Sample records for supervised principal component

  1. Principals' Perceptions Regarding Their Supervision and Evaluation

    ERIC Educational Resources Information Center

    Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann

    2015-01-01

    This study examined the perceptions of principals concerning principal evaluation and supervisory feedback. Principals were asked two open-ended questions. Respondents included 82 principals in the Rocky Mountain region. The emerging themes were "Superintendent Performance," "Principal Evaluation Components," "Specific…

  2. Genetic Classification of Populations Using Supervised Learning

    PubMed Central

    Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

  3. Principals' Supervision and Evaluation Cycles: Perspectives from Principals

    ERIC Educational Resources Information Center

    Hvidston, David J.; McKim, Courtney Ann; Mette, Ian M.

    2016-01-01

    The goals for this quantitative study were to examine principals' perceptions regarding supervision and evaluation within their own evaluations. Three research questions guided the inquiry: (1) What are the perceptions of principals' regarding their own supervision?; (2) What are the perceptions of principals' regarding their own evaluation?; and…

  4. Teacher Supervision Practices and Principals' Characteristics

    ERIC Educational Resources Information Center

    April, Daniel; Bouchamma, Yamina

    2015-01-01

    A questionnaire was used to determine the individual and collective teacher supervision practices of school principals and vice-principals in Québec (n = 39) who participated in a research-action study on pedagogical supervision. These practices were then analyzed in terms of the principals' sociodemographic and socioprofessional characteristics…

  5. Quality assurance of the clinical learning environment in Austria: Construct validity of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T scale).

    PubMed

    Mueller, Gerhard; Mylonas, Demetrius; Schumacher, Petra

    2018-07-01

    Within nursing education, the clinical learning environment is of a high importance in regards to the development of competencies and abilities. The organization, atmosphere, and supervision in the clinical learning environment are only a few factors that influence this development. In Austria there is currently no valid instrument available for the evaluation of influencing factors. The aim of the study was to test the construct validity with principal component analysis as well as the internal consistency of the German Clinical Learning Environment, Supervision and Teacher Scale (CLES+T scale) in Austria. The present validation study has a descriptive-quantitative cross-sectional design. The sample consisted of 385 nursing students from thirteen training institutions in Austria. The data collection was carried out online between March and April 2016. Starting with a polychoric correlation matrix, a parallel analysis with principal component extraction and promax rotation was carried out due to the ordinal data. The exploratory ordinal factor analysis supported a four-component solution and explained 73% of the total variance. The internal consistency of all 25 items reached a Cronbach's α of 0.95 and the four components ranged between 0.83 and 0.95. The German version of the CLES+T scale seems to be a useful instrument for identifying potential areas of improvement in clinical practice in order to derive specific quality measures for the practical learning environment. Copyright © 2018 Elsevier Ltd. All rights reserved.

  6. Predicting Teacher Job Satisfaction Based on Principals' Instructional Supervision Behaviours: A Study of Turkish Teachers

    ERIC Educational Resources Information Center

    Ilgan, Abdurrahman; Parylo, Oksana; Sungu, Hilmi

    2015-01-01

    This quantitative research examined instructional supervision behaviours of school principals as a predictor of teacher job satisfaction through the analysis of Turkish teachers' perceptions of principals' instructional supervision behaviours. There was a statistically significant difference found between the teachers' job satisfaction level and…

  7. The Principal and Supervision. Elementary Principal Series No. 4.

    ERIC Educational Resources Information Center

    Luehe, Bill

    The fourth of six volumes in the "Elementary Principal Series," this booklet offers new principals a set of ideas, procedures, and examples associated with effective teacher supervision. The principal-teacher supervisory relationship has changed dramatically over recent years. The principal is no longer an inspector, but a colleague…

  8. Improving the professionalism of post-certification teacher through academic supervision in vocational schools

    NASA Astrophysics Data System (ADS)

    Satyawati, Sophia Tri; Widyanto, I. Putu; Suemy

    2017-03-01

    This paper examines the principal's efforts in improving the professionalism of post-certification teachers through academic supervision in vocational school. The certification of educators is expected to improve the professionalism of teachers, there are significant changes between the before and after receiving the certificate of educators. One of the efforts made by the principal on increasing the professionalism of teachers is to carry out academic supervision completely and continuously. This paper examines about how principals at vocational schools carry out the programmed academic supervision, and continuing through mentoring, evaluation and coaching. Academic supervision is performed by individual supervision techniques which includes: classroom or practical visit, classroom or practical observation, individual meetings, inter-class or practical places visit, and self-assessment.

  9. Supervision of Agricultural Educators in Secondary Schools: What Do Teachers Want from Their Principals?

    ERIC Educational Resources Information Center

    Paulsen, Thomas H.; Martin, Robert A.

    2014-01-01

    The purpose of this multi-state study was to identify agricultural education teachers' perceived level of importance regarding selected instructional supervisory practices used in the nonformal components of agricultural education. The theoretical frame supporting this study was the theory of andragogy. Data were reported on the perceived…

  10. Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

    PubMed

    Sun, Xiaochun; Ma, Ping; Mumm, Rita H

    2012-01-01

    Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

  11. Nonparametric Method for Genomics-Based Prediction of Performance of Quantitative Traits Involving Epistasis in Plant Breeding

    PubMed Central

    Sun, Xiaochun; Ma, Ping; Mumm, Rita H.

    2012-01-01

    Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression. PMID:23226325

  12. A stable systemic risk ranking in China's banking sector: Based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

    2018-02-01

    In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

  13. 76 FR 36625 - Application and Termination Notice for Municipal Securities Dealer Principal or Representative

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-06-22

    ... DEPARTMENT OF THE TREASURY Office of Thrift Supervision Application and Termination Notice for Municipal Securities Dealer Principal or Representative AGENCY: Office of Thrift Supervision (OTS), Treasury... Thrift Supervision, 1700 G Street, NW., Washington, DC 20552, by fax to (202) 906-6518, or by e-mail to...

  14. Feature extraction for ultrasonic sensor based defect detection in ceramic components

    NASA Astrophysics Data System (ADS)

    Kesharaju, Manasa; Nagarajah, Romesh

    2014-02-01

    High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

  15. Automated cloud screening of AVHRR imagery using split-and-merge clustering

    NASA Technical Reports Server (NTRS)

    Gallaudet, Timothy C.; Simpson, James J.

    1991-01-01

    Previous methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.

  16. Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

    PubMed

    Taguchi, Y-h; Iwadate, Mitsuo; Umeyama, Hideaki

    2015-04-30

    Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

  17. Teachers' Perceptions Based on Tenure Status and Gender about Principals' Supervision

    ERIC Educational Resources Information Center

    Range, Bret G.; Finch, Kim; Young, Suzanne; Hvidston, David J.

    2014-01-01

    This descriptive study assessed teachers' attitudes about their formative supervision and the observational ability of principals through the constructs of teacher tenure status and gender. In sum, 255 teachers responded to an online survey indicating teachers' desired feedback focused on classroom climate, student engagement, and instructional…

  18. An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

    PubMed

    Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

    2014-01-01

    The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

  19. Aspiring Principals' Perspectives about Teacher Supervision and Evaluation: Insights for Educational Leadership Preparation Programs

    ERIC Educational Resources Information Center

    Range, Bret G.; McKim, Courtney; Mette, Ian M.; Hvidston, David J.

    2014-01-01

    This qualitative study sought to understand the views of aspiring principals about teacher supervision and evaluation issues, including their perceived definitions of each, as well as concerns about performing either duty in their first administrative role. Thirty-­two educational administration graduate students enrolled in an instructional…

  20. An Examination of the Supervision of Special Education Instruction in Urban Public School Districts

    ERIC Educational Resources Information Center

    Payne, Suzette Guy

    2017-01-01

    The purpose of this study was to examine the supervision of special education instruction in urban public elementary schools by interviewing three principals regarding their roles in the supervisory process. Through these interviews the researcher attempted to identify concepts or themes that might guide principals in identifying effective…

  1. An introduction to kernel-based learning algorithms.

    PubMed

    Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

    2001-01-01

    This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

  2. Teachers' Perceptions of Teacher Supervision and Evaluation: A Reflection of School Improvement Practices in the Age of Reform

    ERIC Educational Resources Information Center

    Mette, Ian M.; Range, Bret G.; Anderson, Jason; Hvidston, David J.; Nieuwenhuizen, Lisa

    2015-01-01

    This study examined how principals in eight high-functioning elementary schools provide teacher supervision and evaluation to promote high levels of student achievement. Perceptions of teachers were measured to provide an understanding of which specific principal behaviors translated into better instructional practices within the selected schools.…

  3. Augmenting the decomposition of EMG signals using supervised feature extraction techniques.

    PubMed

    Parsaei, Hossein; Gangeh, Mehrdad J; Stashuk, Daniel W; Kamel, Mohamed S

    2012-01-01

    Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

  4. Difficulties Facing the Educational Supervision Processes in the Public Schools of the Governorate of Jarash Directorate of Education

    ERIC Educational Resources Information Center

    Badah, Ahmad; AL-Awawdeh, Amal; Akroush, Lubna; Al Shobaki, Nayfah

    2013-01-01

    This study attempts to identify the difficulties facing the educational supervision processes in the public schools of the Directorate of Education at Jarash Governorate, Jordan, by surveying the principals of these schools. The sample size consisted of (143) male and female principals in the Governorate of Jarash Directorate of Education. In…

  5. A Study of the Relationship of Perceived Principal Supervision and Support to the Perceived Self-Efficacy of Beginning and Experienced K-12 Teachers

    ERIC Educational Resources Information Center

    Spearing, Leonard M.

    2013-01-01

    In this quantitative study the author examined the relationship between the perceived level of principal supervision and support to the perceived self-efficacy of K-12 teachers in a suburban public school district. The impact of perceived self-efficacy upon the commitment to remain in teaching was also considered. Finally the differential…

  6. Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

    PubMed

    Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

    2015-03-01

    Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

  7. Action Research as Instructional Supervision: Suggestions for Principals

    ERIC Educational Resources Information Center

    Glanz, Jeffrey

    2005-01-01

    Supervision based on collaboration, participative decision making, and reflective practice is the hallmark of a viable school improvement program that is designed to promote teaching and learning. Action research has gradually emerged as an important form of instructional supervision to engage teachers in reflective practice about their teaching…

  8. Principals' Experiences of Being Evaluated: A Phenomenological Study

    ERIC Educational Resources Information Center

    Parylo, Oksana; Zepeda, Sally J.; Bengtson, Ed

    2012-01-01

    This phenomenological study sought to understand principals' lived experiences of being evaluated with reliance on the principles of developmental supervision and adult learning theory. Analysis of interview data from 16 principals revealed 3 major constructs in principal evaluation: evaluation is a complex, constantly changing system; principal…

  9. 12 CFR 561.39 - Principal office.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 12 Banks and Banking 5 2010-01-01 2010-01-01 false Principal office. 561.39 Section 561.39 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY DEFINITIONS FOR REGULATIONS AFFECTING ALL SAVINGS ASSOCIATIONS § 561.39 Principal office. The term principal office means the home...

  10. A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

    PubMed

    Jankovic, M V

    2003-01-01

    A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.

  11. Multidimensional Perspectives on Principal Leadership Effectiveness

    ERIC Educational Resources Information Center

    Beycioglu, Kadir, Ed.; Pashiardis, Petros, Ed.

    2015-01-01

    Exceptional management skills are crucial to success in educational environments. As school leaders, principals are expected to effectively supervise the school system while facing a multitude of issues and demands. "Multidimensional Perspectives on Principal Leadership Effectiveness" combines best practices and the latest approaches in…

  12. Principals' Perceived Supervisory Behaviors Regarding Marginal Teachers in Two States

    ERIC Educational Resources Information Center

    Range, Bret; Hewitt, Paul; Young, Suzie

    2014-01-01

    This descriptive study used an online survey to determine how principals in two states viewed the supervision of marginal teachers. Principals ranked their own evaluation of the teacher as the most important factor when identifying marginal teachers and relied on informal methods to diagnose marginal teaching. Female principals rated a majority of…

  13. The Principal in the Teaching and Learning Process

    ERIC Educational Resources Information Center

    Ediger, Marlow

    2009-01-01

    Today's school principal has a plethora of duties and responsibilities. Among many others, he/she is expected to supervise and monitor teacher progress in the classroom. Too frequently in the past, principals performed largely management duties in schools, but now each principal must also assist in teaching and learning situations. How might the…

  14. Archaeological and Geomorphological Data Recovery at Saylorville Lake, Polk County, Iowa. Volume 1. Technical Report.

    DTIC Science & Technology

    1984-12-01

    IOWA I .1 0 VOLUME! TECHNICAL REPORT Prepared Under the Supervision of With the Assistance of Patricia M. Emerson Harlan R. Finney Principal...POLK COUNTY, IOWA VOLUME I o TECHNICAL REPORT DECEMBER 1984 V Prepared Under the Supervision of Patricia M. Emerson, Principal Investigator With the...U.S. Army Corps of Engineers flood-control reservoir located on the Des Moines River In Polk County, Iowa . The work reported herein was done as a

  15. Assessment of Principals' Supervisory Roles for Quality Assurance in Secondary Schools in Ondo State, Nigeria

    ERIC Educational Resources Information Center

    Ayeni, Adeolu Joshua

    2012-01-01

    This study identified the nature of principals' supervisory roles and the perceived effectiveness of principals in the supervision of teachers' instructional tasks. Furthermore, it investigated the constraints faced by principals in the performance of supervisory duties in the teaching-learning process. This was with a view to providing…

  16. Perceptions of Kentucky High School Principals and Superintendents on the Role of the Superintendent Influencing Principal Instructional Leadership

    ERIC Educational Resources Information Center

    Hamilton, Charles L., Jr.

    2011-01-01

    This exploratory study surveyed the promotion of instructional leadership of high school principals by superintendents, as perceived by self and the principals they supervise. The two-phased study included an initial questionnaire administered to both study groups and comparisons of responses analyzed. All superintendents (N = 173), except the…

  17. Highly Effective Interdisciplinary Teams: Perceptions of Exemplary Middle School Principals.

    ERIC Educational Resources Information Center

    George, Paul S.; Stevenson, Chris

    This study analyzed the opinions of exemplary middle school principals concerning what constitutes highly effective interdisciplinary teams. The schools that the principals represented were chosen according to the Department of Education's, Phi Delta Kappa's, and the Association for Supervision and Curriculum Development's assessment of threshold…

  18. How Six States Are Implementing Principal Evaluation Systems. Integrated Leadership Development Initiative (ILDI)

    ERIC Educational Resources Information Center

    Mattson Almanzán, Heather; Sanders, Nancy; Kearney, Karen

    2011-01-01

    Research and professional leadership standards identify specific ways in which principals directly influence school organization and community relationships and exert less direct, but critically important, influence on teaching quality and instructional effectiveness. Principals' roles are central to supporting and supervising teachers'…

  19. Using Mentor-Coaching to Refine Instructional Supervision Skills of Developing Principals

    ERIC Educational Resources Information Center

    Kissane-Long, Akida Lesli

    2012-01-01

    The current student achievement gap can be attributed, in part, to the perceived and actual shortage of highly qualified principals prepared to be effective instructional leaders (Kearney, 2010). Most school districts within do not offer consistent targeted professional development programs for mid-career principals that will develop…

  20. CHIEF SCHOOL OFFICERS, RECOMMENDATIONS AND REPORT OF A SURVEY.

    ERIC Educational Resources Information Center

    PERKINS, JAMES A.; AND OTHERS

    A SUBCOMMITTEE OF THE NEW YORK REGENTS ADVISORY COMMITTEE ON EDUCATIONAL LEADERSHIP INVESTIGATED THE LEADERSHIP POSITION OF CHIEF SCHOOL OFFICERS (CSO'S). THE DATA COLLECTION PROCESS WAS CONDUCTED IN TWO PHASES. A QUESTIONNAIRE SURVEY WAS MADE OF 818 CSO'S (SUPERINTENDENTS, SUPERVISING PRINCIPALS, DISTRICT PRINCIPALS, AND SCHOOL PRINCIPALS). DATA…

  1. Urban School District's Preparing New Principals Program 2008-2011: Perceptions of Program Completers, Supervising Principals, and Senior Level School District Administrators

    ERIC Educational Resources Information Center

    Taylor, Rosemarye T.; Pelletier, Kelly; Trimble, Todd; Ruiz, Eddie

    2014-01-01

    The purpose of these three parallel mixed method studies was to measure the effectiveness of an urban school district's 2011 Preparing New Principals Program (PNPP). Results supported the premise that preparing principals for school leadership in 2013 must develop them as instructional leaders who can improve teacher performance and student…

  2. The Principal's Role in Helping Teachers Manage Their Classrooms.

    ERIC Educational Resources Information Center

    Klitgaard, Guy C.

    1987-01-01

    The principal should lead in instructional improvement and have a good understanding of the principles and practices of classroom management and a good classroom management system. Discusses instructional supervision and assessing teacher performance. (MD)

  3. Identification of milk origin and process-induced changes in milk by stable isotope ratio mass spectrometry.

    PubMed

    Scampicchio, Matteo; Mimmo, Tanja; Capici, Calogero; Huck, Christian; Innocente, Nadia; Drusch, Stephan; Cesco, Stefano

    2012-11-14

    Stable isotope values were used to develop a new analytical approach enabling the simultaneous identification of milk samples either processed with different heating regimens or from different geographical origins. The samples consisted of raw, pasteurized (HTST), and ultrapasteurized (UHT) milk from different Italian origins. The approach consisted of the analysis of the isotope ratio of δ¹³C and δ¹⁵N for the milk samples and their fractions (fat, casein, and whey). The main finding of this work is that as the heat processing affects the composition of the milk fractions, changes in δ¹³C and δ¹⁵N were also observed. These changes were used as markers to develop pattern recognition maps based on principal component analysis and supervised classification models, such as linear discriminant analysis (LDA), multivariate regression (MLR), principal component regression (PCR), and partial least-squares (PLS). The results give proof of the concept that isotope ratio mass spectroscopy can discriminate simultaneously between milk samples according to their geographical origin and type of processing.

  4. Supervision: Exploring the Effective Components. ERIC/CASS Counseling Digest Series.

    ERIC Educational Resources Information Center

    Borders, L. DiAnne, Ed.

    This document contains a collection of ERIC Digests on supervision, a topic of critical professional importance for counselors. Following an introductory article by the guest editor, L. DiAnne Borders, "Supervision: Exploring the Effective Components," 19 digests address a different facet of supervision. The 19 digests are: (1)…

  5. Catching up to the CCSS: A Principal Navigates Out-of-Subject Instructional Leadership

    ERIC Educational Resources Information Center

    Quebec Fuentes, Sarah; Switzer, J. Matt; Jimerson, Jo Beth

    2015-01-01

    This case provides principals and principal licensure candidates an opportunity to delve into the nuances of supervising teachers in content areas, which may be unfamiliar, and to explore strategies for increasing knowledge about the structures and emphases of the "Common Core State Standards" (CCSS). The case presents issues related to…

  6. Teacher Supervision and Evaluation Challenges: Canadian Perspectives on Overall Instructional Leadership

    ERIC Educational Resources Information Center

    Brandon, Jim; Hollweck, Trista; Donlevy, James Kent; Whalen, Catherine

    2018-01-01

    This inquiry focuses on the "overall instructional leadership" approaches used by exemplary principals in three high performing Canadian provinces to overcome three persistent obstacles to effective teacher supervision and evaluation: (a) the management challenge, (b) the complexity challenge, and (c) the learning challenge. Analysis of…

  7. The Wicked Problem of the Intersection between Supervision and Evaluation

    ERIC Educational Resources Information Center

    Mette, Ian M.; Anderson, Jason; Nieuwenhuizen, Lisa; Range, Bret G.; Hvidston, David J.; Doty, Jon

    2017-01-01

    The purpose of this research was to explore how principals in eight high-functioning elementary schools in one American school district balanced teacher supervision and evaluation in their role as an instructional leader. Using the theoretical framework of "wicked problems," to unpack the circular used to problematize teacher supervision…

  8. [Development of the role scale for municipal supervising public health nurses].

    PubMed

    Hatono, Yoko; Suzuki, Hiroko; Masaki, Naoko

    2013-05-01

    As public health nurses are becoming increasingly decentralized in municipalities, recommendations for allocating supervising public health nurses are being made. This study aimed to develop a scale for measuring the implementation of role of municipal supervising public health nurses and to test its reliability and validity. Scale items were developed using results of a qualitative inductive analysis of interview data, and the items were then revised following an examination of content validity by experts, resulting in a provisional scale of 17 items. A self-administered, written questionnaire was then completed by supervising public health nurses or public health nurses holding the most senior positions in all municipalities nationwide, with the exception of three prefectures in the Tohoku region (total 1,621 locations). In total, 1,036 responses were received, and 931 were used for analysis (valid response rate = 57.4%). Of these, 406 were completed by supervising public health nurses. After deleting one item as a result of item analysis and conducting principal component analysis, factor analysis was conducted using the major factor method and Promax rotation. One item with high loading on multiple factors was deleted, resulting in a scale comprising 15 items and 3 factors. The cumulative contribution ratio was 56.10%. The three factors were labeled "Promotion of health activities across the whole locality," "Coordination as a PHN role leader," and "Development of the skills of public health nurses". The reliability coefficient of the RMSP (Role Scale for Municipal Supervising Public Health Nurses) as a whole was 0.84 using the split-half method (Spearman-Brown formula) and 0.91 using Cronbach's alpha, confirming internal consistency. In terms of validity, an examination was conducted of the correlation of two RMSP scale scores (strength of awareness of role as a supervising public health nurse and confidence as a supervising public health nurse) and scores on existing scales assessing management abilities, and a significant correlation (P < 0.01) was obtained. Additionally, a comparison of the RMSP scores of decentralized local public health nurses according to rank and years of service in areas where there were no supervising public health nurses with the RMSP scores of supervising public health nurses showed that the scores of supervising public health nurses were higher. The developed scale was found to be reliable and valid for measuring the implementation of supervising public health nurses' role.

  9. Effectiveness of Supervisions Conducted by Primary Education Supervisors According to School Principals' Evaluations

    ERIC Educational Resources Information Center

    Yavuz, Mustafa

    2010-01-01

    The author conducted a qualitative case study. The population of the study consisted of 8 randomly selected school principals in the area of primary education supervisors working in Konya, a province of the Turkish Republic. Face-to-face and semistructured interviews were held with the school principals within the population for 90 min. The…

  10. Semi-supervised vibration-based classification and condition monitoring of compressors

    NASA Astrophysics Data System (ADS)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

    Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

  11. Planning, Supervision and Quality of Instructional Leadership of Girls' Day Secondary Schools in Kaduna State, Nigeria

    ERIC Educational Resources Information Center

    Duruh, Benjamin C.

    2018-01-01

    This study investigated the Planning, Supervision and Quality of Instructional Leadership of Girls' Day Secondary Schools in Kaduna State. The research design adopted was a survey design. The independent variables were the respondents which include principals, teachers, students, parents and government officials while dependent variable includes…

  12. Examining Teacher Job Satisfaction and Principals' Instructional Supervision Behaviours: A Comparative Study of Turkish Private and Public School Teachers

    ERIC Educational Resources Information Center

    Sungu, Hilmi; Ilgan, Abdurrahman; Parylo, Oksana; Erdem, Mustafa

    2014-01-01

    In spite of a strong body of research examining teacher job satisfaction and teachers' assessment of their principals' behaviours, most studies focus on the educational systems in the first world countries. This quantitative study focuses on a lesser-examined educational context by comparing school teachers' job satisfaction levels and principals'…

  13. 17 CFR 4.10 - Definitions.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... operator, a principal who participates in making trading decisions for a pool, or who supervises, or has... advisor, a principal who participates in making trading decisions for the account of a client or who..., determined in accord with generally accepted accounting principles, with each position in a commodity...

  14. Assessment of Vulnerability to Coccidioidomycosis in Arizona and California.

    PubMed

    Shriber, Jennifer; Conlon, Kathryn C; Benedict, Kaitlin; McCotter, Orion Z; Bell, Jesse E

    2017-06-23

    Coccidioidomycosis is a fungal infection endemic to the southwestern United States, particularly Arizona and California. Its incidence has increased, potentially due in part to the effects of changing climatic variables on fungal growth and spore dissemination. This study aims to quantify the county-level vulnerability to coccidioidomycosis in Arizona and California and to assess the relationships between population vulnerability and climate variability. The variables representing exposure, sensitivity, and adaptive capacity were combined to calculate county level vulnerability indices. Three methods were used: (1) principal components analysis; (2) quartile weighting; and (3) percentile weighting. Two sets of indices, "unsupervised" and "supervised", were created. Each index was correlated with coccidioidomycosis incidence data from 2000-2014. The supervised percentile index had the highest correlation; it was then correlated with variability measures for temperature, precipitation, and drought. The supervised percentile index was significantly correlated ( p < 0.05) with coccidioidomycosis incidence in both states. Moderate, positive significant associations ( p < 0.05) were found between index scores and climate variability when both states were concurrently analyzed and when California was analyzed separately. This research adds to the body of knowledge that could be used to target interventions to vulnerable counties and provides support for the hypothesis that population vulnerability to coccidioidomycosis is associated with climate variability.

  15. Chemometric characterization of alembic and industrial sugar cane spirits from cape verde and ceará, Brazil.

    PubMed

    Pereira, Regina F R; Vidal, Carla B; de Lima, Ari C A; Melo, Diego Q; Dantas, Allan N S; Lopes, Gisele S; do Nascimento, Ronaldo F; Gomes, Clerton L; da Silva, Maria Nataniela

    2012-01-01

    Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates.

  16. Chemometric Characterization of Alembic and Industrial Sugar Cane Spirits from Cape Verde and Ceará, Brazil

    PubMed Central

    Pereira, Regina F. R.; Vidal, Carla B.; de Lima, Ari C. A.; Melo, Diego Q.; Dantas, Allan N. S.; Lopes, Gisele S.; do Nascimento, Ronaldo F.; Gomes, Clerton L.; da Silva, Maria Nataniela

    2012-01-01

    Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates. PMID:23227051

  17. Screening of oil sources by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and multivariate statistical analysis.

    PubMed

    Zhang, Wanfeng; Zhu, Shukui; He, Sheng; Wang, Yanxin

    2015-02-06

    Using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOFMS), volatile and semi-volatile organic compounds in crude oil samples from different reservoirs or regions were analyzed for the development of a molecular fingerprint database. Based on the GC×GC/TOFMS fingerprints of crude oils, principal component analysis (PCA) and cluster analysis were used to distinguish the oil sources and find biomarkers. As a supervised technique, the geological characteristics of crude oils, including thermal maturity, sedimentary environment etc., are assigned to the principal components. The results show that tri-aromatic steroid (TAS) series are the suitable marker compounds in crude oils for the oil screening, and the relative abundances of individual TAS compounds have excellent correlation with oil sources. In order to correct the effects of some other external factors except oil sources, the variables were defined as the content ratio of some target compounds and 13 parameters were proposed for the screening of oil sources. With the developed model, the crude oils were easily discriminated, and the result is in good agreement with the practical geological setting. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Computerization of School Administration: Impact on the Principal's Role--A Case Study.

    ERIC Educational Resources Information Center

    Telem, Moshe

    2001-01-01

    Describes a study at an Israeli high school that investigated changes in the principal's role as the result of the introduction of a school management information system (SMIS). Discusses information handling and flow, interrelations with teachers, accountability, instruction evaluation, supervision, feedback, frequency of meetings, and shared…

  19. The Effect of Instructional Supervision on Principal Trust

    ERIC Educational Resources Information Center

    Wahnee, Robbie L.

    2010-01-01

    Within-school climates and culture are predicated on organizational structures, distributions of power, and roles that are highly interactive. Hierarchical structures and uneven power distributions, primarily those of teacher-principal, have been found to challenge levels of trust. School interaction patterns form the basis of much of the school…

  20. Instructional Supervision and Curriculum Monitoring: Reinterpreting the Principal's Role through the Arts of Inquiry

    ERIC Educational Resources Information Center

    Brooks, Nancy J.; Solloway, Sharon G.; Allen, Louise Anderson

    2007-01-01

    The gulf between educational leadership theory and contemporary curriculum scholarship is becoming increasingly problematic now that principals have been legally mandated to add curriculum monitoring to their duties as instructional leaders. Lacking familiarity with curriculum theory and practice, many overburdened administrators are turning to…

  1. Critical components of reflective supervision: responses from expert supervisors in the field.

    PubMed

    Tomlin, Angela M; Weatherston, Deborah J; Pavkov, Thomas

    2014-01-01

    This article offers a brief review of the history of supervision, defines reflective supervision, and reports the results of a Delphi study designed to identify critical components of reflective supervision. Academicians and master clinicians skilled in providing reflective supervision participated in a three-phase survey to elicit beliefs about best practice when engaging in reflective supervision. The process yielded consensus descriptions of optimal characteristics and behaviors of supervisors and supervisees when entering into supervisory relationships that encourage reflective practice. These results, although preliminary, suggest that it is possible to identify elements that are integral to effective reflective supervision. These initial findings may be used for future study of the reflective supervisory process. © 2013 Michigan Association for Infant Mental Health.

  2. Handbook for the Supervision of the Intern in School Administration...A Performance Based Guide for the Intern and His Advisors

    ERIC Educational Resources Information Center

    Hensarling, Paul R.; Erlandson, David A.

    This handbook is a comprehensive guide to the supervision of the intern for all positions with school administrative responsibilities: principal, superintendent, and middle administrator. It consists of eight sections, the first five of which are introductory, explaining the concept of the internship as a performance-based evaluative technique and…

  3. Leadership to Build Learning Communities

    ERIC Educational Resources Information Center

    Zepeda, Sally J.

    2004-01-01

    This study examined the work of a principal of a Midwestern urban elementary school who used instructional supervision as a means of developing a learning community for adults. Implementing a variety of approaches adapted to the culture of the school, the principal crafted a process to meet the learning needs of 125 teachers and created an…

  4. A Look at Cuban Schools: What Is Cuba Doing Right?

    ERIC Educational Resources Information Center

    Hunt, Barbara C.

    2003-01-01

    A retired elementary school principal, who first visited Cuba as an exchange student, returns 46 years later as an international consultant and finds that the Cubans have made health care and education the top priorities of their society with strong principals and a solid system of supervision and evaluation. (Author/MLF)

  5. Social Justice: Principals' Perceptions of Their Own Preparedness with Special Education Administration

    ERIC Educational Resources Information Center

    Williams, Kimberly A.

    2015-01-01

    Principals are responsible for supervising the educational and legal requirements for their students who have been identified with special education needs, yet they have not necessarily been specifically trained to do so. The research reviewed in this dissertation suggests that building administrators are often placed in situations in which they…

  6. Instructional Leadership in Compulsory Schools in Iceland and the Role of School Principals

    ERIC Educational Resources Information Center

    Hansen, Börkur; Lárusdóttir, Steinunn Helga

    2015-01-01

    The purpose of this paper is to present findings from a study of instructional leadership in 20 Icelandic compulsory schools. More specifically, the perceptions of staff concerning supervision of instruction, and the views of principals regarding their role as supervisors of instructional development. Data was collected with questionnaires from…

  7. Building Principal Pipelines: A Strategy to Strengthen Education Leadership. Update

    ERIC Educational Resources Information Center

    Syed, Sarosh

    2015-01-01

    School leadership is second only to teaching among school influences on student success, according to research. So what can a school district do to produce a large and steady supply of top-notch school principals--and support their effective supervision? This Wallace Update describes two related Wallace Foundation initiatives seeking answers to…

  8. Primary Care-Mental Health Integration in the Veterans Affairs Health System: Program Characteristics and Performance.

    PubMed

    Cornwell, Brittany L; Brockmann, Laurie M; Lasky, Elaine C; Mach, Jennifer; McCarthy, John F

    2018-06-01

    The Veterans Health Administration (VHA) has achieved substantial national implementation of primary care-mental health integration (PC-MHI) services. However, little is known regarding program characteristics, variation in characteristics across settings, or associations between program fidelity and performance. This study identified core elements of PC-MHI services and evaluated their associations with program characteristics and performance. A principal-components analysis (PCA) of reports from 349 sites identified factors associated with PC-MHI fidelity. Analyses assessed the correlation among factors and between each factor and facility type (medical center or community-based outpatient clinic), primary care population size, and performance indicators (receipt of PC-MHI services, same-day access to mental health and primary care services, and extended duration of services). PCA identified seven factors: core implementation, care management (CM) assessments and supervision, CM supervision receipt, colocated collaborative care (CCC) by prescribing providers, CCC by behavioral health providers, participation in patient aligned care teams (PACTs) for special populations, and treatment of complex mental health conditions. Sites serving larger populations had greater core implementation scores. Medical centers and sites serving larger populations had greater scores for CCC by prescribing providers, CM assessments and supervision, and participation in PACTs. Greater core implementation scores were associated with greater same-day access. Sites with greater scores for CM assessments and supervision had lower scores for treatment of complex conditions. Outpatient clinics and sites serving smaller populations experienced challenges in integrated care implementation. To enhance same-day access, VHA should continue to prioritize PC-MHI implementation. Providing brief, problem-focused care may enhance CM implementation.

  9. Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

    PubMed

    Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

    2014-01-30

    Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

  10. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition.

    PubMed

    Arandjelovic, Relja; Gronat, Petr; Torii, Akihiko; Pajdla, Tomas; Sivic, Josef

    2018-06-01

    We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.

