We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. Conclusions This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway

South African farm workers' interpretation of risk assessment data expressed as pictograms on pesticide labels

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

Rother, Hanna-Andrea

Pesticide companies and regulators in developing countries use the United Nations Food and Agricultural Organization (FAO) recommended pictograms on pesticide labels to communicate risk information based on toxicological and environmental risk assessment data. The pesticide label not only is often the only access people have to pesticide risk information, but also in many countries is a legally binding document. As a result of the crucial role pesticide labels play in protecting health and the environment and as a legal instrument, pictograms are used to overcome literacy challenges in transmitting pesticide risk information. Yet, this risk communication tool is often pronemore » to misinterpretations of the risk information which results in hazardous exposures to pesticides for farm workers and end-users generally. In this paper, results are presented from a study with 115 farm workers on commercial vineyards in the Western Cape, South Africa, assessing their interpretations of 10 commonly used pictograms. A standardized questionnaire based on four commonly used pesticide labels was administered. Overall, 50% or more of the study farm workers had misleading, incorrect and critically confused interpretations of the label pictograms. Interpretations often reflected farm workers' social and cultural frames of reference rather than the technically intended risk information. For example, the pictogram indicating a pesticide's toxicity requires boots must be worn, evoked interpretations of 'dangerous to pedestrians' and 'don't walk through pesticides'. Furthermore, there was a gender variation in pictogram comprehension whereby males generally had more correct interpretations than females. This is a result both of a lack of training for women who are assumed to not work with pesticides, as well as a lack of pictograms relevant for female exposures. These findings challenge the viability of the United Nations current initiative to globally harmonize pictograms used on

An Oracle-based co-training framework for writer identification in offline handwriting

NASA Astrophysics Data System (ADS)

Porwal, Utkarsh; Rajan, Sreeranga; Govindaraju, Venu

2012-01-01

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance of the system for writer identification. Machine learning algorithms have been used extensively to improve the accuracy of such system assuming sufficient amount of data is available for training. Little attention has been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along with improving the accuracy of the system. However, performance of co-training based framework is dependent on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for selection statistically learns the class distribution and predicts the most probable class unlike traditional selection algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM dataset and illustrate the efficacy of the proposed approach.

42 CFR 414.930 - Compendia for determination of medically-accepted indications for off-label uses of drugs and...

Code of Federal Regulations, 2010 CFR

2010-10-01

... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. 414.930... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. (a... specialty compendium, for example a compendium of anti-cancer treatment. A compendium— (i) Includes a...

42 CFR 414.930 - Compendia for determination of medically-accepted indications for off-label uses of drugs and...

Code of Federal Regulations, 2011 CFR

2011-10-01

... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. 414.930... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. (a... specialty compendium, for example a compendium of anti-cancer treatment. A compendium— (i) Includes a...

42 CFR 414.930 - Compendia for determination of medically-accepted indications for off-label uses of drugs and...

Code of Federal Regulations, 2013 CFR

2013-10-01

... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. 414.930... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. (a... specialty compendium, for example a compendium of anti-cancer treatment. A compendium— (i) Includes a...

42 CFR 414.930 - Compendia for determination of medically-accepted indications for off-label uses of drugs and...

Code of Federal Regulations, 2012 CFR

2012-10-01

... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. 414.930... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. (a... specialty compendium, for example a compendium of anti-cancer treatment. A compendium— (i) Includes a...

42 CFR 414.930 - Compendia for determination of medically-accepted indications for off-label uses of drugs and...

Code of Federal Regulations, 2014 CFR

2014-10-01

... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. 414.930... indications for off-label uses of drugs and biologicals in an anti-cancer chemotherapeutic regimen. (a... specialty compendium, for example a compendium of anti-cancer treatment. A compendium— (i) Includes a...

Cross-Training: The Tactical View.

ERIC Educational Resources Information Center

Kaeter, Margaret

1993-01-01

Discusses the advantages of and problems associated with cross-training. Looks at the issue of remuneration and offers examples of how two companies that cross-train presently pay their employees. (JOW)

Using virtual data for training deep model for hand gesture recognition

NASA Astrophysics Data System (ADS)

Nikolaev, E. I.; Dvoryaninov, P. V.; Lensky, Y. Y.; Drozdovsky, N. S.

2018-05-01

Deep learning has shown real promise for the classification efficiency for hand gesture recognition problems. In this paper, the authors present experimental results for a deeply-trained model for hand gesture recognition through the use of hand images. The authors have trained two deep convolutional neural networks. The first architecture produces the hand position as a 2D-vector by input hand image. The second one predicts the hand gesture class for the input image. The first proposed architecture produces state of the art results with an accuracy rate of 89% and the second architecture with split input produces accuracy rate of 85.2%. In this paper, the authors also propose using virtual data for training a supervised deep model. Such technique is aimed to avoid using original labelled images in the training process. The interest of this method in data preparation is motivated by the need to overcome one of the main challenges of deep supervised learning: using a copious amount of labelled data during training.

Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning

NASA Astrophysics Data System (ADS)

Xue, Di-Xiu; Zhang, Rong; Zhao, Yuan-Yuan; Xu, Jian-Ming; Wang, Ya-Lei

2017-07-01

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.

Investigation of RNA Synthesis Using 5-Bromouridine Labelling and Immunoprecipitation.

PubMed

Kofoed, Rikke H; Betzer, Cristine; Lykke-Andersen, Søren; Molska, Ewa; Jensen, Poul H

2018-05-03

When steady state RNA levels are compared between two conditions, it is not possible to distinguish whether changes are caused by alterations in production or degradation of RNA. This protocol describes a method for measurement of RNA production, using 5-Bromouridine labelling of RNA followed by immunoprecipitation, which enables investigation of RNA synthesized within a short timeframe (e.g., 1 h). The advantage of 5-Bromouridine-labelling and immunoprecipitation over the use of toxic transcriptional inhibitors, such as α-amanitin and actinomycin D, is that there are no or very low effects on cell viability during short-term use. However, because 5-Bromouridine-immunoprecipitation only captures RNA produced within the short labelling time, slowly produced as well as rapidly degraded RNA can be difficult to measure by this method. The 5-Bromouridine-labelled RNA captured by 5-Bromouridine-immunoprecipitation can be analyzed by reverse transcription, quantitative polymerase chain reaction, and next generation sequencing. All types of RNA can be investigated, and the method is not limited to measuring mRNA as is presented in this example.

The evaluation of an open source online training system for teaching 12 lead electrocardiographic interpretation.

PubMed

Breen, Cathal; Zhu, Tingting; Bond, Raymond; Finlay, Dewar; Clifford, Gari

2016-01-01

The aim of this study is to present and evaluate the integration of a low resource JavaScript based ECG training interface (CrowdLabel) and a standardised curriculum for self-guided tuition in ECG interpretation. Participants practiced interpreting ECGs weekly using the CrowdLabel interface to assist with the learning of the traditional didactic taught course material during a 6 week training period. To determine competency students were tested during week 7. A total of 245 unique ECG cases were submitted by each student. Accuracy scores during the training period ranged from 0-59.5% (median = 33.3%). Conversely accuracy scores during the test ranged from 30 - 70% (median = 37.5%) (p < 0.05). There was no correlation between students who interpreted high numbers of ECGs during the training period and their marks obtained. CrowdLabel is shown to be a readily accessible dedicated learning platform to support ECG interpretation competency. Copyright © 2016 Elsevier Inc. All rights reserved.

Highly enriched multiply-labeled stable isotopic compounds as atmospheric tracers

DOEpatents

Goldblatt, M.; McInteer, B.B.

1974-01-29

Compounds multiply-labeled with stable isotopes and highly enriched in these isotopes are readily capable of detection in tracer experiments involving high dilutions. Thus, for example, /sup 13/C/sup 18/O/sub 2/ provides a useful tracer for following atmospheric pol lution produced as a result of fossil fuel burning. (Official Gazette)

Off-label use of atypical antipsychotics: cause for concern?

PubMed

McKean, Andrew; Monasterio, Erik

2012-05-01

Licensed indications for medicines were designed to regulate the claims that can be made about a medicine by a pharmaceutical company. Off-label prescribing (i.e. prescribing a drug for an indication outside of that for which it is licensed) is legal and an integral part of medical practice. In psychiatry, off-label prescribing is common and gives clinicians scope to treat patients who are refractory to standard therapy or where there is no licensed medication for an indication. However, efficacy or safety of such off-label use may not be established. There is a growing list of licensed indications for atypical antipsychotics (AAP) beyond schizophrenia and bipolar affective disorder, and also more evidence for other indications where pharmaceutical companies have not obtained a license. Pharmaceutical companies have promoted AAPs for off-label indications to increase sales and consequently have been fined by the US FDA for this. Since the 1990s, AAP use has expanded considerably, for example, the off-label use of quetiapine alone accounted for an estimated 17% of the AAP spend in New Zealand in 2010. There are a number of potential problems with the expanded use of AAPs outside of schizophrenia and related psychoses. A larger population will be exposed to their adverse effects, which include weight gain, type 2 diabetes mellitus, sudden cardiac death and increased mortality rates in the elderly with dementia. There are also concerns with the abuse of these agents, in particular quetiapine. Given that an increasing percentage of the population is being treated with these agents, off-label prescribing of AAPs is a cause for concern; they have a propensity to cause significant side effects and their efficacy and long-term safety for most off-label indications remains largely unknown, and therefore the risks and benefits of their use should be carefully weighed up prior to prescribing these agents off-label.

Integration and baseline management training and transition plan

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

Jech, J.B.

The purpose of the Integration and Baseline Management Training and Transition Plan is to provide a training outline for the Integration and Baseline Management (I and BM) organization and a transition strategy for the Master Equipment List (MEL) Phase 1 application. The training outline includes the following courses: MEL Phase 1 Application Course 1 Master Equipment List General Overview. Course 2 Master Equipment List Editing. Tank Waste Remediation System (TWRS) Labeling Related Course 3 TWRS Equipment Labeling Program (Course Number 350545). As part of courses 1, 2, and 3, it is recommended that a lesson plan be developed and integratedmore » into each of the three courses on the subject of Configuration Management (CM) to include: CM concepts, terminology, definitions, fundamentals and its application with respect to the course. The strategy for the MEL Phase 1 application is to train internal organizations (I and BM) on the MEL-General Overview for read only users and train MEL-Editing for edit users (only on an as needed basis). For external organizations, the strategy is to train selected personnel on the MEL-General Overview and transition them from read only privileges to editing privileges when the appropriate administrative procedures that outline the external organization`s responsibilities (to support MEL) are established. The purpose of this training is to ensure support of the I and BM organization objectives within the TV,IRS Division. These training courses will be added to the existing required training for I and BM personnel only. Other organizations implementing the training will be directed by their management on which training is required.« less

Infants' Acceptance of Phonotactically Illegal Word Forms as Object Labels

ERIC Educational Resources Information Center

Vukatana, Ena; Curtin, Suzanne; Graham, Susan A.

2016-01-01

We investigated 16- and 20-month-olds' flexibility in mapping phonotactically illegal words to objects. Using an associative word-learning task, infants were presented with a training phase that either highlighted or did not highlight the referential status of a novel label. Infants were then habituated to two novel objects, each paired with a…

Independent valine and leucine isotope labeling in Escherichia coli protein overexpression systems.

PubMed

Lichtenecker, Roman J; Weinhäupl, Katharina; Reuther, Lukas; Schörghuber, Julia; Schmid, Walther; Konrat, Robert

2013-11-01

The addition of labeled α-ketoisovalerate to the growth medium of a protein-expressing host organism has evolved into a versatile tool to achieve concomitant incorporation of specific isotopes into valine- and leucine- residues. The resulting target proteins represent excellent probes for protein NMR analysis. However, as the sidechain resonances of these residues emerge in a narrow spectral range, signal overlap represents a severe limitation in the case of high-molecular-weight NMR probes. We present a protocol to eliminate leucine labeling by supplying the medium with unlabeled α-ketoisocaproate. The resulting spectra of a model protein exclusively feature valine signals of increased intensity, confirming the method to be a first example of independent valine and leucine labeling employing α-ketoacid precursor compounds.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

A CCG-Based Method for Training a Semantic Role Labeler in the Absence of Explicit Syntactic Training Data

ERIC Educational Resources Information Center

Boxwell, Stephen A.

2011-01-01

Treebanks are a necessary prerequisite for many NLP tasks, including, but not limited to, semantic role labeling. For many languages, however, treebanks are either nonexistent or too small to be useful. Time-critical applications may require rapid deployment of natural language software for a new critical language--much faster than the development…

Diagnostic Labels, Stigma, and Participation in Research Related to Dementia and Mild Cognitive Impairment

PubMed Central

Garand, Linda; Lingler, Jennifer H.; Conner, Kyaien O.; Dew, Mary Amanda

2010-01-01

Health care professionals use diagnostic labels to classify individuals for both treatment and research purposes. Despite their clear benefits, diagnostic labels also serve as cues that activate stigma and stereotypes. Stigma associated with the diagnostic labels of dementia and mild cognitive impairment (MCI) can have a significant and negative impact on interpersonal relationships, interactions with the health care community, attitudes about service utilization, and participation in clinical research. The impact of stigma also extends to the family caregivers of individuals bearing such labels. In this article, we use examples from our investigations of individuals with dementia or MCI and their family caregivers to examine the impact of labeling and stigma on clinical research participation. We also discuss how stigma can affect numerous aspects of the nursing research process. Strategies are presented for addressing stigma-related barriers to participation in clinical research on dementia and MCI. PMID:20077972

Impact of Health Labels on Flavor Perception and Emotional Profiling: A Consumer Study on Cheese

PubMed Central

Schouteten, Joachim J.; De Steur, Hans; De Pelsmaeker, Sara; Lagast, Sofie; De Bourdeaudhuij, Ilse; Gellynck, Xavier

2015-01-01

The global increase of cardiovascular diseases is linked to the shift towards unbalanced diets with increasing salt and fat intake. This has led to a growing consumers’ interest in more balanced food products, which explains the growing number of health-related claims on food products (e.g., “low in salt” or “light”). Based on a within-subjects design, consumers (n = 129) evaluated the same cheese product with different labels. Participants rated liking, saltiness and fat flavor intensity before and after consuming four labeled cheeses. Even though the cheese products were identical, inclusion of health labels influenced consumer perceptions. Cheese with a “light” label had a lower overall expected and perceived liking compared to regular cheese. Although cheese with a “salt reduced” label had a lower expected liking compared to regular cheese, no lower liking was found when consumers actually consumed the labeled cheese. All labels also influenced the perceived intensities of the attributes related to these labels, e.g., for example salt intensity for reduced salt label. While emotional profiles of the labeled cheeses differed before tasting, little differences were found when actual tasting these cheeses. In conclusion, this study shows that health-related labels might influence the perceived flavor and emotional profiles of cheese products. PMID:26690211

Measuring the labeling efficiency of pseudocontinuous arterial spin labeling.

PubMed

Chen, Zhensen; Zhang, Xingxing; Yuan, Chun; Zhao, Xihai; van Osch, Matthias J P

2017-05-01

Optimization and validation of a sequence for measuring the labeling efficiency of pseudocontinuous arterial spin labeling (pCASL) perfusion MRI. The proposed sequence consists of a labeling module and a single slice Look-Locker echo planar imaging readout. A model-based algorithm was used to calculate labeling efficiency from the signal acquired from the main brain-feeding arteries. Stability of the labeling efficiency measurement was evaluated with regard to the use of cardiac triggering, flow compensation and vein signal suppression. Accuracy of the measurement was assessed by comparing the measured labeling efficiency to mean brain pCASL signal intensity over a wide range of flip angles as applied in the pCASL labeling. Simulations show that the proposed algorithm can effectively calculate labeling efficiency when correcting for T1 relaxation of the blood spins. Use of cardiac triggering and vein signal suppression improved stability of the labeling efficiency measurement, while flow compensation resulted in little improvement. The measured labeling efficiency was found to be linearly (R = 0.973; P < 0.001) related to brain pCASL signal intensity over a wide range of pCASL flip angles. The optimized labeling efficiency sequence provides robust artery-specific labeling efficiency measurement within a short acquisition time (∼30 s), thereby enabling improved accuracy of pCASL CBF quantification. Magn Reson Med 77:1841-1852, 2017. © 2016 International Society for Magnetic Resonance in Medicine Magn Reson Med 77:1841-1852, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

2015-03-01

The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

Aggregating and Predicting Sequence Labels from Crowd Annotations

PubMed Central

Nguyen, An T.; Wallace, Byron C.; Li, Junyi Jessy; Nenkova, Ani; Lease, Matthew

2017-01-01

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online1. PMID:29093611

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Developing a computer security training program

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

Not Available

1990-01-01

We all know that training can empower the computer protection program. However, pushing computer security information outside the computer security organization into the rest of the company is often labeled as an easy project or a dungeon full of dragons. Used in part or whole, the strategy offered in this paper may help the developer of a computer security training program ward off dragons and create products and services. The strategy includes GOALS (what the result of training will be), POINTERS (tips to ensure survival), and STEPS (products and services as a means to accomplish the goals).

Using Distance Education to Teach the New Food Label to Extension Educators.

ERIC Educational Resources Information Center

Struempler, Barbara; And Others

1997-01-01

Satellite training about the new national food labeling system was provided to 97 Alabama extension agents and 67 program assistants. The program, which consisted of a 30-minute video and 25-minute question/answer call-in, proved an effective means of distance education. (SK)

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

PubMed

Sedai, Suman; Garnavi, Rahil; Roy, Pallab; Xi Liang

2015-08-01

Multi-atlas segmentation first registers each atlas image to the target image and transfers the label of atlas image to the coordinate system of the target image. The transferred labels are then combined, using a label fusion algorithm. In this paper, we propose a novel label fusion method which aggregates discriminative learning and generative modeling for segmentation of cardiac MR images. First, a probabilistic Random Forest classifier is trained as a discriminative model to obtain the prior probability of a label at the given voxel of the target image. Then, a probability distribution of image patches is modeled using Gaussian Mixture Model for each label, providing the likelihood of the voxel belonging to the label. The final label posterior is obtained by combining the classification score and the likelihood score under Bayesian rule. Comparative study performed on MICCAI 2013 SATA Segmentation Challenge demonstrates that our proposed hybrid label fusion algorithm is accurate than other five state-of-the-art label fusion methods. The proposed method obtains dice similarity coefficient of 0.94 and 0.92 in segmenting epicardium and endocardium respectively. Moreover, our label fusion method achieves more accurate segmentation results compared to four other label fusion methods.

Pervasive Sound Sensing: A Weakly Supervised Training Approach.

PubMed

Kelly, Daniel; Caulfield, Brian

2016-01-01

Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

Training the gastrointestinal endoscopy trainer.

PubMed

Waschke, Kevin A; Anderson, John; Macintosh, Donald; Valori, Roland M

2016-06-01

Endoscopy training has traditionally been accomplished by an informal process in the endoscopy unit that parallels apprenticeship training seen in other areas of professional education. Subsequent to an audit, a series of interventions were implemented in the English National Health Service to support both service delivery and to improve endoscopy training. The resulting training centers deliver a variety of hands-on endoscopy courses, established in parallel with the roll out of a colon cancer screening program that monitors and documents quality outcomes among endoscopists. The program developed a 'training the trainer' module that subsequently became known as the Training the Colonoscopy Trainer course (TCT). Several years after its implementation, colonoscopy quality outcomes in the UK have improved substantially. The core TCT program has spread to other countries with demonstration of a marked impact on endoscopy training and performance. The aim of this chapter is to describe the principles that underlie effective endoscopy training in this program using the TCT as an example. While the review focuses on the specific example of colonoscopy training, the approach is generic to the teaching of any technical skill; it has been successfully transferred to the teaching of laparoscopic surgery as well as other endoscopic techniques. Copyright © 2016 Elsevier Ltd. All rights reserved.

Human subjects protection training for community workers: an example from "Faith Moves Mountains".

PubMed

Hatcher, Jennifer; Schoenberg, Nancy E

2007-01-01

Despite widespread agreement on the necessity of protecting human subjects, questions regarding ethical treatment and protection of human subjects remain and are particularly vexing for community-based participatory research (CBPR). There has been a notable lack of attention paid to what type of training should be provided and how to balance "real-life" concerns with official requirements. The purpose of this article is to demonstrate how, in consultation with the Office of Research Integrity (ORI) at our institution and our community partners, we developed training that overcame concerns related to instruction of community workers on protection of human subjects. We developed a training module written in lay terms and containing only information pertinent to non-key personnel and their role in the CBPR project. We designed and piloted this material in collaboration with our community partners who work with us to recruit and train lay health advisors (LHAs) and oversee the day-to-day operations of the CBPR project. The educational module was presented to the community workers as a part of a day-long training session. The written materials were a part of a notebook of information accompanied by an oral Power Point presentation. Each of the workers was given a written test to evaluate knowledge of the content presented. The test was administered by the project director, a community member herself, and then sent to our institution for grading by personnel not involved in this project. To date, all community workers have passed the written test. The community members, research partners, and the ORI are satisfied with the scope and simplicity of the training program developed. Our team's collaborative approach to community-based human subjects training contributes to advancing a grounded, feasible, and rigorous process of protecting human subjects while implementing CBPR ideals.

Incremental concept learning with few training examples and hierarchical classification

NASA Astrophysics Data System (ADS)

Bouma, Henri; Eendebak, Pieter T.; Schutte, Klamer; Azzopardi, George; Burghouts, Gertjan J.

2015-10-01

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classifier scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classification. We introduce a new dataset, which we call TOSO, and use it to demonstrate the effectiveness of the proposed method for the localization and recognition of multiple objects in images.

SimLabel: a graphical user interface to simulate continuous wave EPR spectra from site-directed spin labeling experiments.