  11. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

    PubMed

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-03-15

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  12. Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

    PubMed Central

    Sparks, Rachel; Madabhushi, Anant

    2016-01-01

    Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

  13. Data-Based Personnel Decisions: Baker Middle's Intensive Support List

    ERIC Educational Resources Information Center

    Hewitt, Kimberly Kappler; Chopin, Scarlet Lilian

    2015-01-01

    Focused on the use of teacher evaluation data, this case was designed for use in two principal licensure courses, one on data literacy and the other on supervision and personnel. The principal of Baker Middle School has been instructed by the superintendent to use data from the state's new teacher evaluation system to determine which teachers…

  14. Multicultural Supervision: What Difference Does Difference Make?

    ERIC Educational Resources Information Center

    Eklund, Katie; Aros-O'Malley, Megan; Murrieta, Imelda

    2014-01-01

    Multicultural sensitivity and competency represent critical components to contemporary practice and supervision in school psychology. Internship and supervision experiences are a capstone experience for many new school psychologists; however, few receive formal training and supervision in multicultural competencies. As an increased number of…

  15. Supervising Knowledge Work.

    ERIC Educational Resources Information Center

    Duffy, Francis M.

    This paper summarizes a new paradigm of instructional supervision, which shifts the focus on supervision from an examination of individual behavior to the improvement of work processes and social system components of the school district. The paradigm, called "Knowledge Work Supervision," helps teams of teachers and specially trained supervisors…

  16. Evidence-based Practices Addressed in Community-based Children’s Mental Health Clinical Supervision

    PubMed Central

    Accurso, Erin C.; Taylor, Robin M.; Garland, Ann F.

    2013-01-01

    Context Clinical supervision is the principal method of training for psychotherapeutic practice, however there is virtually no research on supervision practice in community settings. Of particular interest is the role supervision might play in facilitating implementation of evidence-based (EB) care in routine care settings. Objective This study examines the format and functions of clinical supervision sessions in routine care, as well as the extent to which supervision addresses psychotherapeutic practice elements common to EB care for children with disruptive behavior problems, who represent the majority of patients served in publicly-funded routine care settings. Methods Supervisors (n=7) and supervisees (n=12) from four publicly-funded community-based child mental health clinics reported on 130 supervision sessions. Results Supervision sessions were primarily individual in-person meetings lasting one hour. The most common functions included case conceptualization and therapy interventions. Coverage of practice elements common to EB treatments was brief. Discussion Despite the fact that most children presenting to public mental health services are referred for disruptive behavior problems, supervision sessions are infrequently focused on practice elements consistent with EB treatments for this population. Supervision is a promising avenue through which training in EB practices could be supported to improve the quality of care for children in community-based “usual care” clinics. PMID:24761163

  17. RG-inspired machine learning for lattice field theory

    NASA Astrophysics Data System (ADS)

    Foreman, Sam; Giedt, Joel; Meurice, Yannick; Unmuth-Yockey, Judah

    2018-03-01

    Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More generally, we discuss the relationship between PCA and observables in Monte Carlo simulations and the possibility of reducing the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.

  18. Landsat-4 MSS and Thematic Mapper data quality and information content analysis

    NASA Technical Reports Server (NTRS)

    Anuta, P. E.; Bartolucci, L. A.; Dean, M. E.; Lozano, D. F.; Malaret, E.; Mcgillem, C. D.; Valdes, J. A.; Valenzuela, C. R.

    1984-01-01

    Landsat-4 Thematic Mapper and Multispectral Scanner data were analyzed to obtain information on data quality and information content. Geometric evaluations were performed to test band-to-band registration accuracy. Thematic Mapper overall system resolution was evaluated using scene objects which demonstrated sharp high contrast edge responses. Radiometric evaluation included detector relative calibration, effects of resampling, and coherent noise effects. Information content evaluation was carried out using clustering, principal components, transformed divergence separability measure, and numerous supervised classifiers on data from Iowa and Illinois. A detailed spectral class analysis (multispectral classification) was carried out on data from the Des Moines, IA area to compare the information content of the MSS and TM for a large number of scene classes.

  19. Principals' Supervisory Techniques as Correlates of Teachers' Job Performance in Secondary Schools in Ebonyi State, Nigeria

    ERIC Educational Resources Information Center

    Chidi, Nnebedum; Victor, Akinfolarin Akinwale

    2017-01-01

    The persistent and prolonged pitiable state of teachers' job performance leading to poor academic achievement of secondary school students in Ebonyi State has become a source of concern and worry among stakeholders and parents. This could be that instructional supervision is not regularly performed by the principals in order to provide…

  20. What Successful School Principals Do and What Unsuccessful Ones Fail to Do

    ERIC Educational Resources Information Center

    Ponomareva, G. M.

    2015-01-01

    School administration is a special process from the standpoint of management; it is no wonder that it is called both a science and an art. The chief distinguishing characteristic of the process of administration in education is that quite often in the not too distant past, school principals had been teachers in the collective they supervised.…

  1. Supervised detection of exoplanets in high-contrast imaging sequences

    NASA Astrophysics Data System (ADS)

    Gomez Gonzalez, C. A.; Absil, O.; Van Droogenbroeck, M.

    2018-06-01

    Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims: In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods: We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results: This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from 2 to 10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions: The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

  2. Understanding the Up, Back, and Forward-Component in Master's Thesis Supervision with Adaptivity

    ERIC Educational Resources Information Center

    de Kleijn, Renske A. M.; Bronkhorst, Larike H.; Meijer, Paulien C.; Pilot, Albert; Brekelmans, Mieke

    2016-01-01

    Despite the importance of goals in educational theories, goals in master's thesis projects are rarely investigated. Therefore, this study explores how goals play a role in master's thesis supervision in terms of: defining the goals (up-component); locating where the student stands in relation to the goals (back-component); and how the student can…

  3. 48 CFR 936.602-70 - DOE selection criteria.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... the firm and its principal members; (2) Experience and technical competence of the firm in comparable... organizations, consulting firms etc.) including key personnel and a competent supervising representative. (c...

  4. 48 CFR 936.602-70 - DOE selection criteria.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... the firm and its principal members; (2) Experience and technical competence of the firm in comparable... organizations, consulting firms etc.) including key personnel and a competent supervising representative. (c...

  5. 48 CFR 936.602-70 - DOE selection criteria.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... the firm and its principal members; (2) Experience and technical competence of the firm in comparable... organizations, consulting firms etc.) including key personnel and a competent supervising representative. (c...

  6. 48 CFR 936.602-70 - DOE selection criteria.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... the firm and its principal members; (2) Experience and technical competence of the firm in comparable... organizations, consulting firms etc.) including key personnel and a competent supervising representative. (c...

  7. 48 CFR 936.602-70 - DOE selection criteria.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... the firm and its principal members; (2) Experience and technical competence of the firm in comparable... organizations, consulting firms etc.) including key personnel and a competent supervising representative. (c...

  8. The Common Factors Discrimination Model: An Integrated Approach to Counselor Supervision

    ERIC Educational Resources Information Center

    Crunk, A. Elizabeth; Barden, Sejal M.

    2017-01-01

    Numerous models of clinical supervision have been developed; however, there is little empirical support indicating that any one model is superior. Therefore, common factors approaches to supervision integrate essential components that are shared among counseling and supervision models. The purpose of this paper is to present an innovative model of…

  9. Supervision of Ethylene Propylene Diene M-Class (EPDM) Rubber Vulcanization and Recovery Processes Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Multivariate Analysis.

    PubMed

    Riba Ruiz, Jordi-Roger; Canals, Trini; Cantero, Rosa

    2017-01-01

    Ethylene propylene diene monomer (EPDM) rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovery processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FT-IR) spectroscopy and suitable multivariate statistical methods. An expedited and nondestructive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples, and microwave treated samples. First the FT-IR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis and canonical variate analysis to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k nearest neighbor algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FT-IR spectroscopy in this area, since the classification accuracy can be as high as 100%.

  10. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

    PubMed Central

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-01-01

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

  11. Evaluation, Supervision, and Staff Development under Mandated Reform: The Perceptions and Practices of Rural Middle School Principals

    ERIC Educational Resources Information Center

    Eady, Charlotte King; Zepeda, Sally J.

    2007-01-01

    The perspectives of three rural middle school principals as they implement Georgia's A Plus Education Reform Act of 2000 were investigated in this study. A case study approach was used, employing both within case and cross case analyses. Three interviews were conducted with each of the three participants, resulting in a total of nine interviews.…

  12. Responding to the Collective and Individual "Best Interests of Students": Revisiting the Tension between Administrative Practice and Ethical Imperatives in Special Education Leadership

    ERIC Educational Resources Information Center

    Frick, William C.; Faircloth, Susan C.; Little, Karen S.

    2013-01-01

    Purpose: Given the increasing role of the principal in the administrative and supervision of special education programs and services, this research examines how elementary principals interpret their experience of leadership decision making as a moral activity in relation to the Ethic of the Profession and Model for Students' Best Interests.…

  13. Automatic age and gender classification using supervised appearance model

    NASA Astrophysics Data System (ADS)

    Bukar, Ali Maina; Ugail, Hassan; Connah, David

    2016-11-01

    Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

  14. A semi-supervised classification algorithm using the TAD-derived background as training data

    NASA Astrophysics Data System (ADS)

    Fan, Lei; Ambeau, Brittany; Messinger, David W.

    2013-05-01

    In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

  15. An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Akbari, D.

    2017-11-01

    In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  16. Supervised chemical pattern recognition in almond ( Prunus dulcis ) Portuguese PDO cultivars: PCA- and LDA-based triennial study.

    PubMed

    Barreira, João C M; Casal, Susana; Ferreira, Isabel C F R; Peres, António M; Pereira, José Alberto; Oliveira, M Beatriz P P

    2012-09-26

    Almonds harvested in three years in Trás-os-Montes (Portugal) were characterized to find differences among Protected Designation of Origin (PDO) Amêndoa Douro and commercial non-PDO cultivars. Nutritional parameters, fiber (neutral and acid detergent fibers, acid detergent lignin, and cellulose), fatty acids, triacylglycerols (TAG), and tocopherols were evaluated. Fat was the major component, followed by carbohydrates, protein, and moisture. Fatty acids were mostly detected as monounsaturated and polyunsaturated forms, with relevance of oleic and linoleic acids. Accordingly, 1,2,3-trioleoylglycerol and 1,2-dioleoyl-3-linoleoylglycerol were the major TAG. α-Tocopherol was the leading tocopherol. To verify statistical differences among PDO and non-PDO cultivars independent of the harvest year, data were analyzed through an analysis of variance, a principal component analysis, and a linear discriminant analysis (LDA). These differences identified classification parameters, providing an important tool for authenticity purposes. The best results were achieved with TAG analysis coupled with LDA, which proved its effectiveness to discriminate almond cultivars.

  17. Self organising maps for visualising and modelling

    PubMed Central

    2012-01-01

    The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. PMID:22594434

  18. Assessment of computer techniques for processing digital LANDSAT MSS data for lithological discrimination of Serra do Ramalho, State of Bahia

    NASA Technical Reports Server (NTRS)

    Paradella, W. R. (Principal Investigator); Vitorello, I.; Monteiro, M. D.

    1984-01-01

    Enhancement techniques and thematic classifications were applied to the metasediments of Bambui Super Group (Upper Proterozoic) in the Region of Serra do Ramalho, SW of the state of Bahia. Linear contrast stretch, band-ratios with contrast stretch, and color-composites allow lithological discriminations. The effects of human activities and of vegetation cover mask and limit, in several ways, the lithological discrimination with digital MSS data. Principal component images and color composite of linear contrast stretch of these products, show lithological discrimination through tonal gradations. This set of products allows the delineations of several metasedimentary sequences to a level superior to reconnaissance mapping. Supervised (maximum likelihood classifier) and nonsupervised (K-Means classifier) classification of the limestone sequence, host to fluorite mineralization show satisfactory results.

  19. The Supervision Partnership as a Phase of Attachment

    ERIC Educational Resources Information Center

    Koehn, Amanda J.; Kerns, Kathryn A.

    2016-01-01

    The supervision partnership in middle childhood was proposed by Waters, Kondo-Ikemura, Posada, and Richters as the last phase of parent-child attachment. The present study elaborates this concept by proposing three components of the supervision partnership: "availability and accessibility," "willingness to communicate," and…

  20. A Model of Instructional Supervision That Meets Today's Needs.

    ERIC Educational Resources Information Center

    Beck, John J.; Seifert, Edward H.

    1983-01-01

    The proposed Instructional Technologist Model is based on a closed loop feedback system allowing for continuous monitoring of teachers by expert instructional technologists. Principals are thereby released for instructional evaluation and general educational management. (MJL)

  1. Using Structural Equation Modeling To Fit Models Incorporating Principal Components.

    ERIC Educational Resources Information Center

    Dolan, Conor; Bechger, Timo; Molenaar, Peter

    1999-01-01

    Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…

  2. The alliance in reflective supervision: a commentary on Tomlin, Weatherston, and Pavkov's critical components of reflective supervision.

    PubMed

    Watkins, C Edward

    2015-01-01

    What are the critical components of reflective supervision? In this commentary, I offer a complementary perspective on A.M. Tomlin, D.J. Weatherston, and T. Pavkov's (2014) seminal study about that very question. I consider their findings within the context of what we now know about the supervisory alliance-a highly robust, heuristic, and eminently practical construct that appears to capture the spirit of reflection, collaboration, and regularity at its best. Matters of alliance theory, practice, and research are briefly addressed, and effort is made to consider the implications of the supervisory alliance as fundamental, foundational, and quintessentially organizational for reflective supervision practice. © 2015 Michigan Association for Infant Mental Health.

  3. Inspired by "El Duende": One-Canvas Process Painting in Art Therapy Supervision

    ERIC Educational Resources Information Center

    Miller, Abbe

    2012-01-01

    This article describes an art-based approach to supervision that combines clinical insights with archetypal awareness arising from painting on a single canvas throughout the internship semester. Supervision is comprised of three main components: (a) spontaneous painting, (b) complex reflective processing, and (c) aesthetically focused attention to…

  4. Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

    PubMed

    Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

    2015-10-01

    Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions.

  5. Implementation of a Contingency Management-Based Intervention in a Community Supervision Setting: Clinical Issues and Recommendations

    ERIC Educational Resources Information Center

    Trotman, Adria J.; Taxman, Faye S.

    2011-01-01

    A cognitive-behaviorally based substance abuse treatment program was implemented within a community supervision setting. This program included a goals group that used a contingency management component and included the probation agent as a part of the treatment. In this article, the authors describe the contingency management component of the…

  6. Supportive supervision and constructive relationships with healthcare workers support CHW performance: Use of a qualitative framework to evaluate CHW programming in Uganda.

    PubMed

    Ludwick, Teralynn; Turyakira, Eleanor; Kyomuhangi, Teddy; Manalili, Kimberly; Robinson, Sheila; Brenner, Jennifer L

    2018-02-13

    While evidence supports community health worker (CHW) capacity to improve maternal and newborn health in less-resourced countries, key implementation gaps remain. Tools for assessing CHW performance and evidence on what programmatic components affect performance are lacking. This study developed and tested a qualitative evaluative framework and tool to assess CHW team performance in a district program in rural Uganda. A new assessment framework was developed to collect and analyze qualitative evidence based on CHW perspectives on seven program components associated with effectiveness (selection; training; community embeddedness; peer support; supportive supervision; relationship with other healthcare workers; retention and incentive structures). Focus groups were conducted with four high/medium-performing CHW teams and four low-performing CHW teams selected through random, stratified sampling. Content analysis involved organizing focus group transcripts according to the seven program effectiveness components, and assigning scores to each component per focus group. Four components, 'supportive supervision', 'good relationships with other healthcare workers', 'peer support', and 'retention and incentive structures' received the lowest overall scores. Variances in scores between 'high'/'medium'- and 'low'-performing CHW teams were largest for 'supportive supervision' and 'good relationships with other healthcare workers.' Our analysis suggests that in the Bushenyi intervention context, CHW team performance is highly correlated with the quality of supervision and relationships with other healthcare workers. CHWs identified key performance-related issues of absentee supervisors, referral system challenges, and lack of engagement/respect by health workers. Other less-correlated program components warrant further study and may have been impacted by relatively consistent program implementation within our limited study area. Applying process-oriented measurement tools are needed to better understand CHW performance-related factors and build a supportive environment for CHW program effectiveness and sustainability. Findings from a qualitative, multi-component tool developed and applied in this study suggest that factors related to (1) supportive supervision and (2) relationships with other healthcare workers may be strongly associated with variances in performance outcomes within a program. Careful consideration of supervisory structure and health worker orientation during program implementation are among strategies proposed to increase CHW performance.

  7. A Step Forward in Teaching Addiction Counselors How to Supervise Motivational Interviewing Using a Clinical Trials Training Approach

    ERIC Educational Resources Information Center

    Martino, Steve; Gallon, Steve; Ball, Samuel A.; Carroll, Kathleen M.

    2007-01-01

    A clinical trials training approach to supervision is a promising and empirically supported method for preparing addiction counselors to implement evidence-based behavioral treatments in community treatment programs. This supervision approach has three main components: (1) direct observation of treatment sessions; (2) structured performance…

  8. Trust: The Power That Binds in Team Supervision of Doctoral Students

    ERIC Educational Resources Information Center

    Robertson, Margaret J.

    2017-01-01

    Team supervision of doctoral students adds new dimensions and complexities to relationships within the teams that impact functionality of the team. Trust emerged as a significant theme in recent qualitative research into the quality of team supervision of doctoral students. Trust was cited as a key component in successful team collaborations, and…

  9. Advances in space robotics

    NASA Technical Reports Server (NTRS)

    Varsi, Giulio

    1989-01-01

    The problem of the remote control of space operations is addressed by identifying the key technical challenge: the management of contact forces and the principal performance parameters. Three principal classes of devices for remote operation are identified: anthropomorphic exoskeletons, computer aided teleoperators, and supervised telerobots. Their fields of application are described, and areas in which progress has reached the level of system or subsystem laboratory demonstrations are indicated. Key test results, indicating performance at a level useful for design tradeoffs, are reported.

  10. A Novel Hybrid Dimension Reduction Technique for Undersized High Dimensional Gene Expression Data Sets Using Information Complexity Criterion for Cancer Classification

    PubMed Central

    Pamukçu, Esra; Bozdogan, Hamparsum; Çalık, Sinan

    2015-01-01

    Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features) but with only a limited number of observations (i.e., samples). Although the classical principal component analysis (PCA) method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA) by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs) to be retained, we further introduce and develop celebrated Akaike's information criterion (AIC), consistent Akaike's information criterion (CAIC), and the information theoretic measure of complexity (ICOMP) criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions. PMID:25838836

  11. Assessment of Vulnerability to Coccidioidomycosis in Arizona and California

    PubMed Central

    Conlon, Kathryn C.; Benedict, Kaitlin; McCotter, Orion Z.; Bell, Jesse E.

    2017-01-01

    Coccidioidomycosis is a fungal infection endemic to the southwestern United States, particularly Arizona and California. Its incidence has increased, potentially due in part to the effects of changing climatic variables on fungal growth and spore dissemination. This study aims to quantify the county-level vulnerability to coccidioidomycosis in Arizona and California and to assess the relationships between population vulnerability and climate variability. The variables representing exposure, sensitivity, and adaptive capacity were combined to calculate county level vulnerability indices. Three methods were used: (1) principal components analysis; (2) quartile weighting; and (3) percentile weighting. Two sets of indices, “unsupervised” and “supervised”, were created. Each index was correlated with coccidioidomycosis incidence data from 2000–2014. The supervised percentile index had the highest correlation; it was then correlated with variability measures for temperature, precipitation, and drought. The supervised percentile index was significantly correlated (p < 0.05) with coccidioidomycosis incidence in both states. Moderate, positive significant associations (p < 0.05) were found between index scores and climate variability when both states were concurrently analyzed and when California was analyzed separately. This research adds to the body of knowledge that could be used to target interventions to vulnerable counties and provides support for the hypothesis that population vulnerability to coccidioidomycosis is associated with climate variability. PMID:28644403

  12. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

    PubMed Central

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  13. Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

    NASA Astrophysics Data System (ADS)

    Zhao, Yiqun; Wang, Zhihui

    2015-12-01

    The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

  14. Micro-Raman spectroscopy of natural and synthetic indigo samples.

    PubMed

    Vandenabeele, Peter; Moens, Luc

    2003-02-01

    In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.

  15. Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

    NASA Astrophysics Data System (ADS)

    Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

    2015-01-01

    Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

  16. TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

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

    Chen, H; Tan, J; Kavanaugh, J

    Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-timemore » and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.« less

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

  18. Discrimination of a chestnut-oak forest unit for geologic mapping by means of a principal component enhancement of Landsat multispectral scanner data.

    USGS Publications Warehouse

    Krohn, M.D.; Milton, N.M.; Segal, D.; Enland, A.

    1981-01-01

    A principal component image enhancement has been effective in applying Landsat data to geologic mapping in a heavily forested area of E Virginia. The image enhancement procedure consists of a principal component transformation, a histogram normalization, and the inverse principal componnet transformation. The enhancement preserves the independence of the principal components, yet produces a more readily interpretable image than does a single principal component transformation. -from Authors

  19. PERCEPTIONS OF THE ELEMENTARY SCHOOL COUNSELOR.

    ERIC Educational Resources Information Center

    BRADEN, BILLY; AND OTHERS

    FACTORS ASSOCIATED WITH THE ROLE AND FUNCTION OF THE ELEMENTARY SCHOOL COUNSELOR AS THEY WERE PERCEIVED BY SELECTED ELEMENTARY SCHOOL COUNSELORS, ELEMENTARY SCHOOL PRINCIPALS, COUNSELOR EDUCATORS, AND STATE SUPERVISORS IN THE SOUTHERN ASSOCIATION FOR COUNSELOR EDUCATION AND SUPERVISION (SACES) REGION WERE IDENTIFIED. THREE INSTRUMENTS WERE…

  20. Updating the Role of Rural Supervision: Perspectives from Alaska

    ERIC Educational Resources Information Center

    Kaden, Ute I.; Patterson, Philip P.; Healy, Joanne

    2014-01-01

    Despite the documented importance of professional experiences in teacher preparation, numerous questions persist as to how university supervisors can effectively contribute to rural preservice teachers' development and to establish lasting collaborations between involved stakeholders (e.g., collaborating teacher, principal, community). This paper…

  1. Principal component regression analysis with SPSS.

    PubMed

    Liu, R X; Kuang, J; Gong, Q; Hou, X L

    2003-06-01

    The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

  2. Reflections on supervision in psychotherapy.

    PubMed

    Fernández-Alvarez, Héctor

    2016-01-01

    The aim of the author is to share his reflections on supervision as a central topic in therapists' education and training programs. The concept of supervision, its functions and effects on the training process along with the contributions of different theoretical models to its evolution are addressed. Supervision alliance, the roles of supervisor and supervisee, evaluation as a central component and the influence of socioeconomic factors are discussed. The conclusions depict the most interesting paths for development in the near future and the areas where research needs to be further conducted along with the subjects most worthy of efforts in the supervision field.

  3. An Early Historical Examination of the Educational Intent of Supervised Agricultural Experiences (SAEs) and Project-Based Learning in Agricultural Education

    ERIC Educational Resources Information Center

    Smith, Kasee L.; Rayfield, John

    2016-01-01

    Project-based learning has been a component of agricultural education since its inception. In light of the current call for additional emphasis of the Supervised Agricultural Experience (SAE) component of agricultural education, there is a need to revisit the roots of project-based learning. This early historical research study was conducted to…

  4. Psychotherapy-based supervision models in an emerging competency-based era: a commentary.

    PubMed

    Falender, Carol A; Shafranske, Edward P

    2010-03-01

    As psychology engages in a cultural shift to competency-based education and training supervision practice is being transformed to the use of competency frames and the application of benchmark competencies. In this issue, psychotherapy-based models of supervision are conceptualized in a competency framework. This paper reflects on the translation of key components of each psychotherapy-based supervision approach in terms of foundational and functional competencies articulated in the Competencies Benchmarks (Fouad et al., 2009). The commentary concludes with a discussion of implications for supervision practice and identifies directions for future articulation and development, including evidence-based psychotherapy supervision. PsycINFO Database Record (c) 2010 APA, all rights reserved

  5. Expanded Notions of Strategic Instructional Leadership: The Principal's Role with Student Support Personnel.

    ERIC Educational Resources Information Center

    Geltner, Beverley B.; Shelton, Maria M.

    1991-01-01

    Definitions of effective instructional leadership inadequately address supervision and strategic deployment of student support personnel (counselors, psychologists, social workers, and others) as critical resources for school success. Engaging their skills and understanding expands professional discourse, improves teaching and learning, and…

  6. An exploration of undergraduate medical students' satisfaction with faculty support supervision during community placements in Uganda

    PubMed Central

    Mubuuke, AG; Oria, H; Dhabangi, A; Kiguli, S; Sewankambo, NK

    2015-01-01

    Introduction To produce health professionals who are oriented towards addressing community priority health needs, the training in medical schools has been transformed to include a component of community-based training. During this period, students spend a part of their training in the communities they are likely to serve upon graduation. They engage and empower local people in the communities to address their health needs during their placements, and at the same time learn from the people. During the community-based component, students are constantly supervised by faculty from the university to ensure that the intended objectives are achieved. The purpose of the present study was to explore student experiences of support supervision from university faculty during their community-based education, research and service (COBERS placements) and to identify ways in which the student learning can be improved through improved faculty supervision. Methods This was a cross-sectional study involving students at the College of Health Sciences, Makerere University, Uganda, who had a community-based component during their training. Data were collected using both questionnaires and focus group discussions. Quantitative data were analyzed using statistical software and thematic approaches were used for the analysis of qualitative data. Results Most students reported satisfaction with the COBERS supervision; however, junior students were less satisfied with the supervision than the more senior students with more experience of community-based training. Although many supervisors assisted students before departure to COBERS sites, a significant number of supervisors made little follow-up while students were in the community. Incorporating the use of information technology avenues such as emails and skype sessions was suggested as a potential way of enhancing supervision amidst resource constraints without faculty physically visiting the sites. Conclusions Although many students were satisfied with COBERS supervision, there are still some challenges, mostly seen with the more junior students. Using information technology could be a solution to some of these challenges. PMID:26626014

  7. An exploration of undergraduate medical students' satisfaction with faculty support supervision during community placements in Uganda.

    PubMed

    Mubuuke, Aloysius G; Oria, Hussein; Dhabangi, Aggrey; Kiguli, Sarah; Sewankambo, Nelson K

    2015-01-01

    To produce health professionals who are oriented towards addressing community priority health needs, the training in medical schools has been transformed to include a component of community-based training. During this period, students spend a part of their training in the communities they are likely to serve upon graduation. They engage and empower local people in the communities to address their health needs during their placements, and at the same time learn from the people. During the community-based component, students are constantly supervised by faculty from the university to ensure that the intended objectives are achieved. The purpose of the present study was to explore student experiences of support supervision from university faculty during their community-based education, research and service (COBERS placements) and to identify ways in which the student learning can be improved through improved faculty supervision. This was a cross-sectional study involving students at the College of Health Sciences, Makerere University, Uganda, who had a community-based component during their training. Data were collected using both questionnaires and focus group discussions. Quantitative data were analyzed using statistical software and thematic approaches were used for the analysis of qualitative data. Most students reported satisfaction with the COBERS supervision; however, junior students were less satisfied with the supervision than the more senior students with more experience of community-based training. Although many supervisors assisted students before departure to COBERS sites, a significant number of supervisors made little follow-up while students were in the community. Incorporating the use of information technology avenues such as emails and skype sessions was suggested as a potential way of enhancing supervision amidst resource constraints without faculty physically visiting the sites. Although many students were satisfied with COBERS supervision, there are still some challenges, mostly seen with the more junior students. Using information technology could be a solution to some of these challenges.

  8. Disciplinary supervision following ethics complaints: goals, tasks, and ethical dimensions.

    PubMed

    Thomas, Janet T

    2014-11-01

    Clinical supervision is considered an integral component of the training of psychologists, and most of the professional literature is focused on this type of supervision. But psychologists also may supervise fully credentialed colleagues in other circumstances. One such context occurs when licensing boards mandate supervision as part of a disciplinary order. When supervision is provided in disciplinary cases, there are significant implications for the ethical dimensions of the supervisory relationship and concomitant ethical challenges for supervisors. Not only are the goals, objectives, and supervisory tasks of disciplinary supervision distinct from other types of supervision, but the supervisor's ethical responsibilities also encompass unique dimensions. Competence, informed consent, boundaries, confidentiality, and documentation are examined. Recommendations for reports to licensing boards include a statement of the clinical or ethical problems instigating discipline, description of how these problems have been addressed, and an assessment of the supervisee's current practices and ability to perform competently. © 2014 Wiley Periodicals, Inc.

  9. Towards automatic lithological classification from remote sensing data using support vector machines

    NASA Astrophysics Data System (ADS)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

  10. Application of thematic mapper-type data over a porphyry-molybdenum deposit in Colorado

    NASA Technical Reports Server (NTRS)

    Rickman, D. L.; Sadowski, R. M.

    1983-01-01

    The objective of the study was to evaluate the utility of thematic mapper data as a source of geologically useful information for mountainous areas of varying vegetation density. Much of the processing was done in an a priori manner without prior ground-based information. This approach resulted in a successfull mapping of the alteration associated with the Mt. Emmons molybdenum ore body as well as several other hydrothermal systems. Supervised classification produced a vegetation map at least as accurate as the mapping done for the environmental impact statement. Principal components were used to map zones of general, subtle alteration and to separate hematitically stained rock from staining associated with hydrothermal activity. Decorrelation color composites were found to be useful field mapping aids, easily delineating many lithologies and vegetation classes of interest. The factors restricting the interpretability and computer manipulation of the data are examined.

  11. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    PubMed

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  12. Development of an Integrated Metabolomic Profiling Approach for Infectious Diseases Research

    PubMed Central

    Lv, Haitao; Hung, Chia S.; Chaturvedi, Kaveri S.; Hooton, Thomas M.; Henderson, Jeffrey P.

    2013-01-01

    Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ∼2300 molecular features. Principal components analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts. PMID:21922104

  13. Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

    PubMed

    Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara

    2017-08-02

    This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

  14. Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

    PubMed

    Bagur, M G; Morales, S; López-Chicano, M

    2009-11-15

    Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

  15. LANDSAT-4 MSS and Thematic Mapper data quality and information content analysis

    NASA Technical Reports Server (NTRS)

    Anuta, P.; Bartolucci, L.; Dean, E.; Lozano, F.; Malaret, E.; Mcgillem, C. D.; Valdes, J.; Valenzuela, C.

    1984-01-01

    LANDSAT-4 thematic mapper (TM) and multispectral scanner (MSS) data were analyzed to obtain information on data quality and information content. Geometric evaluations were performed to test band-to-band registration accuracy. Thematic mapper overall system resolution was evaluated using scene objects which demonstrated sharp high contrast edge responses. Radiometric evaluation included detector relative calibration, effects of resampling, and coherent noise effects. Information content evaluation was carried out using clustering, principal components, transformed divergence separability measure, and supervised classifiers on test data. A detailed spectral class analysis (multispectral classification) was carried out to compare the information content of the MSS and TM for a large number of scene classes. A temperature-mapping experiment was carried out for a cooling pond to test the quality of thermal-band calibration. Overall TM data quality is very good. The MSS data are noisier than previous LANDSAT results.

  16. Electronic Nose For Measuring Wine Evolution In Wine Cellars

    NASA Astrophysics Data System (ADS)

    Lozano, J.; Santos, J. P.; Horrillo, M. C.; Cabellos, J. M.; Arroyo, T.

    2009-05-01

    An electronic nose installed in a wine cellar for measuring the wine evolution is presented in this paper. The system extract the aroma directly from the tanks where wine is stored and carry the volatile compounds to the sensors cell. A tin oxide multisensor, prepared with RF sputtering onto an alumina substrate and doped with chromium and indium, is used. The whole system is fully automated and controlled by computer and can be supervised by internet. Linear techniques like principal component analysis (PCA) and nonlinear ones like probabilistic neural networks (PNN) are used for pattern recognition. Results show that system can detect the evolution of two different wines along 9 months stored in tanks. This system could be trained to detect off-odours of wine and warn the wine expert to correct it as soon as possible, improving the final quality of wine.

  17. Clinical Supervision of Mental Health Professionals Serving Youth: Format and Microskills.

    PubMed

    Bailin, Abby; Bearman, Sarah Kate; Sale, Rafaella

    2018-03-21

    Clinical supervision is an element of quality assurance in routine mental health care settings serving children; however, there is limited scientific evaluation of its components. This study examines the format and microskills of routine supervision. Supervisors (n = 13) and supervisees (n = 20) reported on 100 supervision sessions, and trained coders completed observational coding on a subset of recorded sessions (n = 57). Results indicate that microskills shown to enhance supervisee competency in effectiveness trials and experiments were largely absent from routine supervision, highlighting potential missed opportunities to impart knowledge to therapists. Findings suggest areas for quality improvement within routine care settings.

  18. Competencies Used to Evaluate High School Coaches.

    ERIC Educational Resources Information Center

    Gratto, John

    1983-01-01

    Studies of how to evaluate high school coaches' effectiveness found that most respondents felt that principals, athletic directors, and coaches should jointly arrive at a method of evaluation. Coaching competencies rated most highly included prevention and care of athletic injuries, supervision, and consistent discipline. Other valued competencies…

  19. A Long-Range Approach to Instructional Leadership.

    ERIC Educational Resources Information Center

    McDermott, Donald F.

    1984-01-01

    In Anchorage, Alaska, aspiring principals are getting on-the-job experience through a two-year intern program that facilitates long range planning by enabling the district to test and mold its future administrators. Interns serve as administrative assistants, troubleshooters, and participates in instructional supervision and policy decision. (TE)

  20. Evaluation Process of Paraeducators: Perspectives from Paraeducators and Principals

    ERIC Educational Resources Information Center

    Glickman, Lynn

    2017-01-01

    Paraeducators support students in schools throughout the United States by fulfilling duties such as providing instructional reinforcement, assisting with clerical tasks, supervising students, and supporting the mobility and/or hygiene of students with physical disabilities. However, although data show that approximately 91% of schools in the…

  1. Advanced methods in NDE using machine learning approaches

    NASA Astrophysics Data System (ADS)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.

  2. [Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane].

    PubMed

    Liu, Gui-Song; Guo, Hao-Song; Pan, Tao; Wang, Ji-Hua; Cao, Gan

    2014-10-01

    Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was ex- panded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were signif- icantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively. Vis-NIR spectro- scopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.

  3. On the Fallibility of Principal Components in Research

    ERIC Educational Resources Information Center

    Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

    2017-01-01

    The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…

  4. Parent Participation in the Spanish School System: School Councils

    ERIC Educational Resources Information Center

    Cobano-Delgado, Verónica

    2015-01-01

    Parents of pupils participate in the supervision and management of Spanish schools through the School Council ["Consejo Escolar"], which is the principal body through which such participation and oversight is channeled. Through it families, pupils, teachers and non-teaching staff contribute collectively to making the important decisions…

  5. Assistant Principal Dilemma: Walking the Line between Compliance and Leadership

    ERIC Educational Resources Information Center

    Baker, A. Minor; Guerra, Patricia L.; Baray, Sarah

    2018-01-01

    Inspired by real-life events, this case describes the start of a seemingly solid working relationship between two elementary campus administrators and its demise as the result of inadequate communication and distrust. Initially, these two leaders benefit from differing supervision styles, but these differences eventually lead to increased tension…

  6. School Leaders' Perceptions about Incompetent Teachers: Implications for Supervision and Evaluation

    ERIC Educational Resources Information Center

    Range, Bret G.; Duncan, Heather E.; Scherz, Susan Day; Haines, Courtney A.

    2012-01-01

    This study explored Wyoming school leaders' perceptions about the traits of incompetent teachers, strategies used to work with incompetent teachers, and the barriers to their dismissal. Most importantly, this study differentiated how principals and superintendents viewed incompetency issues. Some major findings include school leaders' beliefs that…

  7. Pushing the Horizons of Student Teacher Supervision: Can a Bug-in-Ear System Be an Effective Plug-and-Play Tool for a Novice Electronic-Coach to Use in Student Teacher Supervision?

    ERIC Educational Resources Information Center

    Almendarez Barron, Maria

    2012-01-01

    The National Council for Accreditation of Teacher Education has called for strengthening teacher preparation by incorporating more fieldwork. Supervision with effective instructional feedback is an essential component of meaningful fieldwork, and immediate feedback has proven more efficacious than delayed feedback. Rock and her colleagues have…

  8. Disruption of TCA Cycle and Glutamate Metabolism Identified by Metabolomics in an In Vitro Model of Amyotrophic Lateral Sclerosis.

    PubMed

    Veyrat-Durebex, Charlotte; Corcia, Philippe; Piver, Eric; Devos, David; Dangoumau, Audrey; Gouel, Flore; Vourc'h, Patrick; Emond, Patrick; Laumonnier, Frédéric; Nadal-Desbarats, Lydie; Gordon, Paul H; Andres, Christian R; Blasco, Hélène

    2016-12-01

    This study aims to develop a cellular metabolomics model that reproduces the pathophysiological conditions found in amyotrophic lateral sclerosis in order to improve knowledge of disease physiology. We used a co-culture model combining the motor neuron-like cell line NSC-34 and the astrocyte clone C8-D1A, with each over-expressing wild-type or G93C mutant human SOD1, to examine amyotrophic lateral sclerosis (ALS) physiology. We focused on the effects of mutant human SOD1 as well as oxidative stress induced by menadione on intracellular metabolism using a metabolomics approach through gas chromatography coupled with mass spectrometry (GC-MS) analysis. Preliminary non-supervised analysis by Principal Component Analysis (PCA) revealed that cell type, genetic environment, and time of culture influenced the metabolomics profiles. Supervised analysis using orthogonal partial least squares discriminant analysis (OPLS-DA) on data from intracellular metabolomics profiles of SOD1 G93C co-cultures produced metabolites involved in glutamate metabolism and the tricarboxylic acid cycle (TCA) cycle. This study revealed the feasibility of using a metabolomics approach in a cellular model of ALS. We identified potential disruption of the TCA cycle and glutamate metabolism under oxidative stress, which is consistent with prior research in the disease. Analysis of metabolic alterations in an in vitro model is a novel approach to investigation of disease physiology.

  9. Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

    NASA Astrophysics Data System (ADS)

    Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

    2017-02-01

    Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

  10. Two-echelon logistics service supply chain decision game considering quality supervision

    NASA Astrophysics Data System (ADS)

    Shi, Jiaying

    2017-10-01

    Due to the increasing importance of supply chain logistics service, we established the Stackelberg game model between single integrator and single subcontractors under decentralized and centralized circumstances, and found that logistics services integrators as a leader prefer centralized decision-making but logistics service subcontractors tend to the decentralized decision-making. Then, we further analyzed why subcontractor chose to deceive and rebuilt a principal-agent game model to monitor the logistics services quality of them. Mixed Strategy Nash equilibrium and related parameters were discussed. The results show that strengthening the supervision and coordination can improve the quality level of logistics service supply chain.

  11. Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

    NASA Astrophysics Data System (ADS)

    Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

    2017-07-01

    The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

  12. Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate

    PubMed Central

    Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.

    2009-01-01

    Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786

  13. Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.

    PubMed

    Maurer, Christian; Federolf, Peter; von Tscharner, Vinzenz; Stirling, Lisa; Nigg, Benno M

    2012-05-01

    Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

  14. Principal Component Relaxation Mode Analysis of an All-Atom Molecular Dynamics Simulation of Human Lysozyme

    NASA Astrophysics Data System (ADS)

    Nagai, Toshiki; Mitsutake, Ayori; Takano, Hiroshi

    2013-02-01

    A new relaxation mode analysis method, which is referred to as the principal component relaxation mode analysis method, has been proposed to handle a large number of degrees of freedom of protein systems. In this method, principal component analysis is carried out first and then relaxation mode analysis is applied to a small number of principal components with large fluctuations. To reduce the contribution of fast relaxation modes in these principal components efficiently, we have also proposed a relaxation mode analysis method using multiple evolution times. The principal component relaxation mode analysis method using two evolution times has been applied to an all-atom molecular dynamics simulation of human lysozyme in aqueous solution. Slow relaxation modes and corresponding relaxation times have been appropriately estimated, demonstrating that the method is applicable to protein systems.

  15. Teacher Supervision: If It Ain't Working...

    ERIC Educational Resources Information Center

    Rooney, Joanne

    2005-01-01

    When Joanne Rooney, a principal, asked 17 tenured teachers who were due for their formal supervisory visits at Pleasant Hill School in Palatine, Illinois whether her annual visits and follow-up conferences help them become better teachers," her question was met with muffled laughter. They knew that her rushed, mandatory visits and conferences…

  16. New Jersey's Design for Educational Excellence: Into Action.

    ERIC Educational Resources Information Center

    New Jersey State Dept. of Education, Trenton.

    This booklet describes various new initiatives that the state of New Jersey has launched in the past 18 months, including: (1) proposed revisions of principal certification requirements; (2) a plan for state intervention in deficient school districts; (3) a plan for the supervision of instruction; (4) an adult literacy program; and (5) a plan…

  17. A Structural Analysis of the Determinants of Job Satisfactions in On-Going Organizations.

    ERIC Educational Resources Information Center

    Vaughn, William J.; Dunn, J. D.

    Organizational effectiveness is composed of two concepts: (1) job satisfactions and (2) employee performance. In this paper the concept of job satisfactions is delimited to include five principal areas, viz., work, pay, promotion, people, and supervision. Employee performance is the reciprocal concept. This paper is directed toward the job…

  18. Developing Leadership Strategies inside the Politics of Language, Diversity, and Change

    ERIC Educational Resources Information Center

    Tooms, Autumn

    2004-01-01

    Currently in her third year as principal of the Leighton Elementary School, Georgia Henson faces conflicting pressures in a district where the priorities of Anglo and Hispanic communities may diverge. This case raises questions regarding administration and supervision of schools in multicultural contexts where political tensions are rising. In…

  19. Positive School Leadership: How the Professional Standards for Educational Leaders Can Be Brought to Life

    ERIC Educational Resources Information Center

    Murphy, Joseph; Louis, Karen Seashore; Smylie, Mark

    2017-01-01

    In November 2015, the National Policy Board for Educational Administration--a coalition of nine professional associations--adopted the Professional Standards for Educational Leaders (PSEL), a set of guidelines for the training, certification, hiring, evaluation, and supervision of school principals and superintendents. While it draws heavily from…

  20. Leaders on the Front Line--Managing Emotion for Ethical Decision Making

    ERIC Educational Resources Information Center

    Tenuto, Penny L.; Gardiner, Mary E.; Yamamoto, Julie K.

    2016-01-01

    To build capacity for students in educational leadership programs, we developed a teaching case study focused on managing emotion for ethical decision making in supervision of personnel. The case offers troubling encounters between a secondary assistant principal and a novice teacher, a veteran teacher, and a veteran administrator. Scenarios…

  1. 40 CFR 1.49 - Office of Water.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 40 Protection of Environment 1 2014-07-01 2014-07-01 false Office of Water. 1.49 Section 1.49... INFORMATION Headquarters § 1.49 Office of Water. The Office of Water, under the supervision of the Assistant Administrator for Water who serves as the principal adviser to the Administrator in matters pertaining to water...

  2. 40 CFR 1.49 - Office of Water.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 40 Protection of Environment 1 2012-07-01 2012-07-01 false Office of Water. 1.49 Section 1.49... INFORMATION Headquarters § 1.49 Office of Water. The Office of Water, under the supervision of the Assistant Administrator for Water who serves as the principal adviser to the Administrator in matters pertaining to water...

  3. The Three-Minute Classroom Walk-Through: Changing School Supervisory Practice One Teacher at a Time

    ERIC Educational Resources Information Center

    Downey, Carolyn J.; Steffy, Betty E.; English, Fenwick W.; Frase, Larry E.; Poston, William K.

    2004-01-01

    For years, the classic supervision model has frustrated both principals and teachers by fostering superior-subordinate relationships, focusing on teacher conformity rather than growth, or producing checklist data that is irrelevant to the curriculum. This book offers a practical, time-saving alternative that impacts student achievement by…

  4. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer

    NASA Technical Reports Server (NTRS)

    Ioup, G. E.

    1985-01-01

    Appendix 5 of the Study of One- and Two-Dimensional Filtering and Deconvolution Algorithms for a Streaming Array Computer includes a resume of the professional background of the Principal Investigator on the project, lists of this publications and research papers, graduate thesis supervised, and grants received.

  5. When Supervision Is Conflated with Evaluation: Teacher Candidates' Perceptions of Their Novice Supervisor

    ERIC Educational Resources Information Center

    Burns, Rebecca West; Badiali, Bernard J.

    2015-01-01

    Preparing teachers in clinically rich contexts requires teacher educators who are skilled and knowledgeable about university coursework as well as the complexities of classrooms. Retired teachers or principals have often assumed the role of field supervisor, bringing to their work extensive practitioner knowledge but often lacking theoretical…

  6. Human Resources Administration: A School-Based Perspective. Fourth Edition

    ERIC Educational Resources Information Center

    Smith, Richard

    2009-01-01

    Enhanced and updated, this Fourth Edition of Richard E. Smith's highly successful text examines the growing role of the principal in planning, hiring, staff development, supervision, and other human resource functions. The Fourth Edition includes new sections on ethics, induction, and the role of the mentor teacher. This edition also introduces…

  7. Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

    PubMed

    Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

    2017-01-01

    This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

  8. Wavelet decomposition based principal component analysis for face recognition using MATLAB

    NASA Astrophysics Data System (ADS)

    Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

    2016-03-01

    For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

  9. The Relation between Factor Score Estimates, Image Scores, and Principal Component Scores

    ERIC Educational Resources Information Center

    Velicer, Wayne F.

    1976-01-01

    Investigates the relation between factor score estimates, principal component scores, and image scores. The three methods compared are maximum likelihood factor analysis, principal component analysis, and a variant of rescaled image analysis. (RC)

  10. The Butterflies of Principal Components: A Case of Ultrafine-Grained Polyphase Units

    NASA Astrophysics Data System (ADS)

    Rietmeijer, F. J. M.

    1996-03-01

    Dusts in the accretion regions of chondritic interplanetary dust particles [IDPs] consisted of three principal components: carbonaceous units [CUs], carbon-bearing chondritic units [GUs] and carbon-free silicate units [PUs]. Among others, differences among chondritic IDP morphologies and variable bulk C/Si ratios reflect variable mixtures of principal components. The spherical shapes of the initially amorphous principal components remain visible in many chondritic porous IDPs but fusion was documented for CUs, GUs and PUs. The PUs occur as coarse- and ultrafine-grained units that include so called GEMS. Spherical principal components preserved in an IDP as recognisable textural units have unique proporties with important implications for their petrological evolution from pre-accretion processing to protoplanet alteration and dynamic pyrometamorphism. Throughout their lifetime the units behaved as closed-systems without chemical exchange with other units. This behaviour is reflected in their mineralogies while the bulk compositions of principal components define the environments wherein they were formed.

  11. Is it possible to strengthen psychiatric nursing staff's clinical supervision? RCT of a meta-supervision intervention.

    PubMed

    Gonge, Henrik; Buus, Niels

    2015-04-01

    To test the effects of a meta-supervision intervention in terms of participation, effectiveness and benefits of clinical supervision of psychiatric nursing staff. Clinical supervision is regarded as a central component in developing mental health nursing practices, but the evidence supporting positive outcomes of clinical supervision in psychiatric nursing is not convincing. The study was designed as a randomized controlled trial. All permanently employed nursing staff members at three general psychiatric wards at a Danish university hospital (n = 83) were allocated to either an intervention group (n = 40) receiving the meta-supervision in addition to attending usual supervision or to a control group (n = 43) attending usual supervision. Self-reported questionnaire measures of clinical supervision effectiveness and benefits were collected at base line in January 2012 and at follow-up completed in February 2013. In addition, a prospective registration of clinical supervision participation was carried out over 3 months subsequent to the intervention. The main result was that it was possible to motivate staff in the intervention group to participate significantly more frequently in sessions of the ongoing supervision compared with the control group. However, more frequent participation was not reflected in the experienced effectiveness of the clinical supervision or in the general formative or restorative benefits. The intervention had a positive effect on individuals or wards already actively engaged in clinical supervision, which suggested that individuals and wards without well-established supervision practices may require more comprehensive interventions targeting individual and organizational barriers to clinical supervision. © 2014 John Wiley & Sons Ltd.

  12. Factors influencing the perceived quality of clinical supervision of occupational therapists in a large Australian state.

    PubMed

    Martin, Priya; Kumar, Saravana; Lizarondo, Lucylynn; Tyack, Zephanie

    2016-10-01

    Clinical supervision is important for effective health service delivery, professional development and practice. Despite its importance there is a lack of evidence regarding the factors that improve its quality. This study aimed to investigate the factors that influence the quality of clinical supervision of occupational therapists employed in a large public sector health service covering mental health, paediatrics, adult physical and other practice areas. A mixed method, sequential explanatory study design was used consisting of two phases. This article reports the quantitative phase (Phase One) which involved administration of the Manchester Clinical Supervision Scale (MCSS-26) to 207 occupational therapists. Frequency of supervision sessions, choice of supervisor and the type of supervision were found to be the predictor variables with a positive and significant influence on the quality of clinical supervision. Factors such as age, length of supervision and the area of practice were found to be the predictor variables with a negative and significant influence on the quality of clinical supervision. Factors that influence the perceived quality of clinical supervision among occupational therapists have been identified. High quality clinical supervision is an important component of clinical governance and has been shown to be beneficial to practitioners, patients and the organisation. Information on factors that make clinical supervision effective identified in this study can be added to existing supervision training and practices to improve the quality of clinical supervision. © 2016 Occupational Therapy Australia.

  13. Building Mental Models by Dissecting Physical Models

    ERIC Educational Resources Information Center

    Srivastava, Anveshna

    2016-01-01

    When students build physical models from prefabricated components to learn about model systems, there is an implicit trade-off between the physical degrees of freedom in building the model and the intensity of instructor supervision needed. Models that are too flexible, permitting multiple possible constructions require greater supervision to…

  14. Supervised Agricultural Experience Instruction in Agricultural Teacher Education Programs: A National Descriptive Study

    ERIC Educational Resources Information Center

    Rank, Bryan D.; Retallick, Michael S.

    2017-01-01

    Faculty in agricultural teacher education programs are responsible for preparing future teachers to lead effective school-based agricultural education programs. However, agriculture teachers are having difficulty implementing supervised agricultural experience (SAE), even though they value it conceptually as a program component. In an effort to…

  15. Successful Supervised Agricultural Experience Programs as Defined by American FFA Degree Star Finalists

    ERIC Educational Resources Information Center

    Rubenstein, Eric D.; Thorn, Andrew C.

    2014-01-01

    Within school-based agricultural education, supervised agricultural experience (SAE) programs remain an integral component of the total program. However, researchers have reported that SAE programs lack focus and direction. Furthermore, SAE programs lack a current definition of successful SAE programs. This study was conducted utilizing…

  16. The Invisible Mirror: In-Home Family Therapy and Supervision.

    ERIC Educational Resources Information Center

    Zarski, John J.; And Others

    1991-01-01

    Discusses home-based family therapy intervention programs, designed as a preventive strategy for multiproblem, at-risk families in mental health agencies. Maintains that a review of the literature reveals little information on clinical supervision, which is a major component of home-based family intervention. Focuses on providing an alternative…

  17. Baccalaureate nursing students' perceptions of learning and supervision in the clinical environment.

    PubMed

    Dimitriadou, Maria; Papastavrou, Evridiki; Efstathiou, Georgios; Theodorou, Mamas

    2015-06-01

    This study is an exploration of nursing students' experiences within the clinical learning environment (CLE) and supervision provided in hospital settings. A total of 357 second-year nurse students from all universities in Cyprus participated in the study. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher instrument. The dimension "supervisory relationship (mentor)", as well as the frequency of individualized supervision meetings, were found to be important variables in the students' clinical learning. However, no statistically-significant connection was established between successful mentor relationship and team supervision. The majority of students valued their mentor's supervision more highly than a nurse teacher's supervision toward the fulfillment of learning outcomes. The dimensions "premises of nursing care" and "premises of learning" were highly correlated, indicating that a key component of a quality clinical learning environment is the quality of care delivered. The results suggest the need to modify educational strategies that foster desirable learning for students in response to workplace demands. © 2014 Wiley Publishing Asia Pty Ltd.

  18. Parenting and the parallel processes in parents' counseling supervision for eating-related problems.

    PubMed

    Golan, Moria

    2014-04-01

    This paper presents an integrative model for supervising counselors of parents who face eating-related problems in their families. The model is grounded in the theory of parallel processes which occur during the supervision of health-care professionals as well as the counseling of parents and patients. The aim of this model is to conceptualize components and processes in the supervision space, in order to: (a) create a nurturing environment for health-care facilitators, parents and children, (b) better understand the complex and difficult nature of parenting, the challenge counselors face, and the skills and practices used in parenting and in counseling, and (c) better own practices and oppose the judgment that often dominates in counseling and supervision. This paper reflects upon the tradition of supervision and offers a comprehensive view of this process, including its challenges, skills and practices.

  19. The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

    PubMed

    Foch, Eric; Milner, Clare E

    2014-01-03

    Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

  20. Role Expectations of the College Supervisor of Elementary Student Teachers in the State of Georgia.

    ERIC Educational Resources Information Center

    Waters, Betty H.

    A questionnaire was administered to student teachers, supervising teachers, principals, and college supervisors to ascertain functions which were actually performed and those most desired by the respondents with regard to the role of the college supervisor. The respondents were participating in student teaching experiences in the state of Georgia…

  1. Leadership Effectiveness and Instructional Supervision: The Case of the Failing Twin

    ERIC Educational Resources Information Center

    Bloom, Collette Madeleine

    2011-01-01

    This case study examines the leadership practices of an effective versus an ineffective elementary school principal. The background of this case involves two fourth grade teachers, each teaching one of a set of identical twins. Discrepancies in teaching and grading practices result in one twin failing. The decision-making choices of the principal…

  2. Cluster Supervision Practices in Primary School of Jimma Zone

    ERIC Educational Resources Information Center

    Afework, E. A.; Frew, A. T.; Abeya, G. G.

    2017-01-01

    The main objective of this study was to assess the supervisory practice of cluster resource centre (CRC) supervisors in Jimma Zone primary schools. To achieve this purpose, the descriptive survey design was employed. Data were collected from 238 randomly selected teachers, and 60 school principals with a response rate of 98.6%. Moreover, 12 CRC…

  3. Max's Family Experience: Web-Resources for Working with Special Education Students and Their Families

    ERIC Educational Resources Information Center

    Theoharis, Raschelle; Fitzpatrick, Michael

    2011-01-01

    Today's principals are required to put in longer hours, lead larger schools, and supervise more faculty and staff members. Additionally they need to create a positive learning environment for students, a productive work environment for their employees, and contend with a variety of student behaviors at the building level. Aside from the duties…

  4. Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

    PubMed

    Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

    2017-05-01

    Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

  5. Work-integrated learning (WIL) supervisors and non-supervisors of allied health professional students.

    PubMed

    Smedts, Anna M; Campbell, Narelle; Sweet, Linda

    2013-01-01

    This study sought to characterise the allied health professional (AHP) workforce of the Northern Territory (NT), Australia, in order to understand the influence of student supervision on workload, job satisfaction, and recruitment and retention. The national Rural Allied Health Workforce Study survey was adapted for the NT context and distributed through local AHP networks. Valid responses (n=179) representing 16 professions were collated and categorised into 'supervisor' and 'non-supervisor' groups for further analysis. The NT AHP workforce is predominantly female, non-Indigenous, raised in an urban environment, trained outside the NT, now concentrated in the capital city, and principally engaged in individual patient care. Allied health professionals cited income and type of work or clientele as the most frequent factors for attraction to their current positions. While 62% provided student supervision, only half reported having training in mentoring or supervision. Supervising students accounted for an estimated 9% of workload. Almost 20% of existing supervisors and 33% of non-supervising survey respondents expressed an interest in greater supervisory responsibilities. Despite indicating high satisfaction with their current positions, 67% of respondents reported an intention to leave their jobs in less than 5 years. Student supervision was not linked to perceived job satisfaction; however, this study found that professionals who were engaged in student supervision were significantly more likely to report intention to stay in their current jobs (>5 years; p<0.05). The findings are important for supporting ongoing work-integrated learning opportunities for students in a remote context, and highlight the need for efforts to be focused on the training and retention of AHPs as student supervisors.

  6. Classification of wines according to their production regions with the contained trace elements using laser-induced breakdown spectroscopy

    NASA Astrophysics Data System (ADS)

    Tian, Ye; Yan, Chunhua; Zhang, Tianlong; Tang, Hongsheng; Li, Hua; Yu, Jialu; Bernard, Jérôme; Chen, Li; Martin, Serge; Delepine-Gilon, Nicole; Bocková, Jana; Veis, Pavel; Chen, Yanping; Yu, Jin

    2017-09-01

    Laser-induced breakdown spectroscopy (LIBS) has been applied to classify French wines according to their production regions. The use of the surface-assisted (or surface-enhanced) sample preparation method enabled a sub-ppm limit of detection (LOD), which led to the detection and identification of at least 22 metal and nonmetal elements in a typical wine sample including majors, minors and traces. An ensemble of 29 bottles of French wines, either red or white wines, from five production regions, Alsace, Bourgogne, Beaujolais, Bordeaux and Languedoc, was analyzed together with a wine from California, considered as an outlier. A non-supervised classification model based on principal component analysis (PCA) was first developed for the classification. The results showed a limited separation power of the model, which however allowed, in a step by step approach, to understand the physical reasons behind each step of sample separation and especially to observe the influence of the matrix effect in the sample classification. A supervised classification model was then developed based on random forest (RF), which is in addition a nonlinear algorithm. The obtained classification results were satisfactory with, when the parameters of the model were optimized, a classification accuracy of 100% for the tested samples. We especially discuss in the paper, the effect of spectrum normalization with an internal reference, the choice of input variables for the classification models and the optimization of parameters for the developed classification models.

  7. Differentiation of Crataegus spp. guided by nuclear magnetic resonance spectrometry with chemometric analyses.

    PubMed

    Lund, Jensen A; Brown, Paula N; Shipley, Paul R

    2017-09-01

    For compliance with US Current Good Manufacturing Practice regulations for dietary supplements, manufacturers must provide identity of source plant material. Despite the popularity of hawthorn as a dietary supplement, relatively little is known about the comparative phytochemistry of different hawthorn species, and in particular North American hawthorns. The combination of NMR spectrometry with chemometric analyses offers an innovative approach to differentiating hawthorn species and exploring the phytochemistry. Two European and two North American species, harvested from a farm trial in late summer 2008, were analyzed by standard 1D 1 H and J-resolved (JRES) experiments. The data were preprocessed and modelled by principal component analysis (PCA). A supervised model was then generated by partial least squares-discriminant analysis (PLS-DA) for classification and evaluated by cross validation. Supervised random forests models were constructed from the dataset to explore the potential of machine learning for identification of unique patterns across species. 1D 1 H NMR data yielded increased differentiation over the JRES data. The random forests results correlated with PLS-DA results and outperformed PLS-DA in classification accuracy. In all of these analyses differentiation of the Crataegus spp. was best achieved by focusing on the NMR spectral region that contains signals unique to plant phenolic compounds. Identification of potentially significant metabolites for differentiation between species was approached using univariate techniques including significance analysis of microarrays and Kruskall-Wallis tests. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

    PubMed

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-04-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

  9. Developing a Language Learning Rationale for African Language Tutorials.

    ERIC Educational Resources Information Center

    Dwyer, David

    1999-01-01

    Presents a rationale for the supervised tutorial component of the African language program at Michigan State University. The supervised tutorial is one of two modes through which African languages are offered at Michigan State University. The other, which is teacher led, is offered for high enrollment languages such as Arabic, Swahili, and Hausa.…

  10. Enhancing Student Teacher Supervision through Hybridization: Adding E-Supervision to the Mix

    ERIC Educational Resources Information Center

    Paulsen, Thomas H.; Schmidt-Crawford, Denise A.

    2017-01-01

    Student teaching is a critical component of the preservice teacher preparation program which has a major role in preparing novices to teach. This capstone experience has been frequently examined and the subject of numerous reform measures. Clinical experiences for preservice teachers have recently seen new recommendations for increased supervisor…

  11. Spectral gene set enrichment (SGSE).

    PubMed

    Frost, H Robert; Li, Zhigang; Moore, Jason H

    2015-03-03

    Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

  12. Automotive System for Remote Surface Classification.

    PubMed

    Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

    2017-04-01

    In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.

  13. Mapping the Similarities of Spectra: Global and Locally-biased Approaches to SDSS Galaxies

    NASA Astrophysics Data System (ADS)

    Lawlor, David; Budavári, Tamás; Mahoney, Michael W.

    2016-12-01

    We present a novel approach to studying the diversity of galaxies. It is based on a novel spectral graph technique, that of locally-biased semi-supervised eigenvectors. Our method introduces new coordinates that summarize an entire spectrum, similar to but going well beyond the widely used Principal Component Analysis (PCA). Unlike PCA, however, this technique does not assume that the Euclidean distance between galaxy spectra is a good global measure of similarity. Instead, we relax that condition to only the most similar spectra, and we show that doing so yields more reliable results for many astronomical questions of interest. The global variant of our approach can identify very finely numerous astronomical phenomena of interest. The locally-biased variants of our basic approach enable us to explore subtle trends around a set of chosen objects. The power of the method is demonstrated in the Sloan Digital Sky Survey Main Galaxy Sample, by illustrating that the derived spectral coordinates carry an unprecedented amount of information.

  14. Automotive System for Remote Surface Classification

    PubMed Central

    Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

    2017-01-01

    In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions. PMID:28368297

  15. GAC: Gene Associations with Clinical, a web based application.

    PubMed

    Zhang, Xinyan; Rupji, Manali; Kowalski, Jeanne

    2017-01-01

    We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

  16. Paper spray mass spectrometry and chemometric tools for a fast and reliable identification of counterfeit blended Scottish whiskies.

    PubMed

    Teodoro, Janaína Aparecida Reis; Pereira, Hebert Vinicius; Sena, Marcelo Martins; Piccin, Evandro; Zacca, Jorge Jardim; Augusti, Rodinei

    2017-12-15

    A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Reducing the Anaerobic Digestion Model No. 1 for its application to an industrial wastewater treatment plant treating winery effluent wastewater.

    PubMed

    García-Diéguez, Carlos; Bernard, Olivier; Roca, Enrique

    2013-03-01

    The Anaerobic Digestion Model No. 1 (ADM1) is a complex model which is widely accepted as a common platform for anaerobic process modeling and simulation. However, it has a large number of parameters and states that hinder its calibration and use in control applications. A principal component analysis (PCA) technique was extended and applied to simplify the ADM1 using data of an industrial wastewater treatment plant processing winery effluent. The method shows that the main model features could be obtained with a minimum of two reactions. A reduced stoichiometric matrix was identified and the kinetic parameters were estimated on the basis of representative known biochemical kinetics (Monod and Haldane). The obtained reduced model takes into account the measured states in the anaerobic wastewater treatment (AWT) plant and reproduces the dynamics of the process fairly accurately. The reduced model can support on-line control, optimization and supervision strategies for AWT plants. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

    PubMed

    Zhou, Pan; Lin, Zhouchen; Zhang, Chao

    2016-05-01

    Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

  19. Supervising community health workers in low-income countries – a review of impact and implementation issues

    PubMed Central

    Hill, Zelee; Dumbaugh, Mari; Benton, Lorna; Källander, Karin; Strachan, Daniel; Asbroek, Augustinus ten; Tibenderana, James; Kirkwood, Betty; Meek, Sylvia

    2014-01-01

    Background Community health workers (CHWs) are an increasingly important component of health systems and programs. Despite the recognized role of supervision in ensuring CHWs are effective, supervision is often weak and under-supported. Little is known about what constitutes adequate supervision and how different supervision strategies influence performance, motivation, and retention. Objective To determine the impact of supervision strategies used in low- and middle-income countries and discuss implementation and feasibility issues with a focus on CHWs. Design A search of peer-reviewed, English language articles evaluating health provider supervision strategies was conducted through November 2013. Included articles evaluated the impact of supervision in low- or middle-income countries using a controlled, pre-/post- or observational design. Implementation and feasibility literature included both peer-reviewed and gray literature. Results A total of 22 impact papers were identified. Papers were from a range of low- and middle-income countries addressing the supervision of a variety of health care providers. We classified interventions as testing supervision frequency, the supportive/facilitative supervision package, supervision mode (peer, group, and community), tools (self-assessment and checklists), focus (quality assurance/problem solving), and training. Outcomes included coverage, performance, and perception of quality but were not uniform across studies. Evidence suggests that improving supervision quality has a greater impact than increasing frequency of supervision alone. Supportive supervision packages, community monitoring, and quality improvement/problem-solving approaches show the most promise; however, evaluation of all strategies was weak. Conclusion Few supervision strategies have been rigorously tested and data on CHW supervision is particularly sparse. This review highlights the diversity of supervision approaches that policy makers have to choose from and, while choices should be context specific, our findings suggest that high-quality supervision that focuses on supportive approaches, community monitoring, and/or quality assurance/problem solving may be most effective. PMID:24815075

  20. Nonlinear Principal Components Analysis: Introduction and Application

    ERIC Educational Resources Information Center

    Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Koojj, Anita J.

    2007-01-01

    The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal…

  1. Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

    USDA-ARS?s Scientific Manuscript database

    Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

  2. Similarities between principal components of protein dynamics and random diffusion

    NASA Astrophysics Data System (ADS)

    Hess, Berk

    2000-12-01

    Principal component analysis, also called essential dynamics, is a powerful tool for finding global, correlated motions in atomic simulations of macromolecules. It has become an established technique for analyzing molecular dynamics simulations of proteins. The first few principal components of simulations of large proteins often resemble cosines. We derive the principal components for high-dimensional random diffusion, which are almost perfect cosines. This resemblance between protein simulations and noise implies that for many proteins the time scales of current simulations are too short to obtain convergence of collective motions.

  3. Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images

    PubMed Central

    Tagare, Hemant D.; Kucukelbir, Alp; Sigworth, Fred J.; Wang, Hongwei; Rao, Murali

    2015-01-01

    Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the (posterior) likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the inluenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. PMID:26049077

  4. Scott O'Neill and Lincoln Elementary School: Preventing a Slide from Good to Worse

    ERIC Educational Resources Information Center

    Salmonowicz, Michael J.

    2007-01-01

    This case was developed for use in courses on the study of organizations, with a focus on school administration and supervision. It focuses on the challenges--most notably, changing demographics and a resistant faculty--faced by a new principal whose school has a record of success but is in danger of sliding into mediocrity. Varied data are…

  5. Influence of Pedagogical Supervisors' Practices and Perceptions on the Use of Results-Based Management

    ERIC Educational Resources Information Center

    April, Daniel; Bouchamma, Yamina

    2017-01-01

    In the province of Québec, Canada, school principals are obligated by law to ensure pedagogical supervision. This law, the Public Education Act (Government of Québec, 2017), also advocates a contractual Results-Based Management approach (RBM). We examined how the practices and perceptions of these supervisors influence the implementation of this…

  6. Parameters of the Teacher Aide Role: A Study of Teacher Aides in Selected Gulf Coast School Districts. Final Report.

    ERIC Educational Resources Information Center

    DeHart, Ruth

    A survey was conducted to provide information of use to school administrators as they plan for initial or continued use of paraprofessional personnel. A questionnaire was developed for use in interviews with 63 principals, supervising teachers and librarians, and teacher aides in 17 randomly selected Texas school districts in the GUSREDA (Gulf…

  7. Towards a Theoretical Framework for the Use of ICT Strategies for Teaching Practicum Supervision

    ERIC Educational Resources Information Center

    Mabunda, P. L.