PubMed

Etienne, E; Le Breton, N; Martinho, M; Mileo, E; Belle, V

2017-08-01

Site-directed spin labeling (SDSL) combined with continuous wave electron paramagnetic resonance (cw EPR) spectroscopy is a powerful technique to reveal, at the residue level, structural transitions in proteins. SDSL-EPR is based on the selective grafting of a paramagnetic label on the protein under study, followed by cw EPR analysis. To extract valuable quantitative information from SDSL-EPR spectra and thus give reliable interpretation on biological system dynamics, numerical simulations of the spectra are required. Such spectral simulations can be carried out by coding in MATLAB using functions from the EasySpin toolbox. For non-expert users of MATLAB, this could be a complex task or even impede the use of such simulation tool. We developed a graphical user interface called SimLabel dedicated to run cw EPR spectra simulations particularly coming from SDSL-EPR experiments. Simlabel provides an intuitive way to visualize, simulate, and fit such cw EPR spectra. An example of SDSL-EPR spectra simulation concerning the study of an intrinsically disordered region undergoing a local induced folding is described and discussed. We believe that this new tool will help the users to rapidly obtain reliable simulated spectra and hence facilitate the interpretation of their results. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Learning to rank image tags with limited training examples.

PubMed

Songhe Feng; Zheyun Feng; Rong Jin

2015-04-01

With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.

Label fusion based brain MR image segmentation via a latent selective model

NASA Astrophysics Data System (ADS)

Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

2018-04-01

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

[Information perceived by consumers through food labeling on fats: a systematic review].

PubMed

Sebastian-Ponce, Miren Itxaso; Sanz-Valero, Javier; Wanden-Berghe, Carmina

2014-11-22

To review the scientific literature related to the information given to consumers about different types of fats in foods through food labeling. Systematic review of the data found in MEDLINE (via PubMed), EMBASE, CINAHL, FSTA, Web of Science, Cochrane Library, SCOPUS and LILACS databasis, until September 2013. The terms used as descriptors and free text were "dietary fats", "dietary fats, unsaturated" and "food labeling". The limit "human" was used. 549 references were retrieved, of which 36 articles were selected after applying the inclusion and exclusion criteria. The main effects related to labeling information were linked to the price and place of purchase/ consumption, sensory dimensions, dietary habits, interpretation and education logo. Food labeling on fat content helps when making consumption decisions. Nutrition education and the meanings of food labels are essential and were effective although the "informed consumer" is yet to be achieved. Training activities should be directed towards prior beliefs and attitudes of consumers in order to make the health and nutrition message consistent. Food labels should be homogeneous and truthful in terms of expressing composition or presenting logos, and messages included in the packaging should be clear and not misleading. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Intensive Training in Youth Sport: An Example of Unequal Opportunity.

ERIC Educational Resources Information Center

Rowley, Stephen R. W.; Graham, Philip J.

1999-01-01

Examined the social composition of an unselected sample of 282 English 8- to 16-year olds involved in intensive training in football, swimming, tennis, and gymnastics. Found that working-class children and those from single-parent families were underrepresented in all sports. Concluded that financial considerations and difficulties in accessing…

Training in the Air Force--The Example of Graduate Education.

ERIC Educational Resources Information Center

Hanushek, Eric A.

The study applies models for rational investing in human capital to Air Force decisions on training and particularly decisions about graduate education for officers. Two separate questions are addressed in depth. First, among the possible types of officers (rated-nonrated, reserve-regular, by length of service) who could be sent to school, are…

Effect of novel inhaler technique reminder labels on the retention of inhaler technique skills in asthma: a single-blind randomized controlled trial.

PubMed

Basheti, Iman A; Obeidat, Nathir M; Reddel, Helen K

2017-02-09

Inhaler technique can be corrected with training, but skills drop off quickly without repeated training. The aim of our study was to explore the effect of novel inhaler technique labels on the retention of correct inhaler technique. In this single-blind randomized parallel-group active-controlled study, clinical pharmacists enrolled asthma patients using controller medication by Accuhaler [Diskus] or Turbuhaler. Inhaler technique was assessed using published checklists (score 0-9). Symptom control was assessed by asthma control test. Patients were randomized into active (ACCa; THa) and control (ACCc; THc) groups. All patients received a "Show-and-Tell" inhaler technique counseling service. Active patients also received inhaler labels highlighting their initial errors. Baseline data were available for 95 patients, 68% females, mean age 44.9 (SD 15.2) years. Mean inhaler scores were ACCa:5.3 ± 1.0; THa:4.7 ± 0.9, ACCc:5.5 ± 1.1; THc:4.2 ± 1.0. Asthma was poorly controlled (mean ACT scores ACCa:13.9 ± 4.3; THa:12.1 ± 3.9; ACCc:12.7 ± 3.3; THc:14.3 ± 3.7). After training, all patients had correct technique (score 9/9). After 3 months, there was significantly less decline in inhaler technique scores for active than control groups (mean difference: Accuhaler -1.04 (95% confidence interval -1.92, -0.16, P = 0.022); Turbuhaler -1.61 (-2.63, -0.59, P = 0.003). Symptom control improved significantly, with no significant difference between active and control patients, but active patients used less reliever medication (active 2.19 (SD 1.78) vs. control 3.42 (1.83) puffs/day, P = 0.002). After inhaler training, novel inhaler technique labels improve retention of correct inhaler technique skills with dry powder inhalers. Inhaler technique labels represent a simple, scalable intervention that has the potential to extend the benefit of inhaler training on asthma outcomes. REMINDER LABELS IMPROVE INHALER TECHNIQUE: Personalized

Food Labels

MedlinePlus

... Staying Safe Videos for Educators Search English Español Food Labels KidsHealth / For Teens / Food Labels What's in ... to have at least 95% organic ingredients. Making Food Labels Work for You The first step in ...

Technical Training for Managers.

ERIC Educational Resources Information Center

Haverland, Edgar M.

The question has arisen as to what kind of information a manager without extensive technical training needs to learn to supervise effectively. For example, the Nike Hercules fire control platoon leader, usually an officer in his first active duty assignment, seldom has had extensive technical training. Yet he is responsibile for the…

Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

PubMed

Smith, Kevin; Horvath, Peter

2014-06-01

High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

Continuing Training in Enterprises for Technological Change.

ERIC Educational Resources Information Center

Behrens, A.; And Others

This document contains a series of papers on the topic of continuing training for technological change in business and industry. The papers focus on examples of training for technological change in several countries of Western Europe. The five papers included in the report are "Training for Continuing Training and Education" (A.…

A label distance maximum-based classifier for multi-label learning.

PubMed

Liu, Xiaoli; Bao, Hang; Zhao, Dazhe; Cao, Peng

2015-01-01

Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.

International Review of the Development and Implementation of Energy Efficiency Standards and Labeling Programs

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

Zhou, Nan; Zheng, Nina; Fridley, David

2012-02-28

Appliance energy efficiency standards and labeling (S&L) programs have been important policy tools for regulating the efficiency of energy-using products for over 40 years and continue to expand in terms of geographic and product coverage. The most common S&L programs include mandatory minimum energy performance standards (MEPS) that seek to push the market for efficient products, and energy information and endorsement labels that seek to pull the market. This study seeks to review and compare some of the earliest and most well-developed S&L programs in three countries and one region: the U.S. MEPS and ENERGY STAR, Australia MEPS and Energymore » Label, European Union MEPS and Ecodesign requirements and Energy Label and Japanese Top Runner programs. For each program, key elements of S&L programs are evaluated and comparative analyses across the programs undertaken to identify best practice examples of individual elements as well as cross-cutting factors for success and lessons learned in international S&L program development and implementation. The international review and comparative analysis identified several overarching themes and highlighted some common factors behind successful program elements. First, standard-setting and programmatic implementation can benefit significantly from a legal framework that stipulates a specific timeline or schedule for standard-setting and revision, product coverage and legal sanctions for non-compliance. Second, the different MEPS programs revealed similarities in targeting efficiency gains that are technically feasible and economically justified as the principle for choosing a standard level, in many cases at a level that no product on the current market could reach. Third, detailed survey data such as the U.S. Residential Energy Consumption Survey (RECS) and rigorous analyses provide a strong foundation for standard-setting while incorporating the participation of different groups of stakeholders further strengthen the

Underinvestment in Employer Training: A Mandate to Spend? Invited Reaction: Employer Training--Is a Mandated Tax the Only Solution?

ERIC Educational Resources Information Center

Bishop, John H.; Lynch, Lisa M.

1993-01-01

Using the example of France, Bishop recommends a U.S. training mandate involving a training tax and incentives. Lynch argues that a broader array of options is needed to meet the training needs of new workers, displaced workers, and the unemployed. (SK)

Emerging applications of label-free optical biosensors

NASA Astrophysics Data System (ADS)

Zanchetta, Giuliano; Lanfranco, Roberta; Giavazzi, Fabio; Bellini, Tommaso; Buscaglia, Marco

2017-01-01

Innovative technical solutions to realize optical biosensors with improved performance are continuously proposed. Progress in material fabrication enables developing novel substrates with enhanced optical responses. At the same time, the increased spectrum of available biomolecular tools, ranging from highly specific receptors to engineered bioconjugated polymers, facilitates the preparation of sensing surfaces with controlled functionality. What remains often unclear is to which extent this continuous innovation provides effective breakthroughs for specific applications. In this review, we address this challenging question for the class of label-free optical biosensors, which can provide a direct signal upon molecular binding without using secondary probes. Label-free biosensors have become a consolidated approach for the characterization and screening of molecular interactions in research laboratories. However, in the last decade, several examples of other applications with high potential impact have been proposed. We review the recent advances in label-free optical biosensing technology by focusing on the potential competitive advantage provided in selected emerging applications, grouped on the basis of the target type. In particular, direct and real-time detection allows the development of simpler, compact, and rapid analytical methods for different kinds of targets, from proteins to DNA and viruses. The lack of secondary interactions facilitates the binding of small-molecule targets and minimizes the perturbation in single-molecule detection. Moreover, the intrinsic versatility of label-free sensing makes it an ideal platform to be integrated with biomolecular machinery with innovative functionality, as in case of the molecular tools provided by DNA nanotechnology.

Comparative analysis of the labelling of nanotechnologies across four stakeholder groups

NASA Astrophysics Data System (ADS)

Capon, Adam; Gillespie, James; Rolfe, Margaret; Smith, Wayne

2015-08-01

Societies are constantly challenged to develop policies around the introduction of new technologies, which by their very nature contain great uncertainty. This uncertainty gives prominence to varying viewpoints which are value laden and have the ability to drastically shift policy. The issue of nanotechnologies is a prime example. The labelling of products that contain new technologies has been one policy tool governments have used to address concerns around uncertainty. Our study develops evidence regarding opinions on the labelling of products made by nanotechnologies. We undertook a computer-assisted telephone (CATI) survey of the Australian public and those involved in nanotechnologies from the academic, business and government sectors using a standardised questionnaire. Analysis was undertaken using descriptive and logistic regression techniques. We explored reluctance to purchase as a result of labelling products which contained manufactured nanomaterials both generally and across five broad products (food, cosmetics/sunscreens, medicines, pesticides, tennis racquets/computers) which represent the broad categories of products regulated by differing government agencies in Australia. We examined the relationship between reluctance to purchase and risk perception, trust, and familiarity. We found irrespective of stakeholder, most supported the labelling of products which contained manufactured nanomaterials. Perception of risk was the main driver of reluctance to purchase, while trust and familiarity were likely to have an indirect effect through risk perception. Food is likely to be the greatest product impacted by labelling. Risk perception surrounding nanotechnologies and label `framing' on the product are key issues to be addressed in the implementation of a labelling scheme.

Galactic Train Wrecks

NASA Image and Video Library

2011-05-25

This montage combines observations from NASA Spitzer Space Telescope and NASA Galaxy Evolution Explorer GALEX spacecraft showing three examples of colliding galaxies from a new photo atlas of galactic train wrecks.

SIRB, sans iron oxide rhodamine B, a novel cross-linked dextran nanoparticle, labels human neuroprogenitor and SH-SY5Y neuroblastoma cells and serves as a USPIO cell labeling control.

PubMed

Shen, Wei-Bin; Vaccaro, Dennis E; Fishman, Paul S; Groman, Ernest V; Yarowsky, Paul

2016-05-01

This is the first report of the synthesis of a new nanoparticle, sans iron oxide rhodamine B (SIRB), an example of a new class of nanoparticles. SIRB is designed to provide all of the cell labeling properties of the ultrasmall superparamagnetic iron oxide (USPIO) nanoparticle Molday ION Rhodamine B (MIRB) without containing the iron oxide core. MIRB was developed to label cells and allow them to be tracked by MRI or to be manipulated by magnetic gradients. SIRB possesses a similar size, charge and cross-linked dextran coating as MIRB. Of great interest is understanding the biological and physiological changes in cells after they are labeled with a USPIO. Whether these effects are due to the iron oxide buried within the nanoparticle or to the surface coating surrounding the iron oxide core has not been considered previously. MIRB and SIRB represent an ideal pairing of nanoparticles to identify nanoparticle anatomy responsible for post-labeling cytotoxicity. Here we report the effects of SIRB labeling on the SH-SY5Y neuroblastoma cell line and primary human neuroprogenitor cells (hNPCs). These effects are contrasted with the effects of labeling SH-SY5Y cells and hNPCs with MIRB. We find that SIRB labeling, like MIRB labeling, (i) occurs without the use of transfection reagents, (ii) is packaged within lysosomes distributed within cell cytoplasm, (iii) is retained within cells with no loss of label after cell storage, and (iv) does not alter cellular viability or proliferation, and (v) SIRB labeled hNPCs differentiate normally into neurons or astrocytes. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Systematic Comparison of Label-Free, Metabolic Labeling, and Isobaric Chemical Labeling for Quantitative Proteomics on LTQ Orbitrap Velos

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

Li, Zhou; Adams, Rachel M; Chourey, Karuna

2012-01-01

A variety of quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling using iTRAQ or TMT. Here, these methods were compared in terms of the depth of proteome coverage, quantification accuracy, precision, and reproducibility using a high-performance hybrid mass spectrometer, LTQ Orbitrap Velos. Our results show that (1) the spectral counting method provides the deepest proteome coverage for identification, but its quantification performance is worse than labeling-based approaches, especially the quantification reproducibility; (2) metabolic labeling and isobaric chemical labeling are capable of accurate, precise, and reproducible quantification and provide deep proteome coverage for quantification. Isobaricmore » chemical labeling surpasses metabolic labeling in terms of quantification precision and reproducibility; (3) iTRAQ and TMT perform similarly in all aspects compared in the current study using a CID-HCD dual scan configuration. Based on the unique advantages of each method, we provide guidance for selection of the appropriate method for a quantitative proteomics study.« less

A simple procedure for parallel sequence analysis of both strands of 5'-labeled DNA.

PubMed

Razvi, F; Gargiulo, G; Worcel, A

1983-08-01

Ligation of a 5'-labeled DNA restriction fragment results in a circular DNA molecule carrying the two 32Ps at the reformed restriction site. Double digestions of the circular DNA with the original enzyme and a second restriction enzyme cleavage near the labeled site allows direct chemical sequencing of one 5'-labeled DNA strand. Similar double digestions, using an isoschizomer that cleaves differently at the 32P-labeled site, allows direct sequencing of the now 3'-labeled complementary DNA strand. It is possible to directly sequence both strands of cloned DNA inserts by using the above protocol and a multiple cloning site vector that provides the necessary restriction sites. The simultaneous and parallel visualization of both DNA strands eliminates sequence ambiguities. In addition, the labeled circular molecules are particularly useful for single-hit DNA cleavage studies and DNA footprint analysis. As an example, we show here an analysis of the micrococcal nuclease-induced breaks on the two strands of the somatic 5S RNA gene of Xenopus borealis, which suggests that the enzyme may recognize and cleave small AT-containing palindromes along the DNA helix.

Modeling-Mainstreaming: A Teacher Training Proposal.

ERIC Educational Resources Information Center

Bireley, Marlene; Mahan, Virginia

This document presents a learning model for training teachers to effectively deal with physically handicapped and mildly retarded children in their regular classroom. The modules are organized in the following fashion: Phase One; Development of an awareness of the concept of mainstreaming, of labels and their consequences, and of the psychological…

From Concrete Examples to Abstract Relations: The Rostrolateral Prefrontal Cortex Integrates Novel Examples into Relational Categories.

PubMed

Davis, Tyler; Goldwater, Micah; Giron, Josue

2017-04-01

The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrating novel examples during relational categorization whereas other PFC regions support more general decision functions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Detection of CdSe quantum dot photoluminescence for security label on paper

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

Isnaeni,, E-mail: isnaeni@lipi.go.id; Sugiarto, Iyon Titok; Bilqis, Ratu

CdSe quantum dot has great potential in various applications especially for emitting devices. One example potential application of CdSe quantum dot is security label for anti-counterfeiting. In this work, we present a practical approach of security label on paper using one and two colors of colloidal CdSe quantum dot, which is used as stamping ink on various types of paper. Under ambient condition, quantum dot is almost invisible. The quantum dot security label can be revealed by detecting emission of quantum dot using photoluminescence and cnc machine. The recorded quantum dot emission intensity is then analyzed using home-made program tomore » reveal quantum dot pattern stamp having the word ’RAHASIA’. We found that security label using quantum dot works well on several types of paper. The quantum dot patterns can survive several days and further treatment is required to protect the quantum dot. Oxidation of quantum dot that occurred during this experiment reduced the emission intensity of quantum dot patterns.« less

Aptamer fluorescence anisotropy sensors for adenosine triphosphate by comprehensive screening tetramethylrhodamine labeled nucleotides.

PubMed

Zhao, Qiang; Lv, Qin; Wang, Hailin

2015-08-15

We previously reported a fluorescence anisotropy (FA) approach for small molecules using tetramethylrhodamine (TMR) labeled aptamer. It relies on target-binding induced change of intramolecular interaction between TMR and guanine (G) base. TMR-labeling sites are crucial for this approach. Only terminal ends and thymine (T) bases could be tested for TMR labeling in our previous work, possibly causing limitation in analysis of different targets with this FA strategy. Here, taking the analysis of adenosine triphosphate (ATP) as an example, we demonstrated a success of conjugating TMR on other bases of aptamer adenine (A) or cytosine (C) bases and an achievement of full mapping various labeling sites of aptamers. We successfully constructed aptamer fluorescence anisotropy (FA) sensors for adenosine triphosphate (ATP). We conjugated single TMR on adenine (A), cytosine (C), or thymine (T) bases or terminals of a 25-mer aptamer against ATP and tested FA responses of 14 TMR-labeled aptamer to ATP. The aptamers having TMR labeled on the 16th base C or 23rd base A were screened out and exhibited significant FA-decreasing or FA-increasing responses upon ATP, respectively. These two favorable TMR-labeled aptamers enabled direct FA sensing ATP with a detection limit of 1 µM and the analysis of ATP in diluted serum. The comprehensive screening various TMR labeling sites of aptamers facilitates the successful construction of FA sensors using TMR-labeled aptamers. It will expand application of TMR-G interaction based aptamer FA strategy to a variety of targets. Copyright © 2015 Elsevier B.V. All rights reserved.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

PubMed

Christiansen, Eric M; Yang, Samuel J; Ando, D Michael; Javaherian, Ashkan; Skibinski, Gaia; Lipnick, Scott; Mount, Elliot; O'Neil, Alison; Shah, Kevan; Lee, Alicia K; Goyal, Piyush; Fedus, William; Poplin, Ryan; Esteva, Andre; Berndl, Marc; Rubin, Lee L; Nelson, Philip; Finkbeiner, Steven

2018-04-19

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire. Copyright © 2018 Elsevier Inc. All rights reserved.

Example Elaboration as a Neglected Instructional Strategy

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

Girill, T R

Over the last decade an unfolding cognitive-psychology research program on how learners use examples to develop effective problem solving expertise has yielded well-established empirical findings. Chi et al., Renkl, Reimann, and Neubert (in various papers) have confirmed statistically significant differences in how good and poor learners inferentially elaborate (self explain) example steps as they study. Such example elaboration is highly relevant to software documentation and training, yet largely neglected in the current literature. This paper summarizes the neglected research on example use and puts its neglect in a disciplinary perspective. The author then shows that differences in support for examplemore » elaboration in commercial software documentation reveal previously over looked usability issues. These issues involve example summaries, using goals and goal structures to reinforce example elaborations, and prompting readers to recognize the role of example parts. Secondly, I show how these same example elaboration techniques can build cognitive maturity among underperforming high school students who study technical writing. Principle based elaborations, condition elaborations, and role recognition of example steps all have their place in innovative, high school level, technical writing exercises, and all promote far transfer problem solving. Finally, I use these studies to clarify the constructivist debate over what writers and readers contribute to text meaning. I argue that writers can influence how readers elaborate on examples, and that because of the great empirical differences in example study effectiveness (and reader choices) writers should do what they can (through within text design features) to encourage readers to elaborate examples in the most successful ways. Example elaboration is a uniquely effective way to learn from worked technical examples. This paper summarizes years of research that clarifies example elaboration. I then show how

Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial.