    2013-01-01

    Teaching Practice is a core component of pre-service or initial teacher education programmes worldwide. The requirements set by the South African Committee on Higher Education effected changes to the coordination and supervision of Teaching Practice. The purpose of this conceptual study was to review and gain insight into the use of various…

  8. An Introductory Application of Principal Components to Cricket Data

    ERIC Educational Resources Information Center

    Manage, Ananda B. W.; Scariano, Stephen M.

    2013-01-01

    Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

  9. Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

    ERIC Educational Resources Information Center

    Olson, Jeffery E.

    Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

  10. Identifying apple surface defects using principal components analysis and artifical neural networks

    USDA-ARS?s Scientific Manuscript database

    Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

  11. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

    PubMed Central

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-01-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

  12. Finding Planets in K2: A New Method of Cleaning the Data

    NASA Astrophysics Data System (ADS)

    Currie, Miles; Mullally, Fergal; Thompson, Susan E.

    2017-01-01

    We present a new method of removing systematic flux variations from K2 light curves by employing a pixel-level principal component analysis (PCA). This method decomposes the light curves into its principal components (eigenvectors), each with an associated eigenvalue, the value of which is correlated to how much influence the basis vector has on the shape of the light curve. This method assumes that the most influential basis vectors will correspond to the unwanted systematic variations in the light curve produced by K2’s constant motion. We correct the raw light curve by automatically fitting and removing the strongest principal components. The strongest principal components generally correspond to the flux variations that result from the motion of the star in the field of view. Our primary method of calculating the strongest principal components to correct for in the raw light curve estimates the noise by measuring the scatter in the light curve after using an algorithm for Savitsy-Golay detrending, which computes the combined photometric precision value (SG-CDPP value) used in classic Kepler. We calculate this value after correcting the raw light curve for each element in a list of cumulative sums of principal components so that we have as many noise estimate values as there are principal components. We then take the derivative of the list of SG-CDPP values and take the number of principal components that correlates to the point at which the derivative effectively goes to zero. This is the optimal number of principal components to exclude from the refitting of the light curve. We find that a pixel-level PCA is sufficient for cleaning unwanted systematic and natural noise from K2’s light curves. We present preliminary results and a basic comparison to other methods of reducing the noise from the flux variations.

  13. Directly reconstructing principal components of heterogeneous particles from cryo-EM images.

    PubMed

    Tagare, Hemant D; Kucukelbir, Alp; Sigworth, Fred J; Wang, Hongwei; Rao, Murali

    2015-08-01

    Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the posterior likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the influenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Processes that Inform Multicultural Supervision: A Qualitative Meta-Analysis.

    PubMed

    Tohidian, Nilou B; Quek, Karen Mui-Teng

    2017-10-01

    As the fields of counseling and psychotherapy have become more cognizant that individuals, couples, and families bring with them a myriad of diversity factors into therapy, multicultural competency has also become a crucial component in the development of clinicians during clinical supervision and training. We employed a qualitative meta-analysis to provide a detailed and comprehensive description of similar themes identified in primary qualitative studies that have investigated supervisory practices with an emphasis on diversity. Findings revealed six meta-categories, namely: (a) Supervisor's Multicultural Stances; (b) Supervisee's Multicultural Encounters; (c) Competency-Based Content in Supervision; (d) Processes Surrounding Multicultural Supervision; (e) Culturally Attuned Interventions; and (f) Multicultural Supervisory Alliance. Implications for practice are discussed. © 2017 American Association for Marriage and Family Therapy.

  15. Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion.

    PubMed

    Fierimonte, Roberto; Scardapane, Simone; Uncini, Aurelio; Panella, Massimo

    2016-08-26

    Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.

  16. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule... management plan. (c) Operator training and qualification. (d) Emission limitations and operating limits. (e...

  17. 40 CFR 60.2570 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of... (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan. (c...

  18. Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

    PubMed

    Maisuradze, Gia G; Leitner, David M

    2007-05-15

    Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

  19. Just How Much Can School Pupils Learn from School Gardening? A Study of Two Supervised Agricultural Experience Approaches in Uganda

    ERIC Educational Resources Information Center

    Okiror, John James; Matsiko, Biryabaho Frank; Oonyu, Joseph

    2011-01-01

    School systems in Africa are short of skills that link well with rural communities, yet arguments to vocationalize curricula remain mixed and school agriculture lacks the supervised practical component. This study, conducted in eight primary (elementary) schools in Uganda, sought to compare the learning achievement of pupils taught using…

  20. Mass spectrometric evidence for the modification of small molecules in a cobalt-60-irradiated rodent diet.

    PubMed

    Prasain, J K; Wilson, L S; Arabshahi, A; Grubbs, C; Barnes, S

    2017-08-01

    The purpose of this study was to investigate the effect of radiation on the content of animal diet constituents using global metabolomics. Aqueous methanolic extracts of control and cobalt-60-irradiated Teklad 7001 diets were comprehensively analyzed using nano-liquid chromatography-MS/MS. Among the over 2000 ions revealed by XCMS followed by data preprocessing, 94 positive and 143 negative metabolite ions had greater than 1.5-fold changes and p-values <0.01. Use of MetaboAnalyst statistical software demonstrated complete separation of the irradiated and non-radiated diets in unsupervised principal components analysis and supervised partial least squares discriminant analysis. Irradiation led to an increase in the content of phytochemicals such as glucosinolates and oxidized lipids in the diet. Twenty-eight metabolites that were significantly changed in the irradiated samples were putatively identified at the level of molecular formulae by MS/MS. MS/MS ALL analysis of chloroform-methanol extracts of the irradiated diet showed increased levels of a number of unique linoleic acid-derived branched fatty acid esters of hydroxy fatty acids. These data imply that gamma irradiation of animal diets causes chemical changes to dietary components, which in turn may influence the risk of mammary cancer. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  1. Government Contract Law Cases.

    DTIC Science & Technology

    1983-10-01

    have no power of supervision or control ere the fact otherwise, we would cease to have a popular nment, but instead would be governed by a group of...article. The principal question presented is appellant’s right to repudiate the contract without liability. The operative questions controlling our...and quality control actions. Paragraph 5 of the document read as follows: THE TRAINING, DOCUMENTATION, SOFTWARE, PACKAGING QUOTED WILL BE DGC

  2. Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

    PubMed Central

    Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

    2015-01-01

    Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

  3. Principal Workload: Components, Determinants and Coping Strategies in an Era of Standardization and Accountability

    ERIC Educational Resources Information Center

    Oplatka, Izhar

    2017-01-01

    Purpose: In order to fill the gap in theoretical and empirical knowledge about the characteristics of principal workload, the purpose of this paper is to explore the components of principal workload as well as its determinants and the coping strategies commonly used by principals to face this personal state. Design/methodology/approach:…

  4. Sensor supervision and multiagent commanding by means of projective virtual reality

    NASA Astrophysics Data System (ADS)

    Rossmann, Juergen

    1998-10-01

    When autonomous systems with multiple agents are considered, conventional control- and supervision technologies are often inadequate because the amount of information available is often presented in a way that the user is effectively overwhelmed by the displayed data. New virtual reality (VR) techniques can help to cope with this problem, because VR offers the chance to convey information in an intuitive manner and can combine supervision capabilities and new, intuitive approaches to the control of autonomous systems. In the approach taken, control and supervision issues were equally stressed and finally led to the new ideas and the general framework for Projective Virtual Reality. The key idea of this new approach for an intuitively operable man machine interface for decentrally controlled multi-agent systems is to let the user act in the virtual world, detect the changes and have an action planning component automatically generate task descriptions for the agents involved to project actions that have been carried out by users in the virtual world into the physical world, e.g. with the help of robots. Thus the Projective Virtual Reality approach is to split the job between the task deduction in the VR and the task `projection' onto the physical automation components by the automatic action planning component. Besides describing the realized projective virtual reality system, the paper will also describe in detail the metaphors and visualization aids used to present different types of (e.g. sensor-) information in an intuitively comprehensible manner.

  5. Can clinical supervision sustain our workforce in the current healthcare landscape? Findings from a Queensland study of allied health professionals.

    PubMed

    Saxby, Christine; Wilson, Jill; Newcombe, Peter

    2015-09-01

    Clinical supervision is widely recognised as a mechanism for providing professional support, professional development and clinical governance for healthcare workers. There have been limited studies about the effectiveness of clinical supervision for allied health and minimal studies conducted within the Australian health context. The aim of the present study was to identify whether clinical supervision was perceived to be effective by allied health professionals and to identify components that contributed to effectiveness. Participants completed an anonymous online questionnaire, administered through the health service's intranet. A cross-sectional study was conducted with community allied health workers (n = 82) 8 months after implementation of structured clinical supervision. Demographic data (age, gender), work-related history (profession employment level, years of experience), and supervision practice (number and length of supervision sessions) were collected through an online survey. The outcome measure, clinical supervision effectiveness, was operationalised using the Manchester Clinical Supervision Scale-26 (MCSS-26). Data were analysed with Pearson correlation (r) and independent sample t-tests (t) with significance set at 0.05 (ie the probability of significant difference set at P < 0.05). The length of the supervision sessions (r(s) ≥ 0.44), the number of sessions (r(s) ≥ 0.35) and the total period supervision had been received (r(s) ≥ 0.42) were all significantly positively correlated with the MCSS-26 domains of clinical supervision effectiveness. Three individual variables, namely 'receiving clinical supervision', 'having some choice in the allocation of clinical supervisor' and 'having a completed clinical supervision agreement', were also significantly associated with higher total MCSS-26 scores (P(s) < 0.014). The results of the study demonstrate that when clinical supervision uses best practice principles, it can provide professional support for allied health workers, even during times of rapid organisational change.

  6. Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

    PubMed

    Saccenti, Edoardo; Timmerman, Marieke E

    2017-03-01

    Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

  7. A systematic review of evidence relating to clinical supervision for nurses, midwives and allied health professionals.

    PubMed

    Pollock, Alex; Campbell, Pauline; Deery, Ruth; Fleming, Mick; Rankin, Jean; Sloan, Graham; Cheyne, Helen

    2017-08-01

    The aim of this study was to systematically review evidence relating to clinical supervision for nurses, midwives and allied health professionals. Since 1902 statutory supervision has been a requirement for UK midwives, but this is due to change. Evidence relating to clinical supervision for nurses and allied health professions could inform a new model of clinical supervision for midwives. A systematic review with a contingent design, comprising a broad map of research relating to clinical supervision and two focussed syntheses answering specific review questions. Electronic databases were searched from 2005 - September 2015, limited to English-language peer-reviewed publications. Systematic reviews evaluating the effectiveness of clinical supervision were included in Synthesis 1. Primary research studies including a description of a clinical supervision intervention were included in Synthesis 2. Quality of reviews were judged using a risk of bias tool and review results summarized in tables. Data describing the key components of clinical supervision interventions were extracted from studies included in Synthesis 2, categorized using a reporting framework and a narrative account provided. Ten reviews were included in Synthesis 1; these demonstrated an absence of convincing empirical evidence and lack of agreement over the nature of clinical supervision. Nineteen primary studies were included in Synthesis 2; these highlighted a lack of consistency and large variations between delivered interventions. Despite insufficient evidence to directly inform the selection and implementation of a framework, the limited available evidence can inform the design of a new model of clinical supervision for UK-based midwives. © 2017 John Wiley & Sons Ltd.

  8. The effect of the learner license Graduated Driver Licensing components on teen drivers' crashes.

    PubMed

    Ehsani, Johnathon Pouya; Bingham, C Raymond; Shope, Jean T

    2013-10-01

    Most studies evaluating the effectiveness of Graduated Driver Licensing (GDL) have focused on the overall system. Studies examining individual components have rarely accounted for the confounding of multiple, simultaneously implemented components. The purpose of this paper is to quantify the effects of a required learner license duration and required hours of supervised driving on teen driver fatal crashes. States that introduced a single GDL component independent of any other during the period 1990-2009 were identified. Monthly and quarterly fatal crash rates per 100,000 population of 16- and 17-year-old drivers were analyzed using single-state time series analysis, adjusting for adult crash rates and gasoline prices. Using the parameter estimates from each state's time series model, the pooled effect of each GDL component on 16- and 17-year-old drivers' fatal crashes was estimated using a random effects meta-analytic model to combine findings across states. In three states, a six-month minimum learner license duration was associated with a significant decline in combined 16- and 17-year-old drivers' fatal crash rates. The pooled effect of the minimum learner license duration across all states in the sample was associated with a significant change in combined 16- and 17-year-old driver fatal crash rates of -.07 (95% Confidence Interval [CI] -.11, -.03). Following the introduction of 30 h of required supervised driving in one state, novice drivers' fatal crash rates increased 35%. The pooled effect across all states in the study sample of having a supervised driving hour requirement was not significantly different from zero (.04, 95% CI -.15, .22). These findings suggest that a learner license duration of at least six-months may be necessary to achieve a significant decline in teen drivers' fatal crash rates. Evidence of the effect of required hours of supervised driving on teen drivers' fatal crash rates was mixed. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Complex dynamics in supervised work groups

    NASA Astrophysics Data System (ADS)

    Dal Forno, Arianna; Merlone, Ugo

    2013-07-01

    In supervised work groups many factors concur to determine productivity. Some of them may be economical and some psychological. According to the literature, the heterogeneity in terms of individual capacity seems to be one of the principal causes for chaotic dynamics in a work group. May sorting groups of people with same capacity for effort be a solution? In the organizational psychology literature an important factor is the engagement in the task, while expectations are central in the economics literature. Therefore, we propose a dynamical model which takes into account both engagement in the task and expectations. An important lesson emerges. The intolerance deriving from the exposure to inequity may not be only caused by differences in individual capacities, but also by these factors combined. Consequently, solutions have to be found in this new direction.

  10. Clinical learning environment and supervision: experiences of Norwegian nursing students - a questionnaire survey.

    PubMed

    Skaalvik, Mari Wolff; Normann, Hans Ketil; Henriksen, Nils

    2011-08-01

    To measure nursing students' experiences and satisfaction with their clinical learning environments. The primary interest was to compare the results between students with respect to clinical practice in nursing homes and hospital wards. Clinical learning environments are important for the learning processes of nursing students and for preferences for future workplaces. Working with older people is the least preferred area of practice among nursing students in Norway. A cross-sectional design. A validated questionnaire was distributed to all nursing students from five non-randomly selected university colleges in Norway. A total of 511 nursing students completed a Norwegian version of the questionnaire, Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) evaluation scale in 2009. Data including descriptive statistics were analysed using the Statistical Program for the Social Sciences. Factor structure was analysed by principal component analysis. Differences across sub-groups were tested with chi-square tests and Mann-Whitney U test for categorical variables and t-tests for continuous variables. Ordinal logistic regression analysis of perceptions of the ward as a good learning environment was performed with supervisory relationships and institutional contexts as independent variables, controlling for age, sex and study year. The participating nursing students with clinical placements in nursing homes assessed their clinical learning environment significantly more negatively than those with hospital placements on nearby all sub-dimensions. The evidence found in this study indicates that measures should be taken to strengthen nursing homes as learning environments for nursing students. To recruit more graduated nurses to work in nursing homes, actions to improve the learning environment are needed. © 2011 Blackwell Publishing Ltd.

  11. The Influence Function of Principal Component Analysis by Self-Organizing Rule.

    PubMed

    Higuchi; Eguchi

    1998-07-28

    This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

  12. Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

    USGS Publications Warehouse

    Brown, C. Erwin

    1993-01-01

    Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

  13. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

    PubMed

    Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

    2003-01-01

    A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

  14. Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.

    PubMed

    Salvatore, Stefania; Bramness, Jørgen G; Røislien, Jo

    2016-07-12

    Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally. We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

  15. 40 CFR 62.14505 - What are the principal components of this subpart?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 40 Protection of Environment 8 2010-07-01 2010-07-01 false What are the principal components of this subpart? 62.14505 Section 62.14505 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... components of this subpart? This subpart contains the eleven major components listed in paragraphs (a...

  16. Clinical supervision for allied health staff: necessary but not sufficient.

    PubMed

    Leggat, Sandra G; Phillips, Bev; Pearce, Philippa; Dawson, Margaret; Schulz, Debbie; Smith, Jenni

    2016-09-01

    Objectives The aim of the present study was to explore the perspectives of allied health professionals on appropriate content for effective clinical supervision of staff. Methods A set of statements regarding clinical supervision was identified from the literature and confirmed through a Q-sort process. The final set was administered as an online survey to 437 allied health professionals working in two Australian health services. Results Of the 120 respondents, 82 had experienced six or more clinical supervision sessions and were included in the analysis. Respondents suggested that clinical supervision was beneficial to both staff and patients, and was distinct from line management performance monitoring and development. Curiously, some of the respondents did not agree that observation of the supervisee's clinical practice was an aspect of clinical supervision. Conclusions Although clinical supervision is included as a pillar of clinical governance, current practice may not be effective in addressing clinical risk. Australian health services need clear organisational policies that outline the relationship between supervisor and supervisee, the role and responsibilities of managers, the involvement of patients and the types of situations to be communicated to the line managers. What is known about the topic? Clinical supervision for allied health professionals is an essential component of clinical governance and is aimed at ensuring safe and high-quality care. However, there is varied understanding of the relationship between clinical supervision and performance management. What does this paper add? This paper provides the perspectives of allied health professionals who are experienced as supervisors or who have experienced supervision. The findings suggest a clear role for clinical supervision that needs to be better recognised within organisational policy and procedure. What are the implications for practitioners? Supervisors and supervisees must remember their duty of care and ensure compliance with organisational policies in their clinical supervisory practices.

  17. Hierarchical Regularity in Multi-Basin Dynamics on Protein Landscapes

    NASA Astrophysics Data System (ADS)

    Matsunaga, Yasuhiro; Kostov, Konstatin S.; Komatsuzaki, Tamiki

    2004-04-01

    We analyze time series of potential energy fluctuations and principal components at several temperatures for two kinds of off-lattice 46-bead models that have two distinctive energy landscapes. The less-frustrated "funnel" energy landscape brings about stronger nonstationary behavior of the potential energy fluctuations at the folding temperature than the other, rather frustrated energy landscape at the collapse temperature. By combining principal component analysis with an embedding nonlinear time-series analysis, it is shown that the fast fluctuations with small amplitudes of 70-80% of the principal components cause the time series to become almost "random" in only 100 simulation steps. However, the stochastic feature of the principal components tends to be suppressed through a wide range of degrees of freedom at the transition temperature.

  18. Geographical classification of apple based on hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  19. Rock type discrimination techniques using Landsat and Seasat image data

    NASA Technical Reports Server (NTRS)

    Blom, R.; Abrams, M.; Conrad, C.

    1981-01-01

    Results of a sedimentary rock type discrimination project using Seasat radar and Landsat multispectral image data of the San Rafael Swell, in eastern Utah, are presented, which has the goal of determining the potential contribution of radar image data to Landsat image data for rock type discrimination, particularly when the images are coregistered. The procedure employs several images processing techniques using the Landsat and Seasat data independently, and then both data sets are coregistered. The images are evaluated according to the ease with which contacts can be located and rock units (not just stratigraphically adjacent ones) separated. Results show that of the Landsat images evaluated, the image using a supervised classification scheme is the best for sedimentary rock type discrimination. Of less value, in decreasing order, are color ratio composites, principal components, and the standard color composite. In addition, for rock type discrimination, the black and white Seasat image is less useful than any of the Landsat color images by itself. However, it is found that the incorporation of the surface textural measures made from the Seasat image provides a considerable and worthwhile improvement in rock type discrimination.

  20. Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

    USGS Publications Warehouse

    Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

    2008-01-01

    Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

  1. Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

    PubMed Central

    Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

    2008-01-01

    Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

  2. System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

    PubMed

    Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

    2018-01-01

    Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

  3. GAC: Gene Associations with Clinical, a web based application

    PubMed Central

    Zhang, Xinyan; Rupji, Manali; Kowalski, Jeanne

    2018-01-01

    We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC. PMID:29263780

  4. Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectrometry combined with chemometrics.

    PubMed

    Longobardi, F; Casiello, G; Cortese, M; Perini, M; Camin, F; Catucci, L; Agostiano, A

    2015-12-01

    The aim of this study was to predict the geographic origin of lentils by using isotope ratio mass spectrometry (IRMS) in combination with chemometrics. Lentil samples from two origins, i.e. Italy and Canada, were analysed obtaining the stable isotope ratios of δ(13)C, δ(15)N, δ(2)H, δ(18)O, and δ(34)S. A comparison between median values (U-test) highlighted statistically significant differences (p<0.05) for all isotopic parameters between the lentils produced in these two different geographic areas, except for δ(15)N. Applying principal component analysis, grouping of samples was observed on the basis of origin but with overlapping zones; consequently, two supervised discriminant techniques, i.e. partial least squares discriminant analysis and k-nearest neighbours algorithm were used. Both models showed good performances with external prediction abilities of about 93% demonstrating the suitability of the methods developed. Subsequently, isotopic determinations were also performed on the protein and starch fractions and the relevant results are reported. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Multi-element, multi-compound isotope profiling as a means to distinguish the geographical and varietal origin of fermented cocoa (Theobroma cacao L.) beans.

    PubMed

    Diomande, Didier; Antheaume, Ingrid; Leroux, Maël; Lalande, Julie; Balayssac, Stéphane; Remaud, Gérald S; Tea, Illa

    2015-12-01

    Multi-element stable isotope ratios have been assessed as a means to distinguish between fermented cocoa beans from different geographical and varietal origins. Isotope ratios and percentage composition for C and N were measured in different tissues (cotyledons, shells) and extracts (pure theobromine, defatted cocoa solids, protein, lipids) obtained from fermented cocoa bean samples. Sixty-one samples from 24 different geographical origins covering all four continental areas producing cocoa were analyzed. Treatment of the data with unsupervised (Principal Component Analysis) and supervised (Partial Least Squares Discriminant Analysis) multiparametric statistical methods allowed the cocoa beans from different origins to be distinguished. The most discriminant variables identified as responsible for geographical and varietal differences were the δ(15)N and δ(13)C values of cocoa beans and some extracts and tissues. It can be shown that the isotope ratios are correlated with the altitude and precipitation conditions found in the different cocoa-growing regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics.

    PubMed

    Bajoub, Aadil; Medina-Rodríguez, Santiago; Gómez-Romero, María; Ajal, El Amine; Bagur-González, María Gracia; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

    2017-01-15

    High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using "Low-level" and "Mid-Level" data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination "chromatographic dataset+chemometric technique" allowing the most accurate classification for each monovarietal extra-VOO was highlighted. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes.

    PubMed

    De Luca, Michele; Restuccia, Donatella; Clodoveo, Maria Lisa; Puoci, Francesco; Ragno, Gaetano

    2016-07-01

    Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

    NASA Technical Reports Server (NTRS)

    Ranson, K. Jon; Sun, Guoqing; Kharuk, V. I.; Kovacs, Katalin

    1998-01-01

    This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

  9. Conformational states and folding pathways of peptides revealed by principal-independent component analyses.

    PubMed

    Nguyen, Phuong H

    2007-05-15

    Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail. 2007 Wiley-Liss, Inc.

  10. [Assessment of the strength of tobacco control on creating smoke-free hospitals using principal components analysis].

    PubMed

    Liu, Hui-lin; Wan, Xia; Yang, Gong-huan

    2013-02-01

    To explore the relationship between the strength of tobacco control and the effectiveness of creating smoke-free hospital, and summarize the main factors that affect the program of creating smoke-free hospitals. A total of 210 hospitals from 7 provinces/municipalities directly under the central government were enrolled in this study using stratified random sampling method. Principle component analysis and regression analysis were conducted to analyze the strength of tobacco control and the effectiveness of creating smoke-free hospitals. Two principal components were extracted in the strength of tobacco control index, which respectively reflected the tobacco control policies and efforts, and the willingness and leadership of hospital managers regarding tobacco control. The regression analysis indicated that only the first principal component was significantly correlated with the progression in creating smoke-free hospital (P<0.001), i.e. hospitals with higher scores on the first principal component had better achievements in smoke-free environment creation. Tobacco control policies and efforts are critical in creating smoke-free hospitals. The principal component analysis provides a comprehensive and objective tool for evaluating the creation of smoke-free hospitals.

  11. Critical Factors Explaining the Leadership Performance of High-Performing Principals

    ERIC Educational Resources Information Center

    Hutton, Disraeli M.

    2018-01-01

    The study explored critical factors that explain leadership performance of high-performing principals and examined the relationship between these factors based on the ratings of school constituents in the public school system. The principal component analysis with the use of Varimax Rotation revealed that four components explain 51.1% of the…

  12. Molecular dynamics in principal component space.

    PubMed

    Michielssens, Servaas; van Erp, Titus S; Kutzner, Carsten; Ceulemans, Arnout; de Groot, Bert L

    2012-07-26

    A molecular dynamics algorithm in principal component space is presented. It is demonstrated that sampling can be improved without changing the ensemble by assigning masses to the principal components proportional to the inverse square root of the eigenvalues. The setup of the simulation requires no prior knowledge of the system; a short initial MD simulation to extract the eigenvectors and eigenvalues suffices. Independent measures indicated a 6-7 times faster sampling compared to a regular molecular dynamics simulation.

  13. Optimized principal component analysis on coronagraphic images of the fomalhaut system

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

    Meshkat, Tiffany; Kenworthy, Matthew A.; Quanz, Sascha P.

    We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases themore » background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.« less

  14. [A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

    PubMed

    Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

    2010-04-01

    It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

  15. Real-Time 3D Sonar Modeling And Visualization

    DTIC Science & Technology

    1998-06-01

    looking back towards Manta sonar beam, Manta plus sonar from 1000m off track. 185 NUWC sponsor Erik Chaum Principal investigator Don Brutzman...USN Sonar Officer LT Kevin Byrne USN Intelligence Officer CPT Russell Storms USA Erik Chaum works in NUWC Code 22. He supervised the design and...McGhee, Bob, "The Phoenix Autonomous Underwater Vehicle," chapter 13, AI-BasedMobile Robots, editors David Kortenkamp, Pete Bonasso and Robin Murphy

  16. Chronic disease self-management and exercise in COPD as pulmonary rehabilitation: a randomized controlled trial.

    PubMed

    Cameron-Tucker, Helen L; Wood-Baker, Richard; Owen, Christine; Joseph, Lyn; Walters, E Haydn

    2014-01-01

    Both exercise and self-management are advocated in pulmonary rehabilitation for people with chronic obstructive pulmonary disease (COPD). The widely used 6-week, group-based Chronic Disease Self-Management Program (CDSMP) increases self-reported exercise, despite supervised exercise not being a program component. This has been little explored in COPD. Whether adding supervised exercise to the CDSMP would add benefit is unknown. We investigated the CDSMP in COPD, with and without a formal supervised exercise component, to address this question. Adult outpatients with COPD were randomized to the CDSMP with or without one hour of weekly supervised exercise over 6 weeks. The primary outcome measure was 6-minute walk test distance (6MWD). Secondary outcomes included self-reported exercise, exercise stage of change, exercise self-efficacy, breathlessness, quality of life, and self-management behaviors. Within- and between-group differences were analyzed on an intention-to-treat basis. Of 84 subjects recruited, 15 withdrew. 6MWD increased similarly in both groups: CDSMP-plus-exercise (intervention group) by 18.6±46.2 m; CDSMP-alone (control group) by 20.0±46.2 m. There was no significant difference for any secondary outcome. The CDSMP produced à small statistically significant increase in 6MWD. The addition of a single supervised exercise session did not further increase exercise capacity. Our findings confirm the efficacy of a behaviorally based intervention in COPD, but this would seem to be less than expected from conventional exercise-based pulmonary rehabilitation, raising the question of how, if at all, the small gains observed in this study may be augmented.

  17. Professional Supervision as Storied Experience: Narrative Analysis Findings for Australian-Based Registered Music Therapists.

    PubMed

    Kennelly, Jeanette D; Baker, Felicity A; Daveson, Barbara A

    2017-03-01

    Limited research exists to inform a music therapist's supervision story from their pre-professional training to their practice as a professional. Evidence is needed to understand the complex nature of supervision experiences and their impact on professional practice. This qualitative study explored the supervisory experiences of Australian-based Registered Music Therapists, according to the: 1) themes that characterize their experiences, 2) influences of the supervisor's professional background, 3) outcomes of supervision, and 4) roles of the employer, the professional music therapy association, and the university in supervision standards and practice. Seven professionals were interviewed for this study. Five stages of narrative analysis were used to create their supervision stories: a life course graph, narrative psychological analysis, component story framework and narrative analysis, analysis of narratives, and final integration of the seven narrative summaries. Findings revealed that supervision practice is influenced by a supervisee's personal and professional needs. A range of supervision models or approaches is recommended, including the access of supervisors from different professional backgrounds to support each stage of learning and development. A quality supervisory experience facilitates shifts in awareness and insight, which results in improved or increased skills, confidence, and accountability of practice. Participants' concern about stakeholders included a limited understanding of the role of the supervisor, a lack of clarity about accountability of supervisory practice, and minimal guidelines, which monitor professional competencies. The benefits of supervision in music therapy depend on the quality of the supervision provided, and clarity about the roles of those involved. Research and guidelines are recommended to target these areas. © the American Music Therapy Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  18. Effects of clinical supervision on resident learning and patient care during simulated ICU scenarios.

    PubMed

    Piquette, Dominique; Tarshis, Jordan; Regehr, Glenn; Fowler, Robert A; Pinto, Ruxandra; LeBlanc, Vicki R

    2013-12-01

    Closer supervision of residents' clinical activities has been promoted to improve patient safety, but may additionally affect resident participation in patient care and learning. The objective of this study was to determine the effects of closer supervision on patient care, resident participation, and the development of resident ability to care independently for critically ill patients during simulated scenarios. This quantitative study represents a component of a larger mixed-methods study. Residents were randomized to one of three levels of supervision, defined by the physical proximity of the supervisor (distant, immediately available, and direct). Each resident completed a simulation scenario under the supervision of a critical care fellow, immediately followed by a modified scenario of similar content without supervision. The simulation center of a tertiary, university-affiliated academic center in a large urban city. Fifty-three residents completing a critical care rotation and 24 critical care fellows were recruited between April 2009 and June 2010. None. During the supervised scenarios, lower team performance checklist scores were obtained for distant supervision compared with immediately available and direct supervision (mean [SD], direct: 72% [12%] vs immediately available: 77% [10%] vs distant: 61% [11%]; p = 0.0013). The percentage of checklist items completed by the residents themselves was significantly lower during direct supervision (median [interquartile range], direct: 40% [21%] vs immediately available: 58% [16%] vs distant: 55% [11%]; p = 0.005). During unsupervised scenarios, no significant differences were found on the outcome measures. Care delivered in the presence of senior supervising physicians was more comprehensive than care delivered without access to a bedside supervisor, but was associated with lower resident participation. However, subsequent resident performance during unsupervised scenarios was not adversely affected. Direct supervision of residents leads to improved care process and does not diminish the subsequent ability of residents to function independently.

  19. How multi segmental patterns deviate in spastic diplegia from typical developed.

    PubMed

    Zago, Matteo; Sforza, Chiarella; Bona, Alessia; Cimolin, Veronica; Costici, Pier Francesco; Condoluci, Claudia; Galli, Manuela

    2017-10-01

    The relationship between gait features and coordination in children with Cerebral Palsy is not sufficiently analyzed yet. Principal Component Analysis can help in understanding motion patterns decomposing movement into its fundamental components (Principal Movements). This study aims at quantitatively characterizing the functional connections between multi-joint gait patterns in Cerebral Palsy. 65 children with spastic diplegia aged 10.6 (SD 3.7) years participated in standardized gait analysis trials; 31 typically developing adolescents aged 13.6 (4.4) years were also tested. To determine if posture affects gait patterns, patients were split into Crouch and knee Hyperextension group according to knee flexion angle at standing. 3D coordinates of hips, knees, ankles, metatarsal joints, pelvis and shoulders were submitted to Principal Component Analysis. Four Principal Movements accounted for 99% of global variance; components 1-3 explained major sagittal patterns, components 4-5 referred to movements on frontal plane and component 6 to additional movement refinements. Dimensionality was higher in patients than in controls (p<0.01), and the Crouch group significantly differed from controls in the application of components 1 and 4-6 (p<0.05), while the knee Hyperextension group in components 1-2 and 5 (p<0.05). Compensatory strategies of children with Cerebral Palsy (interactions between main and secondary movement patterns), were objectively determined. Principal Movements can reduce the effort in interpreting gait reports, providing an immediate and quantitative picture of the connections between movement components. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Enforcer, manager or leader? The judicial role in family violence courts.

    PubMed

    King, Michael; Batagol, Becky

    2010-01-01

    Judicial supervision of offenders is an important component of many family violence courts. Skepticism concerning the ability of offenders to reform and a desire to protect victims has led to some judges to use supervision as a form of deterrence. Supervision is also used to hold offenders accountable for following court orders. Some family violence courts apply processes used in drug courts, such as rewards and sanctions, to promote offender rehabilitation. This article suggests that while protection and support of victims should be the prime concern of family violence courts, a form of judging that engages offenders in the development and implementation of solutions for their problems and supports their implementation is more likely to promote their positive behavioral change than other approaches to judicial supervision. The approach to judging proposed in this article draws from therapeutic jurisprudence, feminist theory, transformational leadership and solution-focused brief therapy principles. Copyright © 2010 Elsevier Ltd. All rights reserved.

  1. Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

    PubMed

    Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

    2018-05-30

    The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

    NASA Astrophysics Data System (ADS)

    Karmakar, Mampi; Maiti, Saumen; Singh, Amrita; Ojha, Maheswar; Maity, Bhabani Sankar

    2017-07-01

    Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for the detailed study to get deeper insight into the Bering Sea' sediment deposition and sequence.

  3. A reduction in ag/residential signature conflict using principal components analysis of LANDSAT temporal data

    NASA Technical Reports Server (NTRS)

    Williams, D. L.; Borden, F. Y.

    1977-01-01

    Methods to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas were considered. The application of principal components analysis to multidate LANDSAT imagery was investigated as a means of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis were discussed, as well as the results of this analysis when applied to multidate LANDSAT imagery of the Washington, D.C. metropolitan area.

  4. Constrained Principal Component Analysis: Various Applications.

    ERIC Educational Resources Information Center

    Hunter, Michael; Takane, Yoshio

    2002-01-01

    Provides example applications of constrained principal component analysis (CPCA) that illustrate the method on a variety of contexts common to psychological research. Two new analyses, decompositions into finer components and fitting higher order structures, are presented, followed by an illustration of CPCA on contingency tables and the CPCA of…

  5. Providing effective supervision in clinical neuropsychology.

    PubMed

    Stucky, Kirk J; Bush, Shane; Donders, Jacobus

    2010-01-01

    A specialty like clinical neuropsychology is shaped by its selection of trainees, educational standards, expected competencies, and the structure of its training programs. The development of individual competency in this specialty is dependent to a considerable degree on the provision of competent supervision to its trainees. In clinical neuropsychology, as in other areas of professional health-service psychology, supervision is the most frequently used method for teaching a variety of skills, including assessment, report writing, differential diagnosis, and treatment. Although much has been written about the provision of quality supervision in clinical and counseling psychology, very little published guidance is available regarding the teaching and provision of supervision in clinical neuropsychology. The primary focus of this article is to provide a framework and guidance for the development of suggested competency standards for training of neuropsychological supervisors, particularly at the residency level. In this paper we outline important components of supervision for neuropsychology trainees and suggest ways in which clinicians can prepare for supervisory roles. Similar to Falender and Shafranske (2004), we propose a competency-based approach to supervision that advocates for a science-informed, formalized, and objective process that clearly delineates the competencies required for good supervisory practice. As much as possible, supervisory competencies are related to foundational and functional competencies in professional psychology, as well as recent legislative initiatives mandating training in supervision. It is our hope that this article will foster further discussion regarding this complex topic, and eventually enhance training in clinical neuropsychology.

  6. Tracking fluorescent dissolved organic matter in multistage rivers using EEM-PARAFAC analysis: implications of the secondary tributary remediation for watershed management.

    PubMed

    Nie, Zeyu; Wu, Xiaodong; Huang, Haomin; Fang, Xiaomin; Xu, Chen; Wu, Jianyu; Liang, Xinqiang; Shi, Jiyan

    2016-05-01

    Profound understanding of behaviors of organic matter from sources to multistage rivers assists watershed management for improving water quality of river networks in rural areas. Ninety-one water samples were collected from the three orders of receiving rivers in a typical combined polluted subcatchment (diffuse agricultural pollutants and domestic sewage) located in China. Then, the fluorescent dissolved organic matter (FDOM) information for these samples was determined by the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Consequently, two typical humic-like (C1 and C2) and other two protein-like (C3 and C4) components were separated. Their fluorescence peaks were located at λ ex/em = 255(360)/455, <250(320)/395, 275/335, and <250/305 nm, which resembled the traditional peaks of A + C, A + M, T, and B, respectively. In addition, C1 and C2 accounted for the dominant contributions to FDOM (>60 %). Principal component analysis (PCA) further demonstrated that, except for the autochthonous produced C4, the allochthonous components (C1 and C2) had the same terrestrial origins, but C3 might possess the separate anthropogenic and biological sources. Moreover, the spatial heterogeneity of contamination levels was noticeable in multistage rivers, and the allochthonous FDOM was gradually homogenized along the migration directions. Interestingly, the average content of the first three PARAFAC components in secondary tributaries and source pollutants had significantly higher levels than that in subsequent receiving rivers, thus suggesting that the supervision and remediation for secondary tributaries would play a prominent role in watershed management works.

  7. Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

    PubMed Central

    Lhermitte, L; Mejstrikova, E; van der Sluijs-Gelling, A J; Grigore, G E; Sedek, L; Bras, A E; Gaipa, G; Sobral da Costa, E; Novakova, M; Sonneveld, E; Buracchi, C; de Sá Bacelar, T; te Marvelde, J G; Trinquand, A; Asnafi, V; Szczepanski, T; Matarraz, S; Lopez, A; Vidriales, B; Bulsa, J; Hrusak, O; Kalina, T; Lecrevisse, Q; Martin Ayuso, M; Brüggemann, M; Verde, J; Fernandez, P; Burgos, L; Paiva, B; Pedreira, C E; van Dongen, J J M; Orfao, A; van der Velden, V H J

    2018-01-01

    Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications. PMID:29089646

  8. Turkish Population Structure and Genetic Ancestry Reveal Relatedness among Eurasian Populations

    PubMed Central

    Hodoğlugil, Uğur; Mahley, Robert W.

    2013-01-01

    Summary Turkey connects the Middle East, Europe, and Asia and has experienced major population movements. We examined the population structure and genetic relatedness of samples from three regions of Turkey using over 500,000 SNP genotypes. The data were analyzed together with Human Genome Diversity Panel data. To obtain a more representative sampling from Central Asia, Kyrgyz samples (Bishkek, Kyrgyzstan) were genotyped and analyzed. Principal component (PC) analysis reveals a significant overlap between Turks and Middle Easterners and a relationship with Europeans and South and Central Asians; however, the Turkish genetic structure is unique. FRAPPE, STRUCTURE, and phylogenetic analyses support the PC analysis depending upon the number of parental ancestry components chosen. For example, supervised STRUCTURE (K = 3) illustrates a genetic ancestry for the Turks of 45% Middle Eastern (95% CI, 42–49), 40% European (95% CI, 36–44), and 15% Central Asian (95% CI, 13–16), whereas at K = 4 the genetic ancestry of the Turks was 38% European (95% CI, 35–42), 35% Middle Eastern (95% CI, 33–38), 18% South Asian (95% CI, 16–19), and 9% Central Asian (95% CI, 7–11). PC analysis and FRAPPE/STRUCTURE results from three regions in Turkey (Aydin, Istanbul, and Kayseri) were superimposed, without clear subpopulation structure, suggesting the selected samples were rather homogeneous. Thus, this study demonstrates admixture of Turkish people reflecting the population migration patterns. PMID:22332727

  9. A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

    NASA Astrophysics Data System (ADS)

    Ginanjar, Irlandia; Pasaribu, Udjianna S.; Indratno, Sapto W.

    2017-03-01

    This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and filter. The third group is the buses with low maintenance costs, especially for lube, and brake canvass. The fourth group is buses with low maintenance costs, especially for tire, and filter.

  10. Survey to Identify Substandard and Falsified Tablets in Several Asian Countries with Pharmacopeial Quality Control Tests and Principal Component Analysis of Handheld Raman Spectroscopy.

    PubMed

    Kakio, Tomoko; Nagase, Hitomi; Takaoka, Takashi; Yoshida, Naoko; Hirakawa, Junichi; Macha, Susan; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

    2018-06-01

    The World Health Organization has warned that substandard and falsified medical products (SFs) can harm patients and fail to treat the diseases for which they were intended, and they affect every region of the world, leading to loss of confidence in medicines, health-care providers, and health systems. Therefore, development of analytical procedures to detect SFs is extremely important. In this study, we investigated the quality of pharmaceutical tablets containing the antihypertensive candesartan cilexetil, collected in China, Indonesia, Japan, and Myanmar, using the Japanese pharmacopeial analytical procedures for quality control, together with principal component analysis (PCA) of Raman spectrum obtained with handheld Raman spectrometer. Some samples showed delayed dissolution and failed to meet the pharmacopeial specification, whereas others failed the assay test. These products appeared to be substandard. Principal component analysis showed that all Raman spectra could be explained in terms of two components: the amount of the active pharmaceutical ingredient and the kinds of excipients. Principal component analysis score plot indicated one substandard, and the falsified tablets have similar principal components in Raman spectra, in contrast to authentic products. The locations of samples within the PCA score plot varied according to the source country, suggesting that manufacturers in different countries use different excipients. Our results indicate that the handheld Raman device will be useful for detection of SFs in the field. Principal component analysis of that Raman data clarify the difference in chemical properties between good quality products and SFs that circulate in the Asian market.

  11. Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

    PubMed

    Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

    2017-12-01

    Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

  12. High-Order Numerical Methods for the Simulation of Linear and Nonlinear Waves: High-Frequency Radiation and Dynamic Stability

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

    Nicholls, David P.

    Over the past four years the Principal Investigator (PI) David Nicholls has worked on several projects in connection with award DE-SC0001549. Of the greatest import has been the continued supervision of ve Ph.D. students (Robyn Canning, Travis McBride, Andrew Sward, Zheng Fang, and Venu Tammali). Canning and McBride defended their theses and graduated in May 2012, while Sward defended his thesis and graduated in May 2013. Both Fang and Tammali plan to defend their theses within the year and graduate in May 2015. Fang is now a very experienced graduate researcher with one paper accepted for publication and another inmore » preparation. Tammali is nearly to the point of writing a paper and will work this summer as an intern at Argonne National Laboratory in the Mathematics and Computer Science Division under the supervision of Paul Fischer.« less

  13. Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

    PubMed Central

    Meyer, Karin; Kirkpatrick, Mark

    2005-01-01

    Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

  14. Recognition of units in coarse, unconsolidated braided-stream deposits from geophysical log data with principal components analysis

    USGS Publications Warehouse

    Morin, R.H.

    1997-01-01

    Returns from drilling in unconsolidated cobble and sand aquifers commonly do not identify lithologic changes that may be meaningful for Hydrogeologic investigations. Vertical resolution of saturated, Quaternary, coarse braided-slream deposits is significantly improved by interpreting natural gamma (G), epithermal neutron (N), and electromagnetically induced resistivity (IR) logs obtained from wells at the Capital Station site in Boise, Idaho. Interpretation of these geophysical logs is simplified because these sediments are derived largely from high-gamma-producing source rocks (granitics of the Boise River drainage), contain few clays, and have undergone little diagenesis. Analysis of G, N, and IR data from these deposits with principal components analysis provides an objective means to determine if units can be recognized within the braided-stream deposits. In particular, performing principal components analysis on G, N, and IR data from eight wells at Capital Station (1) allows the variable system dimensionality to be reduced from three to two by selecting the two eigenvectors with the greatest variance as axes for principal component scatterplots, (2) generates principal components with interpretable physical meanings, (3) distinguishes sand from cobble-dominated units, and (4) provides a means to distinguish between cobble-dominated units.

  15. Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom.

    PubMed

    Wang, Jinbin; Li, Wen; Li, Zhengpeng; Wu, Wenhui; Tang, Xueming

    2018-05-10

    To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food. The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom. © 2018 Institute of Food Technologists®.

  16. Applications of principal component analysis to breath air absorption spectra profiles classification

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Y.

    2015-12-01

    The results of numerical simulation of application principal component analysis to absorption spectra of breath air of patients with pulmonary diseases are presented. Various methods of experimental data preprocessing are analyzed.

  17. [The principal components analysis--method to classify the statistical variables with applications in medicine].

    PubMed

    Dascălu, Cristina Gena; Antohe, Magda Ecaterina

    2009-01-01

    Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.

  18. On Using the Average Intercorrelation Among Predictor Variables and Eigenvector Orientation to Choose a Regression Solution.

    ERIC Educational Resources Information Center

    Mugrage, Beverly; And Others

    Three ridge regression solutions are compared with ordinary least squares regression and with principal components regression using all components. Ridge regression, particularly the Lawless-Wang solution, out-performed ordinary least squares regression and the principal components solution on the criteria of stability of coefficient and closeness…

  19. A Note on McDonald's Generalization of Principal Components Analysis

    ERIC Educational Resources Information Center

    Shine, Lester C., II

    1972-01-01

    It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

  20. CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

    PubMed Central

    Peterson, Leif E

    2002-01-01

    CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

  1. "Kicked out into the real world": prostate cancer patients' experiences with transitioning from hospital-based supervised exercise to unsupervised exercise in the community.

    PubMed

    Schmidt, Mette L K; Østergren, Peter; Cormie, Prue; Ragle, Anne-Mette; Sønksen, Jens; Midtgaard, Julie

    2018-06-21

    Regular exercise is recommended to mitigate the adverse effects of androgen deprivation therapy in men with prostate cancer. The purpose of this study was to explore the experience of transition to unsupervised, community-based exercise among men who had participated in a hospital-based supervised exercise programme in order to propose components that supported transition to unsupervised exercise. Participants were selected by means of purposive, criteria-based sampling. Men undergoing androgen deprivation therapy who had completed a 12-week hospital-based, supervised, group exercise intervention were invited to participate. The programme involved aerobic and resistance training using machines and included a structured transition to a community-based fitness centre. Data were collected by means of semi-structured focus group interviews and analysed using thematic analysis. Five focus group interviews were conducted with a total of 29 men, of whom 25 reported to have continued to exercise at community-based facilities. Three thematic categories emerged: Development and practice of new skills; Establishing social relationships; and Familiarising with bodily well-being. These were combined into an overarching theme: From learning to doing. Components suggested to support transition were as follows: a structured transition involving supervised exercise sessions at a community-based facility; strategies to facilitate peer support; transferable tools including an individual exercise chart; and access to 'check-ups' by qualified exercise specialists. Hospital-based, supervised exercise provides a safe learning environment. Transferring to community-based exercise can be experienced as a confrontation with the real world and can be eased through securing a structured transition, having transferable tools, sustained peer support and monitoring.

  2. The Complexity of Human Walking: A Knee Osteoarthritis Study

    PubMed Central

    Kotti, Margarita; Duffell, Lynsey D.; Faisal, Aldo A.; McGregor, Alison H.

    2014-01-01

    This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space. PMID:25232949

  3. Classification of earth terrain using polarimetric synthetic aperture radar images

    NASA Technical Reports Server (NTRS)

    Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

    1989-01-01

    Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

  4. Principal Components Analysis of a JWST NIRSpec Detector Subsystem

    NASA Technical Reports Server (NTRS)

    Arendt, Richard G.; Fixsen, D. J.; Greenhouse, Matthew A.; Lander, Matthew; Lindler, Don; Loose, Markus; Moseley, S. H.; Mott, D. Brent; Rauscher, Bernard J.; Wen, Yiting; hide

    2013-01-01

    We present principal component analysis (PCA) of a flight-representative James Webb Space Telescope NearInfrared Spectrograph (NIRSpec) Detector Subsystem. Although our results are specific to NIRSpec and its T - 40 K SIDECAR ASICs and 5 m cutoff H2RG detector arrays, the underlying technical approach is more general. We describe how we measured the systems response to small environmental perturbations by modulating a set of bias voltages and temperature. We used this information to compute the systems principal noise components. Together with information from the astronomical scene, we show how the zeroth principal component can be used to calibrate out the effects of small thermal and electrical instabilities to produce cosmetically cleaner images with significantly less correlated noise. Alternatively, if one were designing a new instrument, one could use a similar PCA approach to inform a set of environmental requirements (temperature stability, electrical stability, etc.) that enabled the planned instrument to meet performance requirements

  5. Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

    PubMed

    Ghosh, Debasree; Chattopadhyay, Parimal

    2012-06-01

    The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

  6. Training, supervision and quality of care in selected integrated community case management (iCCM) programmes: A scoping review of programmatic evidence.

    PubMed

    Bosch-Capblanch, Xavier; Marceau, Claudine

    2014-12-01

    To describe the training, supervision and quality of care components of integrated Community Case Management (iCCM) programmes and to draw lessons learned from existing evaluations of those programmes. Scoping review of reports from 29 selected iCCM programmes purposively provided by stakeholders containing any information relevant to understand quality of care issues. The number of people reached by iCCM programmes varied from the tens of thousands to more than a million. All programmes aimed at improving access of vulnerable populations to health care, focusing on the main childhood illnesses, managed by Community Health Workers (CHW), often selected bycommunities. Training and supervision were widely implemented, in different ways and intensities, and often complemented with tools (eg, guides, job aids), supplies, equipment and incentives. Quality of care was measured using many outcomes (eg, access or appropriate treatment). Overall, there seemed to be positive effects for those strategies that involved policy change, organisational change, standardisation of clinical practices and alignment with other programmes. Positive effects were mostly achieved in large multi-component programmes. Mild or no effects have been described on mortality reduction amongst the few programmes for which data on this outcome was available to us. Promising strategies included teaming-up of CHW, micro-franchising or social franchising. On-site training and supervision of CHW have been shown to improve clinical practices. Effects on caregivers seemed positive, with increases in knowledge, care seeking behaviour, or caregivers' basic disease management. Evidence on iCCM is often of low quality, cannot relate specific interventions or the ways they are implemented with outcomes and lacks standardisation; this limits the capacity to identify promising strategies to improve quality of care. Large, multi-faceted, iCCM programmes, with strong components of training, supervision, which included additional support of equipment and supplies, seemed to improve selected quality of care outcomes. However, current evaluation and reporting practices need to be revised in a new research agenda to address the methodological challenges of iCCM evaluations.

  7. Training, supervision and quality of care in selected integrated community case management (iCCM) programmes: A scoping review of programmatic evidence

    PubMed Central

    Bosch–Capblanch, Xavier; Marceau, Claudine

    2014-01-01

    Aim To describe the training, supervision and quality of care components of integrated Community Case Management (iCCM) programmes and to draw lessons learned from existing evaluations of those programmes. Methods Scoping review of reports from 29 selected iCCM programmes purposively provided by stakeholders containing any information relevant to understand quality of care issues. Results The number of people reached by iCCM programmes varied from the tens of thousands to more than a million. All programmes aimed at improving access of vulnerable populations to health care, focusing on the main childhood illnesses, managed by Community Health Workers (CHW), often selected bycommunities. Training and supervision were widely implemented, in different ways and intensities, and often complemented with tools (eg, guides, job aids), supplies, equipment and incentives. Quality of care was measured using many outcomes (eg, access or appropriate treatment). Overall, there seemed to be positive effects for those strategies that involved policy change, organisational change, standardisation of clinical practices and alignment with other programmes. Positive effects were mostly achieved in large multi–component programmes. Mild or no effects have been described on mortality reduction amongst the few programmes for which data on this outcome was available to us. Promising strategies included teaming–up of CHW, micro–franchising or social franchising. On–site training and supervision of CHW have been shown to improve clinical practices. Effects on caregivers seemed positive, with increases in knowledge, care seeking behaviour, or caregivers’ basic disease management. Evidence on iCCM is often of low quality, cannot relate specific interventions or the ways they are implemented with outcomes and lacks standardisation; this limits the capacity to identify promising strategies to improve quality of care. Conclusion Large, multi–faceted, iCCM programmes, with strong components of training, supervision, which included additional support of equipment and supplies, seemed to improve selected quality of care outcomes. However, current evaluation and reporting practices need to be revised in a new research agenda to address the methodological challenges of iCCM evaluations. PMID:25520793

  8. Adaptive maritime video surveillance

    NASA Astrophysics Data System (ADS)

    Gupta, Kalyan Moy; Aha, David W.; Hartley, Ralph; Moore, Philip G.

    2009-05-01

    Maritime assets such as ports, harbors, and vessels are vulnerable to a variety of near-shore threats such as small-boat attacks. Currently, such vulnerabilities are addressed predominantly by watchstanders and manual video surveillance, which is manpower intensive. Automatic maritime video surveillance techniques are being introduced to reduce manpower costs, but they have limited functionality and performance. For example, they only detect simple events such as perimeter breaches and cannot predict emerging threats. They also generate too many false alerts and cannot explain their reasoning. To overcome these limitations, we are developing the Maritime Activity Analysis Workbench (MAAW), which will be a mixed-initiative real-time maritime video surveillance tool that uses an integrated supervised machine learning approach to label independent and coordinated maritime activities. It uses the same information to predict anomalous behavior and explain its reasoning; this is an important capability for watchstander training and for collecting performance feedback. In this paper, we describe MAAW's functional architecture, which includes the following pipeline of components: (1) a video acquisition and preprocessing component that detects and tracks vessels in video images, (2) a vessel categorization and activity labeling component that uses standard and relational supervised machine learning methods to label maritime activities, and (3) an ontology-guided vessel and maritime activity annotator to enable subject matter experts (e.g., watchstanders) to provide feedback and supervision to the system. We report our findings from a preliminary system evaluation on river traffic video.

  9. Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

    NASA Astrophysics Data System (ADS)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2017-06-01

    Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

  10. Pepper seed variety identification based on visible/near-infrared spectral technology

    NASA Astrophysics Data System (ADS)

    Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

    2016-11-01

    Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

  11. Analysis of environmental variation in a Great Plains reservoir using principal components analysis and geographic information systems

    USGS Publications Warehouse

    Long, J.M.; Fisher, W.L.

    2006-01-01

    We present a method for spatial interpretation of environmental variation in a reservoir that integrates principal components analysis (PCA) of environmental data with geographic information systems (GIS). To illustrate our method, we used data from a Great Plains reservoir (Skiatook Lake, Oklahoma) with longitudinal variation in physicochemical conditions. We measured 18 physicochemical features, mapped them using GIS, and then calculated and interpreted four principal components. Principal component 1 (PC1) was readily interpreted as longitudinal variation in water chemistry, but the other principal components (PC2-4) were difficult to interpret. Site scores for PC1-4 were calculated in GIS by summing weighted overlays of the 18 measured environmental variables, with the factor loadings from the PCA as the weights. PC1-4 were then ordered into a landscape hierarchy, an emergent property of this technique, which enabled their interpretation. PC1 was interpreted as a reservoir scale change in water chemistry, PC2 was a microhabitat variable of rip-rap substrate, PC3 identified coves/embayments and PC4 consisted of shoreline microhabitats related to slope. The use of GIS improved our ability to interpret the more obscure principal components (PC2-4), which made the spatial variability of the reservoir environment more apparent. This method is applicable to a variety of aquatic systems, can be accomplished using commercially available software programs, and allows for improved interpretation of the geographic environmental variability of a system compared to using typical PCA plots. ?? Copyright by the North American Lake Management Society 2006.

  12. Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

    PubMed

    Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J

    2003-09-01

    As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

  13. Factors associated with successful transition among children with disabilities in eight European countries

    PubMed Central

    2017-01-01

    Introduction This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Methods Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child’s transition, child involvement in transition, child autonomy, school ethos, professionals’ involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Results Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), ‘child inclusive ethos,’ contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43–7.18, p<0.0001). Discussion To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning. PMID:28636649

  14. Factors associated with successful transition among children with disabilities in eight European countries.

    PubMed

    Ravenscroft, John; Wazny, Kerri; Davis, John M

    2017-01-01

    This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child's transition, child involvement in transition, child autonomy, school ethos, professionals' involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), 'child inclusive ethos,' contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43-7.18, p<0.0001). To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning.

  15. Patient phenotypes associated with outcomes after aneurysmal subarachnoid hemorrhage: a principal component analysis.

    PubMed

    Ibrahim, George M; Morgan, Benjamin R; Macdonald, R Loch

    2014-03-01

    Predictors of outcome after aneurysmal subarachnoid hemorrhage have been determined previously through hypothesis-driven methods that often exclude putative covariates and require a priori knowledge of potential confounders. Here, we apply a data-driven approach, principal component analysis, to identify baseline patient phenotypes that may predict neurological outcomes. Principal component analysis was performed on 120 subjects enrolled in a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm. Correlation matrices were created using a combination of Pearson, polyserial, and polychoric regressions among 46 variables. Scores of significant components (with eigenvalues>1) were included in multivariate logistic regression models with incidence of severe angiographic vasospasm, delayed ischemic neurological deficit, and long-term outcome as outcomes of interest. Sixteen significant principal components accounting for 74.6% of the variance were identified. A single component dominated by the patients' initial hemodynamic status, World Federation of Neurosurgical Societies score, neurological injury, and initial neutrophil/leukocyte counts was significantly associated with poor outcome. Two additional components were associated with angiographic vasospasm, of which one was also associated with delayed ischemic neurological deficit. The first was dominated by the aneurysm-securing procedure, subarachnoid clot clearance, and intracerebral hemorrhage, whereas the second had high contributions from markers of anemia and albumin levels. Principal component analysis, a data-driven approach, identified patient phenotypes that are associated with worse neurological outcomes. Such data reduction methods may provide a better approximation of unique patient phenotypes and may inform clinical care as well as patient recruitment into clinical trials. http://www.clinicaltrials.gov. Unique identifier: NCT00111085.

  16. Principal components of wrist circumduction from electromagnetic surgical tracking.

    PubMed

    Rasquinha, Brian J; Rainbow, Michael J; Zec, Michelle L; Pichora, David R; Ellis, Randy E

    2017-02-01

    An electromagnetic (EM) surgical tracking system was used for a functionally calibrated kinematic analysis of wrist motion. Circumduction motions were tested for differences in subject gender and for differences in the sense of the circumduction as clockwise or counter-clockwise motion. Twenty subjects were instrumented for EM tracking. Flexion-extension motion was used to identify the functional axis. Subjects performed unconstrained wrist circumduction in a clockwise and counter-clockwise sense. Data were decomposed into orthogonal flexion-extension motions and radial-ulnar deviation motions. PCA was used to concisely represent motions. Nonparametric Wilcoxon tests were used to distinguish the groups. Flexion-extension motions were projected onto a direction axis with a root-mean-square error of [Formula: see text]. Using the first three principal components, there was no statistically significant difference in gender (all [Formula: see text]). For motion sense, radial-ulnar deviation distinguished the sense of circumduction in the first principal component ([Formula: see text]) and in the third principal component ([Formula: see text]); flexion-extension distinguished the sense in the second principal component ([Formula: see text]). The clockwise sense of circumduction could be distinguished by a multifactorial combination of components; there were no gender differences in this small population. These data constitute a baseline for normal wrist circumduction. The multifactorial PCA findings suggest that a higher-dimensional method, such as manifold analysis, may be a more concise way of representing circumduction in human joints.

  17. Classroom Reading Specialist Program. Year-end Report. Volume III.

    ERIC Educational Resources Information Center

    Earle, Richard; And Others

    As the third in a four volume final report of a Right to Read preservice competency-based, modular reading specialist project, this volume presents the module outlines for components seven, eight and nine of the classroom program. Component seven, administration and supervision, offers on-the-job practical training where students experience…

  18. Task sharing in rural Haiti: Qualitative assessment of a brief, structured training with and without apprenticeship supervision for community health workers

    PubMed Central

    McLean, Kristen E; Kaiser, Bonnie N; Hagaman, Ashley K; Wagenaar, Bradley H; Therosme, Tatiana P; Kohrt, Brandon A

    2015-01-01

    Despite growing support for supervision after task sharing trainings in humanitarian settings, there is limited research on the experience of trainees in apprenticeship and other supervision approaches. Studying apprenticeships from trainees’ perspectives is crucial to refine supervision and enhance motivation for service implementation. The authors implemented a multi-stage, transcultural adaptation for a pilot task sharing training in Haiti entailing three phases: 1) literature review and qualitative research to adapt a mental health and psychosocial support training; 2) implementation and qualitative process evaluation of a brief, structured group training; and 3) implementation and qualitative evaluation of an apprenticeship training, including a two year follow-up of trainees. Structured group training revealed limited knowledge acquisition, low motivation, time and resource constraints on mastery, and limited incorporation of skills into practice. Adding an apprenticeship component was associated with subjective clinical competency, increased confidence regarding utilising skills, and career advancement. Qualitative findings support the added value of apprenticeship according to trainees. PMID:26190953

  19. Introduction to uses and interpretation of principal component analyses in forest biology.

    Treesearch

    J. G. Isebrands; Thomas R. Crow

    1975-01-01

    The application of principal component analysis for interpretation of multivariate data sets is reviewed with emphasis on (1) reduction of the number of variables, (2) ordination of variables, and (3) applications in conjunction with multiple regression.

  20. Principal component analysis of phenolic acid spectra

    USDA-ARS?s Scientific Manuscript database

    Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to...

  1. Optimal pattern synthesis for speech recognition based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Korsun, O. N.; Poliyev, A. V.

    2018-02-01

    The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

  2. Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

    2017-09-01

    Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

  3. Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

    NASA Astrophysics Data System (ADS)

    Ueki, Kenta; Iwamori, Hikaru

    2017-10-01

    In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

  4. CREATING A "NEST" OF EMOTIONAL SAFETY: REFLECTIVE SUPERVISION IN A CHILD-PARENT PSYCHOTHERAPY CASE.

    PubMed

    Many, Michele M; Kronenberg, Mindy E; Dickson, Amy B

    2016-11-01

    Reflective supervision is considered a key practice component for any infant mental health provider to work effectively with young children and their families. This article will provide a brief history and discussion of reflective supervision followed by a case study demonstrating the importance of reflective supervision in the context of child-parent psychotherapy (CPP; A.F. Lieberman, C. Ghosh Ippen, & P. Van Horn, ; A.F. Lieberman & P. Van Horn, , 2008). Given that CPP leverages the caregiver-child relationship as the mechanism for change in young children who have been impacted by stressors and traumas, primary objectives of CPP include assisting caregivers as they understand the meaning of their child's distress and improving the caregiver-child relationship to make it a safe and supportive space in which the child can heal. As this case will demonstrate, when a clinician is emotionally triggered by a family's negative intergenerational patterns of relating, reflective supervision supports a parallel process in which the psychotherapist feels understood and contained by the supervisor so that she or he is able to support the caregiver's efforts to understand and contain the child. © 2016 Michigan Association for Infant Mental Health.

  5. Assessment of Supportive, Conflicted, and Controlling Dimensions of Family Functioning: A Principal Components Analysis of Family Environment Scale Subscales in a College Sample.

    ERIC Educational Resources Information Center

    Kronenberger, William G.; Thompson, Robert J., Jr.; Morrow, Catherine

    1997-01-01

    A principal components analysis of the Family Environment Scale (FES) (R. Moos and B. Moos, 1994) was performed using 113 undergraduates. Research supported 3 broad components encompassing the 10 FES subscales. These results supported previous research and the generalization of the FES to college samples. (SLD)

  6. Time series analysis of collective motions in proteins

    NASA Astrophysics Data System (ADS)

    Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.

    2004-01-01

    The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

  7. Burst and Principal Components Analyses of MEA Data Separates Chemicals by Class

    EPA Science Inventory

    Microelectrode arrays (MEAs) detect drug and chemical induced changes in action potential "spikes" in neuronal networks and can be used to screen chemicals for neurotoxicity. Analytical "fingerprinting," using Principal Components Analysis (PCA) on spike trains recorded from prim...

  8. EVALUATION OF ACID DEPOSITION MODELS USING PRINCIPAL COMPONENT SPACES

    EPA Science Inventory

    An analytical technique involving principal components analysis is proposed for use in the evaluation of acid deposition models. elationships among model predictions are compared to those among measured data, rather than the more common one-to-one comparison of predictions to mea...

  9. Principal components analysis in clinical studies.

    PubMed

    Zhang, Zhongheng; Castelló, Adela

    2017-09-01

    In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

  10. Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

    PubMed

    Nguyen, Phuong H

    2006-12-01

    Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. (c) 2006 Wiley-Liss, Inc.

  11. Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

    PubMed

    Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

    2016-04-19

    The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

  12. Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

    NASA Astrophysics Data System (ADS)

    Li, Jiangtong; Luo, Yongdao; Dai, Honglin

    2018-01-01

    Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

  13. Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling.

    PubMed

    Vargas-Bello-Pérez, Einar; Toro-Mujica, Paula; Enriquez-Hidalgo, Daniel; Fellenberg, María Angélica; Gómez-Cortés, Pilar

    2017-06-01

    We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  14. Use of multivariate statistics to identify unreliable data obtained using CASA.

    PubMed

    Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

    2013-06-01

    In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

  15. [Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

    PubMed

    Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

    2015-02-01

    Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

  16. Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions.

    PubMed

    Putilov, Arcady A; Donskaya, Olga G

    2016-01-01

    Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep-wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.

  17. Piecewise multivariate modelling of sequential metabolic profiling data.

    PubMed

    Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

    2008-02-19

    Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

  18. Robust prediction of protein subcellular localization combining PCA and WSVMs.

    PubMed

    Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

    2011-08-01

    Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. A pattern recognition approach to transistor array parameter variance

    NASA Astrophysics Data System (ADS)

    da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

    2018-06-01

    The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

  20. Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

    PubMed

    Nariya, Maulik K; Kim, Jae Hyun; Xiong, Jian; Kleindl, Peter A; Hewarathna, Asha; Fisher, Adam C; Joshi, Sangeeta B; Schöneich, Christian; Forrest, M Laird; Middaugh, C Russell; Volkin, David B; Deeds, Eric J

    2017-11-01

    There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products. Copyright © 2017 American Pharmacists Association®. All rights reserved.

  1. Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

    PubMed

    Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

    2015-01-01

    Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

  2. A Study of Feature Combination for Vehicle Detection Based on Image Processing

    PubMed Central

    2014-01-01

    Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. PMID:24672299

  3. Improved classification accuracy by feature extraction using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

    2003-05-01

    A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

  4. Contribution of non-negative matrix factorization to the classification of remote sensing images

    NASA Astrophysics Data System (ADS)

    Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

    2008-10-01

    Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

  5. Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

    NASA Astrophysics Data System (ADS)

    Wojciechowski, Adam

    2017-04-01

    In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity were distinguished: hypsometric component (PC1), deciduous forest habitats component (PC2), river valleys and alder habitats component (PC3), and lakes component (PC4). The distinguished factors characterise natural qualities of postglacial area and reflect well the role of the four most important groups of environment components in shaping ecodiversity of the area under study. The map of ecodiversity of Debnica Kaszubska commune was created on the basis of the first four principal component scores and then five classes of diversity were isolated: very low, low, average, high and very high. As a result of the assessment, five commune regions of very high ecodiversity were separated. These regions are also very attractive for tourists and valuable in terms of their rich nature which include protected areas such as Slupia Valley Landscape Park. The suggested method of ecodiversity assessment with the use of principal component analysis may constitute an alternative methodological proposition to other research methods used so far. Literature Jedicke E., 2001. Biodiversität, Geodiversität, Ökodiversität. Kriterien zur Analyse der Landschaftsstruktur - ein konzeptioneller Diskussionsbeitrag. Naturschutz und Landschaftsplanung, 33(2/3), 59-68.