PubMed

Ni Mhurchu, Cliona; Eyles, Helen; Jiang, Yannan; Blakely, Tony

2018-02-01

There are few objective data on how nutrition labels are used in real-world shopping situations, or how they affect dietary choices and patterns. The Starlight study was a four-week randomised, controlled trial of the effects of three different types of nutrition labels on consumer food purchases: Traffic Light Labels, Health Star Rating labels, or Nutrition Information Panels (control). Smartphone technology allowed participants to scan barcodes of packaged foods and receive randomly allocated labels on their phone screen, and to record their food purchases. The study app therefore provided objectively recorded data on label viewing behaviour and food purchases over a four-week period. A post-hoc analysis of trial data was undertaken to assess frequency of label use, label use by food group, and association between label use and the healthiness of packaged food products purchased. Over the four-week intervention, study participants (n = 1255) viewed nutrition labels for and/or purchased 66,915 barcoded packaged products. Labels were viewed for 23% of all purchased products, with decreasing frequency over time. Shoppers were most likely to view labels for convenience foods, cereals, snack foods, bread and bakery products, and oils. They were least likely to view labels for sugar and honey products, eggs, fish, fruit and vegetables, and meat. Products for which participants viewed the label and subsequently purchased the product during the same shopping episode were significantly healthier than products where labels were viewed but the product was not subsequently purchased: mean difference in nutrient profile score -0.90 (95% CI -1.54 to -0.26). In a secondary analysis of a nutrition labelling intervention trial, there was a significant association between label use and the healthiness of products purchased. Nutrition label use may therefore lead to healthier food purchases. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Methods and apparatus for distributed resource discovery using examples

NASA Technical Reports Server (NTRS)

Chang, Yuan-Chi (Inventor); Li, Chung-Sheng (Inventor); Smith, John Richard (Inventor); Hill, Matthew L. (Inventor); Bergman, Lawrence David (Inventor); Castelli, Vittorio (Inventor)

2005-01-01

Distributed resource discovery is an essential step for information retrieval and/or providing information services. This step is usually used for determining the location of an information or data repository which has relevant information. The most fundamental challenge is the usual lack of semantic interoperability of the requested resource. In accordance with the invention, a method is disclosed where distributed repositories achieve semantic interoperability through the exchange of examples and, optionally, classifiers. The outcome of the inventive method can be used to determine whether common labels are referring to the same semantic meaning.

OpenCL based machine learning labeling of biomedical datasets

NASA Astrophysics Data System (ADS)

Amoros, Oscar; Escalera, Sergio; Puig, Anna

2011-03-01

In this paper, we propose a two-stage labeling method of large biomedical datasets through a parallel approach in a single GPU. Diagnostic methods, structures volume measurements, and visualization systems are of major importance for surgery planning, intra-operative imaging and image-guided surgery. In all cases, to provide an automatic and interactive method to label or to tag different structures contained into input data becomes imperative. Several approaches to label or segment biomedical datasets has been proposed to discriminate different anatomical structures in an output tagged dataset. Among existing methods, supervised learning methods for segmentation have been devised to easily analyze biomedical datasets by a non-expert user. However, they still have some problems concerning practical application, such as slow learning and testing speeds. In addition, recent technological developments have led to widespread availability of multi-core CPUs and GPUs, as well as new software languages, such as NVIDIA's CUDA and OpenCL, allowing to apply parallel programming paradigms in conventional personal computers. Adaboost classifier is one of the most widely applied methods for labeling in the Machine Learning community. In a first stage, Adaboost trains a binary classifier from a set of pre-labeled samples described by a set of features. This binary classifier is defined as a weighted combination of weak classifiers. Each weak classifier is a simple decision function estimated on a single feature value. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. In this work, we propose an alternative representation of the Adaboost binary classifier. We use this proposed representation to define a new GPU-based parallelized Adaboost testing stage using OpenCL. We provide numerical experiments based on large available data sets and we compare our results to CPU-based strategies in terms of time and

A Guide to the Identification of Training Needs.

ERIC Educational Resources Information Center

Boydell, T. H.

This comprehensive analysis of training needs, which is illustrated with case studies and factual examples, is directed towards training management, but its concepts are expressed in terms valuable to all management. The first chapter answers the question, "What are training needs?" The following chapters discuss present and future training needs,…

Training for Environmental Impact Assessment (E.I.A.).

ERIC Educational Resources Information Center

Vougias, S.

1988-01-01

Deals with the methodology and practices for Environmental Impact Assessment (EIA). Describes the EIA process, prediction process, alternative assessment methods, training needs, major activities, training provision and material, main deficiencies and the precautions, and real world training examples. (Author/YP)

Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding.

PubMed

Ma, Eddie Y T; Ratnasingham, Sujeevan; Kremer, Stefan C

2018-01-01

This study presents a machine learning method that increases the number of identified bases in Sanger Sequencing. The system post-processes a KB basecalled chromatogram. It selects a recoverable subset of N-labels in the KB-called chromatogram to replace with basecalls (A,C,G,T). An N-label correction is defined given an additional read of the same sequence, and a human finished sequence. Corrections are added to the dataset when an alignment determines the additional read and human agree on the identity of the N-label. KB must also rate the replacement with quality value of in the additional read. Corrections are only available during system training. Developing the system, nearly 850,000 N-labels are obtained from Barcode of Life Datasystems, the premier database of genetic markers called DNA Barcodes. Increasing the number of correct bases improves reference sequence reliability, increases sequence identification accuracy, and assures analysis correctness. Keeping with barcoding standards, our system maintains an error rate of percent. Our system only applies corrections when it estimates low rate of error. Tested on this data, our automation selects and recovers: 79 percent of N-labels from COI (animal barcode); 80 percent from matK and rbcL (plant barcodes); and 58 percent from non-protein-coding sequences (across eukaryotes).

In vivo stationary flux analysis by 13C labeling experiments.

PubMed

Wiechert, W; de Graaf, A A

1996-01-01

Stationary flux analysis is an invaluable tool for metabolic engineering. In the last years the metabolite balancing technique has become well established in the bioengineering community. On the other hand metabolic tracer experiments using 13C isotopes have long been used for intracellular flux determination. Only recently have both techniques been fully combined to form a considerably more powerful flux analysis method. This paper concentrates on modeling and data analysis for the evaluation of such stationary 13C labeling experiments. After reviewing recent experimental developments, the basic equations for modeling carbon labeling in metabolic systems, i.e. metabolite, carbon label and isotopomer balances, are introduced and discussed in some detail. Then the basics of flux estimation from measured extracellular fluxes combined with carbon labeling data are presented and, finally, this method is illustrated by using an example from C. glutamicum. The main emphasis is on the investigation of the extra information that can be obtained with tracer experiments compared with the metabolite balancing technique alone. As a principal result it is shown that the combined flux analysis method can dispense with some rather doubtful assumptions on energy balancing and that the forward and backward flux rates of bidirectional reaction steps can be simultaneously determined in certain situations. Finally, it is demonstrated that the variant of fractional isotopomer measurement is even more powerful than fractional labeling measurement but requires much higher numerical effort to solve the balance equations.

Labeling proteins inside living cells using external fluorophores for microscopy.

PubMed

Teng, Kai Wen; Ishitsuka, Yuji; Ren, Pin; Youn, Yeoan; Deng, Xiang; Ge, Pinghua; Lee, Sang Hak; Belmont, Andrew S; Selvin, Paul R

2016-12-09

Site-specific fluorescent labeling of proteins inside live mammalian cells has been achieved by employing Streptolysin O, a bacterial enzyme which forms temporary pores in the membrane and allows delivery of virtually any fluorescent probes, ranging from labeled IgG's to small ligands, with high efficiency (>85% of cells). The whole process, including recovery, takes 30 min, and the cell is ready to be imaged immediately. A variety of cell viability tests were performed after treatment with SLO to ensure that the cells have intact membranes, are able to divide, respond normally to signaling molecules, and maintains healthy organelle morphology. When combined with Oxyrase, a cell-friendly photostabilizer, a ~20x improvement in fluorescence photostability is achieved. By adding in glutathione, fluorophores are made to blink, enabling super-resolution fluorescence with 20-30 nm resolution over a long time (~30 min) under continuous illumination. Example applications in conventional and super-resolution imaging of native and transfected cells include p65 signal transduction activation, single molecule tracking of kinesin, and specific labeling of a series of nuclear and cytoplasmic protein complexes.

Young Children's Ability to Use Ordinal Labels in a Spatial Search Task

ERIC Educational Resources Information Center

Miller, Stephanie E.; Marcovitch, Stuart; Boseovski, Janet J.; Lewkowicz, David J.

2015-01-01

The use and understanding of ordinal terms (e.g., "first" and "second") is a developmental milestone that has been relatively unexplored in the preschool age range. In the present study, 4- and 5-year-olds watched as a reward was placed in one of three train cars labeled by the experimenter with an ordinal (e.g.,…

Multiple immunofluorescence labelling of formalin-fixed paraffin-embedded (FFPE) tissue

PubMed Central

Robertson, David; Savage, Kay; Reis-Filho, Jorge S; Isacke, Clare M

2008-01-01

Background Investigating the expression of candidate genes in tissue samples usually involves either immunohistochemical labelling of formalin-fixed paraffin-embedded (FFPE) sections or immunofluorescence labelling of cryosections. Although both of these methods provide essential data, both have important limitations as research tools. Consequently, there is a demand in the research community to be able to perform routine, high quality immunofluorescence labelling of FFPE tissues. Results We present here a robust optimised method for high resolution immunofluorescence labelling of FFPE tissues, which involves the combination of antigen retrieval, indirect immunofluorescence and confocal laser scanning microscopy. We demonstrate the utility of this method with examples of immunofluorescence labelling of human kidney, human breast and a tissue microarray of invasive human breast cancers. Finally, we demonstrate that stained slides can be stored in the short term at 4°C or in the longer term at -20°C prior to images being collected. This approach has the potential to unlock a large in vivo database for immunofluorescence investigations and has the major advantages over immunohistochemistry in that it provides higher resolution imaging of antigen localization and the ability to label multiple antigens simultaneously. Conclusion This method provides a link between the cell biology and pathology communities. For the cell biologist, it will enable them to utilise the vast archive of pathology specimens to advance their in vitro data into in vivo samples, in particular archival material and tissue microarrays. For the pathologist, it will enable them to utilise multiple antibodies on a single section to characterise particular cell populations or to test multiple biomarkers in limited samples and define with greater accuracy cellular heterogeneity in tissue samples. PMID:18366689

Dynamic map labeling.

PubMed

Been, Ken; Daiches, Eli; Yap, Chee

2006-01-01

We address the problem of filtering, selecting and placing labels on a dynamic map, which is characterized by continuous zooming and panning capabilities. This consists of two interrelated issues. The first is to avoid label popping and other artifacts that cause confusion and interrupt navigation, and the second is to label at interactive speed. In most formulations the static map labeling problem is NP-hard, and a fast approximation might have O(nlogn) complexity. Even this is too slow during interaction, when the number of labels shown can be several orders of magnitude less than the number in the map. In this paper we introduce a set of desiderata for "consistent" dynamic map labeling, which has qualities desirable for navigation. We develop a new framework for dynamic labeling that achieves the desiderata and allows for fast interactive display by moving all of the selection and placement decisions into the preprocessing phase. This framework is general enough to accommodate a variety of selection and placement algorithms. It does not appear possible to achieve our desiderata using previous frameworks. Prior to this paper, there were no formal models of dynamic maps or of dynamic labels; our paper introduces both. We formulate a general optimization problem for dynamic map labeling and give a solution to a simple version of the problem. The simple version is based on label priorities and a versatile and intuitive class of dynamic label placements we call "invariant point placements". Despite these restrictions, our approach gives a useful and practical solution. Our implementation is incorporated into the G-Vis system which is a full-detail dynamic map of the continental USA. This demo is available through any browser.

Stockpile Dismantlement Database Training Materials

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

Not Available

1993-11-01

This document, the Stockpile Dismantlement Database (SDDB) training materials is designed to familiarize the user with the SDDB windowing system and the data entry steps for Component Characterization for Disposition. The foundation of information required for every part is depicted by using numbered graphic and text steps. The individual entering data is lead step by step through generic and specific examples. These training materials are intended to be supplements to individual on-the-job training.

A uniform procedure for reimbursing the off-label use of antineoplastic drugs according to the value-for-money approach.

PubMed

Messori, A; Fadda, V; Trippoli, S

2011-04-01

National healthcare systems as well as local institutions generally reimburse numerous off-label uses of anticancer drugs, but an explicit framework for managing these payments is still lacking. As in the case of on-label uses, an optimal management of off-label uses should be aimed at a direct proportionality between cost and clinical benefit. Within this framework, assessing the incremental cost/effectiveness ratio becomes mandatory, and measuring the magnitude of the clinical benefit (e.g. gain in overall survival or progression-free survival) is essential.This paper discusses how the standard principles of cost-effectiveness and value-for-money can be applied to manage the reimbursement of off-label treatments in oncology. It also describes a detailed operational scheme to appropriately implement this aim. Two separate approaches are considered: a) a trial-based approach, which is designed for situations where enough information is available from clinical studies about the expected effectiveness of the off-label treatment; b) an individualized payment-by-results approach, which is designed for situations in which adequate information on effectiveness is lacking; this latter approach requires that each patient receiving off-label treatment is followed-up to determine individual outcomes and tailor the extent of payment to individual results.Some examples of application of both approaches are presented in detail, which have been extracted from a list of 184 off-label indications approved in 2010 by the Region of tuscany in italy. these examples support the feasibility of the two methods proposed.In conclusion, the scheme described in this paper represents an operational solution to an unsettled problem in the area of oncology drugs. © E.S.I.F.T. srl - Firenze

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

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

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors

Trends in Interdisciplinary and Integrative Graduate Training: An NSF IGERT Example

ERIC Educational Resources Information Center

Martin, Philip E.; Umberger, Brian R.

2003-01-01

In a report entitled "Reshaping the Graduate Education of Scientists and Engineers" (National Academy of Sciences, 1995), the Committee on Science, Engineering, and Public Policy proposed a modified PhD training model that retains an emphasis on intensive research experiences, while incorporating additional experiences to prepare graduates for an…

21 CFR 500.52 - Use of terms such as “tonic”, “tone”, “toner”, or “conditioner” in the labeling of preparations...

Code of Federal Regulations, 2010 CFR

2010-04-01

... which the term is qualified in the labeling to reflect the product's intended use. (c) An article so... unless the use of the article under the conditions set forth in its labeling is generally recognized as safe and effective among experts qualified by scientific training and experience to evaluate the safety...

Labeled trees and the efficient computation of derivations

NASA Technical Reports Server (NTRS)

Grossman, Robert; Larson, Richard G.

1989-01-01

The effective parallel symbolic computation of operators under composition is discussed. Examples include differential operators under composition and vector fields under the Lie bracket. Data structures consisting of formal linear combinations of rooted labeled trees are discussed. A multiplication on rooted labeled trees is defined, thereby making the set of these data structures into an associative algebra. An algebra homomorphism is defined from the original algebra of operators into this algebra of trees. An algebra homomorphism from the algebra of trees into the algebra of differential operators is then described. The cancellation which occurs when noncommuting operators are expressed in terms of commuting ones occurs naturally when the operators are represented using this data structure. This leads to an algorithm which, for operators which are derivations, speeds up the computation exponentially in the degree of the operator. It is shown that the algebra of trees leads naturally to a parallel version of the algorithm.

Simulation of spin label structure and its implication in molecular characterization

PubMed Central

Fajer, Piotr; Fajer, Mikolai; Zawrotny, Michael; Yang, Wei

2016-01-01

Interpretation of EPR from spin labels in terms of structure and dynamics requires knowledge of label behavior. General strategies were developed for simulation of labels used in EPR of proteins. The criteria for those simulations are: (a) exhaustive sampling of rotamer space; (b) consensus of results independent of starting points; (c) inclusion of entropy. These criteria are satisfied only when the number of transitions in any dihedral angle exceeds 100 and the simulation maintains thermodynamic equilibrium. Methods such as conventional MD do not efficiently cross energetic barriers, Simulated Annealing, Monte Carlo or popular Rotamer Library methodologies are potential energy based and ignore entropy (in addition to their specific shortcomings: environment fluctuations, fixed environment or electrostatics). Simulated Scaling method, avoids above flaws by modulating the forcefields between 0 (allowing crossing energy barriers) and full potential (sampling minima). Spin label diffuses on this surface while remaining in thermodynamic equilibrium. Simulations show that: (a) single conformation is rare, often there are 2–4 populated rotamers; (b) position of the NO varies up to 16Å. These results illustrate necessity for caution when interpreting EPR signals in terms of molecular structure. For example the 10–16Å distance change in DEER should not be interpreted as a large conformational change, it can well be a flip about Cα -Cβ bond. Rigorous exploration of possible rotamer structures of a spin label is paramount in signal interpretation. We advocate use of bifunctional labels, which motion is restricted 10,000-fold and the NO position is restricted to 2–5Å. PMID:26478501

Design validation and labeling comprehension study for a new epinephrine autoinjector.

PubMed

Edwards, Eric S; Edwards, Evan T; Gunn, Ronald; Patterson, Patricia; North, Robert

2013-03-01

To facilitate the correct use of epinephrine autoinjectors (EAIs) by patients and caregivers, a novel EAI (Auvi-Q) was designed to help minimize use-related hazards. To support validation of Auvi-Q final design and assess whether the instructions for use in the patient information leaflet (PIL) are effective in training participants on proper use of Auvi-Q. Healthy participants, 20 adult and 20 pediatric, were assessed for their ability to complete a simulated injection by following the Auvi-Q instructions for use. Participants relied only on the contents of the PIL and other labeling features (device labeling and its instructions for use, electronic voice instructions and visual prompts). The mean ± SD age of the adult and pediatric participants was 39.4 ± 11.6 and 10.9 ± 2.3 years, respectively. In total, 80% of adult and 35% of pediatric participants had prior experience with EAIs. All adults and 95% of pediatric participants completed a simulated injection on the first attempt; 1 pediatric participant required parental training and a second attempt. Three adult and 4 pediatric participants exhibited a noncritical issue while successfully completing the simulated injection. Most participants agreed that the injection steps were easy to follow and the PIL facilitated understanding on using Auvi-Q safely and effectively. The PIL and other labeling features were effective in communicating instructions for successful use of Auvi-Q. This study provided validation support for the final design and anticipated instructions for use of Auvi-Q. Copyright © 2013 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

PubMed

Tajbakhsh, Nima; Shin, Jae Y; Gurudu, Suryakanth R; Hurst, R Todd; Kendall, Christopher B; Gotway, Michael B; Jianming Liang

2016-05-01

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

A Semantic Labeling of the Environment Based on What People Do.

PubMed

Crespo, Jonathan; Gómez, Clara; Hernández, Alejandra; Barber, Ramón

2017-01-29

In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.

Understanding Food Labels

MedlinePlus

... Healthy eating for girls Understanding food labels Understanding food labels There is lots of info on food ... need to avoid because of food allergies. Other food label terms top In addition to the Nutrition ...

78 FR 66826 - Prior Label Approval System: Generic Label Approval

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-07

... container of a misleading form or size.\\1\\ FSIS has interpreted these provisions as requiring that the...-evaluating-labeling . Labels submitted as an extraordinary circumstance are given the highest priority for... submissions to FSIS headquarters, thus increasing the availability of FSIS labeling staff. Upon publication of...

The topology of metabolic isotope labeling networks.

PubMed

Weitzel, Michael; Wiechert, Wolfgang; Nöh, Katharina

2007-08-29

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively

Abandoning a label doesn’t make it disappear: The perseverance of labeling effects

PubMed Central

Foroni, Francesco; Rothbart, Myron

2012-01-01

Labels exert strong influence on perception and judgment. The present experiment examines the possibility that such effects may persist even when labels are abandoned. Participants judged the similarity of pairs of silhouette drawings of female body types, ordered on a continuum from very thin to very heavy, under conditions where category labels were, and were not, superimposed on the ordered stimuli. Consistent with earlier research, labels had strong effects on perceived similarity, with silhouettes sharing the same label judged as more similar than those having different labels. Moreover, when the labels were removed and no longer present, the effect of the labels, although diminished, persisted. It did not make any difference whether the labels were simply abandoned or, in addition, had their validity challenged. The results are important for our understanding of categorization and labeling processes. The potential theoretical and practical implications of these results for social processes are discussed. PMID:23105148

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

2006-01-01

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

The Near-Term Viability and Benefits of eLabels for Patients, Clinical Sites, and Sponsors.

PubMed

Smith-Gick, Jodi; Barnes, Nicola; Barone, Rocco; Bedford, Jeff; James, Jason R; Reisner, Stacy Frankovitz; Stephenson, Michael

2018-01-01

Current clinical trial labels are designed primarily to meet regulatory requirements. These labels have low patient and site utility, few are opened, and they have limited space and small fonts. As our world transitions from paper to electronic, an opportunity exists to provide patients with information about their investigational clinical trial product in a way that is more easily accessible, meets Health Authority requirements, and provides valuable additional information for the patient and caregiver. A TransCelerate initiative was launched to understand the current regulatory and technology landscape for the potential use an electronic label (eLabel) for investigational medicinal products (IMPs). Concepts and an example proof of concept were developed intended to show the "art of the possible" for a foundational eLabel and a "universal printed label." In addition, possible patient-centric enhancements were captured in the eLabel proof of concept. These concepts were shared with Health Authorities as well as patient and site advisory groups to gather feedback and subsequently enhance the concepts. Feedback indicated that the concept of an eLabel provides value and concepts should continue to be pursued. While the Health Authorities engaged with did not express issues with the use of an eLabel per se, the reduction in the content on the paper label is not possible in some geographic locations due to existing regulations. There is nothing that prevents transmitting the label electronically in conjunction with current conventional labeling. While there are still some regulatory barriers that need to be addressed for reducing what is on the paper label, advancement toward a more patient-centric approach benefits stakeholders and will enable a fully connected patient-centric experience. The industry must start now to build the foundation.

Introduction to Pesticide Labels

EPA Pesticide Factsheets

Pesticide product labels provide critical information about how to safely and legally handle and use pesticide products. Unlike most other types of product labels, pesticide labels are legally enforceable. Learn about pesticide product labels.

Producing Videotape Programs for Computer Training: An Example with AMA/NET

PubMed Central

Novey, Donald W.

1990-01-01

To facilitate user proficiency with AMA/Net, an 80-minute training videotape has been produced. The production was designed to use videotape's advantages, where information and emotion are combined; and to accommodate its chief disadvantage, lack of resolution for fine text, with close-ups and graphics. Content of the videotape was conceived, out-lined, demonstrated with simultaneous text capture, edited into script form, narration added, and scripts marked for videotaping and narrating. Videotaping was performed with actual keyboard sounds for realism. The recording was divided into four areas: office mock-up, keyboard close-ups, scan-conversion and screen close-ups. Once the footage was recorded, it was logged and rough-edited. Care was taken to balance the pace of the program with visual stimulation and amount of narration. The final edit was performed as a culmination of all scripts, video materials and rough edit, with graphics and steady change of visual information offsetting the static nature of the screen display. Carefully planned video programs can be a useful and economical adjunct in the training process for online services.

Producing Videotape Programs for Computer Training: An Example with AMA/NET

PubMed Central

Novey, Donald W.

1990-01-01

To facilitate user proficiency with AMA/Net, an 80-minute training videotape has been produced. The production was designed to use videotape's advantages, where information and emotion are combined; and to accommodate its chief disadvantage, lack of resolution for fine text, with close-ups and graphics. Content of the videotape was conceived, outlined, demonstrated with simultaneous text capture, edited into script form, narration added, and scripts marked for videotaping and narrating. Videotaping was performed with actual keyboard sounds for realism. The recording was divided into four areas: office mock-up, keyboard close-ups, scan-conversion and screen close-ups. Once the footage was recorded, it was logged and rough-edited. Care was taken to balance the pace of the program with visual stimulation and amount of narration. The final edit was performed as a culmination of all scripts, video materials and rough edit, with graphics and steady change of visual information offsetting the static nature of the screen display. Carefully planned video programs can be a useful and economical adjunct in the training process for online services.

Treadmill Training Enhances Axon Regeneration In Injured Mouse Peripheral Nerves Without Increased Loss of Topographic Specificity

PubMed Central

English, Arthur W.; Cucoranu, Delia; Mulligan, Amanda; Sabatier, Manning

2009-01-01

We investigated the extent of misdirection of regenerating axons when that regeneration was enhanced using treadmill training. Retrograde fluorescent tracers were applied to the cut proximal stumps of the tibial and common fibular nerves two or four weeks after transection and surgical repair of the mouse sciatic nerve. The spatial locations of retrogradely labeled motoneurons were studied in untreated control mice and in mice receiving two weeks of treadmill training, either according to a continuous protocol (10 m/min, one hour/day, five day/week) or an interval protocol (20 m/min for two minutes, followed by a five minute rest, repeated 4 times, five days/week). More retrogradely labeled motoneurons were found in both treadmill trained groups. The magnitude of this increase was as great as or greater than that found after using other enhancement strategies. In both treadmill trained groups, the proportions of motoneurons labeled from tracer applied to the common fibular nerve that were found in spinal cord locations reserved for tibial motoneurons in intact mice was no greater than in untreated control mice and significantly less than found after electrical stimulation or chondroitinase treatment. Treadmill training in the first two weeks following peripheral nerve injury produces a marked enhancement of motor axon regeneration without increasing the propensity of those axons to choose pathways leading to functionally inappropriate targets. PMID:19731339

An introduction to deep learning on biological sequence data: examples and solutions.

PubMed

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Labeling milk along its production chain with DNA encapsulated in silica.

PubMed

Bloch, Madeleine S; Paunescu, Daniela; Stoessel, Philipp R; Mora, Carlos A; Stark, Wendelin J; Grass, Robert N

2014-10-29

The capability of tracing a food product along its production chain is important to ensure food safety and product authenticity. For this purpose and as an application example, recently developed Silica Particles with Encapsulated DNA (SPED) were added to milk at concentrations ranging from 0.1 to 100 ppb (μg per kg milk). Thereby the milk, as well as the milk-derived products yoghurt and cheese, could be uniquely labeled with a DNA tag. Procedures for the extraction of the DNA tags from the food matrixes were elaborated and allowed identification and quantification of previously marked products by quantitative polymerase chain reaction (qPCR) with detection limits below 1 ppb of added particles. The applicability of synthetic as well as naturally occurring DNA sequences was shown. The usage of approved food additives as DNA carrier (silica = E551) and the low cost of the technology (<0.1 USD per ton of milk labeled with 10 ppb of SPED) display the technical applicability of this food labeling technology.

Person Perception and Verbal Labeling: The Development of Social Labels.

ERIC Educational Resources Information Center

Brooks-Gunn, Jeanne; Lewis, Michael

This study examined the social labels which are first used by infants, social differentiation on the basis of labeling behavior, and overgeneralization of social labels. Subjects were 81 infants from 9 to 36 months of age. The 9- to 24-month-olds were shown slides of themselves, their mothers, their fathers, and unfamiliar children, babies, and…

Automated annotation of functional imaging experiments via multi-label classification

PubMed Central

Turner, Matthew D.; Chakrabarti, Chayan; Jones, Thomas B.; Xu, Jiawei F.; Fox, Peter T.; Luger, George F.; Laird, Angela R.; Turner, Jessica A.

2013-01-01

Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert's annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k-nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text. PMID:24409112

Double labeling of human leukemic cells using /sup 3/H-cytarabine and monoclonal antibody against bromodeoxyuridine

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

Raza, A.; Preisler, H.D.

A new technique using immunofluorescence and autoradiography is described, in which the DNA of cells in S phase are labeled with two different probes. This method makes it possible to study the relationship between DNA synthesis and the uptake and/or incorporation of chemotherapeutic agents into normal or neoplastic cells. An example is provided in which the incorporation of /sup 3/H-cytarabine into DNA is demonstrated to occur only in cells which were synthesizing DNA during exposure to /sup 3/H-cytarabine. Other radioactively labeled probes can be used as well.

Where Do Features Come From?

ERIC Educational Resources Information Center

Hinton, Geoffrey

2014-01-01

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for…

[Labeling of food containing genetically modified organisms: international policies and Brazilian legislation].

PubMed

Costa, Thadeu Estevam Moreira Maramaldo; Marin, Victor Augustus

2011-08-01

The increase in surface area planted with genetically modified crops, with the subsequent transfer of such crops into the general environment for commercial trade, has raised questions about the safety of these products. The introduction of the Cartagena Protocol on Biosafety has led to the need to produce information and ensure training in this area for the implementation of policies on biosafety and for decision-making on the part of governments at the national, regional and international level. This article presents two main standpoints regarding the labeling of GM products (one adopted by the United States and the other by the European Union), as well as the position adopted by Brazil and its current legislation on labeling and commercial release of genetically modified (GM) products.

Evaluation of a novel educational strategy, including inhaler-based reminder labels, to improve asthma inhaler technique.

PubMed

Basheti, Iman A; Armour, Carol L; Bosnic-Anticevich, Sinthia Z; Reddel, Helen K

2008-07-01

To evaluate the feasibility, acceptability and effectiveness of a brief intervention about inhaler technique, delivered by community pharmacists to asthma patients. Thirty-one pharmacists received brief workshop education (Active: n=16, CONTROL: n=15). Active Group pharmacists were trained to assess and teach dry powder inhaler technique, using patient-centered educational tools including novel Inhaler Technique Labels. Interventions were delivered to patients at four visits over 6 months. At baseline, patients (Active: 53, CONTROL: 44) demonstrated poor inhaler technique (mean+/-S.D. score out of 9, 5.7+/-1.6). At 6 months, improvement in inhaler technique score was significantly greater in Active cf. CONTROL patients (2.8+/-1.6 cf. 0.9+/-1.4, p<0.001), and asthma severity was significantly improved (p=0.015). Qualitative responses from patients and pharmacists indicated a high level of satisfaction with the intervention and educational tools, both for their effectiveness and for their impact on the patient-pharmacist relationship. A simple feasible intervention in community pharmacies, incorporating daily reminders via Inhaler Technique Labels on inhalers, can lead to improvement in inhaler technique and asthma outcomes. Brief training modules and simple educational tools, such as Inhaler Technique Labels, can provide a low-cost and sustainable way of changing patient behavior in asthma, using community pharmacists as educators.

Sensing site-specific structural characteristics and chirality using vibrational circular dichroism of isotope labeled peptides.

PubMed

Keiderling, Timothy A

2017-12-01

Isotope labeling has a long history in chemistry as a tool for probing structure, offering enhanced sensitivity, or enabling site selection with a wide range of spectroscopic tools. Chirality sensitive methods such as electronic circular dichroism are global structural tools and have intrinsically low resolution. Consequently, they are generally insensitive to modifications to enhance site selectivity. The use of isotope labeling to modify vibrational spectra with unique resolvable frequency shifts can provide useful site-specific sensitivity, and these methods have been recently more widely expanded in biopolymer studies. While the spectral shifts resulting from changes in isotopic mass can provide resolution of modes from specific parts of the molecule and can allow detection of local change in structure with perturbation, these shifts alone do not directly indicate structure or chirality. With vibrational circular dichroism (VCD), the shifted bands and their resultant sign patterns can be used to indicate local conformations in labeled biopolymers, particularly if multiple labels are used and if their coupling is theoretically modeled. This mini-review discusses selected examples of the use of labeling specific amides in peptides to develop local structural insight with VCD spectra. © 2017 Wiley Periodicals, Inc.

Feature Acquisition with Imbalanced Training Data

NASA Technical Reports Server (NTRS)

Thompson, David R.; Wagstaff, Kiri L.; Majid, Walid A.; Jones, Dayton L.

2011-01-01

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

A comparison of methods for teaching receptive labeling to children with autism spectrum disorders.

PubMed

Grow, Laura L; Carr, James E; Kodak, Tiffany M; Jostad, Candice M; Kisamore, April N

2011-01-01

Many early intervention curricular manuals recommend teaching auditory-visual conditional discriminations (i.e., receptive labeling) using the simple-conditional method in which component simple discriminations are taught in isolation and in the presence of a distracter stimulus before the learner is required to respond conditionally. Some have argued that this procedure might be susceptible to faulty stimulus control such as stimulus overselectivity (Green, 2001). Consequently, there has been a call for the use of alternative teaching procedures such as the conditional-only method, which involves conditional discrimination training from the onset of intervention. The purpose of the present study was to compare the simple-conditional and conditional-only methods for teaching receptive labeling to 3 young children diagnosed with autism spectrum disorders. The data indicated that the conditional-only method was a more reliable and efficient teaching procedure. In addition, several error patterns emerged during training using the simple-conditional method. The implications of the results with respect to current teaching practices in early intervention programs are discussed.

A COMPARISON OF METHODS FOR TEACHING RECEPTIVE LABELING TO CHILDREN WITH AUTISM SPECTRUM DISORDERS

PubMed Central

Grow, Laura L; Carr, James E; Kodak, Tiffany M; Jostad, Candice M; Kisamore, April N

2011-01-01

Many early intervention curricular manuals recommend teaching auditory-visual conditional discriminations (i.e., receptive labeling) using the simple-conditional method in which component simple discriminations are taught in isolation and in the presence of a distracter stimulus before the learner is required to respond conditionally. Some have argued that this procedure might be susceptible to faulty stimulus control such as stimulus overselectivity (Green, 2001). Consequently, there has been a call for the use of alternative teaching procedures such as the conditional-only method, which involves conditional discrimination training from the onset of intervention. The purpose of the present study was to compare the simple-conditional and conditional-only methods for teaching receptive labeling to 3 young children diagnosed with autism spectrum disorders. The data indicated that the conditional-only method was a more reliable and efficient teaching procedure. In addition, several error patterns emerged during training using the simple-conditional method. The implications of the results with respect to current teaching practices in early intervention programs are discussed. PMID:21941380

Correlative fluorescence and scanning transmission electron microscopy of quantum dot-labeled proteins on whole cells in liquid.

PubMed

Peckys, Diana B; Bandmann, Vera; de Jonge, Niels

2014-01-01

Correlative fluorescence microscopy combined with scanning transmission electron microscopy (STEM) of cells fully immersed in liquid is a new methodology with many application areas. Proteins, in live cells immobilized on microchips, are labeled with fluorescent quantum dot nanoparticles. In this protocol, the epidermal growth factor receptor (EGFR) is labeled. The cells are fixed after a selected labeling time, for example, 5 min as needed to form EGFR dimers. The microchip with cells is then imaged with fluorescence microscopy. Thereafter, STEM can be accomplished in two ways. The microchip with the labeled cells and one microchip with a spacer are assembled into a special microfluidic device and imaged with dedicated high-voltage STEM. Alternatively, thin edges of cells can be studied with environmental scanning electron microscopy with a STEM detector, by placing a microchip with cells in a cooled wet environment. © 2014 Elsevier Inc. All rights reserved.

Label-Free Bioanalyte Detection from Nanometer to Micrometer Dimensions-Molecular Imprinting and QCMs †.

PubMed

Mujahid, Adnan; Mustafa, Ghulam; Dickert, Franz L

2018-06-01

Modern diagnostic tools and immunoassay protocols urges direct analyte recognition based on its intrinsic behavior without using any labeling indicator. This not only improves the detection reliability, but also reduces sample preparation time and complexity involved during labeling step. Label-free biosensor devices are capable of monitoring analyte physiochemical properties such as binding sensitivity and selectivity, affinity constants and other dynamics of molecular recognition. The interface of a typical biosensor could range from natural antibodies to synthetic receptors for example molecular imprinted polymers (MIPs). The foremost advantages of using MIPs are their high binding selectivity comparable to natural antibodies, straightforward synthesis in short time, high thermal/chemical stability and compatibility with different transducers. Quartz crystal microbalance (QCM) resonators are leading acoustic devices that are extensively used for mass-sensitive measurements. Highlight features of QCM devices include low cost fabrication, room temperature operation, and most importantly ability to monitor extremely low mass shifts, thus potentially a universal transducer. The combination of MIPs with quartz QCM has turned out as a prominent sensing system for label-free recognition of diverse bioanalytes. In this article, we shall encompass the potential applications of MIP-QCM sensors exclusively label-free recognition of bacteria and virus species as representative micro and nanosized bioanalytes.

Labelling fashion magazine advertisements: Effectiveness of different label formats on social comparison and body dissatisfaction.

PubMed

Tiggemann, Marika; Brown, Zoe

2018-06-01

The experiment investigated the impact on women's body dissatisfaction of different forms of label added to fashion magazine advertisements. Participants were 340 female undergraduate students who viewed 15 fashion advertisements containing a thin and attractive model. They were randomly allocated to one of five label conditions: no label, generic disclaimer label (indicating image had been digitally altered), consequence label (indicating that viewing images might make women feel bad about themselves), informational label (indicating the model in the advertisement was underweight), or a graphic label (picture of a paint brush). Although exposure to the fashion advertisements resulted in increased body dissatisfaction, there was no significant effect of label type on body dissatisfaction; no form of label demonstrated any ameliorating effect. In addition, the consequence and informational labels resulted in increased perceived realism and state appearance comparison. Yet more extensive research is required before the effective implementation of any form of label. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bar Code Labels

NASA Technical Reports Server (NTRS)

1988-01-01

American Bar Codes, Inc. developed special bar code labels for inventory control of space shuttle parts and other space system components. ABC labels are made in a company-developed anodizing aluminum process and consecutively marketed with bar code symbology and human readable numbers. They offer extreme abrasion resistance and indefinite resistance to ultraviolet radiation, capable of withstanding 700 degree temperatures without deterioration and up to 1400 degrees with special designs. They offer high resistance to salt spray, cleaning fluids and mild acids. ABC is now producing these bar code labels commercially or industrial customers who also need labels to resist harsh environments.

21 CFR 1302.04 - Location and size of symbol on label and labeling.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 9 2010-04-01 2010-04-01 false Location and size of symbol on label and labeling. 1302.04 Section 1302.04 Food and Drugs DRUG ENFORCEMENT ADMINISTRATION, DEPARTMENT OF JUSTICE LABELING AND PACKAGING REQUIREMENTS FOR CONTROLLED SUBSTANCES § 1302.04 Location and size of symbol on label...

21 CFR 1302.04 - Location and size of symbol on label and labeling.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 9 2011-04-01 2011-04-01 false Location and size of symbol on label and labeling. 1302.04 Section 1302.04 Food and Drugs DRUG ENFORCEMENT ADMINISTRATION, DEPARTMENT OF JUSTICE LABELING AND PACKAGING REQUIREMENTS FOR CONTROLLED SUBSTANCES § 1302.04 Location and size of symbol on label...

21 CFR 1302.04 - Location and size of symbol on label and labeling.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 9 2012-04-01 2012-04-01 false Location and size of symbol on label and labeling. 1302.04 Section 1302.04 Food and Drugs DRUG ENFORCEMENT ADMINISTRATION, DEPARTMENT OF JUSTICE LABELING AND PACKAGING REQUIREMENTS FOR CONTROLLED SUBSTANCES § 1302.04 Location and size of symbol on label...

40 CFR 60.536 - Permanent label, temporary label, and owner's manual.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Performance for New Residential Wood Heaters § 60.536 Permanent label, temporary label, and owner's manual. (a... section. (2) Except for wood heaters subject to § 60.530 (e), (f), or (g), the permanent label shall... material expected to last the lifetime of the wood heater, (iv) Present required information in a manner so...

40 CFR 60.536 - Permanent label, temporary label, and owner's manual.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Performance for New Residential Wood Heaters § 60.536 Permanent label, temporary label, and owner's manual. (a... section. (2) Except for wood heaters subject to § 60.530 (e), (f), or (g), the permanent label shall... material expected to last the lifetime of the wood heater, (iv) Present required information in a manner so...

40 CFR 60.536 - Permanent label, temporary label, and owner's manual.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Performance for New Residential Wood Heaters § 60.536 Permanent label, temporary label, and owner's manual. (a... section. (2) Except for wood heaters subject to § 60.530 (e), (f), or (g), the permanent label shall... material expected to last the lifetime of the wood heater, (iv) Present required information in a manner so...

40 CFR 60.536 - Permanent label, temporary label, and owner's manual.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Performance for New Residential Wood Heaters § 60.536 Permanent label, temporary label, and owner's manual. (a... section. (2) Except for wood heaters subject to § 60.530 (e), (f), or (g), the permanent label shall... material expected to last the lifetime of the wood heater, (iv) Present required information in a manner so...

40 CFR 60.536 - Permanent label, temporary label, and owner's manual.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Performance for New Residential Wood Heaters § 60.536 Permanent label, temporary label, and owner's manual. (a... section. (2) Except for wood heaters subject to § 60.530 (e), (f), or (g), the permanent label shall... material expected to last the lifetime of the wood heater, (iv) Present required information in a manner so...

A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Min-jae; Kim, Hannah; Kim, Eunso; Ki, Sojung; Shao, Lei; Ozcan, Aydogan

2016-03-01

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform's ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians'. Next, through a national-scale educational outreach program in South Korea we recruited >1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool's effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.

29 CFR 1960.59 - Training of employees and employee representatives.

Code of Federal Regulations, 2010 CFR

2010-07-01

... specialized job safety and health training appropriate to the work performed by the employee, for example: Clerical; printing; welding; crane operation; chemical analysis, and computer operations. Such training...

Training for Hospitality.

ERIC Educational Resources Information Center

Herman, Francine A.; Eller, Martha E.

1991-01-01

The labor problems of hotels and restaurants are being addressed with partnership training initiatives. Examples are the Stratford Chef's School in Ontario, the University of Hawaii's Tourism Industry Management Program, and an English-as-a-Second-Language program sponsored by labor and management in New York City hotels. (SK)

Synthesis of γ-Phosphate-Labeled and Doubly Labeled Adenosine Triphosphate Analogs.

PubMed

Hacker, Stephan M; Welter, Moritz; Marx, Andreas

2015-03-09

This unit describes the synthesis of γ-phosphate-labeled and doubly labeled adenosine triphosphate (ATP) analogs and their characterization using the phosphodiesterase I from Crotalus adamanteus (snake venom phosphodiesterase; SVPD). In the key step of the synthesis, ATP or an ATP analog, bearing a linker containing a trifluoroacetamide group attached to the nucleoside, are modified with an azide-containing linker at the terminal phosphate using an alkylation reaction. Subsequently, different labels are introduced to the linkers by transformation of one functional group to an amine and coupling to an N-hydroxysuccinimide ester. Specifically, the Staudinger reaction of the azide is employed as a straightforward means to obtain an amine in the presence of various labels. Furthermore, the fluorescence characteristics of a fluorogenic, doubly labeled ATP analog are investigated following enzymatic cleavage by SVPD. Copyright © 2015 John Wiley & Sons, Inc.

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

78 FR 24211 - Draft Guidance for Industry on Safety Considerations for Container Labels and Carton Labeling...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-24

... container labels and carton labeling design, is the second in a series of three planned guidance documents...] Draft Guidance for Industry on Safety Considerations for Container Labels and Carton Labeling Design To... entitled ``Safety Considerations for Container Labels and Carton Labeling Design to Minimize Medication...

Advanced Listening Comprehension Training in Occupational ESL.

ERIC Educational Resources Information Center

Smart, Graham

1982-01-01

In the English Language Training Unit of the Bank of Canada, the duties performed by an individual worker are examined closely and a language-use profile developed. Authentic recordings of specific problematic occupational situations are used and found to be highly motivating in training. Examples of exercise discourse are given. (MSE)

Comparison of the hedonic general Labeled Magnitude Scale with the hedonic 9-point scale.