  6. 12 CFR 502.26 - How does OTS calculate the semi-annual assessment for savings and loan holding companies?

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... holding company's business, a component based on its organizational form, and a component based on its...-site supervision of a noncomplex, low risk savings and loan holding company structure. OTS will... company is the registered holding company at the highest level of ownership in a holding company structure...

  7. A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

    EPA Science Inventory

    A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

  8. Rosacea assessment by erythema index and principal component analysis segmentation maps

    NASA Astrophysics Data System (ADS)

    Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

    2017-12-01

    RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

  9. Airborne electromagnetic data levelling using principal component analysis based on flight line difference

    NASA Astrophysics Data System (ADS)

    Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

    2018-04-01

    A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

  10. Multilevel sparse functional principal component analysis.

    PubMed

    Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

    2014-01-29

    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

  11. [Content of mineral elements of Gastrodia elata by principal components analysis].

    PubMed

    Li, Jin-ling; Zhao, Zhi; Liu, Hong-chang; Luo, Chun-li; Huang, Ming-jin; Luo, Fu-lai; Wang, Hua-lei

    2015-03-01

    To study the content of mineral elements and the principal components in Gastrodia elata. Mineral elements were determined by ICP and the data was analyzed by SPSS. K element has the highest content-and the average content was 15.31 g x kg(-1). The average content of N element was 8.99 g x kg(-1), followed by K element. The coefficient of variation of K and N was small, but the Mn was the biggest with 51.39%. The highly significant positive correlation was found among N, P and K . Three principal components were selected by principal components analysis to evaluate the quality of G. elata. P, B, N, K, Cu, Mn, Fe and Mg were the characteristic elements of G. elata. The content of K and N elements was higher and relatively stable. The variation of Mn content was biggest. The quality of G. elata in Guizhou and Yunnan was better from the perspective of mineral elements.

  12. Visualizing Hyolaryngeal Mechanics in Swallowing Using Dynamic MRI

    PubMed Central

    Pearson, William G.; Zumwalt, Ann C.

    2013-01-01

    Introduction Coordinates of anatomical landmarks are captured using dynamic MRI to explore whether a proposed two-sling mechanism underlies hyolaryngeal elevation in pharyngeal swallowing. A principal components analysis (PCA) is applied to coordinates to determine the covariant function of the proposed mechanism. Methods Dynamic MRI (dMRI) data were acquired from eleven healthy subjects during a repeated swallows task. Coordinates mapping the proposed mechanism are collected from each dynamic (frame) of a dynamic MRI swallowing series of a randomly selected subject in order to demonstrate shape changes in a single subject. Coordinates representing minimum and maximum hyolaryngeal elevation of all 11 subjects were also mapped to demonstrate shape changes of the system among all subjects. MophoJ software was used to perform PCA and determine vectors of shape change (eigenvectors) for elements of the two-sling mechanism of hyolaryngeal elevation. Results For both single subject and group PCAs, hyolaryngeal elevation accounted for the first principal component of variation. For the single subject PCA, the first principal component accounted for 81.5% of the variance. For the between subjects PCA, the first principal component accounted for 58.5% of the variance. Eigenvectors and shape changes associated with this first principal component are reported. Discussion Eigenvectors indicate that two-muscle slings and associated skeletal elements function as components of a covariant mechanism to elevate the hyolaryngeal complex. Morphological analysis is useful to model shape changes in the two-sling mechanism of hyolaryngeal elevation. PMID:25090608

  13. Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

    PubMed

    Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

    2012-11-13

    We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCV(max)). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity. In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.

  14. The factorial reliability of the Middlesex Hospital Questionnaire in normal subjects.

    PubMed

    Bagley, C

    1980-03-01

    The internal reliability of the Middlesex Hospital Questionnaire and its component subscales has been checked by means of principal components analyses of data on 256 normal subjects. The subscales (with the possible exception of Hysteria) were found to contribute to the general underlying factor of psychoneurosis. In general, the principal components analysis points to the reliability of the subscales, despite some item overlap.

  15. The Derivation of Job Compensation Index Values from the Position Analysis Questionnaire (PAQ). Report No. 6.

    ERIC Educational Resources Information Center

    McCormick, Ernest J.; And Others

    The study deals with the job component method of establishing compensation rates. The basic job analysis questionnaire used in the study was the Position Analysis Questionnaire (PAQ) (Form B). On the basis of a principal components analysis of PAQ data for a large sample (2,688) of jobs, a number of principal components (job dimensions) were…

  16. Perceptions of the Principal Evaluation Process and Performance Criteria: A Qualitative Study of the Challenge of Principal Evaluation

    ERIC Educational Resources Information Center

    Faginski-Stark, Erica; Casavant, Christopher; Collins, William; McCandless, Jason; Tencza, Marilyn

    2012-01-01

    Recent federal and state mandates have tasked school systems to move beyond principal evaluation as a bureaucratic function and to re-imagine it as a critical component to improve principal performance and compel school renewal. This qualitative study investigated the district leaders' and principals' perceptions of the performance evaluation…

  17. Determinants of effective clinical learning: a student and teacher perspective in Saudi Arabia.

    PubMed

    Alhaqwi, A I; van der Molen, H T; Schmidt, H G; Magzoub, M E

    2010-08-01

    Graduating clinically competent medical students is probably the principal objective of all medical curricula. Training for clinical competence is rather a complex process and to be effective requires involving all stakeholders, including students, in the processes of planning and implanting the curriculum. This study explores the perceptions of students of the College of Medicine at King Abdul-Aziz Bin Saud University for Health Sciences (KASU-HS), Riyadh, Saudi Arabia of the features of effective clinical rotations by inviting them to answer the question: "Which experiences or activities in your opinion have contributed to the development of your clinical competence? This college was established in 2004 and adopted a problem-based learning curriculum. This question was posed to 24 medical students divided into three focus groups. A fourth focus group interview was conducted with five teachers. Transcriptions of the tape-recorded focus group interviews were qualitatively analyzed using a framework analysis approach. Students identified five main themes of factors perceived to affect their clinical learning: (1) the provision of authentic clinical learning experiences, (2) good organization of the clinical sessions, (3) issues related to clinical cases, (4) good supervision and (5) students' own learning skills. These themes were further subdivided into 18 sub-themes. Teachers identified three principal themes: (1) organizational issues, (2) appropriate supervision and (3) providing authentic experiences. Consideration of these themes in the process of planning and development of medical curricula could contribute to medical students' effective clinical learning and skills competency.

  18. 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

    PubMed

    Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

    2017-09-19

    A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

  19. Effect of noise in principal component analysis with an application to ozone pollution

    NASA Astrophysics Data System (ADS)

    Tsakiri, Katerina G.

    This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

  20. Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

    PubMed

    Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

    2017-09-01

    This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  1. Principal forensic physicians as educational supervisors.

    PubMed

    Stark, Margaret M

    2009-10-01

    This research project was performed to assist the Faculty of Forensic and Legal Medicine (FFLM) with the development of a training programme for Principal Forensic Physicians (PFPs) (Since this research was performed the Metropolitan Police Service have dispensed with the services of the Principal Forensic Physicians so currently (as of January 2009) there is no supervision of newly appointed FMEs or the development training of doctors working in London nor any audit or appraisal reviews.) to fulfil their role as educational supervisors. PFPs working in London were surveyed by questionnaire to identify the extent of their knowledge with regard to their role in the development training of all forensic physicians (FPs) in their group, the induction of assistant FPs and their perceptions of their own training needs with regard to their educational role. A focus group was held at the FFLM annual conference to discuss areas of interest that arose from the preliminary results of the questionnaire. There is a clear need for the FFLM to set up a training programme for educational supervisors in clinical forensic medicine, especially with regard to appraisal. 2009 Elsevier Ltd and Faculty of Forensic and Legal Medicine.

  2. Evaluation of Deep Learning Representations of Spatial Storm Data

    NASA Astrophysics Data System (ADS)

    Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

    2017-12-01

    The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

  3. A social marketing approach to quality improvement in family planning services: a case study from Rawalpindi, Pakistan.

    PubMed

    Gulzar, Jamshaid; Ali, Moazzam; Kuroiwa, Chushi

    2008-02-01

    In the 1990s, social marketing approach was introduced in Pakistan to improve the quality and accessibility of family planning methods involving private practitioners. This study measured six quality elements using a Bruce-Jain framework. Cross-sectional survey data were collected from 29 randomly selected Green Star clinics. The study's four components were 1) an inventory of each outlet (infrastructure, equipment, and supplies); 2) an observation guide for interaction between family planning clients and service providers; 3) exit interviews with clients attending the outlet; and 4) interviews with providers at the outlet. Of the 29 clients participating in the exit interviews, 72% were new users of family planning. The clients' mean age was 32 years; all clients were married; 93% had received formal education. Housework was the principal activity of 93% of clients. The mean number of children reported was three. Both hormonal and intrauterine contraceptives (IUCDs) were available in all facilities; 86% of the clients reported being able to obtain their contraceptive of choice. Most facilities had the equipment and supplies needed to deliver services; service personnel were trained and regularly supervised; the service outlets emphasized mechanisms to ensure continuity of use. Notable shortcomings included a shortage of information on alternative methods, contraindications, and side-effect management, as well as a dearth of registration records. In conclusion, this is a good example of public-private partnership involving private practitioners using a social marketing approach. The quality components of a Bruce-Jain framework were achieved, resulting in a satisfied clientele. Involvement of private service outlets increased the accessibility and enhanced the use of services. Social marketing may be expanded to improve quality and access by involving further components of health care.

  4. Experimental Researches on the Durability Indicators and the Physiological Comfort of Fabrics using the Principal Component Analysis (PCA) Method

    NASA Astrophysics Data System (ADS)

    Hristian, L.; Ostafe, M. M.; Manea, L. R.; Apostol, L. L.

    2017-06-01

    The work pursued the distribution of combed wool fabrics destined to manufacturing of external articles of clothing in terms of the values of durability and physiological comfort indices, using the mathematical model of Principal Component Analysis (PCA). Principal Components Analysis (PCA) applied in this study is a descriptive method of the multivariate analysis/multi-dimensional data, and aims to reduce, under control, the number of variables (columns) of the matrix data as much as possible to two or three. Therefore, based on the information about each group/assortment of fabrics, it is desired that, instead of nine inter-correlated variables, to have only two or three new variables called components. The PCA target is to extract the smallest number of components which recover the most of the total information contained in the initial data.

  5. Information extraction from multivariate images

    NASA Technical Reports Server (NTRS)

    Park, S. K.; Kegley, K. A.; Schiess, J. R.

    1986-01-01

    An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

  6. Psychometric evaluation of the Persian version of the Templer's Death Anxiety Scale in cancer patients.

    PubMed

    Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Bahrami, Nasim; Sharif, Saeed Pahlevan; Sharif Nia, Hamid

    2016-10-01

    In this study, 398 Iranian cancer patients completed the 15-item Templer's Death Anxiety Scale (TDAS). Tests of internal consistency, principal components analysis, and confirmatory factor analysis were conducted to assess the internal consistency and factorial validity of the Persian TDAS. The construct reliability statistic and average variance extracted were also calculated to measure construct reliability, convergent validity, and discriminant validity. Principal components analysis indicated a 3-component solution, which was generally supported in the confirmatory analysis. However, acceptable cutoffs for construct reliability, convergent validity, and discriminant validity were not fulfilled for the three subscales that were derived from the principal component analysis. This study demonstrated both the advantages and potential limitations of using the TDAS with Persian-speaking cancer patients.

  7. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    ERIC Educational Resources Information Center

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  8. Analysis of the principal component algorithm in phase-shifting interferometry.

    PubMed

    Vargas, J; Quiroga, J Antonio; Belenguer, T

    2011-06-15

    We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.

  9. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  10. Burst and Principal Components Analyses of MEA Data for 16 Chemicals Describe at Least Three Effects Classes.

    EPA Science Inventory

    Microelectrode arrays (MEAs) detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-•of-concept, the current study assessed the utility of analytical "fingerprinting" using Principal Components Analysis (P...

  11. Incremental principal component pursuit for video background modeling

    DOEpatents

    Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt

    2017-03-14

    An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.

  12. Dynamic competitive probabilistic principal components analysis.

    PubMed

    López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

    2009-04-01

    We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

  13. Electronic system for monitoring the frequency and pressure of mastication: study and approach for its design.

    PubMed

    Nakamura, Orlando K; Garcia, Daniel O; Villavicencio, Emilio A; Navarro, Luis A; Torres, Miguel A; Huamani, Robinson; Yabar, Leopoldo F

    2010-01-01

    The objective of this work is to study and design a portable non invasive prototype which allows us to supervise the mastication frequency and pressure for specific meals, performing an analysis of sounds and pressures generated by facial muscles when they are chewing. These variables have a direct influence on people nutritious and dietary habits; also, a quickly eating makes people ingest a lot of food instead he needs generating overweight on him. On the other hand, there is no so much study for upheaval of temporal-mandible joints (TMJ) in Peru, keeping as reference that unilateral mastication is one of the principal causes on myofacial pains but, as obesity, there are no studies in Peru about how to prevent these pathologies. In consequence, we propose the development of this prototype which, additional to supervise variables such as mastication frequency and pressure, will allow to the patient an self-correction of his habits.

  14. Emerging governance approaches for tourism in the protected areas of china.

    PubMed

    Su, Dan; Wall, Geoffrey; Eagles, Paul F J

    2007-06-01

    This paper examines the recent evolution in the governance of protected area tourism in China. China now sees cooperation in the form of public-private partnerships occurring between authorized private tourism enterprises in various organizational forms and the public managers from specific portfolio departments of governments at different levels. Three types of governance models are visible: the Leasing Model, the Non-listed Share-holding Model, and the Public-listed Share-holding Model. Theories of corporate governance were applied to these models to analyze the internal and external mechanisms of supervision and incentives for both the government agencies and the authorized tourism enterprises for nature-based tourism operations. The Principal-Agent problem and the supervision mechanism are the focus of the analysis. The emerging governance approaches for tourism in protected areas of China are all theoretically viable, as explained by the theory of property rights and corporate governance, and practically viable as elaborated in the cases of the three types of governance models summarized in this paper.

  15. A principal components model of soundscape perception.

    PubMed

    Axelsson, Östen; Nilsson, Mats E; Berglund, Birgitta

    2010-11-01

    There is a need for a model that identifies underlying dimensions of soundscape perception, and which may guide measurement and improvement of soundscape quality. With the purpose to develop such a model, a listening experiment was conducted. One hundred listeners measured 50 excerpts of binaural recordings of urban outdoor soundscapes on 116 attribute scales. The average attribute scale values were subjected to principal components analysis, resulting in three components: Pleasantness, eventfulness, and familiarity, explaining 50, 18 and 6% of the total variance, respectively. The principal-component scores were correlated with physical soundscape properties, including categories of dominant sounds and acoustic variables. Soundscape excerpts dominated by technological sounds were found to be unpleasant, whereas soundscape excerpts dominated by natural sounds were pleasant, and soundscape excerpts dominated by human sounds were eventful. These relationships remained after controlling for the overall soundscape loudness (Zwicker's N(10)), which shows that 'informational' properties are substantial contributors to the perception of soundscape. The proposed principal components model provides a framework for future soundscape research and practice. In particular, it suggests which basic dimensions are necessary to measure, how to measure them by a defined set of attribute scales, and how to promote high-quality soundscapes.

  16. ABE Phase III: Progress and Problems. September 1, 1969-April 1, 1970.

    ERIC Educational Resources Information Center

    Southwestern Cooperative Educational Lab., Albuquerque, NM.

    Interim information concerning the ABE III grants is provided in the three parts of this report. Part 1 (outline) describes the goals and objectives of each component; Part 2 describes accomplishments and problems to date; and Part 3 deals with coordination and supervision activities undertaken by the Lab. The components of the program are: (1)…

  17. 12 CFR 502.28 - How does OTS determine the organizational form component for a savings and loan holding company?

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... component for a savings and loan holding company? 502.28 Section 502.28 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY ASSESSMENTS AND FEES Assessments Savings and Loan Holding Companies... savings and loan holding company that OTS regulates under section 10(l) of the HOLA. OTS will compute your...

  18. Dyadic, Triadic, and Group Models of Peer Supervision/Consultation: What Are Their Components, and Is There Evidence of Their Effectiveness?

    ERIC Educational Resources Information Center

    Borders, L. DiAnne

    2012-01-01

    Models that meet the Psychology Board of Australia's definition of peer consultation include dyadic, triadic, and group formats. Components of these models (e.g., goals, theoretical basis, role of leader, members' roles, structure, and steps in procedure, stages in group development) are presented, and evidence of their effectiveness is reviewed.…

  19. 42 CFR 93.407 - HHS administrative actions.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... HEALTH EFFECTS STUDIES OF HAZARDOUS SUBSTANCES RELEASES AND FACILITIES PUBLIC HEALTH SERVICE POLICIES ON... cooperative agreement. (6) Special review of all requests for PHS funding. (7) Imposition of supervision... PHS funding component, and the debarring official. ...

  20. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

    NASA Astrophysics Data System (ADS)

    Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

    2015-01-01

    Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

  1. Application of principal component analysis in protein unfolding: an all-atom molecular dynamics simulation study.

    PubMed

    Das, Atanu; Mukhopadhyay, Chaitali

    2007-10-28

    We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide-ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

  2. Application of principal component analysis in protein unfolding: An all-atom molecular dynamics simulation study

    NASA Astrophysics Data System (ADS)

    Das, Atanu; Mukhopadhyay, Chaitali

    2007-10-01

    We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide—ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

  3. SAS program for quantitative stratigraphic correlation by principal components

    USGS Publications Warehouse

    Hohn, M.E.

    1985-01-01

    A SAS program is presented which constructs a composite section of stratigraphic events through principal components analysis. The variables in the analysis are stratigraphic sections and the observational units are range limits of taxa. The program standardizes data in each section, extracts eigenvectors, estimates missing range limits, and computes the composite section from scores of events on the first principal component. Provided is an option of several types of diagnostic plots; these help one to determine conservative range limits or unrealistic estimates of missing values. Inspection of the graphs and eigenvalues allow one to evaluate goodness of fit between the composite and measured data. The program is extended easily to the creation of a rank-order composite. ?? 1985.

  4. Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy

    NASA Astrophysics Data System (ADS)

    Werth, Alexandra; Liakat, Sabbir; Dong, Anqi; Woods, Callie M.; Gmachl, Claire F.

    2018-05-01

    An integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy. The sphere enhances signal stability by roughly an order of magnitude, allowing us to use a thermoelectrically (TE) cooled detector while maintaining comparable glucose prediction accuracy levels. Using a smaller TE-cooled detector reduces form factor, creating a mobile sensor. Principal component analysis has predicted principal components of spectra taken from human subjects that closely match the absorption peaks of glucose. These principal components are used as regressors in a linear regression algorithm to make glucose concentration predictions, over 75% of which are clinically accurate.

  5. A novel principal component analysis for spatially misaligned multivariate air pollution data.

    PubMed

    Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

    2017-01-01

    We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

  6. Atmospheric electricity at Durham: the scientific contributions and legacy of J. A. ("Skip") Chalmers (1904-1967)

    NASA Astrophysics Data System (ADS)

    Aplin, Karen L.

    2018-03-01

    John Alan Chalmers made major contributions to atmospheric electricity over almost 40 years spent at Durham University, UK. He is particularly remembered in the atmospheric science community for his accessible and insightful textbook, Atmospheric Electricity, and his work on corona currents, which are still regularly cited. He also supervised over 35 research students. This article discusses his background, scientific contributions, and significant legacy to modern atmospheric science within the context of a long and productive career spent at one of England's principal northern universities.

  7. A comparative study of selected Georgia elementary principals' perceptions of environmental knowledge

    NASA Astrophysics Data System (ADS)

    Campbell, Joyce League

    This study sought to establish baseline data on environmental knowledge, opinions, and perceptions of elementary principals and to make comparisons based on academic success rankings of schools and to national results. The self-reported study looked at 200 elementary principals in the state of Georgia. The population selected for the study included principals from the 100 top and 100 bottom academically ranked elementary schools as reported in the Georgia Public Policy Foundation Report Card for Parents. Their scores on the NEETF/Roper Environmental Knowledge Survey were compared between these two Georgia groups and to a national sample. Georgia elementary principals' scores were compared to environmental programs evident in their schools. The two Georgia groups were also compared on environmental opinion and perception responses on mandates, programs in schools and time devoted to these, environmental education as a priority, and the impact of various factors on the strength of environmental studies in schools. Georgia elementary principals leading schools at the bottom of the academic performance scale achieved environmental knowledge scores comparable to the national sample. However, principals of academically successful schools scored significantly higher on environmental knowledge than their colleagues from low performing schools (p < .05) and higher than the national sample (p < .001). Both Georgia principal groups strongly support a mandated environmental education curriculum for Georgia. The two groups were comparable on distributions of time devoted to environmental education across grade levels; however, principals from the more successful schools reported significantly (p < .01) greater amounts of time allotted to environmental studies. Both groups reported the same variety of environmental programs and practices evident in their schools and similar participation in these activities at various grade levels. Most significant (p < .01) was the comparison of ratings each group gave to environmental education as an instructional priority in their schools; principals supervising successful school programs viewed environmental education as a higher priority. These successful principals also recognized the importance of both administrator and staff interest as influencing factors and ranked these two variables as strongly impacting the success or failure of environmental initiatives in schools. Comparison of principals' environmental knowledge scores to numbers of programs shown no significant relationship. (Abstract shortened by UMI.)

  8. Principals' Perceptions of Collegial Support as a Component of Administrative Inservice.

    ERIC Educational Resources Information Center

    Daresh, John C.

    To address the problem of increasing professional isolation of building administrators, the Principals' Inservice Project helps establish principals' collegial support groups across the nation. The groups are typically composed of 6 to 10 principals who meet at least once each month over a 2-year period. One collegial support group of seven…

  9. Training the Trainers: Learning to Be a Principal Supervisor

    ERIC Educational Resources Information Center

    Saltzman, Amy

    2017-01-01

    While most principal supervisors are former principals themselves, few come to the role with specific training in how to do the job effectively. For this reason, both the Washington, D.C., and Tulsa, Oklahoma, principal supervisor programs include a strong professional development component. In this article, the author takes a look inside these…

  10. Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

    ERIC Educational Resources Information Center

    Rodrigue, Christine M.

    2011-01-01

    This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

  11. A Principal Components Analysis and Validation of the Coping with the College Environment Scale (CWCES)

    ERIC Educational Resources Information Center

    Ackermann, Margot Elise; Morrow, Jennifer Ann

    2008-01-01

    The present study describes the development and initial validation of the Coping with the College Environment Scale (CWCES). Participants included 433 college students who took an online survey. Principal Components Analysis (PCA) revealed six coping strategies: planning and self-management, seeking support from institutional resources, escaping…

  12. Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

    2015-11-01

    The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

  13. Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

    NASA Astrophysics Data System (ADS)

    Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

    2013-06-01

    Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

  14. Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap

    ERIC Educational Resources Information Center

    Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.

    2007-01-01

    Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

  15. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

  16. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

  17. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

  18. 40 CFR 60.1580 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... the model rule? 60.1580 Section 60.1580 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines..., 1999 Use of Model Rule § 60.1580 What are the principal components of the model rule? The model rule...

  19. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

  20. Students' Perceptions of Teaching and Learning Practices: A Principal Component Approach

    ERIC Educational Resources Information Center

    Mukorera, Sophia; Nyatanga, Phocenah

    2017-01-01

    Students' attendance and engagement with teaching and learning practices is perceived as a critical element for academic performance. Even with stipulated attendance policies, students still choose not to engage. The study employed a principal component analysis to analyze first- and second-year students' perceptions of the importance of the 12…

  1. Principal Perspectives about Policy Components and Practices for Reducing Cyberbullying in Urban Schools

    ERIC Educational Resources Information Center

    Hunley-Jenkins, Keisha Janine

    2012-01-01

    This qualitative study explores large, urban, mid-western principal perspectives about cyberbullying and the policy components and practices that they have found effective and ineffective at reducing its occurrence and/or negative effect on their schools' learning environments. More specifically, the researcher was interested in learning more…

  2. Principal Component Analysis: Resources for an Essential Application of Linear Algebra

    ERIC Educational Resources Information Center

    Pankavich, Stephen; Swanson, Rebecca

    2015-01-01

    Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

  3. Learning Principal Component Analysis by Using Data from Air Quality Networks

    ERIC Educational Resources Information Center

    Perez-Arribas, Luis Vicente; Leon-González, María Eugenia; Rosales-Conrado, Noelia

    2017-01-01

    With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…

  4. Applications of Nonlinear Principal Components Analysis to Behavioral Data.

    ERIC Educational Resources Information Center

    Hicks, Marilyn Maginley

    1981-01-01

    An empirical investigation of the statistical procedure entitled nonlinear principal components analysis was conducted on a known equation and on measurement data in order to demonstrate the procedure and examine its potential usefulness. This method was suggested by R. Gnanadesikan and based on an early paper of Karl Pearson. (Author/AL)

  5. Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

    ERIC Educational Resources Information Center

    Hendrix, Dean

    2010-01-01

    This study analyzed 2005-2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between…

  6. Principal component analysis for protein folding dynamics.

    PubMed

    Maisuradze, Gia G; Liwo, Adam; Scheraga, Harold A

    2009-01-09

    Protein folding is considered here by studying the dynamics of the folding of the triple beta-strand WW domain from the Formin-binding protein 28. Starting from the unfolded state and ending either in the native or nonnative conformational states, trajectories are generated with the coarse-grained united residue (UNRES) force field. The effectiveness of principal components analysis (PCA), an already established mathematical technique for finding global, correlated motions in atomic simulations of proteins, is evaluated here for coarse-grained trajectories. The problems related to PCA and their solutions are discussed. The folding and nonfolding of proteins are examined with free-energy landscapes. Detailed analyses of many folding and nonfolding trajectories at different temperatures show that PCA is very efficient for characterizing the general folding and nonfolding features of proteins. It is shown that the first principal component captures and describes in detail the dynamics of a system. Anomalous diffusion in the folding/nonfolding dynamics is examined by the mean-square displacement (MSD) and the fractional diffusion and fractional kinetic equations. The collisionless (or ballistic) behavior of a polypeptide undergoing Brownian motion along the first few principal components is accounted for.

  7. Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.

    PubMed

    Tao, Dapeng; Lin, Xu; Jin, Lianwen; Li, Xuelong

    2016-03-01

    Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods.

  8. Dynamic of consumer groups and response of commodity markets by principal component analysis

    NASA Astrophysics Data System (ADS)

    Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

    2017-09-01

    This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

  9. [Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

    PubMed

    Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

    2017-06-01

    The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

  10. [Applications of three-dimensional fluorescence spectrum of dissolved organic matter to identification of red tide algae].

    PubMed

    Lü, Gui-Cai; Zhao, Wei-Hong; Wang, Jiang-Tao

    2011-01-01

    The identification techniques for 10 species of red tide algae often found in the coastal areas of China were developed by combining the three-dimensional fluorescence spectra of fluorescence dissolved organic matter (FDOM) from the cultured red tide algae with principal component analysis. Based on the results of principal component analysis, the first principal component loading spectrum of three-dimensional fluorescence spectrum was chosen as the identification characteristic spectrum for red tide algae, and the phytoplankton fluorescence characteristic spectrum band was established. Then the 10 algae species were tested using Bayesian discriminant analysis with a correct identification rate of more than 92% for Pyrrophyta on the level of species, and that of more than 75% for Bacillariophyta on the level of genus in which the correct identification rates were more than 90% for the phaeodactylum and chaetoceros. The results showed that the identification techniques for 10 species of red tide algae based on the three-dimensional fluorescence spectra of FDOM from the cultured red tide algae and principal component analysis could work well.

  11. Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

    NASA Astrophysics Data System (ADS)

    Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

    2017-06-01

    Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

  12. Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

    NASA Astrophysics Data System (ADS)

    Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.

    2011-07-01

    We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.

  13. Seeing wholes: The concept of systems thinking and its implementation in school leadership

    NASA Astrophysics Data System (ADS)

    Shaked, Haim; Schechter, Chen

    2013-12-01

    Systems thinking (ST) is an approach advocating thinking about any given issue as a whole, emphasising the interrelationships between its components rather than the components themselves. This article aims to link ST and school leadership, claiming that ST may enable school principals to develop highly performing schools that can cope successfully with current challenges, which are more complex than ever before in today's era of accountability and high expectations. The article presents the concept of ST - its definition, components, history and applications. Thereafter, its connection to education and its contribution to school management are described. The article concludes by discussing practical processes including screening for ST-skilled principal candidates and developing ST skills among prospective and currently performing school principals, pinpointing three opportunities for skills acquisition: during preparatory programmes; during their first years on the job, supported by veteran school principals as mentors; and throughout their entire career. Such opportunities may not only provide school principals with ST skills but also improve their functioning throughout the aforementioned stages of professional development.

  14. A modified procedure for mixture-model clustering of regional geochemical data

    USGS Publications Warehouse

    Ellefsen, Karl J.; Smith, David B.; Horton, John D.

    2014-01-01

    A modified procedure is proposed for mixture-model clustering of regional-scale geochemical data. The key modification is the robust principal component transformation of the isometric log-ratio transforms of the element concentrations. This principal component transformation and the associated dimension reduction are applied before the data are clustered. The principal advantage of this modification is that it significantly improves the stability of the clustering. The principal disadvantage is that it requires subjective selection of the number of clusters and the number of principal components. To evaluate the efficacy of this modified procedure, it is applied to soil geochemical data that comprise 959 samples from the state of Colorado (USA) for which the concentrations of 44 elements are measured. The distributions of element concentrations that are derived from the mixture model and from the field samples are similar, indicating that the mixture model is a suitable representation of the transformed geochemical data. Each cluster and the associated distributions of the element concentrations are related to specific geologic and anthropogenic features. In this way, mixture model clustering facilitates interpretation of the regional geochemical data.

  15. Temporal evolution of financial-market correlations.

    PubMed

    Fenn, Daniel J; Porter, Mason A; Williams, Stacy; McDonald, Mark; Johnson, Neil F; Jones, Nick S

    2011-08-01

    We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

  16. Temporal evolution of financial-market correlations

    NASA Astrophysics Data System (ADS)

    Fenn, Daniel J.; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Johnson, Neil F.; Jones, Nick S.

    2011-08-01

    We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

  17. Deterring Online Advertising Fraud through Optimal Payment in Arrears

    NASA Astrophysics Data System (ADS)

    Edelman, Benjamin

    Online advertisers face substantial difficulty in selecting and supervising small advertising partners: Fraud can be well-hidden, and limited reputation systems reduce accountability. But partners are not paid until after their work is complete, and advertisers can extend this delay both to improve detection of improper partner practices and to punish partners who turn out to be rule-breakers. I capture these relationships in a screening model with delayed payments and probabilistic delayed observation of agents’ types. I derive conditions in which an advertising principal can set its payment delay to deter rogue agents and to attract solely or primarily good-type agents. Through the savings from excluding rogue agents, the principal can increase its profits while offering increased payments to good-type agents. I estimate that a leading affiliate network could have invoked an optimal payment delay to eliminate 71% of fraud without decreasing profit.

  18. Non-linear principal component analysis applied to Lorenz models and to North Atlantic SLP

    NASA Astrophysics Data System (ADS)

    Russo, A.; Trigo, R. M.

    2003-04-01

    A non-linear generalisation of Principal Component Analysis (PCA), denoted Non-Linear Principal Component Analysis (NLPCA), is introduced and applied to the analysis of three data sets. Non-Linear Principal Component Analysis allows for the detection and characterisation of low-dimensional non-linear structure in multivariate data sets. This method is implemented using a 5-layer feed-forward neural network introduced originally in the chemical engineering literature (Kramer, 1991). The method is described and details of its implementation are addressed. Non-Linear Principal Component Analysis is first applied to a data set sampled from the Lorenz attractor (1963). It is found that the NLPCA approximations are more representative of the data than are the corresponding PCA approximations. The same methodology was applied to the less known Lorenz attractor (1984). However, the results obtained weren't as good as those attained with the famous 'Butterfly' attractor. Further work with this model is underway in order to assess if NLPCA techniques can be more representative of the data characteristics than are the corresponding PCA approximations. The application of NLPCA to relatively 'simple' dynamical systems, such as those proposed by Lorenz, is well understood. However, the application of NLPCA to a large climatic data set is much more challenging. Here, we have applied NLPCA to the sea level pressure (SLP) field for the entire North Atlantic area and the results show a slight imcrement of explained variance associated. Finally, directions for future work are presented.%}

  19. Evaluating filterability of different types of sludge by statistical analysis: The role of key organic compounds in extracellular polymeric substances.