PubMed

Kalva, Jaclyn J; Sims, Charles A; Puentes, Lorenzo A; Snyder, Derek J; Bartoshuk, Linda M

2014-02-01

The hedonic 9-point scale was designed to compare palatability among different food items; however, it has also been used occasionally to compare individuals and groups. Such comparisons can be invalid because scale labels (for example, "like extremely") can denote systematically different hedonic intensities across some groups. Addressing this problem, the hedonic general Labeled Magnitude Scale (gLMS) frames affective experience in terms of the strongest imaginable liking/disliking of any kind, which can yield valid group comparisons of food palatability provided extreme hedonic experiences are unrelated to food. For each scale, 200 panelists rated affect for remembered food products (including favorite and least favorite foods) and sampled foods; they also sampled taste stimuli (quinine, sucrose, NaCl, citric acid) and rated their intensity. Finally, subjects identified experiences representing the endpoints of the hedonic gLMS. Both scales were similar in their ability to detect within-subject hedonic differences across a range of food experiences, but group comparisons favored the hedonic gLMS. With the 9-point scale, extreme labels were strongly associated with extremes in food affect. In contrast, gLMS data showed that scale extremes referenced nonfood experiences. Perceived taste intensity significantly influenced differences in food liking/disliking (for example, those experiencing the most intense tastes, called supertasters, showed more extreme liking and disliking for their favorite and least favorite foods). Scales like the hedonic gLMS are suitable for across-group comparisons of food palatability. © 2014 Institute of Food Technologists®

Off-Label Drug Use

MedlinePlus

... their drugs for off-label uses. Off-label marketing is very different from off-label use. Why ... at a higher risk for medication errors, side effects, and unwanted drug reactions. It’s important that the ...

Doctoral training in behavior analysis: Training generalized problem-solving skills

PubMed Central

Chase, Philip N.; Wylie, Ruth G.

1985-01-01

This essay provides guidelines for designing a doctoral program in behavior analysis. First, we propose a general accomplishment for all behavior analytic doctoral students: that they be able to solve problems concerning individual behavior within a range of environments. Second, in order to achieve this goal, we propose that students be trained in conceptual and experimental analysis of behavior, the application of behavioral principles and the administration of behavioral programs. This training should include class work, but it should emphasize the immersion of students in a variety of environments in which they are required to use behavior analytic strategies. Third, we provide an example of a hypothetical graduate program that involves the proposed training. Finally, an evaluation plan is suggested for determining whether a training program is in fact producing students who are generalized problem-solvers. At each step, we justify our point of view from a perspective that combines principles from behavior analysis and educational systems design. PMID:22478633

Use of quadrupedal step training to re-engage spinal interneuronal networks and improve locomotor function after spinal cord injury.

PubMed

Shah, Prithvi K; Garcia-Alias, Guillermo; Choe, Jaehoon; Gad, Parag; Gerasimenko, Yury; Tillakaratne, Niranjala; Zhong, Hui; Roy, Roland R; Edgerton, V Reggie

2013-11-01

Can lower limb motor function be improved after a spinal cord lesion by re-engaging functional activity of the upper limbs? We addressed this issue by training the forelimbs in conjunction with the hindlimbs after a thoracic spinal cord hemisection in adult rats. The spinal circuitries were more excitable, and behavioural and electrophysiological analyses showed improved hindlimb function when the forelimbs were engaged simultaneously with the hindlimbs during treadmill step-training as opposed to training only the hindlimbs. Neuronal retrograde labelling demonstrated a greater number of propriospinal labelled neurons above and below the thoracic lesion site in quadrupedally versus bipedally trained rats. The results provide strong evidence that actively engaging the forelimbs improves hindlimb function and that one likely mechanism underlying these effects is the reorganization and re-engagement of rostrocaudal spinal interneuronal networks. For the first time, we provide evidence that the spinal interneuronal networks linking the forelimbs and hindlimbs are amenable to a rehabilitation training paradigm. Identification of this phenomenon provides a strong rationale for proceeding toward preclinical studies for determining whether training paradigms involving upper arm training in concert with lower extremity training can enhance locomotor recovery after neurological damage.

Methodology of shooting training using modern IT techniques

NASA Astrophysics Data System (ADS)

Gudzbeler, Grzegorz; Struniawski, Jarosław

2017-08-01

Mastering, improvement, shaping and preservation of skills of safe, efficient and effective use of the firearm requires the use of relevant methodology of conducting the shooting training. However reality of police trainings does not usually allow for intensive training shooting with the use of ammunition. An alternative solution is the use of modern training technologies. Example of this is the "Virtual system of improvement tactics of intervention services responsible for security and shooting training." Introduction of stimulator to police trainings will enable complete stuff preparation to achieve its tasks, creating potential of knowledge and experience in many areas, far exceeding the capabilities of conventional training.

Bioinformatics training: selecting an appropriate learning content management system--an example from the European Bioinformatics Institute.

PubMed

Wright, Victoria Ann; Vaughan, Brendan W; Laurent, Thomas; Lopez, Rodrigo; Brooksbank, Cath; Schneider, Maria Victoria

2010-11-01

Today's molecular life scientists are well educated in the emerging experimental tools of their trade, but when it comes to training on the myriad of resources and tools for dealing with biological data, a less ideal situation emerges. Often bioinformatics users receive no formal training on how to make the most of the bioinformatics resources and tools available in the public domain. The European Bioinformatics Institute, which is part of the European Molecular Biology Laboratory (EMBL-EBI), holds the world's most comprehensive collection of molecular data, and training the research community to exploit this information is embedded in the EBI's mission. We have evaluated eLearning, in parallel with face-to-face courses, as a means of training users of our data resources and tools. We anticipate that eLearning will become an increasingly important vehicle for delivering training to our growing user base, so we have undertaken an extensive review of Learning Content Management Systems (LCMSs). Here, we describe the process that we used, which considered the requirements of trainees, trainers and systems administrators, as well as taking into account our organizational values and needs. This review describes the literature survey, user discussions and scripted platform testing that we performed to narrow down our choice of platform from 36 to a single platform. We hope that it will serve as guidance for others who are seeking to incorporate eLearning into their bioinformatics training programmes.

U.S. consumer demand for restaurant calorie information: targeting demographic and behavioral segments in labeling initiatives.

PubMed

Kolodinsky, Jane; Reynolds, Travis William; Cannella, Mark; Timmons, David; Bromberg, Daniel

2009-01-01

To identify different segments of U.S. consumers based on food choices, exercise patterns, and desire for restaurant calorie labeling. Using a stratified (by region) random sample of the U.S. population, trained interviewers collected data for this cross-sectional study through telephone surveys. Center for Rural Studies U.S. national health survey. The final sample included 580 responses (22% response rate); data were weighted to be representative of age and gender characteristics of the U.S. population. Self-reported behaviors related to food choices, exercise patterns, desire for calorie information in restaurants, and sample demographics. Clusters were identified using Schwartz Bayesian criteria. Impacts of demographic characteristics on cluster membership were analyzed using bivariate tests of association and multinomial logit regression. Cluster analysis revealed three clusters based on respondents' food choices, activity levels, and desire for restaurant labeling. Two clusters, comprising three quarters of the sample, desired calorie labeling in restaurants. The remaining cluster opposed restaurant labeling. Demographic variables significantly predicting cluster membership included region of residence (p < .10), income (p < .05), gender (p < .01), and age (p < .10). Though limited by a low response and potential self-reporting bias in the phone survey, this study suggests that several groups are likely to benefit from restaurant calorie labeling. Specific demographic clusters could be targeted through labeling initiatives.

Photoaffinity labeling in target- and binding-site identification

PubMed Central

Smith, Ewan; Collins, Ian

2015-01-01

Photoaffinity labeling (PAL) using a chemical probe to covalently bind its target in response to activation by light has become a frequently used tool in drug discovery for identifying new drug targets and molecular interactions, and for probing the location and structure of binding sites. Methods to identify the specific target proteins of hit molecules from phenotypic screens are highly valuable in early drug discovery. In this review, we summarize the principles of PAL including probe design and experimental techniques for in vitro and live cell investigations. We emphasize the need to optimize and validate probes and highlight examples of the successful application of PAL across multiple disease areas. PMID:25686004

High-coverage quantitative proteomics using amine-specific isotopic labeling.

PubMed

Melanson, Jeremy E; Avery, Steven L; Pinto, Devanand M

2006-08-01

Peptide dimethylation with isotopically coded formaldehydes was evaluated as a potential alternative to techniques such as the iTRAQ method for comparative proteomics. The isotopic labeling strategy and custom-designed protein quantitation software were tested using protein standards and then applied to measure proteins levels associated with Alzheimer's disease (AD). The method provided high accuracy (10% error), precision (14% RSD) and coverage (70%) when applied to the analysis of a standard solution of BSA by LC-MS/MS. The technique was then applied to measure protein abundance levels in brain tissue afflicted with AD relative to normal brain tissue. 2-D LC-MS analysis identified 548 unique proteins (p<0.05). Of these, 349 were quantified with two or more peptides that met the statistical criteria used in this study. Several classes of proteins exhibited significant changes in abundance. For example, elevated levels of antioxidant proteins and decreased levels of mitochondrial electron transport proteins were observed. The results demonstrate the utility of the labeling method for high-throughput quantitative analysis.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well

To label or not to label: applications of quantitative proteomics in neuroscience research.

PubMed

Filiou, Michaela D; Martins-de-Souza, Daniel; Guest, Paul C; Bahn, Sabine; Turck, Christoph W

2012-02-01

Proteomics has provided researchers with a sophisticated toolbox of labeling-based and label-free quantitative methods. These are now being applied in neuroscience research where they have already contributed to the elucidation of fundamental mechanisms and the discovery of candidate biomarkers. In this review, we evaluate and compare labeling-based and label-free quantitative proteomic techniques for applications in neuroscience research. We discuss the considerations required for the analysis of brain and central nervous system specimens, the experimental design of quantitative proteomic workflows as well as the feasibility, advantages, and disadvantages of the available techniques for neuroscience-oriented questions. Furthermore, we assess the use of labeled standards as internal controls for comparative studies in humans and review applications of labeling-based and label-free mass spectrometry approaches in relevant model organisms and human subjects. Providing a comprehensive guide of feasible and meaningful quantitative proteomic methodologies for neuroscience research is crucial not only for overcoming current limitations but also for gaining useful insights into brain function and translating proteomics from bench to bedside. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Derivation and evaluation of a labeled hedonic scale.

PubMed

Lim, Juyun; Wood, Alison; Green, Barry G

2009-11-01

The objective of this study was to develop a semantically labeled hedonic scale (LHS) that would yield ratio-level data on the magnitude of liking/disliking of sensation equivalent to that produced by magnitude estimation (ME). The LHS was constructed by having 49 subjects who were trained in ME rate the semantic magnitudes of 10 common hedonic descriptors within a broad context of imagined hedonic experiences that included tastes and flavors. The resulting bipolar scale is statistically symmetrical around neutral and has a unique semantic structure. The LHS was evaluated quantitatively by comparing it with ME and the 9-point hedonic scale. The LHS yielded nearly identical ratings to those obtained using ME, which implies that its semantic labels are valid and that it produces ratio-level data equivalent to ME. Analyses of variance conducted on the hedonic ratings from the LHS and the 9-point scale gave similar results, but the LHS showed much greater resistance to ceiling effects and yielded normally distributed data, whereas the 9-point scale did not. These results indicate that the LHS has significant semantic, quantitative, and statistical advantages over the 9-point hedonic scale.

Hierarchical Multi-atlas Label Fusion with Multi-scale Feature Representation and Label-specific Patch Partition

PubMed Central

Wu, Guorong; Kim, Minjeong; Sanroma, Gerard; Wang, Qian; Munsell, Brent C.; Shen, Dinggang

2014-01-01

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images, after registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it’s critical the chosen patch similarity measurement accurately captures the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchically approach is used to improve the

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2013-01-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

A rater training protocol to assess team performance.

PubMed

Eppich, Walter; Nannicelli, Anna P; Seivert, Nicholas P; Sohn, Min-Woong; Rozenfeld, Ranna; Woods, Donna M; Holl, Jane L

2015-01-01

Simulation-based methodologies are increasingly used to assess teamwork and communication skills and provide team training. Formative feedback regarding team performance is an essential component. While effective use of simulation for assessment or training requires accurate rating of team performance, examples of rater-training programs in health care are scarce. We describe our rater training program and report interrater reliability during phases of training and independent rating. We selected an assessment tool shown to yield valid and reliable results and developed a rater training protocol with an accompanying rater training handbook. The rater training program was modeled after previously described high-stakes assessments in the setting of 3 facilitated training sessions. Adjacent agreement was used to measure interrater reliability between raters. Nine raters with a background in health care and/or patient safety evaluated team performance of 42 in-situ simulations using post-hoc video review. Adjacent agreement increased from the second training session (83.6%) to the third training session (85.6%) when evaluating the same video segments. Adjacent agreement for the rating of overall team performance was 78.3%, which was added for the third training session. Adjacent agreement was 97% 4 weeks posttraining and 90.6% at the end of independent rating of all simulation videos. Rater training is an important element in team performance assessment, and providing examples of rater training programs is essential. Articulating key rating anchors promotes adequate interrater reliability. In addition, using adjacent agreement as a measure allows differentiation between high- and low-performing teams on video review. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Capacitive label reader

DOEpatents

Arlowe, H. Duane

1985-01-01

A capacitive label reader includes an outer ring transmitting portion, an inner ring transmitting portion, and a plurality of insulated receiving portions. A label is the mirror-image of the reader except that identifying portions corresponding to the receiving portions are insulated from only one of two coupling elements. Positive and negative pulses applied, respectively, to the two transmitting rings biased a CMOS shift register positively to either a 1 or 0 condition. The output of the CMOS may be read as an indication of the label.

Capacitive label reader

DOEpatents

Arlowe, H.D.

1983-07-15

A capacitive label reader includes an outer ring transmitting portion, an inner ring transmitting portion, and a plurality of insulated receiving portions. A label is the mirror-image of the reader except that identifying portions corresponding to the receiving portions are insulated from only one of two coupling elements. Positive and negative pulses applied, respectively, to the two transmitting rings biased a CMOS shift register positively to either a 1 or 0 condition. The output of the CMOS may be read as an indication of the label.

16 CFR 460.12 - Labels.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Practices FEDERAL TRADE COMMISSION TRADE REGULATION RULES LABELING AND ADVERTISING OF HOME INSULATION § 460.12 Labels. If you are a manufacturer, you must label all packages of your insulation. The labels must contain: (a) The type of insulation. (b) A chart showing these items: (1) For batts and blankets of any...

16 CFR 460.12 - Labels.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Practices FEDERAL TRADE COMMISSION TRADE REGULATION RULES LABELING AND ADVERTISING OF HOME INSULATION § 460.12 Labels. If you are a manufacturer, you must label all packages of your insulation. The labels must contain: (a) The type of insulation. (b) A chart showing these items: (1) For batts and blankets of any...

16 CFR 460.12 - Labels.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Practices FEDERAL TRADE COMMISSION TRADE REGULATION RULES LABELING AND ADVERTISING OF HOME INSULATION § 460.12 Labels. If you are a manufacturer, you must label all packages of your insulation. The labels must contain: (a) The type of insulation. (b) A chart showing these items: (1) For batts and blankets of any...

16 CFR 460.12 - Labels.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Practices FEDERAL TRADE COMMISSION TRADE REGULATION RULES LABELING AND ADVERTISING OF HOME INSULATION § 460.12 Labels. If you are a manufacturer, you must label all packages of your insulation. The labels must contain: (a) The type of insulation. (b) A chart showing these items: (1) For batts and blankets of any...

16 CFR 460.12 - Labels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Practices FEDERAL TRADE COMMISSION TRADE REGULATION RULES LABELING AND ADVERTISING OF HOME INSULATION § 460.12 Labels. If you are a manufacturer, you must label all packages of your insulation. The labels must contain: (a) The type of insulation. (b) A chart showing these items: (1) For batts and blankets of any...

21 CFR 895.25 - Labeling.

Code of Federal Regulations, 2010 CFR

2010-04-01

... eliminated by labeling or a change in labeling, or change in advertising if the device is a restricted device... person(s) responsible for the labeling or advertising of the device specifying: (1) The deception or risk... labeling, or change in advertising if the device is a restricted device, necessary to correct the deception...

Review of nutrition labeling formats.

PubMed

Geiger, C J; Wyse, B W; Parent, C R; Hansen, R G

1991-07-01

This article examines nutrition labeling history as well as the findings of nine research studies of nutrition labeling formats. Nutrition labeling regulations were announced in 1973 and have been periodically amended since then. In response to requests from consumers and health care professionals for revision of the labeling system, the Food and Drug Administration initiated a three-phase plan for reform of nutrition labeling in 1990. President Bush signed the Nutrition Labeling and Education Act in November 1990. Literature analysis revealed that only nine studies with an experimental design have focused on nutrition labeling since 1971. Four were conducted before 1975, which was the year that nutrition labeling was officially implemented, two were conducted in 1980, and three were conducted after 1986. Only two of the nine studies supported the traditional label format mandated by the Code of Federal Regulations, and one study partially supported it. Four of the nine studies that evaluated graphic presentations of nutrition information found that consumer comprehension of nutrition information was improved with a graphic format for nutrition labeling: three studies supported the use of bar graphs and one study supported the use of a pie chart. Full disclosure (ie, complete nutrient and ingredient labeling) was preferred by consumers in two of the three studies that examined this variable. The third study supported three types of information disclosure dependent upon socioeconomic class. In those studies that tested graphics, a bar graph format was significantly preferred and showed better consumer comprehension than the traditional format.

40 CFR 168.65 - Pesticide export label and labeling requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... toxic pesticides. If the pesticide, device or active ingredient is highly toxic to humans, the skull and... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Pesticide export label and labeling...) PESTICIDE PROGRAMS STATEMENTS OF ENFORCEMENT POLICIES AND INTERPRETATIONS Export Policy and Procedures for...

40 CFR 168.65 - Pesticide export label and labeling requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... toxic pesticides. If the pesticide, device or active ingredient is highly toxic to humans, the skull and... 40 Protection of Environment 24 2011-07-01 2011-07-01 false Pesticide export label and labeling...) PESTICIDE PROGRAMS STATEMENTS OF ENFORCEMENT POLICIES AND INTERPRETATIONS Export Policy and Procedures for...

40 CFR 168.65 - Pesticide export label and labeling requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... toxic pesticides. If the pesticide, device or active ingredient is highly toxic to humans, the skull and... 40 Protection of Environment 25 2012-07-01 2012-07-01 false Pesticide export label and labeling...) PESTICIDE PROGRAMS STATEMENTS OF ENFORCEMENT POLICIES AND INTERPRETATIONS Export Policy and Procedures for...

Assertiveness Training: A Program for High School Students.

ERIC Educational Resources Information Center

Jean-Grant, Deborah S.

1980-01-01

Proposes an assertiveness training program suitable for adolescents in a high school group setting. After role-playing examples, students should begin formulating their own responses. Early work in this area indicates that students eagerly participate in assertiveness training groups, and are quick to pick up the skills required for assertive…

Advanced Pediatric Brain Imaging Research and Training Program

DTIC Science & Technology

2013-10-01

diffusion tensor imaging and perfusion ( arterial spin labeling) MRI data and to relate measures of global and regional brain microstructural organization...AD_________________ Award Number: W81XWH-11-2-0198 TITLE: Advanced Pediatric Brain Imaging...September 2013 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Advanced Pediatric Brain Imaging Research and Training Program 5b. GRANT NUMBER W81XWH

Do nutrition labels improve dietary outcomes?

PubMed

Variyam, Jayachandran N

2008-06-01

The disclosure of nutritional characteristics of most packaged foods became mandatory in the United States with the implementation of the Nutrition Labeling and Education Act (NLEA) in 1994. Under the NLEA regulations, a 'Nutrition Facts' panel displays information on nutrients such as calories, total and saturated fats, cholesterol, and sodium in a standardized format. By providing nutrition information in a credible, distinctive, and easy-to-read format, the new label was expected to help consumers choose healthier, more nutritious diets. This paper examines whether the disclosure of nutrition information through the mandatory labels impacted consumer diets. Assessing the dietary effects of labeling is problematic due to the confounding of the label effect with unobserved label user characteristics. This self-selection problem is addressed by exploiting the fact that the NLEA exempts away-from-home foods from mandatory labeling. Difference-in-differences models that account for zero away-from-home intakes suggest that the labels increase fiber and iron intakes of label users compared with label nonusers. In comparison, a model that does not account for self-selection implies significant label effects for all but two of the 13 nutrients that are listed on the label.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

PubMed Central

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-01-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications. PMID:26525841

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope.

PubMed

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-03

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

NASA Astrophysics Data System (ADS)

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

An interactive three-dimensional virtual body structures system for anatomical training over the internet.

PubMed

Temkin, Bharti; Acosta, Eric; Malvankar, Ameya; Vaidyanath, Sreeram

2006-04-01

The Visible Human digital datasets make it possible to develop computer-based anatomical training systems that use virtual anatomical models (virtual body structures-VBS). Medical schools are combining these virtual training systems and classical anatomy teaching methods that use labeled images and cadaver dissection. In this paper we present a customizable web-based three-dimensional anatomy training system, W3D-VBS. W3D-VBS uses National Library of Medicine's (NLM) Visible Human Male datasets to interactively locate, explore, select, extract, highlight, label, and visualize, realistic 2D (using axial, coronal, and sagittal views) and 3D virtual structures. A real-time self-guided virtual tour of the entire body is designed to provide detailed anatomical information about structures, substructures, and proximal structures. The system thus facilitates learning of visuospatial relationships at a level of detail that may not be possible by any other means. The use of volumetric structures allows for repeated real-time virtual dissections, from any angle, at the convenience of the user. Volumetric (3D) virtual dissections are performed by adding, removing, highlighting, and labeling individual structures (and/or entire anatomical systems). The resultant virtual explorations (consisting of anatomical 2D/3D illustrations and animations), with user selected highlighting colors and label positions, can be saved and used for generating lesson plans and evaluation systems. Tracking users' progress using the evaluation system helps customize the curriculum, making W3D-VBS a powerful learning tool. Our plan is to incorporate other Visible Human segmented datasets, especially datasets with higher resolutions, that make it possible to include finer anatomical structures such as nerves and small vessels. (c) 2006 Wiley-Liss, Inc.

78 FR 47154 - Food Labeling; Gluten-Free Labeling of Foods

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-05

...The Food and Drug Administration (FDA or we) is issuing a final rule to define the term ``gluten-free'' for voluntary use in the labeling of foods. The final rule defines the term ``gluten-free'' to mean that the food bearing the claim does not contain an ingredient that is a gluten-containing grain (e.g., spelt wheat); an ingredient that is derived from a gluten-containing grain and that has not been processed to remove gluten (e.g., wheat flour); or an ingredient that is derived from a gluten-containing grain and that has been processed to remove gluten (e.g., wheat starch), if the use of that ingredient results in the presence of 20 parts per million (ppm) or more gluten in the food (i.e., 20 milligrams (mg) or more gluten per kilogram (kg) of food); or inherently does not contain gluten; and that any unavoidable presence of gluten in the food is below 20 ppm gluten (i.e., below 20 mg gluten per kg of food). A food that bears the claim ``no gluten,'' ``free of gluten,'' or ``without gluten'' in its labeling and fails to meet the requirements for a ``gluten-free'' claim will be deemed to be misbranded. In addition, a food whose labeling includes the term ``wheat'' in the ingredient list or in a separate ``Contains wheat'' statement as required by a section of the Federal Food, Drug, and Cosmetic Act (the FD&C Act) and also bears the claim ``gluten-free'' will be deemed to be misbranded unless its labeling also bears additional language clarifying that the wheat has been processed to allow the food to meet FDA requirements for a ``gluten-free'' claim. Establishing a definition of the term ``gluten-free'' and uniform conditions for its use in food labeling will help ensure that individuals with celiac disease are not misled and are provided with truthful and accurate information with respect to foods so labeled. We are issuing the final rule under the Food Allergen Labeling and Consumer Protection Act of 2004 (FALCPA).

Like your labels?

PubMed

Field, Michele

2010-01-01

The descriptive “conventions” used on food labels are always evolving. Today, however, the changes are so complicated (partly driven by legislation requiring disclosures about environmental impacts, health issues, and geographical provenance) that these labels more often baffle buyers than enlighten them. In a light-handed manner, the article points to how sometimes reading label language can be like deciphering runes—and how if we are familiar with the technical terms, we can find a literal meaning, but still not see the implications. The article could be ten times longer because food labels vary according to cultures—but all food-exporting cultures now take advantage of our short attention-span when faced with these texts. The question is whether less is more—and if so, in this contest for our attention, what “contestant” is voted off.

Training at altitude in practice.

PubMed

Dick, F W

1992-10-01

There can be little doubt that training at altitude is fundamental to preparing an athlete for competition at altitude. However the value of training at altitude for competition at sea level appears on the one hand to lack total acceptance amongst sports scientists; and on the other to hold some cloak of mystery for coaches who have yet to enjoy first hand experience. The fact is that very few endurance athletes will ignore the critical edge which altitude training affords. Each fraction of a percentage of performance advantage gained through methods which are within the rules of fair play in sport, may shift the balance between failure and achievement. Moreover, there is growing support for application of training at altitude for speed-related disciplines. This paper aims to demystify the subject by dealing with practical aspects of training at altitude. Such aspects include a checklist of what should and should not be done at altitude, when to use altitude relative to target competitions, and specific training examples.

VE at Scope Time (VEST): Three construction examples

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

Sperling, R.B.

1991-04-01

Value Engineering at Scope Time (VEST)'' was published in Value World, January-February-March 1991. That article describes VEST as a four-phase process utilizing the heart'' of VE methodology, which is designed to be used with members of construction design teams to help them focus on the scope of work by doing cost modeling, function analysis, brainstorming and evaluation of ideas. With minimal training designers, architects and engineers can become energized to find creative design solutions and learn an effective, synergistic team approach to facilities design projects using VEST. If time is available, the team can begin the development of some highermore » ranked ideas into preliminary proposals. This paper is an expansion of that article, adding a brief section on training and presenting three examples of VEST on construction projects at a federally-funded research Laboratory.« less

Capacitive label reader

DOEpatents

Arlowe, H.D.

1985-11-12

A capacitive label reader includes an outer ring transmitting portion, an inner ring transmitting portion, and a plurality of insulated receiving portions. A label is the mirror-image of the reader except that identifying portions corresponding to the receiving portions are insulated from only one of two coupling elements. Positive and negative pulses applied, respectively, to the two transmitting rings biased a CMOS shift register positively to either a 1 or 0 condition. The output of the CMOS may be read as an indication of the label. 5 figs.

Erathostenes: An Example of Work with University Students in Didactics and History of Astronomy

NASA Astrophysics Data System (ADS)

Lanciano, Nicoletta; Berardo, Mariangela

2016-12-01

We present below, through an example, the richness of the use of a method of clues to enter the history of Astronomy, tested with university students and teachers in training. The question presented as an example is the study of the work of Eratosthenes to measure the Earth's meridian. It shows how the course generates a chain of questions and new questions and problems arise as the students learn to look for answers and solutions.

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.

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Production of isotopically labeled standards from a uniformly labeled precursor for quantitative volatile metabolomic studies.

PubMed

Gómez-Cortés, Pilar; Brenna, J Thomas; Sacks, Gavin L

2012-06-19

Optimal accuracy and precision in small-molecule profiling by mass spectrometry generally requires isotopically labeled standards chemically representative of all compounds of interest. However, preparation of mixed standards from commercially available pure compounds is often prohibitively expensive and time-consuming, and many labeled compounds are not available in pure form. We used a single-prototype uniformly labeled [U-(13)C]compound to generate [U-(13)C]-labeled volatile standards for use in subsequent experimental profiling studies. [U-(13)C]-α-Linolenic acid (18:3n-3, ALA) was thermally oxidized to produce labeled lipid degradation volatiles which were subsequently characterized qualitatively and quantitatively. Twenty-five [U-(13)C]-labeled volatiles were identified by headspace solid-phase microextraction-gas chromatography/time-of-flight mass spectrometry (HS-SPME-GC/TOF-MS) by comparison of spectra with unlabeled volatiles. Labeled volatiles were quantified by a reverse isotope dilution procedure. Using the [U-(13)C]-labeled standards, limits of detection comparable to or better than those of previous HS-SPME reports were achieved, 0.010-1.04 ng/g. The performance of the [U-(13)C]-labeled volatile standards was evaluated using a commodity soybean oil (CSO) oxidized at 60 °C from 0 to 15 d. Relative responses of n-decane, an unlabeled internal standard otherwise absent from the mixture, and [U-(13)C]-labeled oxidation products changed by up to 8-fold as the CSO matrix was oxidized, demonstrating that reliance on a single standard in volatile profiling studies yields inaccurate results due to changing matrix effects. The [U-(13)C]-labeled standard mixture was used to quantify 25 volatiles in oxidized CSO and low-ALA soybean oil with an average relative standard deviation of 8.5%. Extension of this approach to other labeled substrates, e.g., [U-(13)C]-labeled sugars and amino acids, for profiling studies should be feasible and can dramatically improve

Fluorescence labeling of colloidal core-shell particles with defined isoelectric points for in vitro studies.

PubMed

Daberkow, Timo; Meder, Fabian; Treccani, Laura; Schowalter, Marco; Rosenauer, Andreas; Rezwan, Kurosch

2012-02-01

In the light of in vitro nanotoxicological studies fluorescence labeling has become standard for particle localization within the cell environment. However, fluorescent labeling is also known to significantly alter the particle surface chemistry and therefore potentially affect the outcome of cell studies. Hence, fluorescent labeling is ideally carried out without changing, for example, the isoelectric point. A simple and straightforward method for obtaining fluorescently labeled spherical metal oxide particles with well-defined isoelectric points and a narrow size distribution is presented in this study. Spherical amorphous silica (SiO2, 161 nm diameter) particles were used as the substrate material and were coated with silica, alumina (Al2O3), titania (TiO2), or zirconia (ZrO2) using sol-gel chemistry. Fluorescent labeling was achieved by directly embedding rhodamine 6G dye in the coating matrix without affecting the isoelectric point of the metal oxide coatings. The coating quality was confirmed by high resolution transmission electron microscopy, energy filtered transmission electron microscopy and electrochemical characterization. The coatings were proven to be stable for at least 240 h under different pH conditions. The well-defined fluorescent particles can be directly used for biomedical investigations, e.g. elucidation of particle-cell interactions in vitro. Copyright © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Dietary Supplement Label Database (DSLD)

MedlinePlus

... be an educational and research tool for students, academics, and other professionals. Disclaimer: All information contained in the Dietary Supplement Label Database (DSLD) comes from product labels. Label information has ...

27 CFR 31.212 - Labeling.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Labeling. Every dealer packaging alcohol for industrial use must affix to each package filled a label... label other appropriate statements; however, such statements must not obscure or contradict the data...

27 CFR 31.212 - Labeling.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Labeling. Every dealer packaging alcohol for industrial use must affix to each package filled a label... label other appropriate statements; however, such statements must not obscure or contradict the data...

27 CFR 31.212 - Labeling.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Labeling. Every dealer packaging alcohol for industrial use must affix to each package filled a label... label other appropriate statements; however, such statements must not obscure or contradict the data...

27 CFR 31.212 - Labeling.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Labeling. Every dealer packaging alcohol for industrial use must affix to each package filled a label... label other appropriate statements; however, such statements must not obscure or contradict the data...

27 CFR 31.212 - Labeling.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Labeling. Every dealer packaging alcohol for industrial use must affix to each package filled a label... label other appropriate statements; however, such statements must not obscure or contradict the data...

Applications of neural networks in training science.

PubMed

Pfeiffer, Mark; Hohmann, Andreas

2012-04-01

Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming. Copyright © 2011 Elsevier B.V. All rights reserved.

Porosity estimation by semi-supervised learning with sparsely available labeled samples

NASA Astrophysics Data System (ADS)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

[From the Residency Training in the United States to See the Challenges and Directions of China Residency Standardized Training].

PubMed

Cui, Yong; Wang, Tianyou

2016-06-20

Resident standardization training has been started and spreaded out gradually in China. Resident standardization training is crucial to ensure the clinician homogenization, improve medical service quality and level of medical treatment and health care, so it received much attention from all sides. Residency training in American has a history of nearly a century. Systematic model of residency training in the United States had been established for nearly 50 years, and it is a typical representative and successful example of the western medical education. The purpose of this paper is to discuss the institutional arrangements and development direction of the resident standardization training in China, based on comparison of the two residency training system between the two countries on target, schedule, management institution, evaluation and remuneration.

Virtual reality as a tool for cross-cultural communication: an example from military team training

NASA Astrophysics Data System (ADS)

Downes-Martin, Stephen; Long, Mark; Alexander, Joanna R.

1992-06-01

A major problem with communication across cultures, whether professional or national, is that simple language translation if often insufficient to communicate the concepts. This is especially true when the communicators come from highly specialized fields of knowledge or from national cultures with long histories of divergence. This problem becomes critical when the goal of the communication is national negotiation dealing with such high risk items as arms negotiation or trade wars. Virtual Reality technology has considerable potential for facilitating communication across cultures, by immersing the communicators within multiple visual representations of the concepts, and providing control over those representations. Military distributed team training provides a model for virtual reality suitable for cross cultural communication such as negotiation. In both team training and negotiation, the participants must cooperate, agree on a set of goals, and achieve mastery over the concepts being negotiated. Team training technologies suitable for supporting cross cultural negotiation exist (branch wargaming, computer image generation and visualization, distributed simulation), and have developed along different lines than traditional virtual reality technology. Team training de-emphasizes the realism of physiological interfaces between the human and the virtual reality, and emphasizes the interaction of humans with each other and with intelligent simulated agents within the virtual reality. This approach to virtual reality is suggested as being more fruitful for future work.

Does the Drug Facts Label for nonprescription drugs meet its design objectives? A new procedure for assessing label effectiveness

PubMed Central

Ryan, Michael P; Costello-White, Reagan N

2017-01-01

We demonstrate an expanded procedure for assessing drug-label comprehension. Innovations include a pretest of drug preconceptions, verbal ability and label attentiveness measures, a label-scanning task, a free-recall test, category-clustering measures, and preconception-change scores. In total, 55 female and 39 male undergraduates read a facsimile Drug Facts Label for aspirin, a Cohesive-Prose Label, or a Scrambled-Prose Label. The Drug Facts Label outperformed the Scrambled-Prose Label, but not the Cohesive-Prose Label, in scanning effectiveness. The Drug Facts Label was no better than the Cohesive-Prose Label or the Scrambled-Prose Label in promoting attentiveness, recall and organization of drug facts, or misconception refutation. Discussion focuses on the need for refutational labels based on a sequence-of-events text schema. PMID:29379613

Pictorial Prescription Labels.

ERIC Educational Resources Information Center

Bratt, Jeremy

1978-01-01

Describes an experimental system which uses pictorial representation for labeling prescribed medicines in the United Kingdom. Since the pictorial approach breaks the language barrier, the labels should present no problems either to illiterates or minority groups who have difficulty in understanding English. (JEG)

21 CFR 201.72 - Potassium labeling.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 4 2013-04-01 2013-04-01 false Potassium labeling. 201.72 Section 201.72 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.72 Potassium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the potassium content...

21 CFR 201.72 - Potassium labeling.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 4 2014-04-01 2014-04-01 false Potassium labeling. 201.72 Section 201.72 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.72 Potassium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the potassium content...

21 CFR 201.72 - Potassium labeling.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 4 2012-04-01 2012-04-01 false Potassium labeling. 201.72 Section 201.72 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.72 Potassium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the potassium content...

Interactive Multimedia Instruction for Training Self-Directed Learning Techniques

DTIC Science & Technology

2016-06-01

feedback and input on the content, format, and pedagogical approach of the lesson. This survey could be e-mailed to the principal ARI researcher for...peers in self-directed learning. Some examples of the metaphorical relationships and common examples woven into this IMI are identified in Table 1...20 Table 1 Metaphorical Relationships and Illustrations Used in Self-Directed Learning Training Military or Common Example Self-Directed

Training Injury Control Practitioners: The Indian Health Service Model.

ERIC Educational Resources Information Center

Smith, Richard J., III; Dellapenna, Alan J., Jr.; Berger, Lawrence R.

2000-01-01

Describes an innovative training program for injury prevention specialists developed by the Indian Health Service (IHS), noting its applicability to other community-based settings. Examines injuries and American Indians; designing the IHS program; IHS training courses; examples of community-based interventions organized by people who had completed…

Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

PubMed Central

Sridhar, Vivek Kumar Rangarajan; Bangalore, Srinivas; Narayanan, Shrikanth S.

2009-01-01

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling. PMID:19603083

Multiscale analysis of neural spike trains.

PubMed

Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

2014-01-30

This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. Copyright © 2013 John Wiley & Sons, Ltd.

Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images.

PubMed

He, Lifeng; Chao, Yuyan; Suzuki, Kenji

2011-08-01

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

21 CFR 201.64 - Sodium labeling.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 4 2014-04-01 2014-04-01 false Sodium labeling. 201.64 Section 201.64 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.64 Sodium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the sodium content per...

21 CFR 201.64 - Sodium labeling.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 4 2013-04-01 2013-04-01 false Sodium labeling. 201.64 Section 201.64 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.64 Sodium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the sodium content per...

21 CFR 201.64 - Sodium labeling.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 4 2012-04-01 2012-04-01 false Sodium labeling. 201.64 Section 201.64 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.64 Sodium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the sodium content per...

21 CFR 201.64 - Sodium labeling.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 4 2011-04-01 2011-04-01 false Sodium labeling. 201.64 Section 201.64 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.64 Sodium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the sodium content per...

21 CFR 201.64 - Sodium labeling.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 4 2010-04-01 2010-04-01 false Sodium labeling. 201.64 Section 201.64 Food and... LABELING Labeling Requirements for Over-the-Counter Drugs § 201.64 Sodium labeling. (a) The labeling of over-the-counter (OTC) drug products intended for oral ingestion shall contain the sodium content per...

Choosing front-of-package food labelling nutritional criteria: how smart were 'Smart Choices'?

PubMed

Roberto, Christina A; Bragg, Marie A; Livingston, Kara A; Harris, Jennifer L; Thompson, Jackie M; Seamans, Marissa J; Brownell, Kelly D

2012-02-01

The 'Smart Choices' programme was an industry-driven, front-of-package (FOP) nutritional labelling system introduced in the USA in August 2009, ostensibly to help consumers select healthier options during food shopping. Its nutritional criteria were developed by members of the food industry in collaboration with nutrition and public health experts and government officials. The aim of the present study was to test the extent to which products labelled as 'Smart Choices' could be classified as healthy choices on the basis of the Nutrient Profile Model (NPM), a non-industry-developed, validated nutritional standard. A total of 100 packaged products that qualified for a 'Smart Choices' designation were sampled from eight food and beverage categories. All products were evaluated using the NPM method. In all, 64 % of the products deemed 'Smart Choices' did not meet the NPM standard for a healthy product. Within each 'Smart Choices' category, 0 % of condiments, 8·70 % of fats and oils, 15·63 % of cereals and 31·58 % of snacks and sweets met NPM thresholds. All sampled soups, beverages, desserts and grains deemed 'Smart Choices' were considered healthy according to the NPM standard. The 'Smart Choices' programme is an example of industries' attempts at self-regulation. More than 60 % of foods that received the 'Smart Choices' label did not meet standard nutritional criteria for a 'healthy' food choice, suggesting that industries' involvement in designing labelling systems should be scrutinized. The NPM system may be a good option as the basis for establishing FOP labelling criteria, although more comparisons with other systems are needed.

Training for the Self-Catering Industry. An Example of College/Employer Collaboration in Training for Unemployed Adults. FEU/REPLAN.

ERIC Educational Resources Information Center

Further Education Unit, London (England).

A Local Collaborative Project was developed by an employers' association (Best of British Holidays), Evesham College of Further Education, the Department of Education and Science PICKUP Unit, and Hereford and Worcester Local Education Authority to train workers for the self-catering (travel and tourism) industry in England. During the project,…

Tracing photosynthetic carbon in leaves with nanoSIMS after 13CO2 labelling

NASA Astrophysics Data System (ADS)

Dannoura, Masako; Takeuchi, Miyuki; Kominami, Yuji; Takanashi, Satoru; Kenichi, Yoshimura; Ataka, Mioko

2015-04-01

To understand the carbon allocation of the tree and forest ecosystem, it is important to consider the residence time of carbon in different pools at suitable time scales. For example the carbon used for respiration will stay a few minutes to a few days in the tree, the carbon used for storage or structure of leaves will stay months to years, and the carbon used for wood structure, it will stay over the whole lifespan of the tree. The leaves are the entrance of carbon in trees where it can be used for foliage growth and maintenance or exported to the other organs or the other forest ecosystem compartments. Tracing carbon isotope using NanoSIMS technique is one of useful methods to estimate where and how long the carbon stay in the tree organs. In this study, 13CO2 pulse labelling were conducted and 13C was measured by IRMS to see the amount of C remaining in the leaves with time.NanoSIMS was used to localize where the labelled C remained within the leaf tissue. Twice labelling were done on branches of Quercus serrata at FFPRI(Forest and Forest Products research Institute) in Kyoto, Japan. The first labelling was in 30 April 2012 when the leaves start flushing and the second one was in 29 May 2012 when the leaves were completely deployed. For both labelling experiment, one branch was selected and covered with transparent plastic bag. CO2 concentration was recorded with IRGA and air temperature inside the chamber was monitored. Then 13CO2 was injected into the bag, and after 1 hour, the bag was removed and the branch was again exposed to ambient air. Leaves were collected before and 10-12 times after labelling and their isotope compositions were measured by IRMS. The leaf collected just after labelling and 6 days after labelling were used for NanoSIMS observation. Samples for nanoSIMS were preserved in glutaraldehyde and then embed in epoxy resin. The sliced sample were placed on the silicon wafer and observed by NanoSIMS 50L(Cameca, France). The 13C was highest just

Designing strategies and tools for teacher training: The role of critical details, examples in optics

NASA Astrophysics Data System (ADS)

Viennot, Laurence; Chauvet, Françoise; Colin, Philippe; Rebmann, Gérard

2005-01-01

Within the overall STTIS (Science Teacher Training in an Information Society) framework, this paper focuses on transformations of innovative teaching of optics, following a recommended change of approach to optics in the French curriculum. The empirical investigation of how teachers responded to this change, the main results of which are briefly presented here, identified a crucial aspect of the problem. This is the importance of critical detail'': that is, the fact that the linkage between certain critical details of practice and the fundamental rationale of a teaching sequence is often not easily understood by teachers, even those who are strongly motivated. The paper then discusses the development of guidelines for the design of training materials based on these research findings, which show how teachers typically tend to transform innovations when putting them into practice. We describe the rationale behind and structure of some teacher training materials intended to facilitate awareness and mastery in this respect.

Off-label use of vaccines.