    PubMed

    Xiao, Keke; Chen, Yun; Jiang, Xie; Zhou, Yan

    2017-03-01

    An investigation was conducted for 20 different types of sludge in order to identify the key organic compounds in extracellular polymeric substances (EPS) that are important in assessing variations of sludge filterability. The different types of sludge varied in initial total solids (TS) content, organic composition and pre-treatment methods. For instance, some of the sludges were pre-treated by acid, ultrasonic, thermal, alkaline, or advanced oxidation technique. The Pearson's correlation results showed significant correlations between sludge filterability and zeta potential, pH, dissolved organic carbon, protein and polysaccharide in soluble EPS (SB EPS), loosely bound EPS (LB EPS) and tightly bound EPS (TB EPS). The principal component analysis (PCA) method was used to further explore correlations between variables and similarities among EPS fractions of different types of sludge. Two principal components were extracted: principal component 1 accounted for 59.24% of total EPS variations, while principal component 2 accounted for 25.46% of total EPS variations. Dissolved organic carbon, protein and polysaccharide in LB EPS showed higher eigenvector projection values than the corresponding compounds in SB EPS and TB EPS in principal component 1. Further characterization of fractionized key organic compounds in LB EPS was conducted with size-exclusion chromatography-organic carbon detection-organic nitrogen detection (LC-OCD-OND). A numerical multiple linear regression model was established to describe relationship between organic compounds in LB EPS and sludge filterability. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. QSAR modeling of flotation collectors using principal components extracted from topological indices.

    PubMed

    Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

    2002-01-01

    Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

  1. Being and becoming a psychotherapy supervisor: the crucial triad of learning difficulties.

    PubMed

    Watkins, C Edward

    2013-01-01

    More than 40 years ago eminent psychiatrist Richard Chessick penned a classic, highly prescient psychotherapy supervision paper (that appeared in this journal) in which he identified for supervisors the crucial triad of learning difficulties that tend to confront beginning therapists in their training. These are (a) dealing with the anxiety attendant to the development of psychological mindedness; (b) developing a psychotherapist identity; and (c) developing conviction about the meaningfulness of psychodynamics and psychotherapy. In this paper, I would like to revisit Chessick's seminal contribution about the teaching and learning of psychotherapy and extrapolate his triad of learning difficulties to the process of teaching and learning supervision. The process of being and becoming a psychotherapist has been likened to a developmental journey, and similarly being and becoming a supervisor is increasingly recognized as a developmental journey that is best stimulated by means of didactic and practical experiences (i.e., supervision coursework, seminars, or workshops and the supervision of supervision). In what follows, I would like to explore how Chessick's crucial triad of learning difficulties can be meaningfully extrapolated to and used to inform the supervision training situation. In extrapolating Chessick's triad, beginning supervisors or supervisor trainees can be conceptualized as confronting three critical issues: (a) dealing with the anxiety and demoralization attendant to the development of supervisory mindedness; (b) developing a supervisory identity; and (c) developing conviction about the meaningfulness of psychotherapy supervision. This triadic conceptualization appears to capture nicely core concerns that extend across the arc of the supervisor development process and provides a useful and usable way of thinking about supervisor training and informing it. Each component of the triadic conceptualization is described, and some supervisor education intervention possibilities are considered.

  2. Workplace training for senior trainees: a systematic review and narrative synthesis of current approaches to promote patient safety.

    PubMed

    Walton, Merrilyn; Harrison, Reema; Burgess, Annette; Foster, Kirsty

    2015-10-01

    Preventable harm is one of the top six health problems in the developed world. Developing patient safety skills and knowledge among advanced trainee doctors is critical. Clinical supervision is the main form of training for advanced trainees. The use of supervision to develop patient safety competence has not been established. To establish the use of clinical supervision and other workplace training to develop non-technical patient safety competency in advanced trainee doctors. Keywords, synonyms and subject headings were used to search eight electronic databases in addition to hand-searching of relevant journals up to 1 March 2014. Titles and abstracts of retrieved publications were screened by two reviewers and checked by a third. Full-text articles were screened against the eligibility criteria. Data on design, methods and key findings were extracted. Clinical supervision documents were assessed against components common to established patient safety frameworks. Findings from the reviewed articles and document analysis were collated in a narrative synthesis. Clinical supervision is not identified as an avenue for embedding patient safety skills in the workplace and is consequently not evaluated as a method to teach trainees these skills. Workplace training in non-technical patient safety skills is limited, but one-off training courses are sometimes used. Clinical supervision is the primary avenue for learning in postgraduate medical education but the most overlooked in the context of patient safety learning. The widespread implementation of short courses is not matched by evidence of rigorous evaluation. Supporting supervisors to identify teaching moments during supervision and to give weight to non-technical skills and technical skills equally is critical. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  3. The research component of specialist registration--a question of alligators and swamps? A personal view.

    PubMed

    Aldous, C M; Adhikari, M; Rout, C C

    2015-01-01

    The recent implementation of the research requirement for specialist registration presents difficulties with regard to the provision of research supervision, particularly in those medical schools that previously followed the path of qualification via the Colleges of Medicine of South Africa examinations. The differences between the requirements for research supervision as stated in the Health Professions Council of South Africa memorandum and those of the Committee for Higher Education are causing disparities between medical schools similar to those that led to the memorandum in the first place. While the research component of specialist training can only improve the quality of both patient care and academic endeavour, it requires an enormous investment of time on the part of both the specialist trainees and their supervisors. In order to deal with this, specific issues outlined in the article need to be addressed.

  4. Building mental models by dissecting physical models.

    PubMed

    Srivastava, Anveshna

    2016-01-01

    When students build physical models from prefabricated components to learn about model systems, there is an implicit trade-off between the physical degrees of freedom in building the model and the intensity of instructor supervision needed. Models that are too flexible, permitting multiple possible constructions require greater supervision to ensure focused learning; models that are too constrained require less supervision, but can be constructed mechanically, with little to no conceptual engagement. We propose "model-dissection" as an alternative to "model-building," whereby instructors could make efficient use of supervisory resources, while simultaneously promoting focused learning. We report empirical results from a study conducted with biology undergraduate students, where we demonstrate that asking them to "dissect" out specific conceptual structures from an already built 3D physical model leads to a significant improvement in performance than asking them to build the 3D model from simpler components. Using questionnaires to measure understanding both before and after model-based interventions for two cohorts of students, we find that both the "builders" and the "dissectors" improve in the post-test, but it is the latter group who show statistically significant improvement. These results, in addition to the intrinsic time-efficiency of "model dissection," suggest that it could be a valuable pedagogical tool. © 2015 The International Union of Biochemistry and Molecular Biology.

  5. Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

    PubMed Central

    Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Wolf, Ronald L.; Bilello, Michel; Verma, Ragini; O’Rourke, Donald M.

    2014-01-01

    Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. Materials and Methods Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. Results The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. Conclusion Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication. © RSNA, 2014 PMID:24955928

  6. Population Structure and Genomic Breed Composition in an Angus-Brahman Crossbred Cattle Population.

    PubMed

    Gobena, Mesfin; Elzo, Mauricio A; Mateescu, Raluca G

    2018-01-01

    Crossbreeding is a common strategy used in tropical and subtropical regions to enhance beef production, and having accurate knowledge of breed composition is essential for the success of a crossbreeding program. Although pedigree records have been traditionally used to obtain the breed composition of crossbred cattle, the accuracy of pedigree-based breed composition can be reduced by inaccurate and/or incomplete records and Mendelian sampling. Breed composition estimation from genomic data has multiple advantages including higher accuracy without being affected by missing, incomplete, or inaccurate records and the ability to be used as independent authentication of breed in breed-labeled beef products. The present study was conducted with 676 Angus-Brahman crossbred cattle with genotype and pedigree information to evaluate the feasibility and accuracy of using genomic data to determine breed composition. We used genomic data in parametric and non-parametric methods to detect population structure due to differences in breed composition while accounting for the confounding effect of close familial relationships. By applying principal component analysis (PCA) and the maximum likelihood method of ADMIXTURE to genomic data, it was possible to successfully characterize population structure resulting from heterogeneous breed ancestry, while accounting for close familial relationships. PCA results offered additional insight into the different hierarchies of genetic variation structuring. The first principal component was strongly correlated with Angus-Brahman proportions, and the second represented variation within animals that have a relatively more extended Brangus lineage-indicating the presence of a distinct pattern of genetic variation in these cattle. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals. This was most likely due to inaccuracies in the pedigree-based estimation of breed composition, which supported the case for using genomic information to complement and/or replace pedigree information when estimating breed composition. Comparison with a supervised analysis where purebreds are used as the training set suggest that accurate predictions can be achieved even in the absence of purebred population information.

  7. Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra.

    PubMed

    Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R

    2010-01-01

    The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.

  8. Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

    PubMed

    Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

    2018-01-01

    This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. © 2017 American Association of Physicists in Medicine.

  9. Cultivating an Environment that Contributes to Teaching and Learning in Schools: High School Principals' Actions

    ERIC Educational Resources Information Center

    Lin, Mind-Dih

    2012-01-01

    Improving principal leadership is a vital component to the success of educational reform initiatives that seek to improve whole-school performance, as principal leadership often exercises positive but indirect effects on student learning. Because of the importance of principals within the field of school improvement, this article focuses on…

  10. Measuring Principals' Effectiveness: Results from New Jersey's First Year of Statewide Principal Evaluation. REL 2016-156

    ERIC Educational Resources Information Center

    Herrmann, Mariesa; Ross, Christine

    2016-01-01

    States and districts across the country are implementing new principal evaluation systems that include measures of the quality of principals' school leadership practices and measures of student achievement growth. Because these evaluation systems will be used for high-stakes decisions, it is important that the component measures of the evaluation…

  11. The Views of Novice and Late Career Principals Concerning Instructional and Organizational Leadership within Their Evaluation

    ERIC Educational Resources Information Center

    Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann; Mette, Ian M.

    2015-01-01

    This study examined the perspectives of novice and late career principals concerning instructional and organizational leadership within their performance evaluations. An online survey was sent to 251 principals with a return rate of 49%. Instructional leadership components of the evaluation that were most important to all principals were:…

  12. Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

    PubMed

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2013-04-01

    Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Checking Dimensionality in Item Response Models with Principal Component Analysis on Standardized Residuals

    ERIC Educational Resources Information Center

    Chou, Yeh-Tai; Wang, Wen-Chung

    2010-01-01

    Dimensionality is an important assumption in item response theory (IRT). Principal component analysis on standardized residuals has been used to check dimensionality, especially under the family of Rasch models. It has been suggested that an eigenvalue greater than 1.5 for the first eigenvalue signifies a violation of unidimensionality when there…

  14. Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

    ERIC Educational Resources Information Center

    Brusco, Michael J.; Singh, Renu; Steinley, Douglas

    2009-01-01

    The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

  15. Relaxation mode analysis of a peptide system: comparison with principal component analysis.

    PubMed

    Mitsutake, Ayori; Iijima, Hiromitsu; Takano, Hiroshi

    2011-10-28

    This article reports the first attempt to apply the relaxation mode analysis method to a simulation of a biomolecular system. In biomolecular systems, the principal component analysis is a well-known method for analyzing the static properties of fluctuations of structures obtained by a simulation and classifying the structures into some groups. On the other hand, the relaxation mode analysis has been used to analyze the dynamic properties of homopolymer systems. In this article, a long Monte Carlo simulation of Met-enkephalin in gas phase has been performed. The results are analyzed by the principal component analysis and relaxation mode analysis methods. We compare the results of both methods and show the effectiveness of the relaxation mode analysis.

  16. Matrix partitioning and EOF/principal component analysis of Antarctic Sea ice brightness temperatures

    NASA Technical Reports Server (NTRS)

    Murray, C. W., Jr.; Mueller, J. L.; Zwally, H. J.

    1984-01-01

    A field of measured anomalies of some physical variable relative to their time averages, is partitioned in either the space domain or the time domain. Eigenvectors and corresponding principal components of the smaller dimensioned covariance matrices associated with the partitioned data sets are calculated independently, then joined to approximate the eigenstructure of the larger covariance matrix associated with the unpartitioned data set. The accuracy of the approximation (fraction of the total variance in the field) and the magnitudes of the largest eigenvalues from the partitioned covariance matrices together determine the number of local EOF's and principal components to be joined by any particular level. The space-time distribution of Nimbus-5 ESMR sea ice measurement is analyzed.

  17. Fast principal component analysis for stacking seismic data

    NASA Astrophysics Data System (ADS)

    Wu, Juan; Bai, Min

    2018-04-01

    Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

  18. Multivariate analyses of salt stress and metabolite sensing in auto- and heterotroph Chenopodium cell suspensions.

    PubMed

    Wongchai, C; Chaidee, A; Pfeiffer, W

    2012-01-01

    Global warming increases plant salt stress via evaporation after irrigation, but how plant cells sense salt stress remains unknown. Here, we searched for correlation-based targets of salt stress sensing in Chenopodium rubrum cell suspension cultures. We proposed a linkage between the sensing of salt stress and the sensing of distinct metabolites. Consequently, we analysed various extracellular pH signals in autotroph and heterotroph cell suspensions. Our search included signals after 52 treatments: salt and osmotic stress, ion channel inhibitors (amiloride, quinidine), salt-sensing modulators (proline), amino acids, carboxylic acids and regulators (salicylic acid, 2,4-dichlorphenoxyacetic acid). Multivariate analyses revealed hirarchical clusters of signals and five principal components of extracellular proton flux. The principal component correlated with salt stress was an antagonism of γ-aminobutyric and salicylic acid, confirming involvement of acid-sensing ion channels (ASICs) in salt stress sensing. Proline, short non-substituted mono-carboxylic acids (C2-C6), lactic acid and amiloride characterised the four uncorrelated principal components of proton flux. The proline-associated principal component included an antagonism of 2,4-dichlorphenoxyacetic acid and a set of amino acids (hydrophobic, polar, acidic, basic). The five principal components captured 100% of variance of extracellular proton flux. Thus, a bias-free, functional high-throughput screening was established to extract new clusters of response elements and potential signalling pathways, and to serve as a core for quantitative meta-analysis in plant biology. The eigenvectors reorient research, associating proline with development instead of salt stress, and the proof of existence of multiple components of proton flux can help to resolve controversy about the acid growth theory. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

  19. 45 CFR 95.505 - Definitions.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ..., and allocating all State agency costs incurred in support of all programs administered or supervised... Department of Health and Human Services (HHS) organizational components responsible for administering public... Services, Office of Child Support Enforcement,Centers for Medicare & Medicaid Services, and Office of...

  20. 29 CFR 1919.90 - Documentation.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ...(f) Test Supervision .10(b) Annual Examinations: (see Examinations). Assistant Secretary .2(d) Blocks...). Cargo Gear: Braking Devices .22 Chains .25 Damaged Components .20 Definition .2(b) Derrick Attachment...: (see Gear Certification). Shore-Based Materials Handling Devices .70(a) Chains, Limitations .25...

  1. 29 CFR 1919.90 - Documentation.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ...(f) Test Supervision .10(b) Annual Examinations: (see Examinations). Assistant Secretary .2(d) Blocks...). Cargo Gear: Braking Devices .22 Chains .25 Damaged Components .20 Definition .2(b) Derrick Attachment...: (see Gear Certification). Shore-Based Materials Handling Devices .70(a) Chains, Limitations .25...

  2. [The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

    PubMed

    Surzhikov, V D; Surzhikov, D V

    2014-01-01

    The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

  3. Priority of VHS Development Based in Potential Area using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Meirawan, D.; Ana, A.; Saripudin, S.

    2018-02-01

    The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

  4. Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

    PubMed

    Azevedo, C F; Nascimento, M; Silva, F F; Resende, M D V; Lopes, P S; Guimarães, S E F; Glória, L S

    2015-10-09

    A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

  5. Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.

    PubMed

    Dawes, Timothy J W; de Marvao, Antonio; Shi, Wenzhe; Fletcher, Tristan; Watson, Geoffrey M J; Wharton, John; Rhodes, Christopher J; Howard, Luke S G E; Gibbs, J Simon R; Rueckert, Daniel; Cook, Stuart A; Wilkins, Martin R; O'Regan, Declan P

    2017-05-01

    Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. Materials and Methods The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). Conclusion A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.

  6. Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

    2013-12-01

    Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.

  7. Performance-Based Preparation of Principals: A Framework for Improvement. A Special Report of the NASSP Consortium for the Performance-Based Preparation of Principals.

    ERIC Educational Resources Information Center

    National Association of Secondary School Principals, Reston, VA.

    Preparation programs for principals should have excellent academic and performance based components. In examining the nature of performance based principal preparation this report finds that school administration programs must bridge the gap between conceptual learning in the classroom and the requirements of professional practice. A number of…

  8. Principal component greenness transformation in multitemporal agricultural Landsat data

    NASA Technical Reports Server (NTRS)

    Abotteen, R. A.

    1978-01-01

    A data compression technique for multitemporal Landsat imagery which extracts phenological growth pattern information for agricultural crops is described. The principal component greenness transformation was applied to multitemporal agricultural Landsat data for information retrieval. The transformation was favorable for applications in agricultural Landsat data analysis because of its physical interpretability and its relation to the phenological growth of crops. It was also found that the first and second greenness eigenvector components define a temporal small-grain trajectory and nonsmall-grain trajectory, respectively.

  9. Nurse supervisors' actions in relation to their decision-making style and ethical approach to clinical supervision.

    PubMed

    Berggren, Ingela; Severinsson, Elisabeth

    2003-03-01

    The aim of the study was to explore the decision-making style and ethical approach of nurse supervisors by focusing on their priorities and interventions in the supervision process. Clinical supervision promotes ethical awareness and behaviour in the nursing profession. A focus group comprised of four clinical nurse supervisors with considerable experience was studied using qualitative hermeneutic content analysis. The essence of the nurse supervisors' decision-making style is deliberations and priorities. The nurse supervisors' willingness, preparedness, knowledge and awareness constitute and form their way of creating a relationship. The nurse supervisors' ethical approach focused on patient situations and ethical principles. The core components of nursing supervision interventions, as demonstrated in supervision sessions, are: guilt, reconciliation, integrity, responsibility, conscience and challenge. The nurse supervisors' interventions involved sharing knowledge and values with the supervisees and recognizing them as nurses and human beings. Nurse supervisors frequently reflected upon the ethical principle of autonomy and the concept and substance of integrity. The nurse supervisors used an ethical approach that focused on caring situations in order to enhance the provision of patient care. They acted as role models, shared nursing knowledge and ethical codes, and focused on patient related situations. This type of decision-making can strengthen the supervisees' professional identity. The clinical nurse supervisors in the study were experienced and used evaluation decisions as their form of clinical decision-making activity. The findings underline the need for further research and greater knowledge in order to improve the understanding of the ethical approach to supervision.

  10. Characteristics of good supervision: a multi-perspective qualitative exploration of the Masters in Public Health dissertation.

    PubMed

    Katikireddi, Srinivasa Vittal; Reilly, Jacqueline

    2017-09-01

    A dissertation is often a core component of the Masters in Public Health (MPH) qualification. This study aims to explore its purpose, from the perspective of both students and supervisors, and identify practices viewed as constituting good supervision. A multi-perspective qualitative study drawing on in-depth one-to-one interviews with MPH supervisors (n = 8) and students (n = 10), with data thematically analysed. The MPH dissertation was viewed as providing generic as well as discipline-specific knowledge and skills. It provided an opportunity for in-depth study on a chosen topic but different perspectives were evident as to whether the project should be grounded in public health practice rather than academia. Good supervision practice was thought to require topic knowledge, generic supervision skills (including clear communication of expectations and timely feedback) and adaptation of supervision to meet student needs. Two ideal types of the MPH dissertation process were identified. Supervisor-led projects focus on achieving a clearly defined output based on a supervisor-identified research question and aspire to harmonize research and teaching practice, but often have a narrower focus. Student-led projects may facilitate greater learning opportunities and better develop skills for public health practice but could be at greater risk of course failure. © The Author 2016. Published by Oxford University Press on behalf of Faculty of Public Health.

  11. Staff supervision in residential care.

    PubMed

    Myers, Peter G; Bibbs, Tonya; Orozco, Candy

    2004-04-01

    Residential care workers must be offered opportunities for formalized and systematic supervision in individual and group formats to provide the highest possible level of care to children and adolescents whom they serve. Effective supervision with residential care staff should be open to exploring issues at all levels of their experience and in relation to each component of the broader organizational structure within which they work. Systems theory offers a useful lens through which to view supervising staff in residential treatment. Systems theory proposes that human behavior is shaped by interactional processes and internal factors. Although the development of the individual occurs within intrinsic cognitive and emotional spheres, it also is believed to be related to several other elements. These additional variables include the point at which the family and system function in their own life cycle, the historical and current emotional context, the current and changing structure of the system, narratives, and the cultural context. This article discussed how methods of training and supervision would be most effective if they were designed specifically for the developmental level of the participants. Some literature reviews have concluded that youth care workers, like all professionals, pass through developmental stages and progress through them in their work. To assist youth care workers in their jobs, supervisors must understand these stages and the ways in which they may be enacted in the workplace.

  12. Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.

    PubMed

    Pintus, M A; Gaspa, G; Nicolazzi, E L; Vicario, D; Rossoni, A; Ajmone-Marsan, P; Nardone, A; Dimauro, C; Macciotta, N P P

    2012-06-01

    The large number of markers available compared with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data editing, 37,254 and 40,179 single nucleotide polymorphisms (SNP) were retained for Brown and Simmental, respectively. Principal component analysis carried out on the SNP genotype matrix extracted 2,257 and 3,596 new variables in the 2 breeds, respectively. Bulls were sorted by birth year to create reference and prediction populations. The effect of principal components on deregressed proofs in reference animals was estimated with a BLUP model. Results were compared with those obtained by using SNP genotypes as predictors with either the BLUP or Bayes_A method. Traits considered were milk, fat, and protein yields, fat and protein percentages, and somatic cell score. The GEBV were obtained for prediction population by blending direct genomic prediction and pedigree indexes. No substantial differences were observed in squared correlations between GEBV and EBV in prediction animals between the 3 methods in the 2 breeds. The principal component analysis method allowed for a reduction of about 90% in the number of independent variables when predicting direct genomic values, with a substantial decrease in calculation time and without loss of accuracy. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  13. Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

    PubMed

    Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

    2014-01-01

    Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

  14. Sparse modeling of spatial environmental variables associated with asthma

    PubMed Central

    Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

    2014-01-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

  15. Sparse modeling of spatial environmental variables associated with asthma.

    PubMed

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

    2015-02-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Experimental Investigation of Principal Residual Stress and Fatigue Performance for Turned Nickel-Based Superalloy Inconel 718.

    PubMed

    Hua, Yang; Liu, Zhanqiang

    2018-05-24

    Residual stresses of turned Inconel 718 surface along its axial and circumferential directions affect the fatigue performance of machined components. However, it has not been clear that the axial and circumferential directions are the principle residual stress direction. The direction of the maximum principal residual stress is crucial for the machined component service life. The present work aims to focuses on determining the direction and magnitude of principal residual stress and investigating its influence on fatigue performance of turned Inconel 718. The turning experimental results show that the principal residual stress magnitude is much higher than surface residual stress. In addition, both the principal residual stress and surface residual stress increase significantly as the feed rate increases. The fatigue test results show that the direction of the maximum principal residual stress increased by 7.4%, while the fatigue life decreased by 39.4%. The maximum principal residual stress magnitude diminished by 17.9%, whereas the fatigue life increased by 83.6%. The maximum principal residual stress has a preponderant influence on fatigue performance as compared to the surface residual stress. The maximum principal residual stress can be considered as a prime indicator for evaluation of the residual stress influence on fatigue performance of turned Inconel 718.

  17. Principal component analysis for designed experiments.

    PubMed

    Konishi, Tomokazu

    2015-01-01

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

  18. The role of supervised driving in a graduated driver licensing program : traffic tech.

    DOT National Transportation Integrated Search

    2012-04-01

    Traditional Graduated Driver Licensing (GDL) systems, : in place in all the States, include three licensing stages: the : initial learner stage, an intermediate or provisional phase, : and full licensure. An important component of GDL systems : is th...

  19. Coping with Multicollinearity: An Example on Application of Principal Components Regression in Dendroecology

    Treesearch

    B. Desta Fekedulegn; J.J. Colbert; R.R., Jr. Hicks; Michael E. Schuckers

    2002-01-01

    The theory and application of principal components regression, a method for coping with multicollinearity among independent variables in analyzing ecological data, is exhibited in detail. A concrete example of the complex procedures that must be carried out in developing a diagnostic growth-climate model is provided. We use tree radial increment data taken from breast...

  20. Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014

    ERIC Educational Resources Information Center

    Rahayu, Sri; Sugiarto, Teguh; Madu, Ludiro; Holiawati; Subagyo, Ahmad

    2017-01-01

    This study aims to apply the model principal component analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hong Kong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield…

  1. Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

    NASA Technical Reports Server (NTRS)

    Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Smith, William L., Sr.; Mango, Stephen A.

    2009-01-01

    A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

  2. Principal component analysis of Raman spectra for TiO2 nanoparticle characterization

    NASA Astrophysics Data System (ADS)

    Ilie, Alina Georgiana; Scarisoareanu, Monica; Morjan, Ion; Dutu, Elena; Badiceanu, Maria; Mihailescu, Ion

    2017-09-01

    The Raman spectra of anatase/rutile mixed phases of Sn doped TiO2 nanoparticles and undoped TiO2 nanoparticles, synthesised by laser pyrolysis, with nanocrystallite dimensions varying from 8 to 28 nm, was simultaneously processed with a self-written software that applies Principal Component Analysis (PCA) on the measured spectrum to verify the possibility of objective auto-characterization of nanoparticles from their vibrational modes. The photo-excited process of Raman scattering is very sensible to the material characteristics, especially in the case of nanomaterials, where more properties become relevant for the vibrational behaviour. We used PCA, a statistical procedure that performs eigenvalue decomposition of descriptive data covariance, to automatically analyse the sample's measured Raman spectrum, and to interfere the correlation between nanoparticle dimensions, tin and carbon concentration, and their Principal Component values (PCs). This type of application can allow an approximation of the crystallite size, or tin concentration, only by measuring the Raman spectrum of the sample. The study of loadings of the principal components provides information of the way the vibrational modes are affected by the nanoparticle features and the spectral area relevant for the classification.

  3. Testing for Non-Random Mating: Evidence for Ancestry-Related Assortative Mating in the Framingham Heart Study

    PubMed Central

    Sebro, Ronnie; Hoffman, Thomas J.; Lange, Christoph; Rogus, John J.; Risch, Neil J.

    2013-01-01

    Population stratification leads to a predictable phenomenon—a reduction in the number of heterozygotes compared to that calculated assuming Hardy-Weinberg Equilibrium (HWE). We show that population stratification results in another phenomenon—an excess in the proportion of spouse-pairs with the same genotypes at all ancestrally informative markers, resulting in ancestrally related positive assortative mating. We use principal components analysis to show that there is evidence of population stratification within the Framingham Heart Study, and show that the first principal component correlates with a North-South European cline. We then show that the first principal component is highly correlated between spouses (r=0.58, p=0.0013), demonstrating that there is ancestrally related positive assortative mating among the Framingham Caucasian population. We also show that the single nucleotide polymorphisms loading most heavily on the first principal component show an excess of homozygotes within the spouses, consistent with similar ancestry-related assortative mating in the previous generation. This nonrandom mating likely affects genetic structure seen more generally in the North American population of European descent today, and decreases the rate of decay of linkage disequilibrium for ancestrally informative markers. PMID:20842694

  4. Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

    PubMed

    Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

    2016-02-01

    Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p < 0.05) difference in sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

  5. Statistical analysis of major ion and trace element geochemistry of water, 1986-2006, at seven wells transecting the freshwater/saline-water interface of the Edwards Aquifer, San Antonio, Texas

    USGS Publications Warehouse

    Mahler, Barbara J.

    2008-01-01

    The statistical analyses taken together indicate that the geochemistry at the freshwater-zone wells is more variable than that at the transition-zone wells. The geochemical variability at the freshwater-zone wells might result from dilution of ground water by meteoric water. This is indicated by relatively constant major ion molar ratios; a preponderance of positive correlations between SC, major ions, and trace elements; and a principal components analysis in which the major ions are strongly loaded on the first principal component. Much of the variability at three of the four transition-zone wells might result from the use of different laboratory analytical methods or reporting procedures during the period of sampling. This is reflected by a lack of correlation between SC and major ion concentrations at the transition-zone wells and by a principal components analysis in which the variability is fairly evenly distributed across several principal components. The statistical analyses further indicate that, although the transition-zone wells are less well connected to surficial hydrologic conditions than the freshwater-zone wells, there is some connection but the response time is longer. 

  6. Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

    PubMed Central

    Matsen IV, Frederick A.; Evans, Steven N.

    2013-01-01

    Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

  7. Time Management Ideas for Assistant Principals.

    ERIC Educational Resources Information Center

    Cronk, Jerry

    1987-01-01

    Prioritizing the use of time, effective communication, delegating authority, having detailed job descriptions, and good secretarial assistance are important components of time management for assistant principals. (MD)

  8. The principal components model: a model for advancing spirituality and spiritual care within nursing and health care practice.

    PubMed

    McSherry, Wilfred

    2006-07-01

    The aim of this study was to generate a deeper understanding of the factors and forces that may inhibit or advance the concepts of spirituality and spiritual care within both nursing and health care. This manuscript presents a model that emerged from a qualitative study using grounded theory. Implementation and use of this model may assist all health care practitioners and organizations to advance the concepts of spirituality and spiritual care within their own sphere of practice. The model has been termed the principal components model because participants identified six components as being crucial to the advancement of spiritual health care. Grounded theory was used meaning that there was concurrent data collection and analysis. Theoretical sampling was used to develop the emerging theory. These processes, along with data analysis, open, axial and theoretical coding led to the identification of a core category and the construction of the principal components model. Fifty-three participants (24 men and 29 women) were recruited and all consented to be interviewed. The sample included nurses (n=24), chaplains (n=7), a social worker (n=1), an occupational therapist (n=1), physiotherapists (n=2), patients (n=14) and the public (n=4). The investigation was conducted in three phases to substantiate the emerging theory and the development of the model. The principal components model contained six components: individuality, inclusivity, integrated, inter/intra-disciplinary, innate and institution. A great deal has been written on the concepts of spirituality and spiritual care. However, rhetoric alone will not remove some of the intrinsic and extrinsic barriers that are inhibiting the advancement of the spiritual dimension in terms of theory and practice. An awareness of and adherence to the principal components model may assist nurses and health care professionals to engage with and overcome some of the structural, organizational, political and social variables that are impacting upon spiritual care.

  9. Applications of machine learning and data mining methods to detect associations of rare and common variants with complex traits.

    PubMed

    Lu, Ake Tzu-Hui; Austin, Erin; Bonner, Ashley; Huang, Hsin-Hsiung; Cantor, Rita M

    2014-09-01

    Machine learning methods (MLMs), designed to develop models using high-dimensional predictors, have been used to analyze genome-wide genetic and genomic data to predict risks for complex traits. We summarize the results from six contributions to our Genetic Analysis Workshop 18 working group; these investigators applied MLMs and data mining to analyses of rare and common genetic variants measured in pedigrees. To develop risk profiles, group members analyzed blood pressure traits along with single-nucleotide polymorphisms and rare variant genotypes derived from sequence and imputation analyses in large Mexican American pedigrees. Supervised MLMs included penalized regression with varying penalties, support vector machines, and permanental classification. Unsupervised MLMs included sparse principal components analysis and sparse graphical models. Entropy-based components analyses were also used to mine these data. None of the investigators fully capitalized on the genetic information provided by the complete pedigrees. Their approaches either corrected for the nonindependence of the individuals within the pedigrees or analyzed only those who were independent. Some methods allowed for covariate adjustment, whereas others did not. We evaluated these methods using a variety of metrics. Four contributors conducted primary analyses on the real data, and the other two research groups used the simulated data with and without knowledge of the underlying simulation model. One group used the answers to the simulated data to assess power and type I errors. Although the MLMs applied were substantially different, each research group concluded that MLMs have advantages over standard statistical approaches with these high-dimensional data. © 2014 WILEY PERIODICALS, INC.

  10. The Behavioural Assessment of Self-Structuring (BASS): psychometric properties in a post-acute brain injury rehabilitation programme.

    PubMed

    Jackson, Howard F; Tunstall, Victoria; Hague, Gemma; Daniels, Leanne; Crompton, Stacey; Taplin, Kimberly

    2014-01-01

    Jackson et al. (this edition) argue that structure is an important component in reducing the handicaps caused by cognitive impairments following acquired brain injury and that post-acute neuropsychological brain injury rehabilitation programmes should not only endeavour to provide structure but also aim to develop self-structuring. However, at present there is no standardized device for assessing self-structuring. To provide preliminary analysis of the psychometric properties of the Behavioural Assessment of Self-Structuring (BASS) staff rating scale (a 26 item informant five point rating scale based on the degree of support client requires to achieve self-structuring item). BASS data was utilised for clients attending residential rehabilitation. Reliability (inter-rarer and intra-rater), validity (construct, concurrent and discriminate) and sensitivity to change were investigated. Initial results indicate that the BASS has reasonably good reliability, good construct validity (via principal components analysis), good discriminant validity, and good concurrent validity correlating well with a number of other outcome measures (HoNOS; NPDS, Supervision Rating Scale, MPAI, FIM and FAM). The BASS did not correlate well with the NPCNA. Finally, the BASS was shown to demonstrate sensitivity to change. Although some caution is required in drawing firm conclusions at the present time and further exploration of the psychometric properties of the BASS is required, initial results are encouraging for the use of the BASS in assessing rehabilitation progress. These findings are discussed in terms of the value of the concept of self-structuring to the rehabilitation process for individuals with neuropsychological impairments consequent on acquired brain injury.

  11. Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints

    PubMed Central

    Cheng, Jianjun; Leng, Mingwei; Li, Longjie; Zhou, Hanhai; Chen, Xiaoyun

    2014-01-01

    Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods. PMID:25329660

  12. Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions

    NASA Astrophysics Data System (ADS)

    BoŻek, Piotr

    2018-03-01

    The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb + Pb collisions at √{sN N}=2760 GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of v22 and v4, of v2v3 and v5, or of v23,v33 , and v6. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.