PubMed

Neels, Pieter; Southern, James; Abramson, Jon; Duclos, Philippe; Hombach, Joachim; Marti, Melanie; Fitzgerald-Husek, Alanna; Fournier-Caruana, Jacqueline; Hanquet, Germaine

2017-04-25

This article reviews the off-label recommendations and use of vaccines, and focuses on the differences between the labelled instructions on how to use the vaccine as approved by the regulatory authorities (or "label" 1 ), and the recommendations for use issued by public health advisory bodies at national and international levels. Differences between public health recommendations and the product label regarding the vaccine use can lead to confusion at the level of vaccinators and vaccinees and possibly result in lower compliance with national vaccination schedules. In particular, in many countries, the label may contain regulatory restrictions and warnings against vaccination of specific population groups (e.g. pregnant women) due to a lack of evidence of safety from controlled trials at the time of initial licensure of the vaccine, while public health authorities may recommend the same vaccine for that group, based on additional post-marketing data and benefit risk analyses. We provide an overview of the different responsibilities between regulatory authorities and public health advisory bodies, and the rationale for off-label use 2 of vaccines, the challenges involved based on the impact of off-label use in real-life. We propose to reduce off-label use of vaccines by requiring the manufacturer to regularly adapt the label as much as possible to the public health needs as supported by new evidence. This would require manufacturers to collect and report post-marketing data, communicate them to all stakeholders and regulators to extrapolate existing evidence (when acceptable) to other groups or to other brands of a vaccine (class effect 3 ). Regulatory authorities have a key role to play by requesting additional post-marketing data, e.g. in specific target groups. When public health recommendations for vaccine use that are outside labelled indications are considered necessary, good communication between regulatory bodies, public health authorities, companies and

In vitro labelling and detection of mesenchymal stromal cells: a comparison between magnetic resonance imaging of iron-labelled cells and magnetic resonance spectroscopy of fluorine-labelled cells.

PubMed

Rizzo, Stefania; Petrella, Francesco; Zucca, Ileana; Rinaldi, Elena; Barbaglia, Andrea; Padelli, Francesco; Baggi, Fulvio; Spaggiari, Lorenzo; Bellomi, Massimo; Bruzzone, Maria Grazia

2017-01-01

Among the various stem cell populations used for cell therapy, adult mesenchymal stromal cells (MSCs) have emerged as a major new cell technology. These cells must be tracked after transplantation to monitor their migration within the body and quantify their accumulation at the target site. This study assessed whether rat bone marrow MSCs can be labelled with superparamagnetic iron oxide (SPIO) nanoparticles and perfluorocarbon (PFC) nanoemulsion formulations without altering cell viability and compared magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) results from iron-labelled and fluorine-labelled MSCs, respectively. Of MSCs, 2 × 10 6 were labelled with Molday ION Rhodamine-B (MIRB) and 2 × 10 6 were labelled with Cell Sense. Cell viability was evaluated by trypan blue exclusion method. Labelled MSCs were divided into four samples containing increasing cell numbers (0.125 × 10 6 , 0.25 × 10 6 , 0.5 × 10 6 , 1 × 10 6 ) and scanned on a 7T MRI: for MIRB-labelled cells, phantoms and cells negative control, T1, T2 and T2* maps were acquired; for Cell Sense labelled cells, phantoms and unlabelled cells, a 19 F non-localised single-pulse MRS sequence was acquired. In total, 86.8% and 83.6% of MIRB-labelled cells and Cell Sense-labelled cells were viable, respectively. MIRB-labelled cells were visible in all samples with different cell numbers; pellets containing 0.5 × 10 6 and 1 × 10 6 of Cell Sense-labelled cells showed a detectable 19 F signal. Our data support the use of both types of contrast material (SPIO and PFC) for MSCs labelling, although further efforts should be dedicated to improve the efficiency of PFC labelling.

49 CFR 172.430 - POISON label.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 2 2014-10-01 2014-10-01 false POISON label. 172.430 Section 172.430... SECURITY PLANS Labeling § 172.430 POISON label. (a) Except for size and color, the POISON label must be as follows: EC02MR91.029 (b) In addition to complying with § 172.407, the background on the POISON label must...

49 CFR 172.430 - POISON label.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 2 2011-10-01 2011-10-01 false POISON label. 172.430 Section 172.430... SECURITY PLANS Labeling § 172.430 POISON label. (a) Except for size and color, the POISON label must be as follows: EC02MR91.029 (b) In addition to complying with § 172.407, the background on the POISON label must...

49 CFR 172.430 - POISON label.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 2 2012-10-01 2012-10-01 false POISON label. 172.430 Section 172.430... SECURITY PLANS Labeling § 172.430 POISON label. (a) Except for size and color, the POISON label must be as follows: EC02MR91.029 (b) In addition to complying with § 172.407, the background on the POISON label must...

49 CFR 172.430 - POISON label.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 2 2013-10-01 2013-10-01 false POISON label. 172.430 Section 172.430... SECURITY PLANS Labeling § 172.430 POISON label. (a) Except for size and color, the POISON label must be as follows: EC02MR91.029 (b) In addition to complying with § 172.407, the background on the POISON label must...

49 CFR 172.430 - POISON label.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false POISON label. 172.430 Section 172.430... SECURITY PLANS Labeling § 172.430 POISON label. (a) Except for size and color, the POISON label must be as follows: EC02MR91.029 (b) In addition to complying with § 172.407, the background on the POISON label must...

Flexible-rate optical packet generation/detection and label swapping for optical label switching networks

NASA Astrophysics Data System (ADS)

Wu, Zhongying; Li, Juhao; Tian, Yu; Ge, Dawei; Zhu, Paikun; Chen, Yuanxiang; Chen, Zhangyuan; He, Yongqi

2017-03-01

In recent years, optical label switching (OLS) gains lots of attentions due to its intrinsic advantages to implement protocol, bit-rate, granularity and data format transparency packet switching. In this paper, we propose a novel scheme to realize flexible-rate optical packet switching for OLS networks. At the transmitter node, flexible-rate packet is generated by parallel modulating different combinations of optical carriers generated from the optical multi-carrier generator (OMCG), among which the low-speed optical label occupies one carrier. At the switching node, label is extracted and re-generated in label processing unit (LPU). The payloads are switched based on routing information and new label is added after switching. At the receiver node, another OMCG serves as local oscillators (LOs) for optical payloads coherent detection. The proposed scheme offers good flexibility for dynamic optical packet switching by adjusting the payload bandwidth and could also effectively reduce the number of lasers, modulators and receivers for packet generation/detection. We present proof-of-concept demonstrations of flexible-rate packet generation/detection and label swapping in 12.5 GHz grid. The influence of crosstalk for cascaded label swapping is also investigated.

In Their Words: Student Choice in Training Markets--Victorian Examples. NCVER Research Report

ERIC Educational Resources Information Center

Brown, Justin

2017-01-01

This research offers insights into the options available to individuals as they navigate the vocational education and training (VET) market. Importantly, this study directly represents the voice of students, asking how their choices were made and whether their choice was sufficiently "informed." The student voice is contrasted with…

SIMulation of Medication Error induced by Clinical Trial drug labeling: the SIMME-CT study.

PubMed

Dollinger, Cecile; Schwiertz, Vérane; Sarfati, Laura; Gourc-Berthod, Chloé; Guédat, Marie-Gabrielle; Alloux, Céline; Vantard, Nicolas; Gauthier, Noémie; He, Sophie; Kiouris, Elena; Caffin, Anne-Gaelle; Bernard, Delphine; Ranchon, Florence; Rioufol, Catherine

2016-06-01

To assess the impact of investigational drug labels on the risk of medication error in drug dispensing. A simulation-based learning program focusing on investigational drug dispensing was conducted. The study was undertaken in an Investigational Drugs Dispensing Unit of a University Hospital of Lyon, France. Sixty-three pharmacy workers (pharmacists, residents, technicians or students) were enrolled. Ten risk factors were selected concerning label information or the risk of confusion with another clinical trial. Each risk factor was scored independently out of 5: the higher the score, the greater the risk of error. From 400 labels analyzed, two groups were selected for the dispensing simulation: 27 labels with high risk (score ≥3) and 27 with low risk (score ≤2). Each question in the learning program was displayed as a simulated clinical trial prescription. Medication error was defined as at least one erroneous answer (i.e. error in drug dispensing). For each question, response times were collected. High-risk investigational drug labels correlated with medication error and slower response time. Error rates were significantly 5.5-fold higher for high-risk series. Error frequency was not significantly affected by occupational category or experience in clinical trials. SIMME-CT is the first simulation-based learning tool to focus on investigational drug labels as a risk factor for medication error. SIMME-CT was also used as a training tool for staff involved in clinical research, to develop medication error risk awareness and to validate competence in continuing medical education. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

Automatic prevention of label overlap

DOT National Transportation Integrated Search

1976-03-01

The project comprised a number of simulation exercises : designed to evaluate methods of either preventing or : resolving the problems likely to be caused by label overlap on : Labelled Plan Displays (LPD). The automatic prevention of : label overlap...

Pharmaceutical Industry Off-label Promotion and Self-regulation: A Document Analysis of Off-label Promotion Rulings by the United Kingdom Prescription Medicines Code of Practice Authority 2003-2012.

PubMed

Vilhelmsson, Andreas; Davis, Courtney; Mulinari, Shai

2016-01-01

European Union law prohibits companies from marketing drugs off-label. In the United Kingdom--as in some other European countries, but unlike the United States--industry self-regulatory bodies are tasked with supervising compliance with marketing rules. The objectives of this study were to (1) characterize off-label promotion rulings in the UK compared to the whistleblower-initiated cases in the US and (2) shed light on the UK self-regulatory mechanism for detecting, deterring, and sanctioning off-label promotion. We conducted structured reviews of rulings by the UK self-regulatory authority, the Prescription Medicines Code of Practice Authority (PMCPA), between 2003 and 2012. There were 74 off-label promotion rulings involving 43 companies and 65 drugs. Nineteen companies were ruled in breach more than once, and ten companies were ruled in breach three or more times over the 10-y period. Drawing on a typology previously developed to analyse US whistleblower complaints, we coded and analysed the apparent strategic goals of each off-label marketing scheme and the practices consistent with those alleged goals. 50% of rulings cited efforts to expand drug use to unapproved indications, and 39% and 38% cited efforts to expand beyond approved disease entities and dosing strategies, respectively. The most frequently described promotional tactic was attempts to influence prescribers (n = 72, 97%), using print material (70/72, 97%), for example, advertisements (21/70, 30%). Although rulings cited prescribers as the prime target of off-label promotion, competing companies lodged the majority of complaints (prescriber: n = 16, 22%, versus companies: n = 42, 57%). Unlike US whistleblower complaints, few UK rulings described practices targeting consumers (n = 3, 4%), payers (n = 2, 3%), or company staff (n = 2, 3%). Eight UK rulings (11%) pertaining to six drugs described promotion of the same drug for the same off-label use as was alleged by whistleblowers in the US. However

Soil Fumigant Labels - Methyl Bromide

EPA Pesticide Factsheets

Search soil fumigant pesticide labels by EPA registration number, product name, or company, and follow the link to The Pesticide Product Label System (PPLS) for details. Updated labels include new safety requirements for buffer zones and related measures.

End labeling procedures: an overview.

PubMed

Hilario, Elena

2004-09-01

There are two ways to label a DNA molecular; by the ends or all along the molecule. End labeling can be performed at the 3'- or 5'-end. Labeling at the 3' end is performed by filling 3'-end recessed ends with a mixture or labeled and unlabeled dNTPs using Klenow or T4 DNA polymerases. Both reactions are template dependent. Terminal deoxynucleotide transferase incorporates dNTPs at the 3' end of any kind of DNA molecule or RNA. Labels incorporated at the 3'-end of the DNA molecule prevent any further extension or ligation to any other molecule, but this can be overcome by labeling the 5'-end of the desired DNA molecule. 5'-end labeling is performed by enzymatic methods (T4 polynucleotide kinase exchange and forward reactions), by chemical modification of sensitized oligonucleotides with phosphoroamidite, or by combined methods. Probe cleanup is recommended when high background problems occur, but caution should be taken not to damage the attached probe with harsh chemicals or by light exposure.

Training Research: Practical Recommendations for Maximum Impact

PubMed Central

Beidas, Rinad S.; Koerner, Kelly; Weingardt, Kenneth R.; Kendall, Philip C.

2011-01-01

This review offers practical recommendations regarding research on training in evidence-based practices for mental health and substance abuse treatment. When designing training research, we recommend: (a) aligning with the larger dissemination and implementation literature to consider contextual variables and clearly defining terminology, (b) critically examining the implicit assumptions underlying the stage model of psychotherapy development, (c) incorporating research methods from other disciplines that embrace the principles of formative evaluation and iterative review, and (d) thinking about how technology can be used to take training to scale throughout all stages of a training research project. An example demonstrates the implementation of these recommendations. PMID:21380792

What is prescription labeling communicating to doctors about hepatotoxic drugs? A study of FDA approved product labeling.

PubMed

Willy, Mary E; Li, Zili

2004-04-01

The objective of this study was to evaluate the informativeness and consistency of product labeling of hepatotoxic drugs marketed in the United States. We searched the Physicians' Desk Reference-2000 for prescription drugs with hepatic failure and/or hepatic necrosis listed in the labeling. We used a six-item checklist to evaluate the 'informativeness' and consistency of the labeling content. An informativeness score equaled the proportion of checklist items present in each drug's labeling. Ninety-five prescription drugs were included in the study. Eleven (12%) of the drugs had information related to hepatic failure in a Black Boxed Warning, 52 (54%) in the Warnings section and 32 (34%) in the Adverse Reactions section of the label. The mean informativeness score was 35%; the score was significantly higher, 61%, when the risk was perceived to be high. The informativeness of labeling was not affected by the time of the labeling, but differed across the Center for Drug Evaluation and Research (CDER) Review Division responsible for the labeling. The information provided in labeling is variable and affected by many factors, including the perceived level of risk and review division strategy. Product labeling may benefit from current FDA initiatives to improve the consistency of risk-related labeling.

Influence of Interpretation Aids on Attentional Capture, Visual Processing, and Understanding of Front-of-Package Nutrition Labels.

PubMed

Antúnez, Lucía; Giménez, Ana; Maiche, Alejandro; Ares, Gastón

2015-01-01

To study the influence of 2 interpretational aids of front-of-package (FOP) nutrition labels (color code and text descriptors) on attentional capture and consumers' understanding of nutritional information. A full factorial design was used to assess the influence of color code and text descriptors using visual search and eye tracking. Ten trained assessors participated in the visual search study and 54 consumers completed the eye-tracking study. In the visual search study, assessors were asked to indicate whether there was a label high in fat within sets of mayonnaise labels with different FOP labels. In the eye-tracking study, assessors answered a set of questions about the nutritional content of labels. The researchers used logistic regression to evaluate the influence of interpretational aids of FOP nutrition labels on the percentage of correct answers. Analyses of variance were used to evaluate the influence of the studied variables on attentional measures and participants' response times. Response times were significantly higher for monochromatic FOP labels compared with color-coded ones (3,225 vs 964 ms; P < .001), which suggests that color codes increase attentional capture. The highest number and duration of fixations and visits were recorded on labels that did not include color codes or text descriptors (P < .05). The lowest percentage of incorrect answers was observed when the nutrient level was indicated using color code and text descriptors (P < .05). The combination of color codes and text descriptors seems to be the most effective alternative to increase attentional capture and understanding of nutritional information. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Kinematic precision of gear trains

NASA Technical Reports Server (NTRS)

Litvin, F. L.; Goldrich, R. N.; Coy, J. J.; Zaretsky, E. V.

1982-01-01

Kinematic precision is affected by errors which are the result of either intentional adjustments or accidental defects in manufacturing and assembly of gear trains. A method for the determination of kinematic precision of gear trains is described. The method is based on the exact kinematic relations for the contact point motions of the gear tooth surfaces under the influence of errors. An approximate method is also explained. Example applications of the general approximate methods are demonstrated for gear trains consisting of involute (spur and helical) gears, circular arc (Wildhaber-Novikov) gears, and spiral bevel gears. Gear noise measurements from a helicopter transmission are presented and discussed with relation to the kinematic precision theory.

Examples of Cross-Cultural Problems Encountered by Americans Working Overseas; An Instructor's Handbook.

ERIC Educational Resources Information Center

Foster, Robert J.

Intended mainly as a source book for instructors in area training programs, this handbook contains summary accounts of events illustrating problems frequently met by Americans working overseas, especially those providing technical assistance in developing nations. Examples are drawn from case studies, interviews, anthropology texts, and other…

Randomized trial on the effects of a combined physical/cognitive training in aged MCI subjects: the Train the Brain study

PubMed Central

Maffei, L.; Picano, E.; Andreassi, M. G.; Angelucci, A.; Baldacci, F.; Baroncelli, L.; Begenisic, T.; Bellinvia, P. F.; Berardi, N.; Biagi, L.; Bonaccorsi, J.; Bonanni, E.; Bonuccelli, U.; Borghini, A.; Braschi, C.; Broccardi, M.; Bruno, R. M.; Caleo, M.; Carlesi, C.; Carnicelli, L.; Cartoni, G.; Cecchetti, L.; Cenni, M. C.; Ceravolo, R.; Chico, L.; Cintoli, S.; Cioni, G.; Coscia, M.; Costa, M.; D’Angelo, G.; D’Ascanio, P.; Nes, M. De; Turco, S. Del; Coscio, E. Di; Galante, M. Di; Lascio, N. di; Faita, F.; Falorni, I.; Faraguna, U.; Fenu, A.; Fortunato, L.; Franco, R.; Gargani, L.; Gargiulo, R.; Ghiadoni, L.; Giorgi, F. S.; Iannarella, R.; Iofrida, C.; Kusmic, C.; Limongi, F.; Maestri, M.; Maffei, M.; Maggi, S.; Mainardi, M.; Mammana, L.; Marabotti, A.; Mariotti, V.; Melissari, E.; Mercuri, A.; Micera, S.; Molinaro, S.; Narducci, R.; Navarra, T.; Noale, M.; Pagni, C.; Palumbo, S.; Pasquariello, R.; Pellegrini, S.; Pietrini, P.; Pizzorusso, T.; Poli, A.; Pratali, L.; Retico, A.; Ricciardi, E.; Rota, G.; Sale, A.; Sbrana, S.; Scabia, G.; Scali, M.; Scelfo, D.; Sicari, R.; Siciliano, G.; Stea, F.; Taddei, S.; Tognoni, G.; Tonacci, A.; Tosetti, M.; Turchi, S.; Volpi, L.

2017-01-01

Age-related cognitive impairment and dementia are an increasing societal burden. Epidemiological studies indicate that lifestyle factors, e.g. physical, cognitive and social activities, correlate with reduced dementia risk; moreover, positive effects on cognition of physical/cognitive training have been found in cognitively unimpaired elders. Less is known about effectiveness and action mechanisms of physical/cognitive training in elders already suffering from Mild Cognitive Impairment (MCI), a population at high risk for dementia. We assessed in 113 MCI subjects aged 65–89 years, the efficacy of combined physical-cognitive training on cognitive decline, Gray Matter (GM) volume loss and Cerebral Blood Flow (CBF) in hippocampus and parahippocampal areas, and on brain-blood-oxygenation-level-dependent (BOLD) activity elicited by a cognitive task, measured by ADAS-Cog scale, Magnetic Resonance Imaging (MRI), Arterial Spin Labeling (ASL) and fMRI, respectively, before and after 7 months of training vs. usual life. Cognitive status significantly decreased in MCI-no training and significantly increased in MCI-training subjects; training increased parahippocampal CBF, but no effect on GM volume loss was evident; BOLD activity increase, indicative of neural efficiency decline, was found only in MCI-no training subjects. These results show that a non pharmacological, multicomponent intervention improves cognitive status and indicators of brain health in MCI subjects. PMID:28045051

Teaching Generalized Reading of Product Warning Labels to Young Adults with Autism Using the Constant Time Delay Procedure

ERIC Educational Resources Information Center

Dogoe, Maud S.; Banda, Devender R.; Lock, Robin H.; Feinstein, Rita

2011-01-01

This study examined the effectiveness of the constant timed delay procedure for teaching two young adults with autism to read, define, and state the contextual meaning of keywords on product warning labels of common household products. Training sessions were conducted in the dyad format using flash cards. Results indicated that both participants…

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

PubMed

Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

Ion mobility-enhanced MS(E)-based label-free analysis reveals effects of low-dose radiation post contextual fear conditioning training on the mouse hippocampal proteome.

PubMed

Huang, Lin; Wickramasekara, Samanthi I; Akinyeke, Tunde; Stewart, Blair S; Jiang, Yuan; Raber, Jacob; Maier, Claudia S

2016-05-17

Recent advances in the field of biodosimetry have shown that the response of biological systems to ionizing radiation is complex and depends on the type and dose of radiation, the tissue(s) exposed, and the time lapsed after exposure. The biological effects of low dose radiation on learning and memory are not well understood. An ion mobility-enhanced data-independent acquisition (MS(E)) approach in conjunction with the ISOQuant software tool was utilized for label-free quantification of hippocampal proteins with the goal of determining protein alteration associated with low-dose whole body ionizing radiation (X-rays, 1Gy) of 5.5-month-old male C57BL/6J mice post contextual fear conditioning training. Global proteome analysis revealed deregulation of 73 proteins (out of 399 proteins). Deregulated proteins indicated adverse effects of irradiation on myelination and perturbation of energy metabolism pathways involving a shift from the TCA cycle to glutamate oxidation. Our findings also indicate that proteins associated with synaptic activity, including vesicle recycling and neurotransmission, were altered in the irradiated mice. The elevated LTP and decreased LTD suggest improved synaptic transmission and enhanced efficiency of neurotransmitter release which would be consistent with the observed comparable contextual fear memory performance of the mice following post-training whole body or sham-irradiation. This study is significant because the biological consequences of low dose radiation on learning and memory are complex and not yet well understood. We conducted a IMS-enhanced MS(E)-based label-free quantitative proteomic analysis of hippocampal tissue with the goal of determining protein alteration associated with low-dose whole body ionizing radiation (X-ray, 1Gy) of 5.5-month-old male C57BL/6J mice post contextual fear conditioning training. The IMS-enhanced MS(E) approach in conjunction with ISOQuant software was robust and accurate with low median CV values of

Motor Cortical Plasticity to Training Started in Childhood: The Example of Piano Players.