  13. Analysis and improvement measures of flight delay in China

    NASA Astrophysics Data System (ADS)

    Zang, Yuhang

    2017-03-01

    Firstly, this paper establishes the principal component regression model to analyze the data quantitatively, based on principal component analysis to get the three principal component factors of flight delays. Then the least square method is used to analyze the factors and obtained the regression equation expression by substitution, and then found that the main reason for flight delays is airlines, followed by weather and traffic. Aiming at the above problems, this paper improves the controllable aspects of traffic flow control. For reasons of traffic flow control, an adaptive genetic queuing model is established for the runway terminal area. This paper, establish optimization method that fifteen planes landed simultaneously on the three runway based on Beijing capital international airport, comparing the results with the existing FCFS algorithm, the superiority of the model is proved.

  14. An efficient classification method based on principal component and sparse representation.

    PubMed

    Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

    2016-01-01

    As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

  15. Polyhedral gamut representation of natural objects based on spectral reflectance database and its application

    NASA Astrophysics Data System (ADS)

    Haneishi, Hideaki; Sakuda, Yasunori; Honda, Toshio

    2002-06-01

    Spectral reflectance of most reflective objects such as natural objects and color hardcopy is relatively smooth and can be approximated by several numbers of principal components with high accuracy. Though the subspace spanned by those principal components represents a space in which reflective objects can exist, it dos not provide the bound in which the samples distribute. In this paper we propose to represent the gamut of reflective objects in more distinct form, i.e., as a polyhedron in the subspace spanned by several principal components. Concept of the polyhedral gamut representation and its application to calculation of metamer ensemble are described. Color-mismatch volume caused by different illuminant and/or observer for a metamer ensemble is also calculated and compared with theoretical one.

  16. Evaluation of Low-Voltage Distribution Network Index Based on Improved Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Fan, Hanlu; Gao, Suzhou; Fan, Wenjie; Zhong, Yinfeng; Zhu, Lei

    2018-01-01

    In order to evaluate the development level of the low-voltage distribution network objectively and scientifically, chromatography analysis method is utilized to construct evaluation index model of low-voltage distribution network. Based on the analysis of principal component and the characteristic of logarithmic distribution of the index data, a logarithmic centralization method is adopted to improve the principal component analysis algorithm. The algorithm can decorrelate and reduce the dimensions of the evaluation model and the comprehensive score has a better dispersion degree. The clustering method is adopted to analyse the comprehensive score because the comprehensive score of the courts is concentrated. Then the stratification evaluation of the courts is realized. An example is given to verify the objectivity and scientificity of the evaluation method.

  17. Online signature recognition using principal component analysis and artificial neural network

    NASA Astrophysics Data System (ADS)

    Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

    2016-12-01

    In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

  18. Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

    NASA Astrophysics Data System (ADS)

    Othman, Arsalan A.; Gloaguen, Richard

    2017-09-01

    Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

  19. Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

    NASA Astrophysics Data System (ADS)

    Deshmukh, Atul; Singh, S. P.; Chaturvedi, Pankaj; Krishna, C. Murali

    2011-12-01

    Oral squamous cell carcinoma is one of among the top 10 malignancies. Optical spectroscopy, including Raman, is being actively pursued as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex vivo tissues. Spectral features showed predominance of lipids and proteins in normal and cancer conditions, respectively, which were attributed to membrane lipids and surface proteins. In view of recent developments in deep tissue Raman spectroscopy, we have recorded Raman spectra from superior and inferior surfaces of 10 normal oral tissues on intact, as well as incised, biopsies after separation of epithelium from connective tissue. Spectral variations and similarities among different groups were explored by unsupervised (principal component analysis) and supervised (linear discriminant analysis, factorial discriminant analysis) methodologies. Clusters of spectra from superior and inferior surfaces of intact tissues show a high overlap; whereas spectra from separated epithelium and connective tissue sections yielded clear clusters, though they also overlap on clusters of intact tissues. Spectra of all four groups of normal tissues gave exclusive clusters when tested against malignant spectra. Thus, this study demonstrates that spectra recorded from the superior surface of an intact tissue may have contributions from deeper layers but has no bearing from the classification of a malignant tissues point of view.

  20. Rapid authentication of edible bird's nest by FTIR spectroscopy combined with chemometrics.

    PubMed

    Guo, Lili; Wu, Yajun; Liu, Mingchang; Ge, Yiqiang; Chen, Ying

    2018-06-01

    Edible bird's nests (EBNs) have been traditionally regarded as a kind of medicinal and healthy food in China. For economic reasons, they are frequently subjected to adulteration with some cheaper substitutes, such as Tremella fungus, agar, fried pigskin, and egg white. As a kind of precious and functional product, it is necessary to establish a robust method for the rapid authentication of EBNs with small amounts of samples by simple processes. In this study, the Fourier transform infrared spectroscopy (FTIR) system was utilized and its feasibility for identification of EBNs was verified. FTIR spectra data of authentic and adulterated EBNs were analyzed by chemometrics analyses including principal component analysis, linear discriminant analysis (LDA), support vector machine (SVM) and one-class partial least squares (OCPLS). The results showed that the established LDA and SVM models performed well and had satisfactory classification ability, with the former 94.12% and the latter 100%. The OCPLS model was developed with prediction sensitivity of 0.937 and specificity of 0.886. Further detection of commercial EBN samples confirmed these results. FTIR is applicable in the scene of rapid authentication of EBNs, especially for quality supervision departments, entry-exit inspection and quarantine, and customs administration. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  1. Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

    PubMed Central

    Myakalwar, Ashwin Kumar; Sreedhar, S.; Barman, Ishan; Dingari, Narahara Chari; Rao, S. Venugopal; Kiran, P. Prem; Tewari, Surya P.; Kumar, G. Manoj

    2012-01-01

    We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen to nitrogen compositional values yielded an optimal value (at 746.83 nm) with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry. PMID:22099648

  2. Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue.

    PubMed

    Bougrini, Madiha; Tahri, Khalid; Haddi, Zouhair; El Bari, Nezha; Llobet, Eduard; Jaffrezic-Renault, Nicole; Bouchikhi, Benachir

    2014-12-01

    A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. In vivo Raman spectroscopic identification of premalignant lesions in oral buccal mucosa

    NASA Astrophysics Data System (ADS)

    Singh, S. P.; Deshmukh, Atul; Chaturvedi, Pankaj; Murali Krishna, C.

    2012-10-01

    Cancers of oral cavities are one of the most common malignancies in India and other south-Asian countries. Tobacco habits are the main etiological factors for oral cancer. Identification of premalignant lesions is required for improving survival rates related to oral cancer. Optical spectroscopy methods are projected as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex-vivo tissues. We intend to evaluate potentials of Raman spectroscopy in detecting premalignant conditions. Spectra were recorded from premalignant patches, contralateral normal (opposite to tumor site), and cancerous sites of subjects with oral cancers and also from age-matched healthy subjects with and without tobacco habits. A total of 861 spectra from 104 subjects were recorded using a fiber-optic probe-coupled HE-785 Raman spectrometer. Spectral differences in the 1200- to 1800-cm-1 region were subjected to unsupervised principal component analysis and supervised linear discriminant analysis followed by validation with leave-one-out and an independent test data set. Results suggest that premalignant conditions can be objectively discriminated with both normal and cancerous sites as well as from healthy controls with and without tobacco habits. Findings of the study further support efficacy of Raman spectroscopic approaches in oral-cancer applications.

  4. A gene expression signature associated with survival in metastatic melanoma

    PubMed Central

    Mandruzzato, Susanna; Callegaro, Andrea; Turcatel, Gianluca; Francescato, Samuela; Montesco, Maria C; Chiarion-Sileni, Vanna; Mocellin, Simone; Rossi, Carlo R; Bicciato, Silvio; Wang, Ena; Marincola, Francesco M; Zanovello, Paola

    2006-01-01

    Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells. PMID:17129373

  5. Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

    NASA Astrophysics Data System (ADS)

    Siedliska, Anna; Zubik, Monika; Baranowski, Piotr; Mazurek, Wojciech

    2017-10-01

    The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: `Łutówka', `Pandy 103', and `Groniasta', differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained via the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries.

  6. Perceptions of fish habitat conditions in Oklahoma tailwater fisheries: a survey of fisheries managers

    USGS Publications Warehouse

    Long, James M.

    2011-01-01

    While the downstream effects of dams on fish habitat have long been recognized, broad-scale assessments of tailwater fish habitat have rarely been conducted. In this paper, I report on the status of tailwater fisheries in Oklahoma as determined through a web-based survey of fisheries biologists with the Oklahoma Department of Wildlife Conservation conducted in July 2010. Respondents addressed 38 tailwaters, encompassing all major areas of the state. The majority of fish species comprising these fisheries included blue catfish (Ictalurus furcatus), followed by white bass (Morone chrysops), channel catfish (I. punctatus) and flathead catfish (Pylodictis olivaris). Most respondents indicated no or low concerns with fish habitat in tailwaters under their management supervision; only two tailwaters (Tenkiller Ferry and Fort Gibson) had the majority of concerns with fish habitat identified as high to moderately high. Principal components analysis and subsequent correlation analysis showed that tailwaters that scored high for issues related to shoreline erosion, change in water depth, flow fluctuations, and flow timing were associated with dams with large maximum discharge ability. No other factors related to fish habitat condition in tailwaters were found. In Oklahoma, dams with maximum discharge of at least 6,767.5 m3 sec–1 were more likely to have flow-related fish habitat concerns in the tailwater.

  7. Spectral Data Reduction via Wavelet Decomposition

    NASA Technical Reports Server (NTRS)

    Kaewpijit, S.; LeMoigne, J.; El-Ghazawi, T.; Rood, Richard (Technical Monitor)

    2002-01-01

    The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

  8. Discrimination of magnoliae officinalis cortex based on the quantitative profiles of magnolosides by two-channel liquid chromatography with electrochemical detection.

    PubMed

    Xue, Zhenzhen; Kotani, Akira; Yang, Bin; Hakamata, Hideki

    2018-05-31

    A two-channel liquid chromatography with electrochemical detection system (2LC-ECD) was newly designed for the simultaneous determination of magnolosides A, B, F, H, and L in the first channel and other magnolosides D and M in the second channel, respectively. Peak heights had linear relationships to the magnoloside concentrations in a range of 0.02-16 μmol/L for H, 0.01-12 μmol/L for A, 0.02-12 μmol/L for F and L, 0.01-8 μmol/L for B, 0.002-6 μmol/L for D, and 0.002-4 μmol/L for M, respectively. Seven magnolosides in magnoliae officinalis cortex (MOC) were determined by the 2LC-ECD, and the obtained quantitative profiles of magnolosides were applied to the discrimination between the MOC samples harvested from Hubei and Sichuan (called Chuan po) and from Zhejiang and Fujian (called Wen po). By principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) based on the quantitative profiles of the magnolosides, Chuan po were clearly discriminated from Wen po on the plots obtained from our multivariable analyses. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.

    PubMed

    Jesse, Stephen; Kalinin, Sergei V

    2009-02-25

    An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

  10. The Artistic Nature of the High School Principal.

    ERIC Educational Resources Information Center

    Ritschel, Robert E.

    The role of high school principals can be compared to that of composers of music. For instance, composers put musical components together into a coherent whole; similarly, principals organize high schools by establishing class schedules, assigning roles to subordinates, and maintaining a safe and orderly learning environment. Second, composers…

  11. Collaborative Relationships between Principals and School Counselors: Facilitating a Model for Developing a Working Alliance

    ERIC Educational Resources Information Center

    Odegard-Koester, Melissa A.; Watkins, Paul

    2016-01-01

    The working relationship between principals and school counselors have received some attention in the literature, however, little empirical research exists that examines specifically the components that facilitate a collaborative working relationship between the principal and school counselor. This qualitative case study examined the unique…

  12. The Retention and Attrition of Catholic School Principals

    ERIC Educational Resources Information Center

    Durow, W. Patrick; Brock, Barbara L.

    2004-01-01

    This article reports the results of a study of the retention of principals in Catholic elementary and secondary schools in one Midwestern diocese. Findings revealed that personal needs, career advancement, support from employer, and clearly defined role expectations were key factors in principals' retention decisions. A profile of components of…

  13. Countertransference. Its continued importance in psychiatric education

    PubMed Central

    Rao, Nyapati R.; Meinzer, Arthur E.; Berman, Sheldon S.

    1997-01-01

    Psychotherapy is likely to play a minor or nonexistent role in the future general psychiatrist's training and practice. However, the component skills of recognition and management of the countertransference will remain important. Because psychotherapy training and supervision have been the venues for countertransference learning, the field is in danger of losing its teaching laboratory and hence losing these skills. The authors examine the concept of countertransference and discuss its importance in four increasingly significant areas: managed care, psychopharmacological treatment, emergency intervention, and the management of professional boundaries regarding sexual misconduct. Methods are discussed for enhancing residents' countertransference skills through supervision, training groups, and the resident's personal psychotherapy. PMID:9058556

  14. Satellite spectral data and archaeological reconnaissance in western Greece

    NASA Technical Reports Server (NTRS)

    Cooper, Frederick A.; Bauer, M. E.; Cullen, Brenda C.

    1991-01-01

    A Macro-geographical reconnaissance of the Western Peloponnesos adopts spectral signatures taken by Landsat-5 Thematic Mapper as a new instrument of archaeological survey in Greece. Ancient records indicate that indigenous resources contributed to the prosperity of the region. Natural resources and Ancient, Medieval, and Pre-modern Folklife in the Western Peloponnesos describes the principal lines of research. For a supervised classification of attested ancient resources, a variety of biophysical surface features were pinpointed: stone quarries, coal mines, forests of oak and silver fir, terracotta-producing clay beds, crops, and various wild but exploited shrubs such as flax.

  15. Course Design for Critical Thinking.

    ERIC Educational Resources Information Center

    Furedy, John J.; Furedy, Christine

    1979-01-01

    A fourth year honors thesis research course in psychology at the University of Toronto uses the device of adversarial interaction to improve critical thinking. Course components, including thesis submission, research seminar, student relations, and supervision, are designed to simulate the constraints, criticism, and relationships of actual…

  16. 42 CFR 410.47 - Pulmonary rehabilitation program: Conditions for coverage.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ...-prescribed exercise means physical activity, including aerobic exercise, prescribed and supervised by a... components: (1) Physician-prescribed exercise. This physical activity includes techniques such as exercise... program for COPD and certain other chronic respiratory diseases designed to optimize physical and social...

  17. Diet Quality and Physical Activity Outcome Improvements Resulting From a Church-Based Diet and Supervised Physical Activity Intervention for Rural, Southern, African American Adults: Delta Body and Soul III.

    PubMed

    Thomson, Jessica L; Goodman, Melissa H; Tussing-Humphreys, Lisa

    2015-09-01

    We assessed the effects of a 6-month, church-based, diet and supervised physical activity intervention, conducted between 2011 and 2012, on improving diet quality and increasing physical activity of Southern, African American adults. Using a quasi-experimental design, eight self-selected, eligible churches were assigned to intervention or control. Assessments included dietary, physical activity, anthropometric, and clinical measures. Mixed model regression analysis and McNemar's test were used to determine if within and between group differences were significant. Cohen's d effect sizes for selected outcomes also were computed and compared with an earlier, lower dose intervention. Retention rates were 84% (102/122) for control and 76% (219/287) for intervention participants. Diet quality components, including fruits, vegetables, discretionary calories, and total quality, improved significantly in the intervention group. Strength/flexibility physical activity also increased in the intervention group, while both aerobic and strength/flexibility physical activity significantly decreased in the control group. Effect sizes for selected health outcomes were larger in the current intervention as compared to an earlier, less intense iteration of the study. Results suggest that more frequent education sessions as well as supervised group physical activity may be key components to increasing the efficacy of behavioral lifestyle interventions in rural, Southern, African American adults. © 2015 Society for Public Health Education.

  18. The Psychometric Assessment of Children with Learning Disabilities: An Index Derived from a Principal Components Analysis of the WISC-R.

    ERIC Educational Resources Information Center

    Lawson, J. S.; Inglis, James

    1984-01-01

    A learning disability index (LDI) for the assessment of intellectual deficits on the Wechsler Intelligence Scale for Children-Revised (WISC-R) is described. The Factor II score coefficients derived from an unrotated principal components analysis of the WISC-R normative data, in combination with the individual's scaled scores, are used for this…

  19. Perturbation analyses of intermolecular interactions

    NASA Astrophysics Data System (ADS)

    Koyama, Yohei M.; Kobayashi, Tetsuya J.; Ueda, Hiroki R.

    2011-08-01

    Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

  20. Perturbation analyses of intermolecular interactions.

    PubMed

    Koyama, Yohei M; Kobayashi, Tetsuya J; Ueda, Hiroki R

    2011-08-01

    Conformational fluctuations of a protein molecule are important to its function, and it is known that environmental molecules, such as water molecules, ions, and ligand molecules, significantly affect the function by changing the conformational fluctuations. However, it is difficult to systematically understand the role of environmental molecules because intermolecular interactions related to the conformational fluctuations are complicated. To identify important intermolecular interactions with regard to the conformational fluctuations, we develop herein (i) distance-independent and (ii) distance-dependent perturbation analyses of the intermolecular interactions. We show that these perturbation analyses can be realized by performing (i) a principal component analysis using conditional expectations of truncated and shifted intermolecular potential energy terms and (ii) a functional principal component analysis using products of intermolecular forces and conditional cumulative densities. We refer to these analyses as intermolecular perturbation analysis (IPA) and distance-dependent intermolecular perturbation analysis (DIPA), respectively. For comparison of the IPA and the DIPA, we apply them to the alanine dipeptide isomerization in explicit water. Although the first IPA principal components discriminate two states (the α state and PPII (polyproline II) + β states) for larger cutoff length, the separation between the PPII state and the β state is unclear in the second IPA principal components. On the other hand, in the large cutoff value, DIPA eigenvalues converge faster than that for IPA and the top two DIPA principal components clearly identify the three states. By using the DIPA biplot, the contributions of the dipeptide-water interactions to each state are analyzed systematically. Since the DIPA improves the state identification and the convergence rate with retaining distance information, we conclude that the DIPA is a more practical method compared with the IPA. To test the feasibility of the DIPA for larger molecules, we apply the DIPA to the ten-residue chignolin folding in explicit water. The top three principal components identify the four states (native state, two misfolded states, and unfolded state) and their corresponding eigenfunctions identify important chignolin-water interactions to each state. Thus, the DIPA provides the practical method to identify conformational states and their corresponding important intermolecular interactions with distance information.

  1. Identification of Best Practices for Resident Aesthetic Clinics in Plastic Surgery Training: The ACAPS National Survey

    PubMed Central

    Wu, Cindy; Bentz, Michael L.; Redett, Richard J.; Shack, R. Bruce; David, Lisa R.; Taub, Peter J.; Janis, Jeffrey E.

    2015-01-01

    Introduction: Resident aesthetic clinics (RACs) have demonstrated good outcomes and acceptable patient satisfaction, but few studies have evaluated their educational, financial, or medicolegal components. We sought to determine RAC best practices. Methods: We surveyed American Council of Academic Plastic Surgeon members (n = 399), focusing on operational details, resident supervision, patient safety, medicolegal history, financial viability, and research opportunities. Of the 96 respondents, 63 reported having a RAC, and 56% of plastic surgery residency program directors responded. Results: RACs averaged 243 patient encounters and 53.9 procedures annually, having been in existence for 19.6 years (mean). Full-time faculty (73%) supervised chief residents (84%) in all aspects of care (65%). Of the 63 RACs, 45 were accredited, 40 had licensed procedural suites, 28 had inclusion/exclusion criteria, and 31 used anesthesiologists. Seventeen had overnight capability, and 17 had a Life Safety Plan. No cases of malignant hyperthermia occurred, but 1 facility death was reported. Sixteen RACs had been involved in a lawsuit, and 33 respondents reported financial viability of the RACs. Net revenue was transferred to both the residents’ educational fund (41%) and divisional/departmental overhead (37%). Quality measures included case logs (78%), morbidity/mortality conference (62%), resident surveys (52%), and patient satisfaction scores (46%). Of 63 respondents, 14 have presented or published RAC-specific research; 80 of 96 of those who were surveyed believed RACs enhanced education. Conclusions: RACs are an important component of plastic surgery education. Most clinics are financially viable but carry high malpractice risk and consume significant resources. Best practices, to maximize patient safety and optimize resident education, include use of accredited procedural rooms and direct faculty supervision of all components of care. PMID:26146599

  2. Intelligent on-board system for driving assistance

    NASA Astrophysics Data System (ADS)

    Rombaut, Michele; Le Fort-Piat, N.

    1995-09-01

    We present in this paper, an electronic copilot embedded in a real car. The system objective is to help the driver by sending alarms or warnings in order to avoid dangerous situtations. An onboard perception system based on CCD cameras and proprioceptive sensors is used ot provide information concerning the environment and the internal state of the vehicle. From this set of information, the copilot is able to analyze the situation and to generate adequate warnings to the driver according to the circumstances. The definition and the development of such a system deal with multisensor data fusion and supervision strategies. The framework of this work was the European Prometheus Pro-Art program. The electronic copilot has been integrated in a prototype vehicle called Prolab2. This French demonstrator integrates the works of nine research laboratories and two car companies: PSA and RENAULT. After a brief presentation of the global demonstrator, we present the two principal parts developed in our laboratory corresponding to the high level modules of the system: the dynamic data manager and the situation supervision.

  3. [Role of school lunch in primary school education: a trial analysis of school teachers' views using an open-ended questionnaire].

    PubMed

    Inayama, T; Kashiwazaki, H; Sakamoto, M

    1998-12-01

    We tried to analyze synthetically teachers' view points associated with health education and roles of school lunch in primary education. For this purpose, a survey using an open-ended questionnaire consisting of eight items relating to health education in the school curriculum was carried out in 100 teachers of ten public primary schools. Subjects were asked to describe their view regarding the following eight items: 1) health and physical guidance education, 2) school lunch guidance education, 3) pupils' attitude toward their own health and nutrition, 4) health education, 5) role of school lunch in education, 6) future subjects of health education, 7) class room lesson related to school lunch, 8) guidance in case of pupil with unbalanced dieting and food avoidance. Subjects described their own opinions on an open-ended questionnaire response sheet. Keywords in individual descriptions were selected, rearranged and classified into categories according to their own meanings, and each of the selected keywords were used as the dummy variable. To assess individual opinions synthetically, a principal component analysis was then applied to the variables collected through the teachers' descriptions, and four factors were extracted. The results were as follows. 1) Four factors obtained from the repeated principal component analysis were summarized as; roles of health education and school lunch program (the first principal component), cooperation with nurse-teachers and those in charge of lunch service (the second principal component), time allocation for health education in home-room activity and lunch time (the third principal component) and contents of health education and school lunch guidance and their future plan (the fourth principal component). 2) Teachers regarded the role of school lunch in primary education as providing daily supply of nutrients, teaching of table manners and building up friendships with classmates, health education and food and nutrition education, and developing food preferences through eating lunch together with classmates. 3) Significant positive correlation was observed between "the teachers' opinion about the role of school lunch of providing opportunity to learn good behavior for food preferences through eating lunch together with classmates" and the first principal component "roles of health education and school lunch program" (r = 0.39, p < 0.01). The variable "the role of school lunch is health education and food and nutrition education" showed positive correlation with the principle component "cooperation with nurse-teachers and those in charge of lunch service" (r = 0.27, p < 0.01). Interesting relationships obtained were that teachers with longer educational experience tended to place importance in health education and food and nutrition education as the role of school lunch, and that male teachers regarded the roles of school lunch more importantly for future education in primary education than female teachers did.

  4. Phenomenology of mixed states: a principal component analysis study.

    PubMed

    Bertschy, G; Gervasoni, N; Favre, S; Liberek, C; Ragama-Pardos, E; Aubry, J-M; Gex-Fabry, M; Dayer, A

    2007-12-01

    To contribute to the definition of external and internal limits of mixed states and study the place of dysphoric symptoms in the psychopathology of mixed states. One hundred and sixty-five inpatients with major mood episodes were diagnosed as presenting with either pure depression, mixed depression (depression plus at least three manic symptoms), full mixed state (full depression and full mania), mixed mania (mania plus at least three depressive symptoms) or pure mania, using an adapted version of the Mini International Neuropsychiatric Interview (DSM-IV version). They were evaluated using a 33-item inventory of depressive, manic and mixed affective signs and symptoms. Principal component analysis without rotation yielded three components that together explained 43.6% of the variance. The first component (24.3% of the variance) contrasted typical depressive symptoms with typical euphoric, manic symptoms. The second component, labeled 'dysphoria', (13.8%) had strong positive loadings for irritability, distressing sensitivity to light and noise, impulsivity and inner tension. The third component (5.5%) included symptoms of insomnia. Median scores for the first component significantly decreased from the pure depression group to the pure mania group. For the dysphoria component, scores were highest among patients with full mixed states and decreased towards both patients with pure depression and those with pure mania. Principal component analysis revealed that dysphoria represents an important dimension of mixed states.

  5. A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample

    DOE PAGES

    Chang, Yu-Yen; Chao, Rikon; Wang, Wei-Hao; ...

    2012-01-01

    Disney emore » t al. (2008) have found a striking correlation among global parameters of H i -selected galaxies and concluded that this is in conflict with the CDM model. Considering the importance of the issue, we reinvestigate the problem using the principal component analysis on a fivefold larger sample and additional near-infrared data. We use databases from the Arecibo Legacy Fast Arecibo L -band Feed Array Survey for the gas properties, the Sloan Digital Sky Survey for the optical properties, and the Two Micron All Sky Survey for the near-infrared properties. We confirm that the parameters are indeed correlated where a single physical parameter can explain 83% of the variations. When color ( g - i ) is included, the first component still dominates but it develops a second principal component. In addition, the near-infrared color ( i - J ) shows an obvious second principal component that might provide evidence of the complex old star formation. Based on our data, we suggest that it is premature to pronounce the failure of the CDM model and it motivates more theoretical work.« less

  6. Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

    NASA Astrophysics Data System (ADS)

    Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

    2016-04-01

    Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

  7. Effects of prehabilitation and rehabilitation including a home-based component on physical fitness, adherence, treatment tolerance, and recovery in patients with non-small cell lung cancer: A systematic review.

    PubMed

    Driessen, Elisabeth J; Peeters, Marieke E; Bongers, Bart C; Maas, Huub A; Bootsma, Gerbern P; van Meeteren, Nico L; Janssen-Heijnen, Maryska L

    2017-06-01

    This systematic review aimed to examine physical fitness, adherence, treatment tolerance, and recovery for (p)rehabilitation including a home-based component for patients with non-small cell lung cancer (NSCLC). PRISMA and Cochrane guidelines were followed. Studies describing (home-based) prehabilitation or rehabilitation in patients with NSCLC were included from four databases (January 2000-April 2016, N=11). Nine of ten rehabilitation studies and one prehabilitation study (437 NSCLC patients, mean age 59-72 years) showed significantly or clinically relevant improved physical fitness. Three (27%) assessed home-based training and eight (73%) combined training at home, inhospital (intramural) and/or at the physiotherapy practice/department (extramural). Six (55%) applied supervision of home-based components, and four (36%) a personalized training program. Adherence varied strongly (9-125% for exercises, 50-100% for patients). Treatment tolerance and recovery were heterogeneously reported. Although promising results of (p)rehabilitation for improving physical fitness were found (especially in case of supervision and personalization), adequately powered studies for home-based (p)rehabilitation are needed. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Exploratory factor analysis of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T).

    PubMed

    Watson, Paul Barry; Seaton, Philippa; Sims, Deborah; Jamieson, Isabel; Mountier, Jane; Whittle, Rose; Saarikoski, Mikko

    2014-01-01

    The Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) scale measures student nurses' perceptions of clinical learning environments. This study evaluates the construct validity and internal reliability of the CLES+T in hospital settings in New Zealand. Comparisons are made between New Zealand and Finnish data. The CLES+T scale was completed by 416 Bachelor of Nursing students following hospital clinical placements between October 2008 and December 2009. Construct validity and internal reliability were assessed using exploratory factor analysis and Cronbach's alpha. Exploratory factor analysis supports 4 factors. Cronbach's alpha ranged from .82 to .93. All items except 1 loaded on the same factors found in unpublished Finnish data. The first factor combined 2 previous components from the published Finnish component analysis and was renamed: connecting with, and learning in, communities of clinical practice. The remaining 3 factors (Nurse teacher, Supervisory relationship, and Leadership style of the manager) corresponded to previous components and their conceptualizations. The CLES+T has good internal reliability and a consistent factor structure across samples. The consistency across international samples supports faculties and hospitals using the CLES+T to benchmark the quality of clinical learning environments provided to students.

  9. Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

    PubMed

    Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

    2003-01-01

    Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

  10. HT-FRTC: a fast radiative transfer code using kernel regression

    NASA Astrophysics Data System (ADS)

    Thelen, Jean-Claude; Havemann, Stephan; Lewis, Warren

    2016-09-01

    The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a Gaussian Process (GP) regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatterers. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling).

  11. Spectral decomposition of asteroid Itokawa based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Koga, Sumire C.; Sugita, Seiji; Kamata, Shunichi; Ishiguro, Masateru; Hiroi, Takahiro; Tatsumi, Eri; Sasaki, Sho

    2018-01-01

    The heliocentric stratification of asteroid spectral types may hold important information on the early evolution of the Solar System. Asteroid spectral taxonomy is based largely on principal component analysis. However, how the surface properties of asteroids, such as the composition and age, are projected in the principal-component (PC) space is not understood well. We decompose multi-band disk-resolved visible spectra of the Itokawa surface with principal component analysis (PCA) in comparison with main-belt asteroids. The obtained distribution of Itokawa spectra projected in the PC space of main-belt asteroids follows a linear trend linking the Q-type and S-type regions and is consistent with the results of space-weathering experiments on ordinary chondrites and olivine, suggesting that this trend may be a space-weathering-induced spectral evolution track for S-type asteroids. Comparison with space-weathering experiments also yield a short average surface age (< a few million years) for Itokawa, consistent with the cosmic-ray-exposure time of returned samples from Itokawa. The Itokawa PC score distribution exhibits asymmetry along the evolution track, strongly suggesting that space weathering has begun saturated on this young asteroid. The freshest spectrum found on Itokawa exhibits a clear sign for space weathering, indicating again that space weathering occurs very rapidly on this body. We also conducted PCA on Itokawa spectra alone and compared the results with space-weathering experiments. The obtained results indicate that the first principal component of Itokawa surface spectra is consistent with spectral change due to space weathering and that the spatial variation in the degree of space weathering is very large (a factor of three in surface age), which would strongly suggest the presence of strong regional/local resurfacing process(es) on this small asteroid.

  12. Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

    2012-07-01

    The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

  13. Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

    NASA Astrophysics Data System (ADS)

    Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

    2015-03-01

    Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

  14. Leadership Coaching: A Multiple-Case Study of Urban Public Charter School Principals' Experiences

    ERIC Educational Resources Information Center

    Lackritz, Anne D.

    2017-01-01

    This multi-case study seeks to understand the experiences of New York City and Washington, DC public charter school principals who have experienced leadership coaching, a component of leadership development, beyond their novice years. The research questions framing this study address how experienced public charter school principals describe the…

  15. The View from the Principal's Office: An Observation Protocol Boosts Literacy :eadership

    ERIC Educational Resources Information Center

    Novak, Sandi; Houck, Bonnie

    2016-01-01

    The Minnesota Elementary School Principals' Association offered Minnesota principals professional learning that placed a high priority on literacy instruction and developing a collegial culture. A key component is the literacy classroom visit, an observation protocol used to gather data to determine the status of literacy teaching and student…

  16. Administrative Obstacles to Technology Use in West Virginia Public Schools: A Survey of West Virginia Principals

    ERIC Educational Resources Information Center

    Agnew, David W.

    2011-01-01

    Public school principals must meet many challenges and make decisions concerning financial obligations while providing the best learning environment for students. A major challenge to principals is implementing technological components successfully while providing teachers the 21st century instructional skills needed to enhance students'…

  17. 34 CFR 76.565 - General management costs-restricted rate.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... similar officer along with immediate support staff of these individuals. The term does not include the...) Components of the grantee are those organizational units supervised directly or indirectly by the chief executive officer. These organizational units generally exist one management level below the executive...

  18. Managing Athletic Liability: An Assessment Guide.

    ERIC Educational Resources Information Center

    Burling, Philip; And Others

    1992-01-01

    A comprehensive risk-management program associated with athletic activities contains the following essential components: (1) policies and procedures; (2) training; (3) supervision; (4) corrective action; (5) review and revision; (6) legal counsel and support. Action steps follow each of these major areas. (29 case references) (MLF)

  19. DEMS - a second generation diabetes electronic management system.

    PubMed

    Gorman, C A; Zimmerman, B R; Smith, S A; Dinneen, S F; Knudsen, J B; Holm, D; Jorgensen, B; Bjornsen, S; Planet, K; Hanson, P; Rizza, R A

    2000-06-01

    Diabetes electronic management system (DEMS) is a component-based client/server application, written in Visual C++ and Visual Basic, with the database server running Sybase System 11. DEMS is built entirely with a combination of dynamic link libraries (DLLs) and ActiveX components - the only exception is the DEMS.exe. DEMS is a chronic disease management system for patients with diabetes. It is used at the point of care by all members of the diabetes team including physicians, nurses, dieticians, clinical assistants and educators. The system is designed for maximum clinical efficiency and facilitates appropriately supervised delegation of care. Dispersed clinical sites may be supervised from a central location. The system is designed for ease of navigation; immediate provision of many types of automatically generated reports; quality audits; aids to compliance with good care guidelines; and alerts, advisories, prompts, and warnings that guide the care provider. The system now contains data on over 34000 patients and is in daily use at multiple sites.

  20. Differential principal component analysis of ChIP-seq.

    PubMed

    Ji, Hongkai; Li, Xia; Wang, Qian-fei; Ning, Yang

    2013-04-23

    We propose differential principal component analysis (dPCA) for analyzing multiple ChIP-sequencing datasets to identify differential protein-DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.
Top