PubMed

Chieffo, Raffaella; Straffi, Laura; Inuggi, Alberto; Gonzalez-Rosa, Javier J; Spagnolo, Francesca; Coppi, Elisabetta; Nuara, Arturo; Houdayer, Elise; Comi, Giancarlo; Leocani, Letizia

2016-01-01

Converging evidence suggest that motor training is associated with early and late changes of the cortical motor system. Transcranial magnetic stimulation (TMS) offers the possibility to study plastic rearrangements of the motor system in physiological and pathological conditions. We used TMS to characterize long-term changes in upper limb motor cortical representation and interhemispheric inhibition associated with bimanual skill training in pianists who started playing in an early age. Ipsilateral silent period (iSP) and cortical TMS mapping of hand muscles were obtained from 30 strictly right-handed subjects (16 pianists, 14 naïve controls), together with electromyographic recording of mirror movements (MMs) to voluntary hand movements. In controls, motor cortical representation of hand muscles was larger on the dominant (DH) than on the non-dominant hemisphere (NDH). On the contrary, pianists showed symmetric cortical output maps, being their DH less represented than in controls. In naïve subjects, the iSP was smaller on the right vs left abductor pollicis brevis (APB) indicating a weaker inhibition from the NDH to the DH. In pianists, interhemispheric inhibition was more symmetric as their DH was better inhibited than in controls. Electromyographic MMs were observed only in naïve subjects (7/14) and only to voluntary movement of the non-dominant hand. Subjects with MM had a lower iSP area on the right APB compared with all the others. Our findings suggest a more symmetrical motor cortex organization in pianists, both in terms of muscle cortical representation and interhemispheric inhibition. Although we cannot disentangle training-related from preexisting conditions, it is possible that long-term bimanual practice may reshape motor cortical representation and rebalance interhemispheric interactions, which in naïve right-handed subjects would both tend to favour the dominant hemisphere.

Motor Cortical Plasticity to Training Started in Childhood: The Example of Piano Players

PubMed Central

Inuggi, Alberto; Gonzalez-Rosa, Javier J.; Spagnolo, Francesca; Coppi, Elisabetta; Nuara, Arturo; Houdayer, Elise; Comi, Giancarlo; Leocani, Letizia

2016-01-01

Converging evidence suggest that motor training is associated with early and late changes of the cortical motor system. Transcranial magnetic stimulation (TMS) offers the possibility to study plastic rearrangements of the motor system in physiological and pathological conditions. We used TMS to characterize long-term changes in upper limb motor cortical representation and interhemispheric inhibition associated with bimanual skill training in pianists who started playing in an early age. Ipsilateral silent period (iSP) and cortical TMS mapping of hand muscles were obtained from 30 strictly right-handed subjects (16 pianists, 14 naïve controls), together with electromyographic recording of mirror movements (MMs) to voluntary hand movements. In controls, motor cortical representation of hand muscles was larger on the dominant (DH) than on the non-dominant hemisphere (NDH). On the contrary, pianists showed symmetric cortical output maps, being their DH less represented than in controls. In naïve subjects, the iSP was smaller on the right vs left abductor pollicis brevis (APB) indicating a weaker inhibition from the NDH to the DH. In pianists, interhemispheric inhibition was more symmetric as their DH was better inhibited than in controls. Electromyographic MMs were observed only in naïve subjects (7/14) and only to voluntary movement of the non-dominant hand. Subjects with MM had a lower iSP area on the right APB compared with all the others. Our findings suggest a more symmetrical motor cortex organization in pianists, both in terms of muscle cortical representation and interhemispheric inhibition. Although we cannot disentangle training-related from preexisting conditions, it is possible that long-term bimanual practice may reshape motor cortical representation and rebalance interhemispheric interactions, which in naïve right-handed subjects would both tend to favour the dominant hemisphere. PMID:27336584

Assessing the applied benefits of perceptual training: Lessons from studies of training working-memory.

PubMed

Jacoby, Nori; Ahissar, Merav

2015-01-01

In the 1980s to 1990s, studies of perceptual learning focused on the specificity of training to basic visual attributes such as retinal position and orientation. These studies were considered scientifically innovative since they suggested the existence of plasticity in the early stimulus-specific sensory cortex. Twenty years later, perceptual training has gradually shifted to potential applications, and research tends to be devoted to showing transfer. In this paper we analyze two key methodological issues related to the interpretation of transfer. The first has to do with the absence of a control group or the sole use of a test-retest group in traditional perceptual training studies. The second deals with claims of transfer based on the correlation between improvement on the trained and transfer tasks. We analyze examples from the general intelligence literature dealing with the impact on general intelligence of training on a working memory task. The re-analyses show that the reports of a significantly larger transfer of the trained group over the test-retest group fail to replicate when transfer is compared to an actively trained group. Furthermore, the correlations reported in this literature between gains on the trained and transfer tasks can be replicated even when no transfer is assumed.

Workshop on active learning: two examples

NASA Astrophysics Data System (ADS)

Ben Lakhdar, Zohra; Lahmar, Souad; Lakshminarayanan, Vasudevan

2014-07-01

Optics is an enabling science that has far ranging importance in many diverse fields. However, many students do not find it to be of great interest. A solution to this problem is to train teachers in active learning methodologies so that the subject matter can be presented to generate student interest. We describe a workshop to present an example of an active learning process in Optics developed for training of teachers in developing countries (a UNESCO project) and will focus on 2 two different activities: 1. Interference and diffraction is considered by students as being very hard to understand and is taught in most developing countries as purely theoretical with almost no experiments. Simple experiments to enhance the conceptual understanding of these wave phenomena will be presented and 2. Image formation by the eye. Here we will discuss myopia, hyperopia and astigmatism as well as accommodation. In this module we will discuss image. The objective of the workshop will be to provide an experience of the use of the active learning method in optics including the use of experiments, mind's on and hands-on exercises, group and class discussions

Characterization of labelling and de-labelling reagents for detection and recovery of tyrosine residue in peptide.

PubMed

Toyo'oka, Toshimasa; Mantani, Tomomi; Kato, Masaru

2003-01-01

This paper characterized the labelling and de-labelling reagents for reversible labelling of tyrosine (Tyr)-containing peptide, which involves detection and recovery. The phenolic hydroxyl group (-OH) in Tyr structure reacted with 4-fluoro-7-nitro-2,1,3-benzoxadiazole (NBD-F), 4-(N,N-dimethylaminosulfonyl)-7-fluoro-2,1,3-benzoxadiazole (DBD-F), and 1-fluoro-2,4-dinitrobenzene (DNFB) under mild conditions at room temperature at pH 9.3. The labels in the resulting derivatives were removed with the treatment of nucleophiles, such as thiols (cysteine, N-acetyl-L-cysteine and dithiothreitol) and amines (dimethylamine, methylamine, diethylamine, ethylamine and pyrrolidine). The de-labelling reactions of NBD-labelled N-acetyl-L-tyrosine (N-AcTyr) with the nucleophiles produced N-AcTyr, accompanied by NBD-nucleophile. Although DBD-F and DNFB also successfully labeled the -OH group in N-AcTyr, the efficiency of Cbond;O bond cleavage and recovery of N-AcTyr by the nucleophiles was relatively low compared with NBD-label. Among the de-labelling reagents, N-acetyl-L-cysteine and dimethylamine were recommended for the elimination of NBD moiety, with respect to the reaction rate, the side reaction, and the yield of recovery. The proposed procedure, which includes the labelling with NBD-F and the removal of NBD moiety by the nucleophiles, was successfully applied to the reversible labelling of N-terminal amine-blocked peptides, i.e. N-AcTyr-Val-Gly, Z-Glu-Tyr, Z-Phe-Tyr, N-Formyl-Met-Leu-Tyr, and N-AcArg-Pro-Pro-Gly-Phe-Ser-Pro-Tyr-Arg. Copyright 2003 John Wiley & Sons, Ltd.

Soil Fumigant Labels - Dazomet

EPA Pesticide Factsheets

Updated labels include new safety requirements for buffer zones and related measures. Find information from the Pesticide Product Labeling System (PPLS) for products such as Basamid G, manufactured by Amvac.

Harmonisation of food labelling regulations in Southeast Asia: benefits, challenges and implications.

PubMed

Kasapila, William; Shaarani, Sharifudin Md

2011-01-01

In the globalised world of the 21st century, issues of food and nutrition labelling are of pre-eminent importance. Several international bodies, including the World Health Organisation and World Trade Organisation, are encouraging countries to harmonise their food and nutrition regulations with international standards, guidelines and recommendations such as those for Codex Alimentarius. Through harmonisation, these organisations envisage fewer barriers to trade and freer movement of food products between countries, which would open doors to new markets and opportunities for the food industry. In turn, increased food trade would enhance economic development and allow consumers a greater choice of products. Inevitably, however, embracing harmonisation brings along cost implications and challenges that have to be overcome. Moreover, the harmonisation process is complex and sporadic in light of the tasks that countries have to undertake; for example, updating legislation, strengthening administrative capabilities and establishing analytical laboratories. This review discusses the legislation and regulations that govern food and nutrition labelling in Southeast Asia, and highlights the discrepancies that exist in this regard, their origin and consequences. It also gives an account of the current status of harmonising labelling of pre-packaged foodstuffs in the region and explains the subsequent benefits, challenges and implications for governments, the food industry and consumers.

Tunable coating of gold nanostars: tailoring robust SERS labels for cell imaging

NASA Astrophysics Data System (ADS)

Bassi, B.; Taglietti, A.; Galinetto, P.; Marchesi, N.; Pascale, A.; Cabrini, E.; Pallavicini, P.; Dacarro, G.

2016-07-01

Surface modification of noble metal nanoparticles with mixed molecular monolayers is one of the most powerful tools in nanotechnology, and is used to impart and tune new complex surface properties. In imaging techniques based on surface enhanced Raman spectroscopy (SERS), precise and controllable surface modifications are needed to carefully design reproducible, robust and adjustable SERS nanoprobes. We report here the attainment of SERS labels based on gold nanostars (GNSs) coated with a mixed monolayer composed of a poly ethylene glycol (PEG) thiol (neutral or negatively charged) that ensure stability in biological environments, and of a signalling unit 7-Mercapto-4-methylcoumarin as a Raman reporter molecule. The composition of the coating mixture is precisely controlled using an original method, allowing the modulation of the SERS intensity and ensuring overall nanoprobe stability. The further addition of a positively charged layer of poly (allylamine hydrocloride) on the surface of negatively charged SERS labels does not change the SERS response, but it promotes the penetration of GNSs in SH-SY5Y neuroblastoma cells. As an example of an application of such an approach, we demonstrate here the internalization of these new labels by means of visualization of cell morphology obtained with SERS mapping.

9 CFR 112.2 - Final container label, carton label, and enclosure.

Code of Federal Regulations, 2010 CFR

2010-01-01

... carton; (10) In the case of a product which contains an antibiotic added during the production process... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Final container label, carton label, and enclosure. 112.2 Section 112.2 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION...

Pharmaceutical Industry Off-label Promotion and Self-regulation: A Document Analysis of Off-label Promotion Rulings by the United Kingdom Prescription Medicines Code of Practice Authority 2003–2012

PubMed Central

Vilhelmsson, Andreas; Davis, Courtney; Mulinari, Shai

2016-01-01

Background European Union law prohibits companies from marketing drugs off-label. In the United Kingdom—as in some other European countries, but unlike the United States—industry self-regulatory bodies are tasked with supervising compliance with marketing rules. The objectives of this study were to (1) characterize off-label promotion rulings in the UK compared to the whistleblower-initiated cases in the US and (2) shed light on the UK self-regulatory mechanism for detecting, deterring, and sanctioning off-label promotion. Methods and Findings We conducted structured reviews of rulings by the UK self-regulatory authority, the Prescription Medicines Code of Practice Authority (PMCPA), between 2003 and 2012. There were 74 off-label promotion rulings involving 43 companies and 65 drugs. Nineteen companies were ruled in breach more than once, and ten companies were ruled in breach three or more times over the 10-y period. Drawing on a typology previously developed to analyse US whistleblower complaints, we coded and analysed the apparent strategic goals of each off-label marketing scheme and the practices consistent with those alleged goals. 50% of rulings cited efforts to expand drug use to unapproved indications, and 39% and 38% cited efforts to expand beyond approved disease entities and dosing strategies, respectively. The most frequently described promotional tactic was attempts to influence prescribers (n = 72, 97%), using print material (70/72, 97%), for example, advertisements (21/70, 30%). Although rulings cited prescribers as the prime target of off-label promotion, competing companies lodged the majority of complaints (prescriber: n = 16, 22%, versus companies: n = 42, 57%). Unlike US whistleblower complaints, few UK rulings described practices targeting consumers (n = 3, 4%), payers (n = 2, 3%), or company staff (n = 2, 3%). Eight UK rulings (11%) pertaining to six drugs described promotion of the same drug for the same off-label use as was alleged by

Advanced Entry Adult Apprenticeship Training Scheme: A Case Study

ERIC Educational Resources Information Center

Sparks, Alan; Ingram, Hadyn; Phillips, Sunny

2009-01-01

Purpose: The purpose of this paper is to evaluate an innovative way to train adult apprentices for the construction industry. Design/methodology/approach: The paper emphasizes that, in order to address skills shortages for international construction, training methods must be improved. It looks at the example of an adult apprenticeship scheme in…

Active Learning by Querying Informative and Representative Examples.

PubMed

Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

2014-10-01

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

The Application of Artificial Intelligence Principles to Teaching and Training

ERIC Educational Resources Information Center

Shaw, Keith

2008-01-01

This paper compares and contrasts the use of AI principles in industrial training with more normal computer-based training (CBT) approaches. A number of applications of CBT are illustrated (for example simulations, tutorial presentations, fault diagnosis, management games, industrial relations exercises) and compared with an alternative approach…

9 CFR 112.2 - Final container label, carton label, and enclosure.

Code of Federal Regulations, 2013 CFR

2013-01-01

... biological product which name shall be identical with that shown in the product license under which such... container label if complete descriptive terms appear on a carton label and enclosures; (2) If the biological... if the biological product is prepared in a foreign country, the name and address of the permittee and...

9 CFR 112.2 - Final container label, carton label, and enclosure.

Code of Federal Regulations, 2012 CFR

2012-01-01

... biological product which name shall be identical with that shown in the product license under which such... container label if complete descriptive terms appear on a carton label and enclosures; (2) If the biological... if the biological product is prepared in a foreign country, the name and address of the permittee and...

9 CFR 112.2 - Final container label, carton label, and enclosure.

Code of Federal Regulations, 2014 CFR

2014-01-01

... biological product which name shall be identical with that shown in the product license under which such... container label if complete descriptive terms appear on a carton label and enclosures; (2) If the biological... if the biological product is prepared in a foreign country, the name and address of the permittee and...

9 CFR 112.2 - Final container label, carton label, and enclosure.

Code of Federal Regulations, 2011 CFR

2011-01-01

... biological product which name shall be identical with that shown in the product license under which such... container label if complete descriptive terms appear on a carton label and enclosures; (2) If the biological... if the biological product is prepared in a foreign country, the name and address of the permittee and...

Food nutrition labelling practice in China.

PubMed

Tao, Yexuan; Li, Ji; Lo, Y Martin; Tang, Qingya; Wang, Youfa

2011-03-01

The present study aimed to scrutinize the food nutrition labelling practice in China before the Chinese Food Nutrition Labeling Regulation (CFNLR) era. Nutrition information of pre-packaged foods collected from a supermarket between December 2007 and January 2008 was analysed and compared with findings from a survey conducted in Beijing. Information collected from a supermarket in Shanghai. A total of 850 pre-packaged foods. In the Shanghai survey, the overall labelling rate was 30·9 %, similar to that found in the Beijing study (29·7 %). While only 20·5 % of the snacks in Shanghai had nutrition labelling, the percentage of food items labelled with SFA (8·6 %), trans fatty acid (4·7 %) or fibre (12·1 %) was very low. Of those food items with nutrition labels, a considerable proportion (7-15 %) did not label energy, fat, carbohydrate or protein. Food products manufactured by Taiwan and Hong Kong companies had a lower labelling rate (13·6 %) than those manufactured by domestic (31·6 %) or international manufacturers (33·8 %). The very low food nutrition labelling rate among products sold in large chain supermarkets in major cities of China before CFNLR emphasizes the need for such critical regulations to be implemented in order to reinforce industrial compliance with accurate nutrition labelling.

Attitude and Behavior Factors Associated with Front-of-Package Label Use with Label Users Making Accurate Product Nutrition Assessments.

PubMed

Roseman, Mary G; Joung, Hyun-Woo; Littlejohn, Emily I

2018-05-01

Front-of-package (FOP) labels are increasing in popularity on retail products. Reductive FOP labels provide nutrient-specific information, whereas evaluative FOP labels summarize nutrient information through icons. Better understanding of consumer behavior regarding FOP labels is beneficial to increasing consumer use of nutrition labeling when making grocery purchasing decisions. We aimed to determine FOP label format effectiveness in aiding consumers at assessing nutrient density of food products. In addition, we sought to determine relationships between FOP label use and attitude toward healthy eating, diet self-assessment, self-reported health and nutrition knowledge, and label and shopping behaviors. A between-subjects experimental design was employed. Participants were randomly assigned to one of four label conditions: Facts Up Front, Facts Up Front Extended, a binary symbol, and no-label control. One hundred sixty-one US primary grocery shoppers, aged 18 to 69 years. Participants were randomly invited to the online study. Participants in one of four label condition groups viewed three product categories (cereal, dairy, and snacks) with corresponding questions. Adults' nutrition assessment of food products based on different FOP label formats, along with label use and attitude toward healthy eating, diet self-assessment, self-reported health and nutrition knowledge, and label and shopping behaviors. Data analyses included descriptive statistics, χ 2 tests, and logistical regression. Significant outcomes were set to α=.05. Participants selected the more nutrient-dense product in the snack food category when it contained an FOP label. Subjective health and nutrition knowledge and frequency of selecting food for healthful reasons were associated with FOP label use (P<0.01 and P<0.05, respectively). Both Facts Up Front (reductive) and binary (evaluative) FOP labels appear effective for nutrition assessment of snack products compared with no label. Specific

Evaluating Varied Label Designs for Use with Medical Devices: Optimized Labels Outperform Existing Labels in the Correct Selection of Devices and Time to Select.

PubMed

Bix, Laura; Seo, Do Chan; Ladoni, Moslem; Brunk, Eric; Becker, Mark W

2016-01-01

Effective standardization of medical device labels requires objective study of varied designs. Insufficient empirical evidence exists regarding how practitioners utilize and view labeling. Measure the effect of graphic elements (boxing information, grouping information, symbol use and color-coding) to optimize a label for comparison with those typical of commercial medical devices. Participants viewed 54 trials on a computer screen. Trials were comprised of two labels that were identical with regard to graphics, but differed in one aspect of information (e.g., one had latex, the other did not). Participants were instructed to select the label along a given criteria (e.g., latex containing) as quickly as possible. Dependent variables were binary (correct selection) and continuous (time to correct selection). Eighty-nine healthcare professionals were recruited at Association of Surgical Technologists (AST) conferences, and using a targeted e-mail of AST members. Symbol presence, color coding and grouping critical pieces of information all significantly improved selection rates and sped time to correct selection (α = 0.05). Conversely, when critical information was graphically boxed, probability of correct selection and time to selection were impaired (α = 0.05). Subsequently, responses from trials containing optimal treatments (color coded, critical information grouped with symbols) were compared to two labels created based on a review of those commercially available. Optimal labels yielded a significant positive benefit regarding the probability of correct choice ((P<0.0001) LSM; UCL, LCL: 97.3%; 98.4%, 95.5%)), as compared to the two labels we created based on commercial designs (92.0%; 94.7%, 87.9% and 89.8%; 93.0%, 85.3%) and time to selection. Our study provides data regarding design factors, namely: color coding, symbol use and grouping of critical information that can be used to significantly enhance the performance of medical device labels.

Readability of prescription labels and medication recall in a population of tertiary referral glaucoma patients.

PubMed

O'Hare, Fleur; Jeganathan, V Swetha E; Rokahr, Catherine G; Rogers, Sophie L; Crowston, Jonathan G

2009-12-01

To evaluate readability of eye drop labels and accurate recall of prescription instructions in a glaucoma population. A hospital-based, cross-sectional study. A trained, interviewer examined patient ability to read standard and larger font medication labels. A questionnaire was administered to ascertain accurate recall of prescribed eye drops. Clinical information was obtained through independent chart review. Glaucoma severity was classified according to a glaucoma staging system. The setting for the study was the glaucoma outpatient clinic, Royal Victorian Eye and Ear Hospital (Melbourne, Australia), a major tertiary referral centre. A total of 200 glaucoma patients (96.2% response), aged 45-90 years, on eye drops took part in the study. Non-English-speaking patients were excluded. The main outcome measure was the ability to read prescribed medication labels and accurately recall treatment regime was compared with glaucoma severity and the number of eye drops. Of the glaucoma patients, 12% were unable to read standard pharmacy labels. Only 5.5% were unable to read the larger font labels. Of the patients, 32% were not able to accurately recall the type of drops or prescribed frequency of instillation. An inability to read standard labels was associated with a threefold reduction in the likelihood of accurate medication recall (95% confidence intervals, 1.40-7.66, P < 0.05). Patients with three or more types of eye drops were five times less likely to recall their medications (95% confidence interval, 0.07-0.57, P < 0.05). Inability to read or recall prescribed eye drops was associated with glaucoma severity and the number of prescribed eye drops. These factors may impact significantly on patients' adherence to glaucoma medications.